Science.gov

Sample records for drought monitor decision

  1. More than just consumers: Integrating local observations into drought monitoring to better support decision making

    NASA Astrophysics Data System (ADS)

    Ferguson, D. B.; Masayesva, A.; Meadow, A. M.; Crimmins, M.

    2016-12-01

    Drought monitoring and drought planning are complex endeavors. Measures of precipitation or streamflow provide little context for understanding how social and environmental systems impacted by drought are responding. In arid and semi-arid regions of the world, this challenge is particularly acute since social-ecological systems are already well-adapted to dry conditions. Understanding what drought means in these regions is an important first step in developing a decision-relevant monitoring system. Traditional drought indices may be of some use, but local observations may ultimately be more relevant for informing difficult decisions in response to unusually dry conditions. This presentation will focus on insights gained from a collaborative project between the University of Arizona and the Hopi Tribe-a Native American community in the U.S. Southwest-to develop a drought information system that is responsive to local needs. The primary goal of the project was to develop a system that: is based on how drought is experienced by Hopi citizens and resource managers, can incorporate local observations of drought impacts as well as conventional indicators, and brings together local expertise with conventional science-based observations. This kind of drought monitoring system can harnesses as much available information as possible to inform resource managers, political leaders, and citizens about drought conditions, but such a system can also engage these local drought stakeholders in observing, thinking about, and helping guide planning for drought.

  2. Building Gateway Tools for Informed Decision Making: The Drought Risk Atlas and U.S. Drought Monitor

    NASA Astrophysics Data System (ADS)

    Svoboda, M.; Fuchs, B.; Poulsen, C.; Nothwehr, J.; Owen, S.

    2014-12-01

    The National Drought Mitigation Center (NDMC) (http://drought.unl.edu) has been working with the National Integrated Drought Information System (NIDIS) (http://drought.gov;) and other partners with a goal of developing tools to enhance drought risk management activities in the U.S. and around the world. The NDMC is a national center founded in 1995 and located at the University of Nebraska-Lincoln. The NDMC conducts basic and applied research, provides a variety of services and produces decision support applications. In addition, the NDMC is involved heavily in education, outreach and planning activities and maintains a number of operational drought-related tools and products including the U.S. Drought Monitor (USDM), Drought Impact Reporter (DIR), Vegetation Drought Response Index (VegDRI) and the Drought Risk Atlas (DRA). The NDMC's recently launched Drought Risk Atlas (DRA) (http://droughtatlas.unl.edu) and the continually evolving U.S. Drought Monitor (http://droughtmonitor.unl.edu;) will be the focus of this presentation. The DRA was launched in 2014 in order to help better answer the common questions of "How does this drought compare to the Dust Bowl years or some other regional drought of record?", or "How often do we see a drought as severe as this?", and "Are we seeing trends in drought frequency?". Access to new digital data sources, geospatial tools and analyses, and dissemination through a web-based interface has allowed us to triple the original National Drought Atlas station sample size and roughly double the period of record in standing up the new DRA. Building off of feedback from the user community, the SPI, SPEI, PDSI, self-calibrated PDSI, Deciles and other climatology (to also include hydrology) products are included. It is anticipated that this tool will heighten awareness and enhance decision support activities with regards to drought risk for policy makers, resource managers, producers, planners, media and the public. Examples of the DRA

  3. From drought indicators to impacts: developing improved tools for monitoring and early warning with decision-makers in mind

    NASA Astrophysics Data System (ADS)

    Hannaford, Jamie; Barker, Lucy; Svensson, Cecilia; Tanguy, Maliko; Laize, Cedric; Bachmair, Sophie; Tijdeman, Erik; Stahl, Kerstin; Collins, Kevin

    2016-04-01

    of M&EW and future aspirations. Different stakeholders clearly have different goals for M&EW, but there are a number of common themes, including a desire to better understand the links between the outputs of large-scale M&EW systems (rainfall, river flow, etc), localised triggers used by decision-makers during drought episodes, and actual impacts of drought. Secondly, we present analyses designed to test the utility of a wide range of drought indicators for their use in UK applications. We demonstrate the suitability of standardised indicators (like the SPI) for use in the UK, addressing the suitability of statistical distributions and using these indicators for drought severity quantification and for understanding propagation from meteorological to hydrological drought; all of which are currently poorly understood aspects that are vital for future monitoring. We then address the extent to which these indicators can be used to predict drought impacts, focusing on several sectors (water supply, agriculture and ecosystems). These analyses test which indicators perform best at predicting drought impacts, and seek to identify indicator thresholds that trigger impact occurrence. Unsurprisingly, we found that no single indicator best predicts impacts, and results are domain, sector and season specific. However, we reveal important linkages between indicators and impacts that could enhance the design and delivery of monitoring and forecasting information and its uptake by decision-makers concerned with drought.

  4. The German drought monitor

    NASA Astrophysics Data System (ADS)

    Zink, Matthias; Samaniego, Luis; Kumar, Rohini; Thober, Stephan; Mai, Juliane; Schäfer, David; Marx, Andreas

    2016-07-01

    The 2003 drought event in Europe had major implications on many societal sectors, including energy production, health, forestry and agriculture. The reduced availability of water accompanied by high temperatures led to substantial economic losses on the order of 1.5 Billion Euros, in agriculture alone. Furthermore, soil droughts have considerable impacts on ecosystems, forest fires and water management. Monitoring soil water availability in near real-time and at high-resolution, i.e., 4 × 4 km2, enables water managers to mitigate the impact of these extreme events. The German drought monitor was established in 2014 as an online platform. It uses an operational modeling system that consists of four steps: (1) a daily update of observed meteorological data by the German Weather Service, with consistency checks and interpolation; (2) an estimation of current soil moisture using the mesoscale hydrological model; (3) calculation of a quantile-based soil moisture index (SMI) based on a 60 year data record; and (4) classification of the SMI into five drought classes ranging from abnormally dry to exceptional drought. Finally, an easy to understand map is produced and published on a daily basis on www.ufz.de/droughtmonitor. Analysis of the ongoing 2015 drought event, which garnered broad media attention, shows that 75% of the German territory underwent drought conditions in July 2015. Regions such as Northern Bavaria and Eastern Saxony, however, have been particularly prone to drought conditions since autumn 2014. Comparisons with historical droughts show that the 2015 event is amongst the ten most severe drought events observed in Germany since 1954 in terms of its spatial extent, magnitude and duration.

  5. Drought: A comprehensive R package for drought monitoring, prediction and analysis

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang

    2015-04-01

    Drought may impose serious challenges to human societies and ecosystems. Due to complicated causing effects and wide impacts, a universally accepted definition of drought does not exist. The drought indicator is commonly used to characterize drought properties such as duration or severity. Various drought indicators have been developed in the past few decades for the monitoring of a certain aspect of drought condition along with the development of multivariate drought indices for drought characterizations from multiple sources or hydro-climatic variables. Reliable drought prediction with suitable drought indicators is critical to the drought preparedness plan to reduce potential drought impacts. In addition, drought analysis to quantify the risk of drought properties would provide useful information for operation drought managements. The drought monitoring, prediction and risk analysis are important components in drought modeling and assessments. In this study, a comprehensive R package "drought" is developed to aid the drought monitoring, prediction and risk analysis (available from R-Forge and CRAN soon). The computation of a suite of univariate and multivariate drought indices that integrate drought information from various sources such as precipitation, temperature, soil moisture, and runoff is available in the drought monitoring component in the package. The drought prediction/forecasting component consists of statistical drought predictions to enhance the drought early warning for decision makings. Analysis of drought properties such as duration and severity is also provided in this package for drought risk assessments. Based on this package, a drought monitoring and prediction/forecasting system is under development as a decision supporting tool. The package will be provided freely to the public to aid the drought modeling and assessment for researchers and practitioners.

  6. Drought Monitoring with VegDRI

    USGS Publications Warehouse

    Brown, Jesslyn F.

    2010-01-01

    Drought strikes somewhere in the United States every year, turning green landscapes brown as precipitation falls below normal levels and water supplies dwindle. Drought is typically a temporary climatic aberration, but it is also an insidious natural hazard. It might last for weeks, months, or years and may have many negative effects. Drought can threaten crops, livestock, and livelihoods, stress wildlife and habitats, and increase wildfire risks and threats to human health. Drought conditions can vary tremendously from place to place and week to week. Accurate drought monitoring is essential to understand a drought's progression and potential effects, and to provide information necessary to support drought mitigation decisions. It is also crucial in light of climate change where droughts could become more frequent, severe, and persistent.

  7. Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Advanced Microwave Scanning Radiometer Data Products for National Drought Monitor Decision Support

    NASA Technical Reports Server (NTRS)

    Estep, Leland

    2007-01-01

    Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.

  8. The German Drought Monitor

    NASA Astrophysics Data System (ADS)

    Marx, Andreas; Zink, Matthias; Pommerencke, Julia; Kumar, Rohini; Thober, Stephan; Samaniego, Luis

    2015-04-01

    Soil moisture droughts reduce the amount of water available to plant growth potentially leading e.g. to crop failure or increased forest fire risk. The threat of human livelihoods in developing countries and large economic losses in developed ones are severe consequences of these events. Monitoring the current state of soil water content allows to improve water management to mitigate the associated damages. Since summer 2014, the German Drought Monitor (GDM, available at: www.ufz.de/droughtmonitor) has been established using an operational hydrological modeling system, which consists of 3 steps: (1) the daily download of meteorological forcing data, consistency check and interpolation of this data, (2) running the mesoscale Hydrologic Model (mHM; Samaniego et al. 2010) and saving the state variables at the end of the model run as restart-file for the next days run, and (3) calculation of the soil moisture index (SMI, Samaniego et al. 2013, JHM) and visualization of the drought data. The hydrological model mHM was used to generate daily soil moisture fields for the period 1954-2013 over the entire area of Germany at a high spatial resolution of 4 x 4 km². The model requires daily precipitation, temperature, and potential evapotranspiration as forcing. A three-layer soil scheme was used to model the soil moisture dynamics over the entire root zone depth. Based on the 60 year simulation of soil moisture, the frequency distributions have been calculated for each grid cell to derive the soil moisture index. In this beta version, we do a monthly online update of the SMI. Furthermore, a trend analysis of drought events for 69 German subregions since 1954 was conducted. It showed that for most parts of Germany, the frequency of abnormally dry conditions increased while the stronger drought situations with SMI<0.2 decreased at the same time. For the coming year, a stakeholder consultation is planned. The aim is to clarify for whom a drought monitor would be useful, what

  9. Enhancing Drought Risk Management: Tools and Services for Decision Support

    NASA Astrophysics Data System (ADS)

    Svoboda, M. D.; Hayes, M. J.

    2011-12-01

    The National Drought Mitigation Center (NDMC) (http://drought.unl.edu) has been working with the National Integrated Drought Information System (NIDIS) (http://drought.gov) and other partners with a goal of developing tools, products, services and outreach with a goal of contributing to a U.S. drought early warning system (DEWS) as well as contributing to efforts underway toward building a virtual and collaborative global drought early warning system (GDEWS). The NDMC's mission is to work to reduce societal vulnerability to drought by helping decision makers at all levels to: develop and implement DEWS, understand and prevent drought impacts and increase long-term resilience to drought through proactive risk management planning. The NDMC is a national center founded in 1995 and located at the University of Nebraska-Lincoln. The NDMC conducts basic and applied research, services and decision support applications, along with the maintaining of a number of operational drought-related tools, products and outreach activities, including the U.S. Drought Monitor (USDM), Drought Impact Reporter (DIR), Vegetation Drought Response Index (VegDRI) along with the newly developed and enhanced National Drought Atlas, Drought Ready Communities Guide to Community Drought Preparedness and our Managing Drought Risk on the Ranch planning section on our newly revamped web site at http://drought.unl.edu. This presentation will describe in more detail the various drought resources, tools, research efforts, services and collaborations already being provided by the NDMC and its partners toward developing a collaborative DEWS in the U.S. and around the world.

  10. Agricultural Productivity Forecasts for Improved Drought Monitoring

    NASA Technical Reports Server (NTRS)

    Limaye, Ashutosh; McNider, Richard; Moss, Donald; Alhamdan, Mohammad

    2010-01-01

    Water stresses on agricultural crops during critical phases of crop phenology (such as grain filling) has higher impact on the eventual yield than at other times of crop growth. Therefore farmers are more concerned about water stresses in the context of crop phenology than the meteorological droughts. However the drought estimates currently produced do not account for the crop phenology. US Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) have developed a drought monitoring decision support tool: The U.S. Drought Monitor, which currently uses meteorological droughts to delineate and categorize drought severity. Output from the Drought Monitor is used by the States to make disaster declarations. More importantly, USDA uses the Drought Monitor to make estimates of crop yield to help the commodities market. Accurate estimation of corn yield is especially critical given the recent trend towards diversion of corn to produce ethanol. Ethanol is fast becoming a standard 10% ethanol additive to petroleum products, the largest traded commodity. Thus the impact of large-scale drought will have dramatic impact on the petroleum prices as well as on food prices. USDA's World Agricultural Outlook Board (WAOB) serves as a focal point for economic intelligence and the commodity outlook for U.S. WAOB depends on Drought Monitor and has emphatically stated that accurate and timely data are needed in operational agrometeorological services to generate reliable projections for agricultural decision makers. Thus, improvements in the prediction of drought will reflect in early and accurate assessment of crop yields, which in turn will improve commodity projections. We have developed a drought assessment tool, which accounts for the water stress in the context of crop phenology. The crop modeling component is done using various crop modules within Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is an agricultural crop

  11. DroughtView: Satellite Based Drought Monitoring and Assessment

    NASA Astrophysics Data System (ADS)

    Hartfield, K. A.; Van Leeuwen, W. J. D.; Crimmins, M.; Marsh, S. E.; Torrey, Y.; Rahr, M.; Orr, B. J.

    2014-12-01

    Drought is an ever growing concern within the United States and Mexico. Extended periods of below-average precipitation can adversely affect agricultural production and ecosystems, impact local water resources and create conditions prime for wildfire. DroughtView (www.droughtview.arizona.edu) is a new on-line resource for scientists, natural resource managers, and the public that brings a new perspective to remote-sensing based drought impact assessment that is not currently available. DroughtView allows users to monitor the impact of drought on vegetation cover for the entire continental United States and the northern regions of Mexico. As a spatially and temporally dynamic geospatial decision support tool, DroughtView is an excellent educational introduction to the relationship between remotely sensed vegetation condition and drought. The system serves up Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data generated from 250 meter 16-day composite Moderate-resolution Imaging Spectroradiometer (MODIS) imagery from 2000 to the present. Calculation of difference from average, previous period and previous year greenness products provide the user with a proxy for drought conditions and insight on the secondary impacts of drought, such as wildfire. The various image products and overlays are served up via the ArcGIS Server platform. DroughtView serves as a useful tool to introduce and teach vegetation time series analysis to those unfamiliar with the science. High spatial resolution imagery is available as a reference layer to locate points of interest, zoom in and export images for implementation in reports and presentations. Animation of vegetation time series allows users to examine ecosystem disturbances and climate data is also available to examine the relationship between precipitation, temperature and vegetation. The tool is mobile friendly allowing users to access the system while in the field. The systems capabilities and

  12. The drought risk atlas: Enhancing decision support for drought risk management in the United States

    NASA Astrophysics Data System (ADS)

    Svoboda, Mark D.; Fuchs, Brian A.; Poulsen, Chris C.; Nothwehr, Jeff R.

    2015-07-01

    Decision makers have continuously asked for better tools and resources to help them assess their risks related to climate variability and extremes. Drought is one of the risks they face, and the need for better drought risk tools and resources has also been expressed. With drought continuing to be one of the most problematic and costly natural disasters within the United States, and building on the work of the original National Drought Atlas (NDA) (1996), an updated and expanded Drought Risk Atlas (DRA) decision support tool for the United States was developed and is housed at the National Drought Mitigation Center. The DRA (1) provides weekly calculations of multiple indices/indicators, with more than a billion records made freely available, including the SPI, SPEI, PDSI, scPDSI, Deciles and U.S. Drought Monitor; (2) houses more than 3000 stations with data through 2012, nearly tripling the station count of the original NDA; (3) utilizes a much longer period of record, nearly double that of the NDA in most cases; (4) when fully completed, will house a cache of more than 500,000 gridded drought index maps; (5) will allow us to analyze and assess trends and various characteristics of drought, including frequency, intensity, duration and magnitude; (6) will become a resource for the National Weather Service (NWS) personnel around the country by transferring the application into the field through integration within the NWS's newly developed Local Climate Analysis Tool (LCAT); and (7) work directly with the National Integrated Drought Information System (NIDIS) program office to include the information contained in the DRA into NIDIS's regional drought early warning system pilot basins and the U.S. Drought Portal for broad dissemination to the user community and general public.

  13. Toward a categorical drought prediction system based on U.S. Drought Monitor (USDM) and climate forecast

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Xia, Youlong; Luo, Lifeng; Singh, Vijay P.; Ouyang, Wei; Hao, Fanghua

    2017-08-01

    Disastrous impacts of recent drought events around the world have led to extensive efforts in drought monitoring and prediction. Various drought information systems have been developed with different indicators to provide early drought warning. The climate forecast from North American Multimodel Ensemble (NMME) has been among the most salient progress in climate prediction and its application for drought prediction has been considerably growing. Since its development in 1999, the U.S. Drought Monitor (USDM) has played a critical role in drought monitoring with different drought categories to characterize drought severity, which has been employed to aid decision making by a wealth of users such as natural resource managers and authorities. Due to wide applications of USDM, the development of drought prediction with USDM drought categories would greatly aid decision making. This study presented a categorical drought prediction system for predicting USDM drought categories in the U.S., based on the initial conditions from USDM and seasonal climate forecasts from NMME. Results of USDM drought categories predictions in the U.S. demonstrate the potential of the prediction system, which is expected to contribute to operational early drought warning in the U.S.

  14. Monitoring and seasonal forecasting of meteorological droughts

    NASA Astrophysics Data System (ADS)

    Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian

    2015-04-01

    Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. Unfortunately, a major constraint in current drought outlooks is the lack of reliable monitoring capability for observed precipitation globally in near-real time. Furthermore, drought monitoring systems requires a long record of past observations to provide mean climatological conditions. We address these constraints by developing a novel drought monitoring approach in which monthly mean precipitation is derived from short-range using ECMWF probabilistic forecasts and then merged with the long term precipitation climatology of the Global Precipitation Climatology Centre (GPCC) dataset. Merging the two makes available a real-time global precipitation product out of which the Standardized Precipitation Index (SPI) can be estimated and used for global or regional drought monitoring work. This approach provides stability in that by-passes problems of latency (lags) in having local rain-gauge measurements available in real time or lags in satellite precipitation products. Seasonal drought forecasts can also be prepared using the common methodology and based upon two data sources used to provide initial conditions (GPCC and the ECMWF ERA-Interim reanalysis (ERAI) combined with either the current ECMWF seasonal forecast or a climatology based upon ensemble forecasts. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar to or better than climatological forecasts. In some cases, particularly for long SPI time

  15. Categorical Drought Monitoring and Prediction in the United States Based on NLDAS-2

    NASA Astrophysics Data System (ADS)

    Hao, Z.; Xia, Y.; Hao, F.; Singh, V. P.

    2015-12-01

    Drought is a pervasive natural hazard and is a billion-dollar disaster in the United States, which is comparable to hurricanes and tropical storms with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined. Drought early warning is of critical importance for drought preparedness planning and mitigation efforts to reduce potential impacts of drought, for which drought monitoring and prediction are the essential components. The U.S. Drought Monitor (USDM) has been widely used to track droughts and their impacts. USDM is a composite product that blends quantitative drought indicators and qualitative drought information from multiple sources and classifies drought conditions into different drought categories. Due to the wide application of USDM products, drought monitoring and prediction in the categorical form would be of great importance to aid decision makers to take appropriate measures for drought managements. Based on drought indices from North American Land Data Assimilation System Phase 2 (NLDAS-2), this study proposes a statistical method for the categorical drought monitoring and prediction in the United States. The probabilities of drought conditions falling into different USDM drought categories can be estimated from the proposed method. The method is found to satisfactorily reconstruct historical USDM drought categories and predict future USDM drought categories, and has considerable potential to aid early drought warning in the United States.

  16. Drought monitoring: Historical and current perspectives

    USDA-ARS?s Scientific Manuscript database

    Given the complex dimensions of drought and the challenges they pose for routine drought monitoring, it is essential that we continue to find innovative and robust ways to quantify and more effectively communicate the impacts of this hazard as part of an operational Drought Early Warning System. Th...

  17. The Global Drought Information System - A Decision Support Tool with Global Applications

    NASA Astrophysics Data System (ADS)

    Arndt, D. S.; Brewer, M.; Heim, R. R., Jr.

    2014-12-01

    underway with an emphasis on information and decision making, and how to effectively provide drought early warning. This talk will provide an update on the status of GDIS and its role in international drought monitoring.

  18. Monitoring vegetation responses to drought -- linking Remotely-sensed Drought Indices with Meteorological drought indices

    NASA Astrophysics Data System (ADS)

    Wang, H.; Lin, H.; Liu, D.

    2013-12-01

    Abstract: Effectively monitoring vegetation drought is of great significance in ecological conservation and agriculture irrigation at the regional scale. Combining meteorological drought indices with remotely sensed drought indices can improve tracking vegetation dynamic under the threat of drought. This study analyzes the dynamics of spatially-defined Temperature Vegetation Dryness Index (TVDI) and temporally-defined Vegetation Health Index (VHI) from remotely sensed NDVI and LST datasets in the dry spells in Southwest China. We analyzed the correlation between remotely sensed drought indices and meteorological drought index of different time scales. The results show that TVDI was limited by the spatial variations of LST and NDVI, while VHI was limited by the temporal variations of LST and NDVI. Station-based buffering analysis indicates that the extracted remotely sensed drought indices and Standard Precipitation Index (SPI) could reach stable correlation with buffering radius larger than 35 km. Three factors affect the spatiotemporal relationship between remotely sensed drought indices and SPI: i) different vegetation types; ii) the timescale of SPI; and iii) remote sensing data noise. Vegetation responds differently to meteorological drought at various time scales. The correlation between SPI6 and VHI is more significant than that between SPI6 and TVDI. Spatial consistency between VHI and TVDI varies with drought aggravation. In early drought period from October to December, VHI and TVDI show limited consistency due to the low quality of remotely sensed images. The study helps to improve monitoring vegetation drought using both meteorological drought indices and remotely sensed drought indices.

  19. Developing a European Drought Observatory for Monitoring, Assessing and Forecasting Droughts across the European Continent

    NASA Astrophysics Data System (ADS)

    Vogt, J.; Barbosa, P.; Hofer, B.; Magni, D.; Jager, A. D.; Singleton, A.; Horion, S.; Sepulcre, G.; Micale, F.; Sokolova, E.; Calcagni, L.; Marioni, M.; Antofie, T. E.

    2011-12-01

    Many European countries have repeatedly been affected by droughts, resulting in considerable ecological and economic damage. Climate change studies indicate a trend towards increasing climate variability most likely resulting in more frequent drought occurrences also in Europe. Against this background, the European Commission's Joint Research Centre (JRC) is developing methods and tools for assessing, monitoring and forecasting droughts in Europe and develops a European Drought Observatory (EDO) to complement national activities with a European view. As droughts affect the entire water cycle continuous monitoring of a suite of indicators is required. Drought indicators at continental scale are supplemented by indicators at national, regional and local scales, providing more detailed information. At the core of the European Drought Observatory (EDO) are a portal and a map server presenting Europe-wide up-to-date drought-relevant information to the public and to decision makers in policy and water resources management. The final portal will include access to metadata catalogues, media reports, a map server and other related resources. The current version of EDO publishes continental information based on data processed and analysed at JRC as well as more detailed information at national and river basin scale processed by the local authorities. Available drought products include monthly updated Standardized Precipitation Indices (SPI), modelled soil moisture anomalies, remote sensing observations on the state of the vegetation cover (i.e. fAPAR and NDWI) and groundwater levels. A one-week soil moisture anomaly forecast complements the picture. Access to information at the national and river basin scale is established through interoperability arrangements with local authorities, making use of a special metadata catalogue and OWS standards (especially WMS and WCS). In addition, time series of drought indices can be retrieved for grid cells and administrative regions in

  20. An Interface to Drought Mitigation: Decision Support Tools from the National Drought Mitigation Center

    NASA Astrophysics Data System (ADS)

    Svoboda, M.; Fuchs, B.; Hayes, M. J.

    2012-12-01

    The National Drought Mitigation Center (NDMC) (http://drought.unl.edu) has been working with the National Integrated Drought Information System (NIDIS) (http://drought.gov) and other partners with a goal of developing tools to enhance drought risk management activities around the world. The NDMC is a national center founded in 1995 and located at the University of Nebraska-Lincoln. The NDMC conducts basic and applied research, services and decision support applications, along with maintaining a number of operational drought-related tools and products including the U.S. Drought Monitor (USDM), Drought Impact Reporter (DIR) and Vegetation Drought Response Index (VegDRI). The NDMC's newly launched National Drought Atlas (NDA) will be the focus of this presentation. Building off the concept of the original National Electronic Drought Atlas (NEDA) developed by the United States Army Corps of Engineers (led by Hoskings, Wallis and Guttman in the early 1990s), the original NEDA consisted of approximately 1000 stations taken from the Historical Climate Network (HCN). The period of record was limited at that time with most stations only having digital data from the late 1940s to present. For the NDMC's NDA, more than 12,000 stations with precipitation and/or temperature records from the National Weather Service Cooperative data (COOP) network were analyzed through the Regional Climate Centers' (RCCs) Applied Climate Information System (ACIS). From the initial sample set of 12,000 sites considered, over 3000 stations had at least 40 years of data and over 1700 sites had over 60 years of data meeting our criteria. A unique period of record (POR) was established for each station based on the screening criteria, with each station having a unique starting date. From the final selection of 3059 stations, all have at least 40+ years of data and 827 sites contain over 80+ years of data. In essence, the new NDA tripled the size and doubled the period of record of those sites used in

  1. The Global Drought Information System - A Decision Support Tool with Global Applications

    NASA Astrophysics Data System (ADS)

    Heim, R. R.; Brewer, M.

    2012-12-01

    Drought is a natural hazard which can cause famine in developing countries and severe economic hardship in developed countries. Given current concerns with the increasing frequency and magnitude of droughts in many regions of the world, especially in the light of expected climate change, drought monitoring and dissemination of early warning information in a timely fashion on a global scale is a critical concern as an important adaptation and mitigation strategy. While a number of nations, and a few continental-scale activities have developed drought information system activities, a global drought early warning system (GDEWS) remains elusive, despite the benefits highlighted by ministers to the Global Earth Observation System of System in 2008. In an effort to begin a process of drought monitoring with international collaboration, the National Integrated Drought Information System's (NIDIS) U.S. Drought Portal, a web-based information system created to address drought services and early warning in the United States, including drought monitoring, forecasting, impacts, mitigation, research, and education, volunteered to develop a prototype Global Drought Monitoring Portal (GDMP). Through integration of data and information at the global level, and with four continental-level partners, the GDMP has proven successful as a tool to monitor drought around the globe. At a recent meeting between NIDIS, the World Meteorological Organization, and the Global Earth Observation System of Systems, it was recommended that the GDMP form the basis for a Global Drought Information System (GDIS). Currently, GDIS activities are focused around incorporating additional drought monitoring information, especially from those areas without regional or continental-scale input, and incorporating drought-specific climate forecast information from the World Climate Research Programme. Additional GDIS pilot activities are underway with an emphasis on information and decision making, and how to

  2. Monitoring groundwater drought with GRACE data assimilation

    NASA Astrophysics Data System (ADS)

    Li, B.; Rodell, M.; Beaudoing, H. K.; Getirana, A.; Zaitchik, B. F.

    2015-12-01

    Groundwater drought is a distinct class of drought, not a sub-class of meteorological, agricultural and hydrological drought and has profound impacts on natural environments and societies. Due to a deficiency of in situ measurements, we developed a groundwater drought indicator using groundwater change estimates derived by assimilating GRACE derived terrestrial water storage (TWS) anomalies into the NASA Catchment land surface model. Data assimilation enables spatial and temporal downscaling of coarse GRACE TWS observations (monthly and ~150,000 km2 effective spatial resolution) and extrapolation to near-real time. In this talk, we will present our latest progress on using GRACE satellite data for groundwater drought monitoring in the U.S. and globally. Characteristics of this groundwater drought indicator will be discussed, including its relationship with other types of drought and how they are influenced by model physics and climate conditions. Results are evaluated using in situ groundwater observations.

  3. Global integrated drought monitoring and prediction system.

    PubMed

    Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza

    2014-01-01

    Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe.

  4. Global integrated drought monitoring and prediction system

    PubMed Central

    Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza

    2014-01-01

    Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe. PMID:25977759

  5. Development and Applications of the U.S. Drought Monitor and North America Drought Monitor

    NASA Astrophysics Data System (ADS)

    Heim, R. R.

    2007-05-01

    Drought is an important climatological phenomenon which has significant socioeconomic and environmental impacts. Several drought indices have been developed during the last hundred years to quantify drought, but all of them either were developed for specific applications and/or regions, or have limitations which restrict their use. In the U.S., a drought monitoring tool was developed in the late 1990s through a federal-state collaborative effort to consolidate and centralize drought monitoring activities. This tool, the U.S. Drought Monitor (USDM), consists of a weekly map and narrative product which describes current drought conditions according to a scale ranging from moderate drought (D1) to exceptional drought (D4). Conditions which are dry but not yet of drought severity are categorized as abnormally dry (D0). The USDM draws its strength from the collaborative input at the federal (USDA, NOAA), regional (NOAA Regional Climate Centers), state, and local levels and from the objective synthesis of several drought-related indices. In late 2002, the methodology of the USDM was extended internationally to develop the North America Drought Monitor (NADM), which is a monthly map and narrative product that describes drought across the U.S., Mexico, and Canada. This paper will describe the development of the USDM and NADM, and discuss examples of their applications.

  6. Climate Engine - Monitoring Drought with Google Earth Engine

    NASA Astrophysics Data System (ADS)

    Hegewisch, K.; Daudert, B.; Morton, C.; McEvoy, D.; Huntington, J. L.; Abatzoglou, J. T.

    2016-12-01

    Drought has adverse effects on society through reduced water availability and agricultural production and increased wildfire risk. An abundance of remotely sensed imagery and climate data are being collected in near-real time that can provide place-based monitoring and early warning of drought and related hazards. However, in an era of increasing wealth of earth observations, tools that quickly access, compute, and visualize archives, and provide answers at relevant scales to better inform decision-making are lacking. We have developed ClimateEngine.org, a web application that uses Google's Earth Engine platform to enable users to quickly compute and visualize real-time observations. A suite of drought indices allow us to monitor and track drought from local (30-meters) to regional scales and contextualize current droughts within the historical record. Climate Engine is currently being used by U.S. federal agencies and researchers to develop baseline conditions and impact assessments related to agricultural, ecological, and hydrological drought. Climate Engine is also working with the Famine Early Warning Systems Network (FEWS NET) to expedite monitoring agricultural drought over broad areas at risk of food insecurity globally.

  7. Advancing Drought Understanding, Monitoring and Prediction

    NASA Technical Reports Server (NTRS)

    Mariotti, Annarita; Schubert, Siegfried D.; Mo, Kingtse; Peters-Lidard, Christa; Wood, Andy; Pulwarty, Roger; Huang, Jin; Barrie, Dan

    2013-01-01

    Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem

  8. The challenges of empirical impact prediction with monitored drought indices

    NASA Astrophysics Data System (ADS)

    Stahl, Kerstin; Bachmair, Sophie; Blauhut, Veit; Kohn, Irene; Tijdeman, Erik

    2017-04-01

    Drought monitoring and early warning still relies primarily on drought indicators selected or combined from hydro-meteorological variables, such as precipitation, modeled soil moisture, observed or modeled streamflow, and in some cases remotely sensed vegetation health. To guide the selection and give these indices more meaning for drought management decisions, a number of studies have investigated empirically the linkage between these indices and records of drought impact occurrence. These studies have been inspired by the damage function approach employed in risk assessments of other natural hazards. In this contribution we systematically review and assess the feasibility of finding impact-indicator link functions suitable for prediction. Impact information was derived from large archives of text-based, coded impact reports, such as the European Drought Impact report Inventory and the US Drought Impact Reporter and link functions were analyzed at various spatial scales for various subsets of impact types and drought events. The identified challenges include the rapid decrease of data when subsetting for specific impact sectors or smaller spatial areas, the choice of the link model, and a variety of potential dynamic changes to the underlying conditions between and even during drought events. Based on the assessment, recommendations for a successful and applicable link model include in particular a careful pre-processing of index and impact data and more systematic impact data collection in the future.

  9. Drought Monitoring in Peru as a Climate Service

    NASA Astrophysics Data System (ADS)

    Lavado, Waldo; Felipe, Oscar; Caycho, Tania; Sosa, Jesus; Fernandez, Carlos; Endara, Sofia

    2015-04-01

    Given the need to reduce socio- economic and environmental drought in Peru as well as the vulnerability and increasing responsiveness and recovery to these events, the National Service of Meteorology and Hydrology of Peru (SENAMHI ) in conjunction with the Peru's Environment Ministry has developed a plan Drought Monitoring nationwide, which consists of two components: 1) Monitoring System and 2 ) Dissemination System . The first component consists of calculating drought indicators at national level; and for that purpose we have selected the following indexes: Normal Precipitation Index (NPI), Standardized Precipitation Index (SPI) , Precipitation Concentration Index (PCI) , Vegetation Condition Index (VCI ) , Temperature Condition Index ( TCI) , Healthy Vegetation Index (VHI ) and Streamflow Drought Index (SDI). In order to estimate these index observed climatological and hydrological data of SENAMHI network is used as well as remote sensing data of precipitation, temperature and vegetation (TRMM, CHIRPS and MODIS). The second component is the spread of these indicators and a compilation thereof to a summary document that integrates all indicators (Monthly Bulletin). This will be done through newsletters and a website (www.senamhi.gob.pe/serviciosclimaticos); in the case of exceptional drought events special notes will be made. A date has launched the first newsletter in September 2014. This drought monitoring system will be used as an instrument of climate service and we intend to make it a useful tool for decision makers and the general population .

  10. Drought, Wetland, and Flood Monitoring with Satellite Scatterometer

    NASA Astrophysics Data System (ADS)

    Nghiem, S. V.; Brakenridge, G. R.; Neumann, G.

    2007-05-01

    Monitoring droughts, wetlands, and floods demands large scale and frequent coverage by satellite observations. Launched in 1999, the National Aeronautics and Space Administration (NASA) SeaWinds scatterometer aboard the QuikSCAT (QSCAT) satellite can collect backscatter data over 90% of the world in a day. The satellite scatterometer has acquired about 8 years of data and is currently measuring the Earth in 14 orbits per day. For drought monitoring, QSCAT data can detect surface soil moisture change and corresponding vegetation change. QSCAT identified drought conditions in the Midwest region of the United States in 2003 as the precipitation frequency observed by QSCAT decreases significantly. In Nairobi, Kenya, long-term QSCAT monitoring shows the severe droughts of 2000 and 2005. QSCAT data will be used together with other data types to enhance the U.S. Drought Monitor (USDM) to be transitioned into the National Integrated Drought Information System (NIDIS). At the other extreme, QSCAT data reveal the timing and patterns of surface soil moisture changes associated with winter storms in California in 2005 and with extreme hurricanes such as Ivan in 2004, Katrina, and Rita in 2005. Flood inundated areas are delineated by QSCAT along the Lena River, and such flooding is related to the snowmelt duration. QSCAT observations show that the Flood of Century along the Lena River in 2001 occurred after an excessively rapid spring melt period. QSCAT data are appropriate for wetland monitoring. The dynamics of wetlands in the Mississippi River basin observed by QSCAT include river discharge lagging the wetland change: first excess surface water is measured, and then streamflow increases. QSCAT data also capture the extreme seasonal wetland dynamics over the region of the Sudd swamps along the upper reaches of the White Nile River in southern Sudan. With the QSCAT capability in monitoring drought, wetland, and flood frequently over the world, QSCAT results will be crucial for

  11. Overview and Update of the North America Drought Monitor and North America Climate Extremes Monitoring System

    NASA Astrophysics Data System (ADS)

    Heim, R. R.

    2006-12-01

    The North America Drought Monitor (NADM) is a joint operational drought monitoring activity between scientists and other specialists in the United States, Mexico, and Canada. Like all weather phenomena, drought occurs irrespective of political and international boundaries. The monthly map and narrative product created by this first-of-its-kind effort provides an integrated continental-scale drought assessment tool for decision-makers in all three countries involved in drought monitoring, drought mitigation, and related climate services. The product is prepared by a rotating primary author who utilizes drought indicators which are computed using standard methodologies for stations across the continent, plus national drought monitoring products and feedback from local experts in each of the three countries. The participants include, within the United States: the NOAA National Climatic Data Center, NOAA Climate Prediction Center, USDA Joint Agricultural Weather Facility, and National Drought Mitigation Center; within Mexico: Servicio Meteorologico Nacional/Comision Nacional del Agua; and within Canada: Agriculture and Agrifood Canada and the Meteorological Service of Canada. The NADM is part of a North America Climate Extremes Monitoring (NACEM) system which will monitor and assess climate extremes across the continent. Several climate indicators are currently computed from station daily data to measure (in addition to drought) heavy precipitation, heat waves, and cold waves. Future efforts will add indicators to monitor storm severity and severe weather, including the creation of a North America Climate Extremes Index (NACEI) patterned after the U.S. Climate Extremes Index (USCEI). This presentation will review the history of the NADM/NACEM effort, the data utilized, the indicators computed, and the product preparation and peer review process.

  12. The Global Integrated Drought Monitoring and Prediction System (GIDMaPS): Overview and Capabilities

    NASA Astrophysics Data System (ADS)

    AghaKouchak, A.; Hao, Z.; Farahmand, A.; Nakhjiri, N.

    2013-12-01

    Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness and management. Motivated by the Global Drought Information Systems (GDIS) activities, this paper presents the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which provides near real-time drought information using both remote sensing observations and model simulations. The monthly data from the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-Land), North American Land Data Assimilation System (NLDAS), and remotely sensed precipitation data are used as input to GIDMaPS. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is based on two input data sets (MERRA and NLDAS) and three drought indicators (SPI, SSI and MSDI). The drought prediction model provides the empirical probability of drought for different severity levels. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The results indicate

  13. Drought monitoring using remote sensing of evapotranspiration

    USDA-ARS?s Scientific Manuscript database

    Drought assessment is a complex endeavor, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture (SM), ground and surface water anomalies reflect deficiencies in moist...

  14. RISA progress in the development of drought indicators to support decision making

    NASA Astrophysics Data System (ADS)

    Close, S.; Simpson, C.

    2015-12-01

    Communities around the country are increasingly recognizing the need to plan for water shortages and long-term drought. To build preparedness and help communities manage risk, researchers funded by NOAA's National Integrated Drought Information System (NIDIS) Coping with Drought initiative through the Regional Integrated Sciences and Assessments (RISA) program are working to better understand these impacts across the country and work with communities and resource managers to develop adaptation strategies that meet their needs. The Coping with Drought initiative supports research involving the use of climate predictions and forecast information in decision-making across a range of sectors including agriculture, natural and water resources management, and public health. As a component of this initiative, the RISA program supported research and engagement to develop indicators of drought designed to be of most use to managers and planners grappling with severe and in some cases ongoing drought in their regions. Indicators are being developed for coastal ecosystems in the Carolinas, water management in California, and native communities in Arizona. For instance, the California Nevada Applications Program (CNAP) RISA developed a percentile-based indicator system for analyzing historic droughts and characterizing the ongoing California drought. And in the Southwest, the Climate Assessment for the Southwest (CLIMAS) RISA has been working with the Hopi community on drought monitoring and planning to develop the first-ever Hopi Quarterly Drought Status Report which integrates scientific and local knowledge about drought. This presentation will discuss RISA's role in developing drought indicators based on engagement with decision makers and how this work fits into the larger role that RISAs are playing in the development of the NIDIS Regional Drought Early Warning Systems across the U.S.

  15. Towards developing drought impact functions to advance drought monitoring and early warning

    NASA Astrophysics Data System (ADS)

    Bachmair, Sophie; Stahl, Kerstin; Hannaford, Jamie; Svoboda, Mark

    2015-04-01

    In natural hazard analysis, damage functions (also referred to as vulnerability or susceptibility functions) relate hazard intensity to the negative effects of the hazard event, often expressed as damage ratio or monetary loss. While damage functions for floods and seismic hazards have gained considerable attention, there is little knowledge on how drought intensity translates into ecological and socioeconomic impacts. One reason for this is the multifaceted nature of drought affecting different domains of the hydrological cycle and different sectors of human activity (for example, recognizing meteorological - agricultural - hydrological - socioeconomic drought) leading to a wide range of drought impacts. Moreover, drought impacts are often non-structural and hard to quantify or monetarize (e.g. impaired navigability of streams, bans on domestic water use, increased mortality of aquatic species). Knowledge on the relationship between drought intensity and drought impacts, i.e. negative environmental, economic or social effects experienced under drought conditions, however, is vital to identify critical thresholds for drought impact occurrence. Such information may help to improve drought monitoring and early warning (M&EW), one goal of the international DrIVER project (Drought Impacts: Vulnerability thresholds in monitoring and Early-warning Research). The aim of this study is to test the feasibility of designing "drought impact functions" for case study areas in Europe (Germany and UK) and the United States to derive thresholds meaningful for drought impact occurrence; to account for the multidimensionality of drought impacts, we use the broader term "drought impact function" over "damage function". First steps towards developing empirical drought impact functions are (1) to identify meaningful indicators characterizing the hazard intensity (e.g. indicators expressing a precipitation or streamflow deficit), (2) to identify suitable variables representing impacts

  16. The Utility of the Real-Time NASA Land Information System Data for Drought Monitoring Applications

    NASA Technical Reports Server (NTRS)

    White, Kristopher D.; Case, Jonathan L.

    2013-01-01

    Measurements of soil moisture are a crucial component for the proper monitoring of drought conditions. The large spatial variability of soil moisture complicates the problem. Unfortunately, in situ soil moisture observing networks typically consist of sparse point observations, and conventional numerical model analyses of soil moisture used to diagnose drought are of coarse spatial resolution. Decision support systems such as the U.S. Drought Monitor contain drought impact resolution on sub-county scales, which may not be supported by the existing soil moisture networks or analyses. The NASA Land Information System, which is run with 3 km grid spacing over the eastern United States, has demonstrated utility for monitoring soil moisture. Some of the more useful output fields from the Land Information System are volumetric soil moisture in the 0-10 cm and 40-100 cm layers, column-integrated relative soil moisture, and the real-time green vegetation fraction derived from MODIS (Moderate Resolution Imaging Spectroradiometer) swath data that are run within the Land Information System in place of the monthly climatological vegetation fraction. While these and other variables have primarily been used in local weather models and other operational forecasting applications at National Weather Service offices, the use of the Land Information System for drought monitoring has demonstrated utility for feedback to the Drought Monitor. Output from the Land Information System is currently being used at NWS Huntsville to assess soil moisture, and to provide input to the Drought Monitor. Since feedback to the Drought Monitor takes place on a weekly basis, weekly difference plots of column-integrated relative soil moisture are being produced by the NASA Short-term Prediction Research and Transition Center and analyzed to facilitate the process. In addition to the Drought Monitor, these data are used to assess drought conditions for monthly feedback to the Alabama Drought Monitoring

  17. The drought calculator: decision support tool for predicting forage growth during drought

    USDA-ARS?s Scientific Manuscript database

    The Drought Calculator (DC), a spreadsheet-based decision support system, was developed to help ranchers and range managers predict reductions in forage production due to drought. Forage growth potential (FGP) is predicted as a weighted average of monthly precipitation during the spring. Precipita...

  18. Advancements in satellite remote sensing for drought monitoring

    USDA-ARS?s Scientific Manuscript database

    Drought monitoring is a key component for effective drought preparedness strategies, providing critical information on current conditions that can be used to trigger mitigation actions to lessen the impact of this natural hazard. However, drought can be both complex and challenging to monitor becau...

  19. Use of decision support systems as a drought management tool

    USGS Publications Warehouse

    Frevert, D.; Lins, H.; ,

    2005-01-01

    Droughts present a unique challenge to water managers throughout the world and the current drought in the western United States is taxing facilities to the limit. Coping with this severe drought requires state of the art decision support systems including efficient and accurate hydrologic process models, detailed hydrologic data bases and effective river systems management modeling frameworks. This paper will outline a system of models developed by the Bureau of Reclamation, the US Geological Survey, the University of Colorado and a number of other governmental and university partners. The application of the technology to drought management in several key western river basins will be discussed.

  20. The Vegetation Drought Response Index for Canada (VegDRI-Canada) to Monitor Agricultural Drought: First Results

    NASA Astrophysics Data System (ADS)

    Tadesse, T.; Wardlow, B.; Brown, J. F.; Champagne, C.; Hadwen, T. A.; Demisse, G. B.; Bayissa, Y. A.

    2016-12-01

    Drought is complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information that is timely and region-specific to identify where and when droughts are occurring. To help decision makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. The Vegetation Drought Response Index for Canada (VegDRI-Canada) has recently been developed for Canada extending the initial VegDRI concept developed for the continental United States to a broader trans-national extent. VegDRI-Canada models are similar to those developed for the United States integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the VegDRI-Canada to isolate the effects of drought on vegetation. Twenty three weekly VegDRI-Canada models were built for the growing season (April-September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (2000 to 2014) was produced using this models and the output was validated by randomly selecting 20% of the historical data (withheld for validation) across the growing season ranged from 0.6 and 0.77. A case study was conducted to assessthe VegDRI-Canada over the Prairie Region of Canada for two drought and one non-drought year for three growing season periods (early-, mid- and late season). A comparison of the VegDRI-Canada map with the Canadian Drought Monitor (CDM) showed that the VegDRI-Canada maps depicted key spatial drought severity patterns during the two targeted drought years consistent with the CDM. The relationship between the VegDRI-Canada values and canola yields was assessed and showed tet VegDRI-Canada values had a

  1. Agricultural drought risk monitoring and yield loss forecast with remote sensing data

    NASA Astrophysics Data System (ADS)

    Nagy, Attila; Tamás, János; Fehér, János

    2015-04-01

    The World Meteorological Organization (WMO) and Global Water Partnership (GWP) have launched a joint Integrated Drought Management Programme (IDMP) to improve monitoring and prevention of droughts. In the frame of this project this study focuses on identification of agricultural drought characteristics and elaborates a monitoring method (with application of remote sensing data), which could result in appropriate early warning of droughts before irreversible yield loss and/or quality degradation occur. The spatial decision supporting system to be developed will help the farmers in reducing drought risk of the different regions by plant specific calibrated drought indexes. The study area was the Tisza River Basin, which is located in Central Europe within the Carpathian Basin. For the investigations normalized difference vegetation index (NDVI) was used calculated from 16 day moving average chlorophyll intensity and biomass quantity data. The results offer concrete identification of remote sensing and GIS data tools for agricultural drought monitoring and forecast, which eventually provides information on physical implementation of drought risk levels. In the first step, we statistically normalized the crop yield maps and the MODIS satellite data. Then the drought-induced crop yield loss values were classified. The crop yield loss data were validated against the regional meteorological drought index values (SPI), the water management and soil physical data. The objective of this method was to determine the congruency of data derived from spectral data and from field measurements. As a result, five drought risk levels were developed to identify the effect of drought on yields: Watch, Early Warning, Warning, Alert and Catastrophe. In the frame of this innovation such a data link and integration, missing from decision process of IDMP, are established, which can facilitate the rapid spatial and temporal monitoring of meteorological, agricultural drought phenomena and its

  2. Satellite Gravimetry Applied to Drought Monitoring

    NASA Technical Reports Server (NTRS)

    Rodell, Matthew

    2010-01-01

    Near-surface wetness conditions change rapidly with the weather, which limits their usefulness as drought indicators. Deeper stores of water, including root-zone soil wetness and groundwater, portend longer-term weather trends and climate variations, thus they are well suited for quantifying droughts. However, the existing in situ networks for monitoring these variables suffer from significant discontinuities (short records and spatial undersampling), as well as the inherent human and mechanical errors associated with the soil moisture and groundwater observation. Remote sensing is a promising alternative, but standard remote sensors, which measure various wavelengths of light emitted or reflected from Earth's surface and atmosphere, can only directly detect wetness conditions within the first few centimeters of the land s surface. Such sensors include the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) C-band passive microwave measurement system on the National Aeronautic and Space Administration's (NASA) Aqua satellite, and the combined active and passive L-band microwave system currently under development for NASA's planned Soil Moisture Active Passive (SMAP) satellite mission. These instruments are sensitive to water as deep as the top 2 cm and 5 cm of the soil column, respectively, with the specific depth depending on vegetation cover. Thermal infrared (TIR) imaging has been used to infer water stored in the full root zone, with limitations: auxiliary information including soil grain size is required, the TIR temperature versus soil water content curve becomes flat as wetness increases, and dense vegetation and cloud cover impede measurement. Numerical models of land surface hydrology are another potential solution, but the quality of output from such models is limited by errors in the input data and tradeoffs between model realism and computational efficiency. This chapter is divided into eight sections, the next of which describes

  3. An extended multivariate framework for drought monitoring in Mexico

    NASA Astrophysics Data System (ADS)

    Real-Rangel, Roberto; Pedrozo-Acuña, Adrián; Breña-Naranjo, Agustín; Alcocer-Yamanaka, Víctor

    2017-04-01

    Around the world, monitoring natural hazards, such as droughts, represents a critical task in risk assessment and management plans. A reliable drought monitoring system allows to identify regions affected by these phenomena so that early response measures can be implemented. In Mexico, this activity is performed using Mexico's Drought Monitor, which is based on a similar methodology as the United States Drought Monitor and the North American Drought Monitor. The main feature of these monitoring systems is the combination of ground-based and remote sensing observations that is ultimately validated by local experts. However, in Mexico in situ records of variables such as precipitation and streamflow are often scarce, or even null, in many regions of the country. Another issue that adds uncertainty in drought monitoring is the arbitrary weight given to each analyzed variable. This study aims at providing an operational framework for drought monitoring in Mexico, based on univariate and multivariate nonparametric standardized indexes proposed in recent studies. Furthermore, the framework has been extended by taking into account the Enhanced Vegetation Index (EVI) for the drought severity assessment. The analyzed variables used for computing the drought indexes are mainly derived from remote sensing (MODIS) and land surface models datasets (NASA MERRA-2). A qualitative evaluation of the results shows that the indexes used are capable of adequately describes the intensity and spatial distribution of past drought documented events.

  4. Global Drought Information System: Influence of Differences in Land Surface Model Dynamics on Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Nijssen, B.; Shukla, S.; Mo, K. C.; Lettenmaier, D. P.

    2014-12-01

    Real-time drought monitoring enables a proactive drought management approach that can lead to timely actions to mitigate the losses due to a drought event. In recent years, the availability of long-term, high quality, satellite and reanalysis based datasets of atmospheric forcings, combined with the development of state-of-the-art hydrologic models have made real-time global drought monitoring feasible. Hydrologic models are invaluable tools for global drought monitoring given the scarcity of long-term moisture observations (e.g. soil moisture, streamflow). However, as valuable as they are for drought monitoring, characteristics of a drought event (i.e. onset, severity and persistence) as estimated by a hydrologic model depend on the model's parameters (e.g. soil and vegetation parameters) and its inherent dynamics that guide the partition of precipitation into evapotranspiration and runoff. One approach to account for the differences in drought estimates due to differences in model dynamics is to use multiple hydrologic models. Each hydrologic model is forced with the same atmospheric forcings to simulate moisture conditions which are converted into objective drought indicators (e.g. soil moisture percentile) with respect to the model's own climatology and then those estimates are combined to provide a multimodel based drought estimates. The University of Washington's Global Drought Information System (GDIS) developed in 2013, is one such prototype drought monitoring system. This system uses the VIC, NOAH and Catchment models. In this presentation we investigate how the differences in the dynamics of the models used in UW's GDIS, influence the drought monitoring estimates. Specifically we answer following questions: 1.What is the level of uncertainties in drought onset, severity and persistence as estimated by different hydrologic models? 2. How do the uncertainties vary spatially and seasonally? 3. What are the sources of the uncertainties?

  5. Improved drought monitoring through advance sensors

    NASA Astrophysics Data System (ADS)

    Aparna, N.; Ramani, A.; Chandrasekaran, D.; Arokiadas, R.

    Remote sensing has been a tool for the monitoring of the Earth Resources and surface conditions. The monitoring of the Earth conditions has become very important in the modern world as the impact of the modern civilization is directly on the resources of the Earth. Many impacts like floods , drought , earth quakes forest fires are to be monitored in and effective way. With the launch of the new advance sensors such monitoring has to be explored. Presently NOAA data is very commo nly used for drought assessment in India. This is being done using the NDVI concept. An attempt has been made to study the drought conditions using IRS-P4 OCM data. IRS-P4 OCM is an advanced sensor launched for OCEAN applications from India. With the launch of the advance sensors with the visible and infra red bands it is essential to know and explore the capabilities of the sensor. A comparative study was made with NOAA AVHRR sensor which also was launched for Ocean applications but widely used for land applications. An NDVI product was generated using the Band 6 and Band 8 of IRS-P4 OCM data , and the results were compared with the NOAA NDVI product. The results shows the radiance values of IRS-P4 band 6 (660- 680 nm) and band 8(845 - 885 nm) are comparable with NOAA band 1 and band2 .The two data sets were resampled and geo corrected and registered with each other. After registration the NOAA data was converted into radiance product and NDVI product was generated. In case of OCM after the data was converted into radiance product the NDVI was generated using the band 6 and band 8 as NDVI will give better results in Infra Red region.. In comparison to AVHRR data we could get more classes because of high radiometric resolution of 12 bit in comparison with NOAA of 10 bit. This also can be attributed to the high spatial resolution of 360 m compared to 1.1Km.In OCM due to the narrow band width the discriminability of vegetation is more. In case of NOAA after the scaling is done the range of

  6. The Drought Task Force and Research on Understanding, Predicting, and Monitoring Drought

    NASA Astrophysics Data System (ADS)

    Barrie, D.; Mariotti, A.; Archambault, H. M.; Hoerling, M. P.; Wood, E. F.; Koster, R. D.; Svoboda, M.

    2016-12-01

    Drought has caused serious social and economic impacts throughout the history of the United States. All Americans are susceptible to the direct and indirect threats drought poses to the Nation. Drought challenges agricultural productivity and reduces the quantity and quality of drinking water supplies upon which communities and industries depend. Drought jeopardizes the integrity of critical infrastructure, causes extensive economic and health impacts, harms ecosystems, and increases energy costs. Ensuring the availability of clean, sufficient, and reliable water resources is a top national and NOAA priority. The Climate Program Office's Modeling, Analysis, Predictions, and Projections (MAPP) program, in partnership with the NOAA-led National Integrated Drought Information System (NIDIS), is focused on improving our understanding of drought causes, evolution, amelioration, and impacts as well as improving our capability to monitor and predict drought. These capabilities and knowledge are critical to providing communities with actionable, reliable information to increase drought preparedness and resilience. This poster will present information on the MAPP-organized Drought Task Force, a consortium of investigators funded by the MAPP program in partnership with NIDIS to advance drought understanding, monitoring, and prediction. Information on Task Force activities, products, and MAPP drought initiatives will be described in the poster, including the Task Force's ongoing focus on the California drought, its predictability, and its causes.

  7. Drought monitoring and assessment: Remote sensing and modeling approaches for the Famine Early Warning Systems Network

    USGS Publications Warehouse

    Senay, Gabriel; Velpuri, Naga Manohar; Bohms, Stefanie; Budde, Michael; Young, Claudia; Rowland, James; Verdin, James

    2015-01-01

    Drought monitoring is an essential component of drought risk management. It is usually carried out using drought indices/indicators that are continuous functions of rainfall and other hydrometeorological variables. This chapter presents a few examples of how remote sensing and hydrologic modeling techniques are being used to generate a suite of drought monitoring indicators at dekadal (10-day), monthly, seasonal, and annual time scales for several selected regions around the world. Satellite-based rainfall estimates are being used to produce drought indicators such as standardized precipitation index, dryness indicators, and start of season analysis. The Normalized Difference Vegetation Index is being used to monitor vegetation condition. Several satellite data products are combined using agrohydrologic models to produce multiple short- and long-term indicators of droughts. All the data sets are being produced and updated in near-real time to provide information about the onset, progression, extent, and intensity of drought conditions. The data and products produced are available for download from the Famine Early Warning Systems Network (FEWS NET) data portal at http://earlywarning.usgs.gov. The availability of timely information and products support the decision-making processes in drought-related hazard assessment, monitoring, and management with the FEWS NET. The drought-hazard monitoring approach perfected by the U.S. Geological Survey for FEWS NET through the integration of satellite data and hydrologic modeling can form the basis for similar decision support systems. Such systems can operationally produce reliable and useful regional information that is relevant for local, district-level decision making.

  8. Enhanced agricultural drought monitoring using a soil water anomaly-based drought index in south-west India

    NASA Astrophysics Data System (ADS)

    Hochstöger, Simon; Pfeil, Isabella; Amarnath, Giriraj; Pani, Peejush; Enenkel, Markus; Wagner, Wolfgang

    2017-04-01

    In India, agriculture accounts for roughly 17% of the GDP and employs around 50% of the total workforce. Especially in the western part of India, most of the agricultural fields are non-irrigated. Hence, agriculture is highly dependent on the monsoon in these areas. However, the absence of rainfall during the monsoon season increases the occurrence of drought periods, which is the main environmental factor affecting agricultural productivity. Rainfall is often not accessible to plants due to runoff or increased rates of evapotranspiration. Therefore, knowledge of the soil moisture state in the root zone of the soil is of great interest in the field of agricultural drought monitoring and operational decision-support. By introducing soil moisture, retrieved via active or passive microwave remote sensors, the gap between rainfall and the subsequent response of vegetation can be closed. Agricultural droughts are strongly influenced by a lack of water availability in the root zone of the soil, making anomalies of the Advanced Scatterometer (ASCAT) soil water index (SWI), representing the water content in lower soil layers, a suitable measure to estimate the water deficit in the soil. These anomalies describe the difference of the actual soil moisture value to the long-term average calculated for the same period. The objective of the study is to investigate the usability of soil moisture anomalies for developing an indicator that is based on critical thresholds, which finally results in a classification with different drought severity levels. In order to evaluate the performance of the drought index, it is compared to the Integrated Drought Severity Index (IDSI), which is developed at the International Water Management Institute in Colombo, Sri Lanka and to rainfall data from the Indian Meteorological Department (IMD). Overall, first analyses show a high potential of using SWI anomalies for agricultural drought monitoring. Most of the drought events detected by negative

  9. The day-to-day monitoring of the 2011 severe drought in China

    NASA Astrophysics Data System (ADS)

    Lu, Er; Cai, Wenyue; Jiang, Zhihong; Zhang, Qiang; Zhang, Cunjie; Higgins, R. Wayne; Halpert, Michael S.

    2014-07-01

    Dry/wet condition has a large interannual variability. Decision-makers need to know the onset, duration, and intensity of drought, and require droughts be monitored at a daily to weekly scale. However, previous tools cannot monitor drought well at this short timescale. The Palmer Drought Severity Index has been found dissatisfactory in monitoring because of its complexity and numerous limitations. The Standardized Precipitation Index (SPI) always asks for a timescale, and precipitation is averaged over the period of the scale. Because of this, the SPI cannot be used for short scales, e.g., several days, and what it tells is the overall drought situation of the period. The weighted average of precipitation (WAP) developed by Lu (Geophys Res Lett 36:L12707, 2009) overcomes the deficiency of the SPI; it does not require a timescale, and can provide the drought (and flood) extent of each day. Therefore, the WAP can monitor drought at scales from daily to weekly, monthly, and any longer scale, and is really "flexible and versatile for all timescales". In this study, the standardized WAP (SWAP) is used to monitor the 2011 drought over China. Drought swept the country during the year from north to south and from east to west. In spring, a once-in-a-fifty-year drought occurred over the Yangtze River basin and the southern region, causing serious shortage of drinking water for people and livestock, as well as tremendous losses in agriculture and the shipping industry. Results show that the SWAP, with its monthly mean plots, can well reproduce the seasonal shift of the 2011 drought across the country. The animation of daily plots demonstrates that the SWAP would have been able to monitor the day-to-day variation of the spring drought around the Yangtze River basin. It can provide the details of the drought, such as when the drought emerged over the region, how long it maintained there (though drought area may move back and forth with extension and contraction of the area

  10. Putting Current North America Drought Conditions Into a Multi-Century Perspective. Part 2: Using the Blended Product in Operational Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Heim, R. R.; Vose, R. S.; Lawrimore, J. H.; Cook, E. R.

    2007-12-01

    Drought is an important climatological phenomenon which has significant socioeconomic and environmental impacts. Several drought indices have been developed to quantify drought, and all of them rely on meteorological observations taken at instrumented in situ weather stations. The instrumental record for drought monitoring in the U.S. extends back only about a hundred years, and the record is even shorter in other countries such as Canada and Mexico. As a result, recurrence intervals and water management compacts (for example, the Colorado River Basin domestic or Rio Grande international compacts) based upon such short records may not be built upon the long-term climatology of a region. Reliable drought information can be derived from paleoclimatic data such as tree-rings, thus enabling researchers and decision-makers to assess drought variability and impacts over a multi-century period. Previous work has developed research-quality paleoclimatic drought reconstructions which have been used in retrospective analyses but, until now, such data have not been used comprehensively in operational monitoring. Part 1 of this paper describes the development of the reconstructed paleoclimatic Palmer drought index gridded dataset for North America from tree-ring data. Part 2 of this paper describes how the reconstructed paleoclimatic data base is blended with a 20th century instrumental- based Palmer drought index gridded dataset for operational drought monitoring applications across North America.

  11. A Vision of International Drought Monitoring Among Pacific Rim Nations

    NASA Astrophysics Data System (ADS)

    Lawrimore, J. H.; Heim, R. R.

    2008-05-01

    Drought is an environmental hazard which results in billions of dollars in economic losses as well as societal suffering and increased mortality in many parts of the world each year. No country is immune from its effects and there is documented evidence of an increasing trend in droughts in many regions since the 1970s. There is also a high likelihood of droughts affecting an ever larger proportion of the world as global temperatures continue to rise. This reality along with the wide-ranging societal and economic impacts of drought upon the world's countries calls out for coordinated international drought monitoring and response. A demonstration of the potential for contributions through international collaboration was made through the establishment of a North American Drought Monitor (NADM) partnership between the United States, Mexico, and Canada in 2002. The United States has also recently initiated development of a National Integrated Drought Information System (NIDIS) to coordinate drought monitoring, mitigation, and research activities at federal, state, and local levels within the United States. Building on the successes and lessons learned from these ongoing activities, efforts are now underway to foster linkages with other countries throughout both North and South America and the Pacific region as an early step toward establishment of a global drought early warnings system. Through the Group on Earth Observations (GEO) new partnerships are supporting the development of a drought monitoring system in the Pacific Rim. Countries in this region are linked by the strong influence of El Niño and La Niña, leading to periods of severe, persistent, and widespread drought, alternating with periods of heavy rain and flooding. As such, the Pacific Rim makes an ideal laboratory for addressing the challenge of drought early warning through capacity building supported by improved drought monitoring tools and data sharing principles.

  12. A Remote Sensing-based Global Agricultural Drought Monitoring and Forecasting System for Supporting GEOSS (Invited)

    NASA Astrophysics Data System (ADS)

    di, L.; Yu, G.; Han, W.; Deng, M.

    2010-12-01

    Group on Earth Observations (GEO) is a voluntary partnership of governments and international organizations. GEO is coordinating the implementation of the Global Earth Observation System of Systems (GEOSS), a worldwide effort to make Earth observation resources more useful to the society. As one of the important technical contributors to GEOSS, the Center for Spatial Information Science and Systems (CSISS), George Mason University, is implementing a remote sensing-based global agricultural drought monitoring and forecasting system (GADMFS) as a GEOSS societal benefit areas (agriculture and water) prototype. The goals of the project are 1) to establish a system as a component of GEOSS for providing global on-demand and systematic agriculture drought information to users worldwide, and 2) to support decision-making with improved monitoring, forecasting, and analyses of agriculture drought. GADMFS has adopted the service-oriented architecture and is based on standard-compliant interoperable geospatial Web services to provide online on-demand drought conditions and forecasting at ~1 km spatial and daily and weekly temporal resolutions for any part of the world to world-wide users through the Internet. Applicable GEOSS recommended open standards are followed in the system implementation. The system’s drought monitoring relies on drought-related parameters, such as surface and root-zone soil moisture and NDVI time series derived from remote sensing data, to provide the current conditions of agricultural drought. The system links to near real-time satellite remote sensing data sources from NASA and NOAA for the monitoring purpose. For drought forecasting, the system utilizes a neural-network based modeling algorithm. The algorithm is trained with inputs of current and historic vegetation-based and climate-based drought index data, biophysical characteristics of the environment, and time-series weather data. The trained algorithm will establish per-pixel model for

  13. Drought monitoring with soil moisture active passive (SMAP) measurements

    NASA Astrophysics Data System (ADS)

    Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

    2017-09-01

    Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an

  14. Improving Drought Monitoring and Early Warning for Water Resource Management in the UK: an Impact Focused Approach

    NASA Astrophysics Data System (ADS)

    Barker, L. J.; Hannaford, J.; Tijdeman, E.; Laize, C.

    2016-12-01

    Drought is a complex natural phenomenon; the many possible manifestations (meteorological, hydrological, agricultural, environmental etc.) and wide range of impacts makes droughts challenging to identify, plan and prepare for. A multitude of indicators have been developed in attempts to identify and quantify droughts, including the Standardised Precipitation Index (SPI) and the Standardised Streamflow Index (SSI). Although these indicators are commonly used around the world in drought monitoring and early warning systems, there is generally little evidence for what these indicators mean in terms of observed drought impacts. The international Belmont Forum-funded DrIVER (Drought Impacts and Vulnerability Thresholds in monitoring and Early warning Research, https://www.drought.uni-freiburg.de/) aims to improve understanding of the relationships between drought indicators and impacts to inform drought monitoring and early warning (M&EW). Here we focus on the UK, a DrIVER case study area, where there are wide range of stakeholders involved in water resources management, using different indicators and triggers, and where there is no systematic collation of drought impacts in real time. We demonstrate the potential of standardised drought indicators for improving UK M&EW, through linkage with observed impacts data and operational triggers used by decision-makers. To achieve this, for several case study regions in England, we analyse drought indicators (SPI, SPEI, SSI) for recent major drought events, alongside datasets of impacts (e.g. ecological monitoring data and impact data from the European Drought Impact report Inventory, EDII) and management triggers and observed restrictions. Results illustrate the benefits of including drought impact data in M&EW systems in addition to more traditional hydro-meteorological-agricultural approaches; more integrated and holistic M&EW should lead to improved drought management.

  15. Monitoring agricultural drought with climate-based drought indices in China

    NASA Astrophysics Data System (ADS)

    Wang, H.; Zhang, C., Sr.; Jeffery, R. C.

    2015-12-01

    Agricultural drought monitoring significantly influences food security in recent decades. Soil moisture shortages adversely affecting agriculture is one important indicator for agricultural drought monitoring. Because of limited soil moisture observations, characterizing soil moisture using climate-based drought indices has great practical meaning. The agricultural area in China was identified by crop identification from remotely sensed data. Drought indices of multiple timescale or from two-layer bucket model were analyzed. In most agricultural areas of China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer time scales. In general, multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0-5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90-100-cm depth than the Z index. Soil moisture-drought indices relationship was significantly affected by soil organic carbon density. Effective agriculture drought monitoring can be conducted with climate-based drought indices from widely available climatic data and crop area identification from remote sensing. Authors:Hongshuo wang1, Chao Zhang1, Jeffery C Rogers2 1 China agricultural university 2 Ohio state University Key words: Agricultural drought, SPI, SPEI, PDSI, Z index, crop identification

  16. Monitoring Drought Conditions in the Navajo Nation Using NASA Earth Observations

    NASA Technical Reports Server (NTRS)

    Ly, Vickie; Gao, Michael; Cary, Cheryl; Turnbull-Appell, Sophie; Surunis, Anton

    2016-01-01

    The Navajo Nation, a 65,700 sq km Native American territory located in the southwestern United States, has been increasingly impacted by severe drought events and changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring drought are dependent on state-based monthly Standardized Precipitation Index value maps calculated by the Western Regional Climate Center. However, these maps do not provide the spatial resolution needed to illustrate differences in drought severity across the vast Nation. To better understand and monitor drought events and drought regime changes in the Navajo Nation, this project created a geodatabase of historical climate information specific to the area, and a decision support tool to calculate average Standardized Precipitation Index values for user-specified areas. The tool and geodatabase use Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Monitor (GPM) observed precipitation data and Parameter-elevation Relationships on Independent Slopes Model modeled historical precipitation data, as well as NASA's modeled Land Data Assimilation Systems deep soil moisture, evaporation, and transpiration data products. The geodatabase and decision support tool will allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources.

  17. Enhanced Drought Monitoring in the Upper Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Doesken, N.; Smith, R.; Ryan, W.; Schwalbe, Z.; Verdin, J. P.

    2012-12-01

    As a part of the National Integrated Drought Information System's Upper Colorado River Basin pilot project, an aggressive collaborative drought monitoring and communication process was initiated in 2010. Weekly climate, drought and water supply assessments were begun which included webinars during critical times of the year -- primarily late January through mid summer. A diverse set of stakeholders ranging from ski area operators, river commissioners, state and federal agency representatives, public land managers, municipal water providers, agricultural interests and media from a 3-state area were invited to participate along with National Weather Service forecast office personal, state climate office representatives and other information providers. The process evolved to become a weekly drought monitoring "committee" providing detailed input to the U.S. Drought Monitor national author. In 2012 this new system was put to the test as dry winter conditions exploded into extreme and widespread drought as the normal spring storms failed to materialize and instead long-duration above average temperatures added evaporative stress to the already limited water supplies. This presentation examines this effort with an emphasis on stakeholder engagement. The overall impact of the 2012 drought appears, so far, to be less than what was experienced in 2002 although measured stream flow appears tp be similar. To what extent this could be attributed to the enhanced drought monitoring and communication will be discussed. The sustainability of this aggressive monitoring effort will also be assessed.

  18. Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

    NASA Astrophysics Data System (ADS)

    Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

    2014-05-01

    Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety

  19. Utilizing multisource remotely sensed data to dynamically monitor drought in China

    NASA Astrophysics Data System (ADS)

    Liu, Sanchao; Li, Wenbo

    2011-12-01

    Drought is one of major nature disaster in the world and China. China has a vast territory and very different spatio-temporal distribution weather condition. Therefore, drought disasters occur frequently throughout China, which may affect large areas and cause great economic loss every year. In this paper, geostationary meteorological remote sensing data, FY-2C/D/E VISSR and three quantitative remotely sensed models including Cloud Parameters Method (CPM), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI) have been used to dynamically monitor severe drought in southwest China from 2009 to 2010. The results have effectively revealed the occurrence, development and disappearance of this drought event. The monitoring results can be used for the relevant disaster management departments' decision-making works.

  20. Drought monitoring in the Northwestern United States

    NASA Astrophysics Data System (ADS)

    Nijssen, B.; Xiao, M.; Lettenmaier, D. P.

    2015-12-01

    Drought along the US Pacific Coast has grabbed headlines this year. During this past spring and summer, California has continued to experience a multi-year drought with water restrictions imposed by the state; the governor of Washington has declared a statewide drought emergency; and the governor of Oregon has issued a drought declaration for most of the counties in Oregon. The region relies on winter precipitation, and especially snow, for most of its water supply during the summer. This past winter has been anomalously warm, leading to record low snow in many places, providing what many have called a preview of snow conditions that may become common by the middle of this century. This talk will review the development of the drought in the Northwest, put the past winter in a historic context, and provide a summary of drought projections under climate change.

  1. Forecasting and Monitoring Agricultural Drought in the Philippines

    NASA Astrophysics Data System (ADS)

    Perez, G. J.; Macapagal, M.; Olivares, R.; Macapagal, E. M.; Comiso, J. C.

    2016-06-01

    A monitoring and forecasting sytem is developed to assess the extent and severity of agricultural droughts in the Philippines at various spacial scales and across different time periods. Using Earth observation satellite data, drought index, hazard and vulnerability maps are created. The drought index, called Standardized Vegetation-Temperature Ratio (SVTR), is derived using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). SVTR is evaluated by correlating its values with existing agricultural drought index, particulary Evaporative Stress Index (ESI). Moreover, the performance of SVTR in detecting drought occurrences was assessed for the 2015-2016 drought event. This period is a strong El Niño year and a large portion of the country was affected by drought at varying degrees, making it a good case study for evaluating drought indices. Satellitederived SVTR was validated through several field visits and surveys across different major agricultural areas in the country, and was found to be 73% accurate. The drought hazard and vulnerability maps are produced by utilizing the evapotranspration product of MODIS, rainfall climatology from the Tropical Rainfall Microwave Mission (TRMM) and ancillary data, including irrigation, water holding capacity and land use. Finally, we used statistical techniques to determine trends in NDVI and LST and generate a sixmonth forecast of drought index. Outputs of this study are being assessed by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and the Department of Agriculture Bureau of Soils and Water Management (DABSWM) for future integration in their operations.

  2. AVHRR-based drought-observing system for monitoring the environment and socioeconomic activities

    NASA Astrophysics Data System (ADS)

    Kogan, F.

    From all natural disaster, drought is the least understandable and the most damaging environmental phenomenon. Although in pre-satellite era, climate data were used for drought monitoring, drought specifics created problems in early drought detection start/end, monitoring its expansion/contraction, intensity and area coverage and the most important, timely estimation of the impacts on the environment and socioeconomic activities. The latest prevented to take prompt measures in mitigating negative consequences of drought for the society. Advances in remote sensing of the past ten years, contributed to the development of comprehensive drought monitoring system and numerous applications, which helped to make decisions for monitoring the environment and predicting sustainable socioeconomic activities. This paper discusses satellite-based land-surface observing system, which provides wells of information used for monitoring such unusual natural disaster as drought. This system was developed from the observations of the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA operational polar-orbiting satellites. The AVHRR data were packed into the Global Vegetation Index (GVI) product, which have served the global community since 1981. The GVI provided reflectances and indices (4 km spacial resolution) every seven days for each 16 km map cell between 75EN and 55ES covering all land ecosystems. The data includes raw and calibrated radiances in the visible, near infrared and infrared spectral bands, processed (with eliminated high frequency noise) radiances, normalized difference vegetation index (NDVI), 20-year climatology, vegetation condition indices and also products, such as vegetation health, drought, vegetation fraction, fire risk etc. In the past ten years, users around the world used this information addressing different issues of drought impacts on socioeconomic activities and responded positively to real time drought information place regularly on the

  3. Statistical Analysis of Drought Indices and Drought Monitoring for Alberta, Canada

    NASA Astrophysics Data System (ADS)

    Shen, S.; Dai, Q.; Yin, H.; Howard, A.

    2006-12-01

    This presentation discusses a statistical analysis of six drought indices for monitoring Alberta drought events from 1901 to 2000. The data used are the interpolated daily precipitation data on the 149 ecodistrict polygons over Alberta. The analyzed indices are standardized precipitation index, rainfall anomaly index, rainfall decile index, standardized anomaly index, principal component index, and optimal index. The historically documented drought records of five sites (Beaver Lodge, Lacombe, Lethbridge, Vegreville, and Swift Current [in Saskatchwan]) are classified into drought categories D4, D3, ?, D0, and wet categories D1, -D2, and D3. The thresholds of the drought categories for different indices are calculated. The wheat drought of Canada's Palliser Triangle was used as a validation analysis of the drought indices. The transitional probability of drought categories from one week to the next is calculated. Some discussions on the theory of calculating SPI are included. It has been found that the while all the drought indices are highly correlated with precipitation, the PCI has the highest correlation. The transitional probability analysis for the south Alberta agricultural region shows that the chance of transition from normal to extremely dry is highest in the mid May, hence this region's spring seeding is extremely vulnerable to precipitation and an effective irrigation system is of great importance to the early stages of crop development.

  4. Multivariate Drought Characterization in India for Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Sreekumaran Unnithan, P.; Mondal, A.

    2016-12-01

    Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.

  5. a Process-Based Drought Early Warning Indicator for Supporting State Drought Mitigation Decision

    NASA Astrophysics Data System (ADS)

    Fu, R.; Fernando, D. N.; Pu, B.

    2014-12-01

    Drought prone states such as Texas requires creditable and actionable drought early warning ranging from seasonal to multi-decadal scales. Such information cannot be simply extracted from the available climate prediction and projections because of their large uncertainties at regional scales and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA national multi-models ensemble experiment (NMME) and the IPCC AR5 (CMIP5) models, are much more reliable for winter and spring than for the summer season for the US Southern Plains. They also show little connection between the droughts in winter/spring and those in summer, in contrast to the observed dry memory from spring to summer over that region. To mitigate the weakness of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies. Based on these key processes and related fields, we have developed a multivariate principle component statistical model to provide a probabilistic summer drought early warning indicator, using the observed or predicted climate conditions in winter and spring on seasonal scale and climate projection for the mid-21stcentury. The summer drought early warning indicator is constructed in a similar way to the NOAA probabilistic predictions that are familiar to water resource managers. The indicator skill is assessed using the standard NOAA climate prediction assessment tools, i.e., the two alternative forced choice (2AFC) and the Receiver Operating Characteristic (ROC). Comparison with long-term observations suggest that this summer drought early warning indicator is able to capture nearly all the strong summer droughts and outperform the dynamic prediction in this regard over the US Southern Plains. This early warning indicator has been used by the state water agency in May 2014 in briefing the state

  6. Monitoring drought intensity in Illinois with a combined index

    NASA Astrophysics Data System (ADS)

    Feng, Guanling

    Many traditional drought assessments are conducted based on climate and hydrologic data. The availability and precision of data limit the spatial and temporal resolution and accuracy of derived drought indices. In this study, Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were generated from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The VCI was derived from Normalized Difference Vegetation Index (NDVI) that was calculated with near infrared and visible red band reflectance from MOD09Q1. The TCI was derived from land surface temperature (LST) product MOD11A2. The VCI and TCI were then combined with reference to the vegetation coverage information from MOD44B to generate the modified Vegetation Health Index (VHI). The modified VHI was applied to quantify the intensity of drought that took place in Illinois from 2000 to 2012. The results showed that the modified VHI identified the major droughts that occurred in Illinois from 2000 to 2012, especially the extreme one taking place in 2012. Moreover, the modified VHI led to the spatial distributions and temporal trends of drought severity, which were overall similar to those from the U.S. Drought Monitor (USDM) maps, but had more detailed spatial variability and much higher spatial resolution. The modified VHI also differentiated the drought impacts between the vegetated and non-vegetated areas, being a lack of the original VHI. Thus, the modified VHI takes advantage of spatially continuous and timely data from satellites and can be applied to conduct the monitoring and detection of drought intensity at local, regional, and national scales. The modified VHI can effectively synthesize the drought information of LST and NDVI to differentiate the effects of land use and land cover (LULC) types and provide the detailed spatial variability of drought intensity and thus enhance the understanding of relationship between drought condition and LULC types.

  7. Ecological and meteorological drought monitoring in East Asia

    NASA Astrophysics Data System (ADS)

    Kim, J. B.; Um, M. J.; Kim, Y.; Chae, Y.

    2016-12-01

    This study aims to how well the ecological drought index can capture the drought status in the East Asia. We estimated the drought severe index (DSI), which uses the evapotranspiration, potential evapotranspiration and the normalized difference vegetation index (NDVI), suggested by Mu et al. (2013) to define the ecological drought. In addition, the meteorological drought index, which is standardized precipitation and evapotranspiration index (SPEI), are estimated and compared to the DSI. The satellite data by moderate resolution imaging spectroradiometer (MODIS) and advanced very-high-resolution radiometer (AVHRR) are used to analyze the DSI and the monthly precipitation and temperature data in the climate research unit (CRU) are applied to estimate the SPEI for 2000-2013 in the East Asia. We conducted the statistical analyses to investigate the drought characteristics of the ecological and meteorological drought indices (i.e. the DSI and SPEI, respectively) and then compared those characteristics drought indices depending on the drought status. We found the DSI did not well captured the drought status when the categories originally suggested by Mu et al. (2013) are applied to divide the drought status in the study area. Consequently, the modified categories for the DSI in this study is suggested and then applied to define the drought status. The modified categories in this study show the great improvement to capture the drought status in the East Asia even though the results cannot be acquired around Taklamakan desert due to the lack of the satellite data. These results illustrate the ecological drought index, such as the DSI, can be applied for the monitoring of the drought in the East Asia and then can give the detailed information of drought status because the satellite data have the relatively high spatial resolutions compared to the observations such as the CRU data. Reference Mu Q, Zhao M, Kimball JS, McDowell NG, Running SW (2013) A remotely sensed global

  8. The Global Drought Monitor Portal - The Foundation for a Global Drought Early Warning System

    NASA Astrophysics Data System (ADS)

    Brewer, M.; Heim, R. R.; Pozzi, W.; Vogt, J.; Sheffield, J.

    2011-12-01

    Drought monitoring, assessment, response, mitigation, adaptation, and early warning systems have been created in a number of countries around the world, and some regional and continental efforts have been successful. However, the creation of a Global Drought Early Warning System (GDEWS) remains elusive. A GDEWS incorporates forecasting and research improvements, in addition to monitoring, impact, planning, mitigation and adaptation and recovery information. At a series of workshops in 2010, the US National Integrated Drought Information System (NIDIS) agreed to take the first step toward a GDEWS, the formation of a Global Drought Monitoring Portal (GDMP). This effort currently covers three continents - North America, Europe, and Africa - and provides global drought indicator information through satellite products and Global Historical Climate Network locations. The GDMP has benefited from coordination with the World Meteorological Organization (WMO) and Group on Earth Observations (GEO). Other nations have expressed interest in contributing and new regional and continental information should be online shortly. This paper presents the capabilities of the GDMP to link the monitoring, forecasting, research, and impacts aspects of international drought as well as the advantages of using common architecture through GEO to facilitate transfer and interoperability of GDEWS-related information.

  9. Remote Sensing of Drought: Progress and Opportunities for Improving Drought Monitoring

    NASA Astrophysics Data System (ADS)

    AghaKouchak, A.

    2015-12-01

    This presentation surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near-surface air relative humidity data from the Atmospheric Infrared Sounder (AIRS) mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment, GRACE) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple datasets and/or assimilate satellite observations into model simulations to generate long-term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.

  10. Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China

    NASA Astrophysics Data System (ADS)

    Wu, Jianjun; Zhou, Lei; Liu, Ming; Zhang, Jie; Leng, Song; Diao, Chunyuan

    2013-08-01

    Accurately monitoring the temporal, spatial distribution and severity of agricultural drought is an effective means to reduce the farmers’ losses. Based on the concept of the new drought index called VegDRI, this paper established a new method, named the Integrated Surface Drought Index (ISDI). In this method, the Palmer Drought Severity Index (PDSI) was selected as the dependent variable; for the independent variables, 12 different combinations of 14 factors were examined, including the traditional climate-based drought indicators, satellite-derived vegetation indices, and other biophysical variables. The final model was established by fully describing drought properties with the smaller average error (relative error) and larger correlation coefficients. The ISDI can be used not only to monitor the main drought features, including precipitation anomalies and vegetation growth conditions but also to indicate the earth surface thermal and water content properties by incorporating temperature information. Then, the ISDI was used for drought monitoring from 2000 to 2009 in mid-eastern China. The results for 2006 (a typical dry year) demonstrate the effectiveness and capability of the ISDI for monitoring drought on both the large and the local scales. Additionally, the multiyear ISDI monitoring results were compared with the actual drought intensity using the agro-meteorological disaster data recorded at the agro-meteorological sites. The investigation results indicated that the ISDI confers advantages in the accuracy and spatial resolution for monitoring drought and has significant potential for drought identification in China.

  11. Real-time monitoring and short-term forecasting of drought in Norway

    NASA Astrophysics Data System (ADS)

    Kwok Wong, Wai; Hisdal, Hege

    2013-04-01

    Drought is considered to be one of the most costly natural disasters. Drought monitoring and forecasting are thus important for sound water management. In this study hydrological drought characteristics applicable for real-time monitoring and short-term forecasting of drought in Norway were developed. A spatially distributed hydrological model (HBV) implemented in a Web-based GIS framework provides a platform for drought analyses and visualizations. A number of national drought maps can be produced, which is a simple and effective way to communicate drought conditions to decision makers and the public. The HBV model is driven by precipitation and air temperature data. On a daily time step it calculates the water balance for 1 x 1 km2 grid cells characterized by their elevation and land use. Drought duration and areal drought coverage for runoff and subsurface storage (sum of soil moisture and groundwater) were derived. The threshold level method was used to specify drought conditions on a grid cell basis. The daily 10th percentile thresholds were derived from seven-day windows centered on that calendar day from the reference period 1981-2010 (threshold not exceeded 10% of the time). Each individual grid cell was examined to determine if it was below its respective threshold level. Daily drought-stricken areas can then be easily identified when visualized on a map. The drought duration can also be tracked and calculated by a retrospective analysis. Real-time observations from synoptic stations interpolated to a regular grid of 1 km resolution constituted the forcing data for the current situation. 9-day meteorological forecasts were used as input to the HBV model to obtain short-term hydrological drought forecasts. Downscaled precipitation and temperature fields from two different atmospheric models were applied. The first two days of the forecast period adopted the forecasts from Unified Model (UM4) while the following seven days were based on the 9-day forecasts

  12. Application of NARR-based NLDAS Ensemble Simulations to Continental-Scale Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Alonge, C. J.; Cosgrove, B. A.

    2008-05-01

    Government estimates indicate that droughts cause billions of dollars of damage to agricultural interests each year. More effective identification of droughts would directly benefit decision makers, and would allow for the more efficient allocation of resources that might mitigate the event. Land data assimilation systems, with their high quality representations of soil moisture, present an ideal platform for drought monitoring, and offer many advantages over traditional modeling systems. The recently released North American Regional Reanalysis (NARR) covers the NLDAS domain and provides all fields necessary to force the NLDAS for 27 years. This presents an ideal opportunity to combine NARR and NLDAS resources into an effective real-time drought monitor. Toward this end, our project seeks to validate and explore the NARR's suitability as a base for drought monitoring applications - both in terms of data set length and accuracy. Along the same lines, the project will examine the impact of the use of different (longer) LDAS model climatologies on drought monitoring, and will explore the advantages of ensemble simulations versus single model simulations in drought monitoring activities. We also plan to produce a NARR- and observation-based high quality 27 year, 1/8th degree, 3-hourly, land surface and meteorological forcing data sets. An investigation of the best way to force an LDAS-type system will also be made, with traditional NLDAS and NLDASE forcing options explored. This presentation will focus on an overview of the drought monitoring project, and will include a summary of recent progress. Developments include the generation of forcing data sets, ensemble LSM output, and production of model-based drought indices over the entire NLDAS domain. Project forcing files use 32km NARR model output as a data backbone, and include observed precipitation (blended CPC gauge, PRISM gauge, Stage II, HPD, and CMORPH) and a GOES-based bias correction of downward solar

  13. A framework for developing an impact-oriented agricultural drought monitoring system from remote sensing

    NASA Astrophysics Data System (ADS)

    Zhang, Jie

    2016-04-01

    With a changing climate, drought has become more intensified, of which agriculture is the major affected sector. Satellite observations have proven great utilities for real-time drought monitoring as well as crop yield estimation, and many remotely sensed indicators have been developed for drought monitoring based on vegetation growth conditions, surface temperature and evapotranspiration information. However, those current drought indicators typically don't take into account the different responses of various input information and the drought impacts during the growing season, revealing some limitations for effective agricultural drought monitoring and impact analysis. Therefore, the goal of this research is to build a framework for the development of an impact-oriented and remote sensing based agricultural drought indicator. Firstly, the global agricultural drought risk was characterized to provide an overview of the agricultural drought prone areas in the world. Then, the responses of different remotely sensed indicators to drought and the impacts of drought on crop yield from the remote sensing perspective during the growing season were explored. Based on previous works on drought risk, drought indicator response and drought impact analysis, an impact-oriented drought indicator will be prototyped from the integration of the drought responses of different indicators and the drought impacts during the growing season. This research can inform an impact-oriented agricultural drought indicator, help prototype an impact-oriented agricultural drought monitoring system, and thus provide valuable inputs for effective agricultural management.

  14. Hydrologic Drought Decision Support System (HyDroDSS)

    USGS Publications Warehouse

    Granato, Gregory E.

    2014-01-01

    The hydrologic drought decision support system (HyDroDSS) was developed by the U.S. Geological Survey (USGS) in cooperation with the Rhode Island Water Resources Board (RIWRB) for use in the analysis of hydrologic variables that may indicate the risk for streamflows to be below user-defined flow targets at a designated site of interest, which is defined herein as data-collection site on a stream that may be adversely affected by pumping. Hydrologic drought is defined for this study as a period of lower than normal streamflows caused by precipitation deficits and (or) water withdrawals. The HyDroDSS is designed to provide water managers with risk-based information for balancing water-supply needs and aquatic-habitat protection goals to mitigate potential effects of hydrologic drought. This report describes the theory and methods for retrospective streamflow-depletion analysis, rank correlation analysis, and drought-projection analysis. All three methods are designed to inform decisions made by drought steering committees and decisionmakers on the basis of quantitative risk assessment. All three methods use estimates of unaltered streamflow, which is the measured or modeled flow without major withdrawals or discharges, to approximate a natural low-flow regime. Retrospective streamflow-depletion analysis can be used by water-resource managers to evaluate relations between withdrawal plans and the potential effects of withdrawal plans on streams at one or more sites of interest in an area. Retrospective streamflow-depletion analysis indicates the historical risk of being below user-defined flow targets if different pumping plans were implemented for the period of record. Retrospective streamflow-depletion analysis also indicates the risk for creating hydrologic drought conditions caused by use of a pumping plan. Retrospective streamflow-depletion analysis is done by calculating the net streamflow depletions from withdrawals and discharges and applying these depletions

  15. Towards an integrated soil moisture drought monitor for East Africa

    NASA Astrophysics Data System (ADS)

    Anderson, W. B.; Zaitchik, B. F.; Hain, C. R.; Anderson, M. C.; Yilmaz, M. T.; Mecikalski, J.; Schultz, L.

    2012-08-01

    Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010-2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of

  16. Towards an integrated soil moisture drought monitor for East Africa

    NASA Astrophysics Data System (ADS)

    Anderson, W. B.; Zaitchik, B. F.; Hain, C. R.; Anderson, M. C.; Yilmaz, M. T.; Mecikalski, J.; Schultz, L.

    2012-04-01

    Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010-2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of

  17. A drought prediction and monitoring system to reduce drought vulnerability and improve water management in Washington State

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Steinemann, A.; Lettenmaier, D. P.

    2009-12-01

    Water resources in Washington State are mostly derived from spring and summer melting of mountain snowpacks that accumulate in the previous winter. As the climate warms, the natural reservoir that is provided by mountain snowpacks is likely to decline, and recent studies have shown that the state’s water resources are particularly vulnerable to climate variability and climate change for this reason. Better use of weather and climate forecasts in the decision-making processes is one strategy for adapting to these ongoing changes in the physical climate. Major strides have been made toward improving climate forecasts, particularly in the Pacific Northwest where the ENSO signal is strong. The NOAA Climate Prediction Center (CPC) provides seasonal climate outlooks over the United States. We examine the potential of these forecasts to provide useful information for Washington State under drought conditions by using CPC forecasts to force the Variable Infiltration Capacity (VIC) land surface model (LSM) based on a Drought Monitoring and Prediction System (DMPS) developed at the University of Washington. This system uses the VIC model to generate the initial hydrologic state, and the CPC seasonal climate ensembles to provide hydrologic assessments of drought conditions up to a lead -time of 6 months. The hydrologic initial state is generated by forcing the model with observed precipitation and temperature forcings. The system uses the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Soil Moisture Percentile (SMP) based on LSM derived soil moisture (SM), runoff over the state as indices for drought characterization. We describe the implementation of DMPS and its evaluation in terms of drought prediction skill over the period 1995-2007 for which CPC forecast archives are available.

  18. A Customized Drought Decision Support Tool for Hsinchu Science Park

    NASA Astrophysics Data System (ADS)

    Huang, Jung; Tien, Yu-Chuan; Lin, Hsuan-Te; Liu, Tzu-Ming; Tung, Ching-Pin

    2016-04-01

    Climate change creates more challenges for water resources management. Due to the lack of sufficient precipitation in Taiwan in fall of 2014, many cities and counties suffered from water shortage during early 2015. Many companies in Hsinchu Science Park were significantly influenced and realized that they need a decision support tool to help them managing water resources. Therefore, a customized computer program was developed, which is capable of predicting the future status of public water supply system and water storage of factories when the water rationing is announced by the government. This program presented in this study for drought decision support (DDSS) is a customized model for a semiconductor company in the Hsinchu Science Park. The DDSS is programmed in Java which is a platform-independent language. System requirements are any PC with the operating system above Windows XP and an installed Java SE Runtime Environment 7. The DDSS serves two main functions. First function is to predict the future storage of Baoshan Reservoir and Second Baoshan Reservoir, so to determine the time point of water use restriction in Hsinchu Science Park. Second function is to use the results to help the company to make decisions to trigger their response plans. The DDSS can conduct real-time scenario simulations calculating the possible storage of water tank for each factory with pre-implementation and post-implementation of those response plans. In addition, DDSS can create reports in Excel to help decision makers to compare results between different scenarios.

  19. Improving Agricultural Drought Monitoring in East Africa with Unbiased Rainfall Fields and Detailed Land Surface Physics

    NASA Astrophysics Data System (ADS)

    McNally, A.; Yatheendradas, S.; Peters-Lidard, C. D.; Michaelsen, J.

    2010-12-01

    Monitoring drought is particularly challenging within rainfed agricultural and pastoral systems, where it can serve the greatest need. Such locations often have sparse or non-existent ground based measurements of precipitation, evapotranspiration (ET), and soil moisture. For more effective drought monitoring with limited hydroclimate observations, we simulate land surface states using the Community Noah Land Surface Model forced with different merged rainfall products inside a Land Information System (LIS). Using model outputs we will answer the questions: How sensitive are soil moisture and ET fields to differences in rainfall forcing and model physics? What are acceptable drought-specific tradeoffs between near-real time availability and skill of rainfall data? Preliminary results with the African Rainfall Estimation Algorithm Version 2 (RFE2.0) outperformed global products, suggesting that sub-global rainfall estimates are the way forward for regional drought monitoring. Specifically, the Noah model forced with RFE2.0 better resolved the heterogeneous patterns in crop stress than the Famine Early Warning System Network (FEWS NET) operational Water Requirement Satisfaction Index (WRSI) model. To further investigate the improvement in drought monitoring while maintaining timeliness, we unbias (using Africa specific climatology) the precipitation products from CPC Merged Analysis of Precipitation (CMAP), Tropical Rainfall Measurement Mission (TRMM), and RFE2.0. The skill (relative accuracy) and reliability (average agreement) of the unbiased rainfall are calculated against an unbiased precipitation product augmented with station data from Ethiopia and Kenya. Soil moisture and ET fields from Noah are compared to the operational FEWS NET WRSI, soil water anomaly index, and the World Food Program’s Crop and Food Security Assessment Mission reports. We anticipate that the unbiased rainfall fields will improve the accuracy, spatio-temporal resolution, and

  20. Drought and vegetation stress monitoring in Portugal using satellite data

    NASA Astrophysics Data System (ADS)

    Gouveia, C.; Trigo, R. M.; Dacamara, C. C.

    2009-02-01

    Remote sensed information on vegetation and soil moisture, namely the Normalised Difference Vegetation Index (NDVI) and the Soil Water Index (SWI), is employed to monitor the spatial extent, severity and persistence of drought episodes over Continental Portugal, from 1999 to 2006. The severity of a given drought episode is assessed by evaluating the cumulative impact over time of drought conditions on vegetation. Special attention is given to the drought episodes that have occurred in the last decade, i.e., 1999, 2002 and particularly the major event of 2005. During both the 1999 and 2005 drought episodes negative anomalies of NDVI are observed over large sectors of Southern Portugal for up to nine months (out of eleven) of the vegetative cycle. On the contrary, the 2002 event was characterized by negative anomalies in the northern half of Portugal and for a shorter period (eight out of eleven months). The impact of soil moisture on vegetation dynamics is evaluated by analyzing monthly anomalies of SWI and by studying the annual cycle of SWI vs. NDVI. While in the case of the drought episode of 1999 the scarcity of water in the soil persisted until spring, in the recent episode of 2005 the deficit in greenness was already apparent at the end of summer. The impact of dry periods on vegetation is clearly observed in both arable land and forest, and it is found that arable land presents a higher sensitivity. From an operational point of view, obtained results reveal the possibility of using the developed methodology to monitor, in quasi real-time, vegetation stress and droughts in Mediterranean ecosystems.

  1. Application of terrestrial microwave remote sensing to agricultural drought monitoring

    USDA-ARS?s Scientific Manuscript database

    Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Suc...

  2. Spatiotemporal drought monitoring for Pinios river basin, Greece

    NASA Astrophysics Data System (ADS)

    Loukas, A.; Vasiliades, L.

    2009-04-01

    This study evaluates the suitability of artificial neural networks for spatiotemporal monthly drought mapping and monitoring in Pinios River Basin, Greece. Pinios River basin has an area of about 9500 km2, is located in Thessaly, an agricultural plain region surrounded by mountains and facing frequent and severe droughts. Monthly precipitation data for the period October 1960 to September 2002 from sixty six (66) precipitation stations were used to calculate the Standardized Precipitation Index (SPI) for multiple time scales as an index of meteorological drought assessment. Previous studies have shown that the 6-month time scale SPI is a suitable hydrological drought index for the study river basin and this index was selected for further analysis on spatiotemporal drought monitoring. A novel interpolation method was employed that accounted for possible non-linear orographic effects at different spatial scales and allowed for regionally and seasonally varying relief-climate relationships. The methodology uses artificial neural networks with inputs spatial coordinates, elevation data, and significant factors derived from principal components analysis of the precipitation data. The spatial and temporal validity of the interpolation method was checked using supervised split sample test. Seventy percent (70%) of the input and output dataseries were used in the development of the model and thirty percent (30%) of the remaining dataseries were used for the spatial and temporal validation of the methodology. The results of the developed methodology were compared with the results of geostatistical and deterministic methods of spatial interpolation and mapping and showed that the proposed technique gave satisfactory spatiotemporal interpolation results in the study basin and could be used for drought assessment and monitoring.

  3. Collaborative processes, research, and applications to improve drought-sensitive decision making in the Upper Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Verdin, J. P.; Pulwarty, R. S.; Doesken, N. J.; Gillespie, M.; Werner, K.; Wilhelmi, O.; Lewis, M. E.; Darby, L. S.; McNutt, C. A.; Schmidt, M.; Redmond, K. T.

    2010-12-01

    The Upper Colorado River Basin (UCRB) is the focus of the first pilot regional drought early warning information system of the National Integrated Drought Information System (NIDIS). In partnership with resource managers from across the basin, a program of needs assessments, collaborative processes, research, and applications was designed and is being implemented. Priority actions involve the drought-sensitive decisions of large reservoir operators, water providers dependent on inter-basin transfers, and ecosystem managers. Identification of drought monitoring and forecasting needs has led to an ongoing UCRB drought monitoring process organized under the leadership of the Colorado state climatologist. Weekly webinars during spring runoff (monthly during the rest of the year) review the latest science, observations and forecasts for variables like streamflow, precipitation, temperature, snowpack, and reservoir storage. Research and applications projects support this collaborative process by developing new insights and tools for drought impact analyses. They include review and improvement of drought indices used in the UCRB, and new tools for making custom, locally-relevant indicators; spatial analysis of water demand in the basin; a low-flow impacts database, including environmental considerations; linkage of National Weather Service climate and hydrological modeling; a monitoring gaps assessment; and enhanced web access to drought information specific to the UCRB via the NIDIS portal. Lessons learned during 2010 will be applied during a second annual cycle in 2011 and then placed in the context of longer-term planning strategies. The findings of the pilot will provide the basis for the design of innovative and sustained practices in the UCRB, as well as expansion of the early warning system to include the Lower Colorado River Basin and to inform adaptation across multiple timescales. The UCRB effort highlights the role of pilot design and implementation as

  4. Coping with drought: A High Resolution Drought Monitoring and Prediction System for the Pacific Northwest

    NASA Astrophysics Data System (ADS)

    Xiao, M.; Nijssen, B.; Shukla, S.; Lettenmaier, D. P.

    2013-12-01

    The Pacific Northwest (PNW) region in North America (defined here as the Columbia and Klamath River basins plus the coastal drainages) is a diverse geographic region with complex topography and a variety of climates. Agriculture (dryland and irrigated), forestry, fisheries, and hydropower provide significant economic benefit to the region and are directly dependent on the availability of sufficient water at the right time. Additional demands are made on water supplies by recreation, ecosystem services and emerging needs such as hydropower generation in support of wind energy integration. Several major droughts have occurred over the region in recent decades (notably 1977, 2001, and 2004), which have had significant consequences for the region's agricultural, hydropower production, and environment. An emerging need for the region is the coordination of existing regional climate activities, including a better awareness of the current water availability conditions across the region. The University of Washington has operated a surface water monitor for the continental United States since 2005, which provides near real-time estimates of surface water conditions at a spatial resolution of 1/2 degree in terms of soil moisture, snow water equivalent, and total moisture based on a suite of land surface models. A higher resolution Drought Monitoring and Prediction System (DMPS) for Washington State was originally implemented at 1/8 degree and later increased to 1/16 degree. This presentation describes the extension of this system to the entire PNW region at 1/16 degree. The expanded system provides daily updates of three primary drought-related indices based on near real-time station observations in the region: Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Soil Moisture Percentiles (SMP). To make the drought measures relevant to water managers, surface water conditions are not only reported on a gridded map, but watershed-level drought summary

  5. Development of a SMAP-Based Drought Monitoring Product

    NASA Astrophysics Data System (ADS)

    Sadri, S.; Wood, E. F.; Pan, M.; Lettenmaier, D. P.

    2016-12-01

    Agricultural drought is defined as a deficit in the amount of soil moisture over a prolonged period of time. Soil moisture information over time and space provides critical insight for agricultural management, including both water availability for crops and moisture conditions that affect management practices such as fertilizer, pesticide applications, and their impact as non-point pollution runoff. Since April of 2015, NASA's Soil Moisture Active Passive (SMAP) mission has retrieved soil moisture using L-band passive radiometric measurements at a 8 day repeat orbit with a swath of 1000 km that maps the Earth in 2-3 days depending on locations. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP soil moisture in terms of probability percentiles for dry (drought) or wet (pluvial) conditions. SMAP observations do result in retrievals that are spatially and temporally discontinuous. Additionally, the short SMAP record length provides a statistical challenge in estimating a drought index and thus drought risk evaluations. In this presentation, we describe a SMAP drought index for the CONUS region based on near-surface soil moisture percentiles. Because the length of the SMAP data record is limited, we use a Bayesian conditional probability approach to extend the SMAP record back to 1979 based on simulated soil moisture of the same period from the Variable Infiltration Capacity (VIC) Land Surface Model (LSM), simulated by Princeton University. This is feasible because the VIC top soil layer (10 cm) is highly correlated with the SMAP 36 km passive microwave during 2015-2016, with more than half the CONUS grids having a cross-correlation greater than 0.6, and over 0.9 in many regions. Given the extended SMAP record, we construct an empirical probability distribution of near-surface soil moisture drought index showing severities similar to those used by the U.S. Drought Monitor (from D0-D4), for a specific SMAP

  6. The current California drought through EDDI's eyes: early warning and monitoring of agricultural and hydrologic drought with the new Evaporative Demand Drought Index.

    NASA Astrophysics Data System (ADS)

    Hobbins, M.; McEvoy, D.; Huntington, J. L.; Wood, A. W.; Morton, C.; Verdin, J. P.

    2015-12-01

    We have developed a physically based, multi-scalar drought index—the Evaporative Demand Drought Index (EDDI)—to improve treatment of evaporative dynamics in drought monitoring. Existing popular drought indices—such as the Palmer Drought Severity Index that informs much of the US Drought Monitor (USDM)—have primarily relyied on precipitation and temperature (T) to represent hydroclimatic anomalies, leaving evaporative demand (E0) most often derived from poorly performing T-based parameterizations then used to derive actual evapotranspiration (ET) from LSMs. Instead, EDDI leverages the inter-relations of E0 and ET, measuring E0's physical response to surface drying anomalies due to two distinct land surface/atmosphere interactions: (i) in sustained drought, limited moisture availability forces E0 and ET into a complementary relation, whereby ET declines as E0 increases; and (ii) in "flash" droughts, E0 increases due to increasing advection or radiation. E0's rise in response to both drought types suggests EDDI's robustness as a monitor and leading indicator of drought. To drive EDDI, we use for E0 daily reference ET from the ASCE Standardized Reference ET equation forced by North American Land Data Assimilation System drivers. EDDI is derived by aggregating E0 anomalies from its long-term mean across a period of interest and normalizing them to a Z-score. Positive EDDI indicates drier than normal conditions (and so drought). We use the current historic California drought as a test-case in which to examine EDDI's performance in monitoring agricultural and hydrologic drought. We observe drought development and decompose the behavior of drought's evaporative drivers during in-drought intensification periods and wetting events. EDDI's performance as a drought leading indicator with respect to the USDM is tested in important agricultural regions. Comparing streamflow from several USGS gauges in the Sierra Nevada to EDDI, we find that EDDI tracks most major

  7. Drought Monitoring and Forecasting: Experiences from the US and Africa

    NASA Astrophysics Data System (ADS)

    Sheffield, Justin; Chaney, Nate; Yuan, Xing; Wood, Eric

    2013-04-01

    Drought has important but very different consequences regionally due to differences in vulnerability. These differences derive from variations in exposure related to climate variability and change, sensitivity of local populations, and coping capacity at all levels. Managing the risk of drought impacts relies on a variety of measures to reduce vulnerability that includes forewarning of drought development through early-warning systems. Existing systems rely on a variety of observing systems from satellites to local observers, modeling tools, and data dissemination methods. They range from sophisticated state-of-the-art systems to simple ground reports. In some regions, systems are virtually non-existent due to limited national capacity. This talk describes our experiences in developing and implementing drought monitoring and seasonal forecast systems in the US and sub-Saharan Africa as contrasting examples of the scientific challenges and user needs in developing early warning systems. In particular, early warning can help improve livelihoods based on subsistence farming in sub-Saharan Africa; whist reduction of economic impacts is generally foremost in the US. For the US, our national drought monitoring and seasonal forecast system has been operational for over 8 years and provides near real-time updates on hydrological states at ~12km resolution and hydrological forecasts out to 9 months. Output from the system contributes to national assessments such as from the NOAA Climate Prediction Center (CPC) and the US National Drought Monitor (USDM). For sub-Saharan Africa, our experimental drought monitoring system was developed as a translation of the US system but presents generally greater challenges due to, for example, lack of ground data and unique user needs. The system provides near real-time updates based on hydrological modeling and satellite based precipitation estimates, and has recently been augmented by a seasonal forecast component. We discuss the

  8. Assessing the utility of meteorological drought indices in monitoring summer drought based on soil moisture in Chongqing, China

    NASA Astrophysics Data System (ADS)

    Chen, Hui; Wu, Wei; Liu, Hong-Bin

    2017-04-01

    Numerous drought indices have been developed to analyze and monitor drought condition, but they are region specific and limited by various climatic conditions. In southwest China, summer drought mainly occurs from June to September, causing destructive and profound impact on agriculture, society, and ecosystems. The current study assesses the availability of meteorological drought indices in monitoring summer drought in this area at 5-day scale. The drought indices include the relative moisture index (M), the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the composite index of meteorological drought (CIspi), and the improved composite index of meteorological drought (CIwap). Long-term daily precipitation and temperature from 1970 to 2014 are used to calculate 30-day M (M 30), SPI (SPI30), SPEI (SPEI30), 90-day SPEI (SPEI90), CIspi, and CIwap. The 5-day soil moisture observations from 2010 to 2013 are applied to assess the performance of these drought indices. Correlation analysis, overall accuracy, and kappa coefficient are utilized to investigate the relationships between soil moisture and drought indices. Correlation analysis indicates that soil moisture is well correlated with CIwap, SPEI30, M 30, SPI30, and CIspi except SPEI90. Moreover, drought classifications identified by M 30 are in agreement with that of the observed soil moisture. The results show that M 30 based on precipitation and potential evapotranspiration is an appropriate indicator for monitoring drought condition at a finer scale in the study area. According to M 30, summer drought during 1970-2014 happened in each year and showed a slightly upward tendency in recent years.

  9. Building Better Drought Resilience Through Improved Monitoring and Early Warning: Learning From Stakeholders in Europe, the USA, and Australia

    NASA Astrophysics Data System (ADS)

    Stahl, K.; Hannaford, J.; Bachmair, S.; Tijdeman, E.; Collins, K.; Svoboda, M.; Knutson, C. L.; Wall, N.; Smith, K. H.; Bernadt, T.; Crossman, N. D.; Overton, I. C.; Barker, L. J.; Acreman, M. C.

    2016-12-01

    With climate projections suggesting that droughts will intensify in many regions in future, improved drought risk management may reduce potential threats to freshwater security across the globe. One aspect that has been called for in this respect is an improvement of the linkage of drought monitoring and early warning, which currently focuses largely on indicators from meteorology and hydrology, to drought impacts on environment and society. However, a survey of existing monitoring and early warning systems globally, that we report on in this contribution, demonstrates that although impacts are being monitored, there is limited work, and certainly little consensus, on how to best achieve this linkage. The Belmont Forum project DrIVER (Drought impacts: Vulnerability thresholds in monitoring and early-warning research) carried out a number of stakeholder workshops in North America, Europe and Australia to elaborate on options for such improvements. A first round of workshops explored current drought management practices among a very diverse range of stakeholders, and their expectations from monitoring and early warning systems (particularly regarding impact characterization). The workshops revealed some disconnects between the indices used in the public early warning systems and those used by local decision-makers, e.g. to trigger drought measures. Follow-up workshops then explored how the links between information at these different scales can be bridged and applied. Impact information plays a key role in this task. This contribution draws on the lessons learned from the transdisciplinary interactions in DrIVER, to enhance the usability of drought monitoring and early-warning systems and other risk management strategies.

  10. Monitoring and modeling agricultural drought for famine early warning (Invited)

    NASA Astrophysics Data System (ADS)

    Verdin, J. P.; Funk, C.; Budde, M. E.; Lietzow, R.; Senay, G. B.; Smith, R.; Pedreros, D.; Rowland, J.; Artan, G. A.; Husak, G. J.; Michaelsen, J.; Adoum, A.; Galu, G.; Magadzire, T.; Rodriguez, M.

    2009-12-01

    The Famine Early Warning Systems Network (FEWS NET) makes quantitative estimates of food insecure populations, and identifies the places and periods during which action must be taken to assist them. Subsistence agriculture and pastoralism are the predominant livelihood systems being monitored, and they are especially drought-sensitive. At the same time, conventional climate observation networks in developing countries are often sparse and late in reporting. Consequently, remote sensing has played a significant role since FEWS NET began in 1985. Initially there was heavy reliance on vegetation index imagery from AVHRR to identify anomalies in landscape greenness indicative of drought. In the latter part of the 1990s, satellite rainfall estimates added a second, independent basis for identification of drought. They are used to force crop water balance models for the principal rainfed staple crops in twenty FEWS NET countries. Such models reveal seasonal moisture deficits associated with yield reduction on a spatially continuous basis. In 2002, irrigated crops in southwest Asia became a concern, and prompted the implementation of a gridded energy balance model to simulate the seasonal mountain snow pack, the main source of irrigation water. MODIS land surface temperature data are also applied in these areas to directly estimate actual seasonal evapotranspiration on the irrigated lands. The approach reveals situations of reduced irrigation water supply and crop production due to drought. The availability of MODIS data after 2000 also brought renewed interest in vegetation index imagery. MODIS NDVI data have proven to be of high quality, thanks to significant spectral and spatial resolution improvements over AVHRR. They are vital to producing rapid harvest assessments for drought-impacted countries in Africa and Asia. The global food crisis that emerged in 2008 has led to expansion of FEWS NET monitoring to over 50 additional countries. Unlike previous practice, these

  11. Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

    2012-12-01

    Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate

  12. Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

    NASA Astrophysics Data System (ADS)

    Aghakouchak, Amir; Tourian, Mohammad J.

    2015-04-01

    Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

  13. Predicting the US Drought Monitor (USDM) using precipitation, soil noisture, and evapotranspiration anomalies, Part II: Intraseasonal drought intensification forecasts

    USDA-ARS?s Scientific Manuscript database

    Probabilistic forecasts of US Drought Monitor (USDM) intensification over two, four and eight week time periods are developed based on recent anomalies in precipitation, evapotranspiration and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. Wh...

  14. A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Xia, Youlong; Ouyang, Wei; Shen, Xinyi

    2016-06-01

    Drought indices have been commonly used to characterize different properties of drought and the need to combine multiple drought indices for accurate drought monitoring has been well recognized. Based on linear combinations of multiple drought indices, a variety of multivariate drought indices have recently been developed for comprehensive drought monitoring to integrate drought information from various sources. For operational drought management, it is generally required to determine thresholds of drought severity for drought classification to trigger a mitigation response during a drought event to aid stakeholders and policy makers in decision making. Though the classification of drought categories based on the univariate drought indices has been well studied, drought classification method for the multivariate drought index has been less explored mainly due to the lack of information about its distribution property. In this study, a theoretical drought classification method is proposed for the multivariate drought index, based on a linear combination of multiple indices. Based on the distribution property of the standardized drought index, a theoretical distribution of the linear combined index (LDI) is derived, which can be used for classifying drought with the percentile approach. Application of the proposed method for drought classification of LDI, based on standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized runoff index (SRI) is illustrated with climate division data from California, United States. Results from comparison with the empirical methods show a satisfactory performance of the proposed method for drought classification.

  15. The Application of ET/VI Products to Better Capture Flash Droughts in the U.S. Drought Monitor

    NASA Astrophysics Data System (ADS)

    Svoboda, M.

    2016-12-01

    The National Drought Mitigation Center (NDMC) (http://drought.unl.edu) has been working with our U.S. Drought Monitor (USDM) (http://droughtmonitor.unl.edu) partners in NOAA and USDA, along with the National Integrated Drought Information System (NIDIS) (http://drought.gov) and others with a goal of developing tools to enhance drought early warning and risk management activities in the U.S. and around the world. The NDMC is a national/international center founded in 1995 and located at the University of Nebraska-Lincoln. The NDMC has been involved in developing and/or vetting various remotely sensed vegetation indices (VI) and evapotranspiration (ET) products over the years as a means of improving our nation's capacity to monitor and detect emerging and/or "flash droughts". Recently, several new ET/VI tools from a variety of platforms have emerged and are beginning to help paint drought in a different light, often times in data poor regions, as a means of augmenting our sparse in situ observation networks. Early returns are promising as these ET/VI products can provide better early warning compared to traditional hydrometeorological indicators, which are the backbone to the USDM's percentile ranking approach. These tools and their role in the making, and enhancing of, the USDM will be explored.

  16. Review of broad-scale drought monitoring of forests: Toward an integrated data mining approach

    Treesearch

    Steve Norman; Frank H. Koch; William W. Hargrove

    2016-01-01

    Efforts to monitor the broad-scale impacts of drought on forests often come up short. Drought is a direct stressor of forests as well as a driver of secondary disturbance agents, making a full accounting of drought impacts challenging. General impacts  can be inferred from moisture deficits quantified using precipitation and temperature measurements. However,...

  17. Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

    NASA Technical Reports Server (NTRS)

    Housborg, Rasmus; Rodell, Matthew

    2010-01-01

    NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

  18. The economic value of drought information: Application to water resources management decisions in Spain

    NASA Astrophysics Data System (ADS)

    Garrote, Luis; Sordo, Alvaro; Iglesias, Ana

    2016-04-01

    Information is valuable when it improves decision-making (e.g., actions can be adjusted to better suit the situation at hand) and enables the mitigation of damage. However, quantifying the value of information is often difficult. Here we explore a general approach to understand the economic value of drought information for water managers framing our approach in the precautionary principle that reminds us that uncertainty is not a reason to postpone or avoid action. We explore how decision making can disregard uncertain effects, taking a short-term approach and focusing instead on the certain costs and benefits of taking action. Two main questions arise: How do we know that advanced drought information is actually helping decisions?; and What is the value of information in the decision process? The approach is applied to several regulated water resources systems in Spain. It first views drought information as a factor in the decision process which can be used by water managers to reduce uncertainty. Second, the value of drought information is the expected gain in a decision outcome (utility) from using additional information. Finally, the gains of improved information are compared with the information collection costs. Here we estimate the value by taking into account the accuracy of the drought information, the subjective probabilities about the value, analyzed as Bayesian probabilities, and the ability or skill of the stakeholders to apply the drought information to modify their actions. Since information may be considered a public good (non-rivalry and non-excludability), it may justify public policy in the provision of information, considering social costs and benefits. The application of the framework to the Spanish case studies shows that information benefits exceeds to costs when drought frequency is 20-40% above normal values; below these values uncertainty in the decisions dominate the results; above these values, the management decisions are limited even

  19. Research on the remote sensing methods of drought monitoring in Chongqing

    NASA Astrophysics Data System (ADS)

    Yang, Shiqi; Tang, Yunhui; Gao, Yanghua; Xu, Yongjin

    2011-12-01

    There are regional and periodic droughts in Chongqing, which impacted seriously on agricultural production and people's lives. This study attempted to monitor the drought in Chongqing with complex terrain using MODIS data. First, we analyzed and compared three remote sensing methods for drought monitoring (time series of vegetation index, temperature vegetation dryness index (TVDI), and vegetation supply water index (VSWI)) for the severe drought in 2006. Then we developed a remote sensing based drought monitoring model for Chongqing by combining soil moisture data and meteorological data. The results showed that the three remote sensing based drought monitoring models performed well in detecting the occurrence of drought in Chongqing on a certain extent. However, Time Series of Vegetation Index has stronger sensitivity in time pattern but weaker in spatial pattern; although TVDI and VSWI can reflect inverse the whole process of severe drought in 2006 summer from drought occurred - increased - relieved - increased again - complete remission in spatial domain, but TVDI requires the situation of extreme drought and extreme moist both exist in study area which it is more difficult in Chongqing; VSWI is simple and practicable, which the correlation coefficient between VSWI and soil moisture data reaches significant levels. In summary, VSWI is the best model for summer drought monitoring in Chongqing.

  20. Introduction of Drought Monitoring and Forecasting System based on Real-time Water Information Using ICT

    NASA Astrophysics Data System (ADS)

    Lee, Y., II; Kim, H. S.; Chun, G.

    2016-12-01

    There were severe damages such as restriction on water supply caused by continuous drought from 2014 to 2015 in South Korea. Through this drought event, government of South Korea decided to establish National Drought Information Analysis Center in K-water(Korea Water Resources Corporation) and introduce a national drought monitoring and early warning system to mitigate those damages. Drought index such as SPI(Standard Precipitation Index), PDSI(Palmer Drought Severity Index) and SMI(Soil Moisture Index) etc. have been developed and are widely used to provide drought information in many countries. However, drought indexes are not appropriate for drought monitoring and early warning in civilized countries with high population density such as South Korea because it could not consider complicated water supply network. For the national drought monitoring and forecasting of South Korea, `Drought Information Analysis System' (D.I.A.S) which is based on the real time data(storage, flowrate, waterlevel etc.) was developed. Based on its advanced methodology, `DIAS' is changing the paradigm of drought monitoring and early warning systems. Because `D.I.A.S' contains the information of water supply network from water sources to the people across the nation and provides drought information considering the real-time hydrological conditions of each and every water source. For instance, in case the water level of a specific dam declines to predetermined level of caution, `D.I.A.S' will notify people who uses the dam as a source of residential or industrial water. It is expected to provide credible drought monitoring and forecasting information with a strong relationship between drought information and the feelings of people rely on water users by `D.I.A.S'.

  1. On the potential application of land surface models for drought monitoring in China

    NASA Astrophysics Data System (ADS)

    Zhang, Liang; Zhang, Huqiang; Zhang, Qiang; Li, Yaohui; Zhao, Jianhua

    2017-05-01

    The potential of using land surface models (LSMs) to monitor near-real-time drought has not been fully assessed in China yet. In this study, we analyze the performance of such a system with a land surface model (LSM) named the Australian Community Atmosphere Biosphere Land Exchange model (CABLE). The meteorological forcing datasets based on reanalysis products and corrected by observational data have been extended to near-real time for semi-operational trial. CABLE-simulated soil moisture (SM) anomalies are used to characterize drought spatial and temporal evolutions. One outstanding feature in our analysis is that with the same meteorological data, we have calculated a range of drought indices including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI). We have assessed the similarity among these indices against observed SM over a number of regions in China. While precipitation is the dominant factor in the drought development, relationships between precipitation, evaporation, and soil moisture anomalies vary significantly under different climate regimes, resulting in different characteristics of droughts in China. The LSM-based trial system is further evaluated for the 1997/1998 drought in northern China and 2009/2010 drought in southwestern China. The system can capture the severities and temporal and spatial evolutions of these drought events well. The advantage of using a LSM-based drought monitoring system is further demonstrated by its potential to monitor other consequences of drought impacts in a more physically consistent manner.

  2. Investigate the Capabilities of Remotely Sensed Crop Indicators for Agricultural Drought Monitoring in Kansas

    NASA Astrophysics Data System (ADS)

    Zhang, J.; Becker-Reshef, I.; Justice, C. O.

    2013-12-01

    Although agricultural production has been rising in the past years, drought remains the primary cause of crop failure, leading to food price instability and threatening food security. The recent 'Global Food Crisis' in 2008, 2011 and 2012 has put drought and its impact on crop production at the forefront, highlighting the need for effective agricultural drought monitoring. Satellite observations have proven a practical, cost-effective and dynamic tool for drought monitoring. However, most satellite based methods are not specially developed for agriculture and their performances for agricultural drought monitoring still need further development. Wheat is the most widely grown crop in the world, and the recent droughts highlight the importance of drought monitoring in major wheat producing areas. As the largest wheat producing state in the US, Kansas plays an important role in both global and domestic wheat markets. Thus, the objective of this study is to investigate the capabilities of remotely sensed crop indicators for effective agricultural drought monitoring in Kansas wheat-grown regions using MODIS data and crop yield statistics. First, crop indicators such as NDVI, anomaly and cumulative metrics were calculated. Second, the varying impacts of agricultural drought at different stages were explored by examining the relationship between the derived indicators and yields. Also, the starting date of effective agricultural drought early detection and the key agricultural drought alert period were identified. Finally, the thresholds of these indicators for agricultural drought early warning were derived and the implications of these indicators for agricultural drought monitoring were discussed. The preliminary results indicate that drought shows significant impacts from the mid-growing-season (after Mid-April); NDVI anomaly shows effective drought early detection from Late-April, and Late-April to Early-June can be used as the key alert period for agricultural

  3. Nonsignificant change of drought in China during 1982-2011 and application of PDSI in monitoring interannual variations of agriculture drought area

    NASA Astrophysics Data System (ADS)

    Yan, Hao

    2017-04-01

    Inspired by concerns of the effects of a warming climate, drought variation and its impacts have gained much attention in China. Arguments about China's drought persist and little work has addressed the relationship between drought index and agricultural drought from a perspective of drought area. Based on a newly revised self-calibrating Palmer Drought Severity Index (PDSI) model driven with ARTS E0 [PDSI_ARTS; Yan et al., 2014], spatial and temporal variations of drought were analyzed for 1982-2011 in China. The results indicate that there was nonsignificant change of drought over this interval but with an extreme drought event happened in 2000-2001. However, using air temperature (Ta)-based Thornthwaite potential evaporation (EP_Th) and Penman-Monteith potential evaporation (EP_PM) to drive the PDSI model, their corresponding PDSI_Th and PDSI_PM all gave a significant drying trend for 1982-2011. This suggests that PDSI model was sensitive to EP parameterization in China. Annual drought-covered area from agriculture survey was initially adopted to evaluate PDSI's capacity in monitoring agriculture drought area in China. The results indicate that PDSI_ARTS drought area (defined as PDSI_ARTS < -0.5) correlated well with the agriculture drought-covered area and PDSI_ARTS successfully detected the extreme agriculture drought in 2000-2001 for 1982-2011, while PDSI_Th and PDSI_PM drought area had no relationship with the agriculture drought-covered area and overestimated the uptrend of agriculture drought, which contrasted with agriculture drought survey. Overall, PDSI_ARTS model had a potential to monitor interannual variations of agricultural drought area and was preferred to EP_Th and EP_PM-driven PDSI models in drought research of China.

  4. Monitoring southwest drought of China using HJ-1A/B and Landsat remote sensing data

    NASA Astrophysics Data System (ADS)

    Huang, He; Zhou, Hongjian; Wang, Ping; Wu, Wei; Yang, Siquan

    2012-10-01

    Drought is one major nature disaster in the world. The affected population and agriculture loss caused by drought are the largest in all natural disasters. Drought has the characteristics of wide affected areas, long duration and periodic strong feature. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster monitoring and forecasting. So, remote sensing data have been widely used and delivered significant benefits in drought prevention and reduction in China. Three drought monitor models including Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Temperature Vegetation Dryness Index (TVDI) had been used to monitor southwest drought occurred in China from 2009 to 2011 based on the small satellite constellation for environment and disaster monitoring and forecasting A/B satellites (HJ-1AB) and Landsat remote sensing data. The results shown that five regions including Sichuan province, Chongqing, Guizhou province, Yunnan province, Guangxi province in southwest of China had suffered different degrees agricultural drought disaster in 2010 and 2011. The comprehensive agricultural disaster situation of five affected areas in 2010 was more serious than drought events occurred in 2011. The many regions in Guizhou province were hardest-hit areas cased by the two consecutive year drought events in southwest China.

  5. Advancing drought monitoring using a Small Unmanned Aerial System (sUAS) in a changing climate

    NASA Astrophysics Data System (ADS)

    Ryu, J.

    2016-12-01

    Drought as a natural hazard, increasingly threatens the sustainability of regional water resources around the world. Given current trends in climate variability and change, droughts are likely to continue and increase. One of the effective ways to mitigate drought impacts may be to use a Small Unmanned Aerial System (sUAS) to improve understanding of the factors that drive the onset and development of drought conditions at local levels would enable planners and end users to more effectively manage and meter out limited water resources. During the presentation, the author will propose a methodological approach to apply sUAS for drought monitoring along with federal regulations and policies.

  6. A Drought Monitoring Tool for Customized Calculation of a Standardized Precipitation Index Value in the Navajo Nation

    NASA Astrophysics Data System (ADS)

    Cary, C.; Ly, V.; Gao, M.; Surunis, A.; Turnbull-Appell, S.; Sodergren, C.; Brooks, A. N.

    2015-12-01

    The Navajo Nation, located in the southwestern United States, has been increasingly impacted by severe drought events and regional changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring climate and drought are dependent on national-scale monthly drought maps calculated by the Western Regional Climate Center (WRCC). These maps do not provide the spatial resolution needed to examine differences in drought severity across the vast Nation. To better understand and monitor drought regime changes in the Navajo Nation, this project comprises of two main components: 1) a geodatabase of historical climate information necessary to calculate Standardized Precipitation Index (SPI) values and 2) a tool that calculates SPI values for a user-selected area within the study site. The tool and geodatabase use TRMM and GPM observed precipitation data, and Parameter-elevation Relationships on Independent Slopes Model (PRISM) modeled historical precipitation data. These products allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources.

  7. Land-atmosphere coupling metrics from satellite remote sensing as a global drought-monitoring tool

    NASA Astrophysics Data System (ADS)

    Roundy, Joshua K.; Santanello, Joseph A.

    2015-04-01

    Drought causes significant economic impact to society that can be reduced through preparations made possible by monitoring and prediction. Most drought monitoring systems utilize a variety of metrics to assess and understand drought. Feedbacks induced through land-atmosphere interactions are an important mechanism of drought intensification and persistence that is often not considered in current drought monitors due to a lack of spatially consistent observations. Recent work has developed a new classification of land-atmosphere interactions that summarizes the net impact of these interactions on drought intensification and recovery through the Coupling Drought Index (CDI). One thing that makes the CDI unique is that it can be calculated based on estimates from satellite remote sensing, which makes it particularly useful for global drought monitoring. Furthermore, the persistent nature of these coupling regimes provides a means of prediction through a Markov Chain Coupling Statistical Model (CSM). Previous work has shown that the CDI based on satellite remote sensing compares well with the U.S. Drought monitor in terms of drought intensification and recovery. On the other hand, the skill of the CSM forecasts over the U.S. is limited and still needs improvement. In this work the extent to which the CDI and CSM can be extended to other areas of the globe are explored. In particular, the ability of the satellite remote sensing based CDI to capture drought intensification and recovery over Africa and Europe are assessed. The benefits and limitations of using a metric of land-atmosphere interactions for global drought monitoring are also discussed.

  8. A quasi-global precipitation time series for drought monitoring

    USGS Publications Warehouse

    Funk, Chris C.; Peterson, Pete J.; Landsfeld, Martin F.; Pedreros, Diego H.; Verdin, James P.; Rowland, James D.; Romero, Bo E.; Husak, Gregory J.; Michaelsen, Joel C.; Verdin, Andrew P.

    2014-01-01

    Estimating precipitation variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in historical context so that the severity of rainfall deficits may quickly be evaluated. To this end, scientists at the U.S. Geological Survey Earth Resources Observation and Science Center, working closely with collaborators at the University of California, Santa Barbara Climate Hazards Group, have developed a quasi-global (50°S–50°N, 180°E–180°W), 0.05° resolution, 1981 to near-present gridded precipitation time series: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data archive.

  9. Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

    NASA Astrophysics Data System (ADS)

    Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

    2014-12-01

    Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and

  10. Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

    NASA Astrophysics Data System (ADS)

    Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

    2015-12-01

    Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and

  11. Using Enhanced Grace Water Storage Data to Improve Drought Detection by the U.S. and North American Drought Monitors

    NASA Technical Reports Server (NTRS)

    Houborg, Rasmus; Rodell, Matthew; Lawrimore, Jay; Li, Bailing; Reichle, Rolf; Heim, Richard; Rosencrans, Matthew; Tinker, Rich; Famiglietti, James S.; Svoboda, Mark; hide

    2011-01-01

    NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations of the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including groundwater. The U.S. and North American Drought Monitors rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors by filling this observational gap. GRACE TWS data were assimilating into the Catchment Land Surface Model using an ensemble Kalman smoother enabling spatial and temporal downscaling and vertical decomposition into soil moisture and groundwater components. The Drought Monitors combine several short- and long-term drought indicators expressed in percentiles as a reference to their historical frequency of occurrence. To be consistent, we generated a climatology of estimated soil moisture and ground water based on a 60-year Catchment model simulation, which was used to convert seven years of GRACE assimilated fields into drought indicator percentiles. At this stage we provide a preliminary evaluation of the GRACE assimilated moisture and indicator fields.

  12. Using Enhanced Grace Water Storage Data to Improve Drought Detection by the U.S. and North American Drought Monitors

    NASA Technical Reports Server (NTRS)

    Houborg, Rasmus; Rodell, Matthew; Lawrimore, Jay; Li, Bailing; Reichle, Rolf; Heim, Richard; Rosencrans, Matthew; Tinker, Rich; Famiglietti, James S.; Svoboda, Mark; Wardlow, Brian; Zaitchik, Benjamin F.

    2011-01-01

    NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations of the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including groundwater. The U.S. and North American Drought Monitors rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors by filling this observational gap. GRACE TWS data were assimilating into the Catchment Land Surface Model using an ensemble Kalman smoother enabling spatial and temporal downscaling and vertical decomposition into soil moisture and groundwater components. The Drought Monitors combine several short- and long-term drought indicators expressed in percentiles as a reference to their historical frequency of occurrence. To be consistent, we generated a climatology of estimated soil moisture and ground water based on a 60-year Catchment model simulation, which was used to convert seven years of GRACE assimilated fields into drought indicator percentiles. At this stage we provide a preliminary evaluation of the GRACE assimilated moisture and indicator fields.

  13. Evaluating the Potential Use of Remotely Sensed Soil Moisture Data for Agricultural Drought Risk Monitoring

    NASA Astrophysics Data System (ADS)

    Yan, H.; Moradkhani, H.

    2015-12-01

    The Pacific Northwest US has received historically low rainfall and snowpack during winter 2015, with drought emergence declared for both states in spring 2015. To mitigate the impacts of drought vulnerability, an operational near-real-time (NRT) drought monitoring with remote sensing technique is investigated. This study provides a comprehensive assessment of the potential of remotely sensed surface soil moisture data in monitoring agricultural drought over the Columbia River Basin (CRB), Pacific Northwest. Two satellite soil moisture datasets were evaluated, the LPRM-AMSR-E (unscaled, 2002-2011) and ESA-CCI (scaled, 1979-2013). The satellite drought monitoring skill is examined with two indices: drought area coverage (the ability of drought detection) and drought severity (according to USDM categories). The effects of satellite sensors (active, passive), multi-satellite combined, and length of climatology are also examined in this study. In order to improve the remote sensing drought monitoring skill, statistical methods including regionalization, with the concept of "trading space for time"; and also bootstrapping are introduced.

  14. Probabilistic drought characterization in the categorical form using ordinal regression

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hong, Yang; Xia, Youlong; Singh, Vijay P.; Hao, Fanghua; Cheng, Hongguang

    2016-04-01

    Drought is an insidious natural hazard that may cause tremendous losses to different sectors, including agriculture and ecosystems. Reliable drought monitoring and early warning are of critical importance for drought preparedness planning and mitigation to reduce potential impacts. Traditional drought monitoring is generally based on drought indices, such as Standardized Precipitation Index (SPI), that are computed from hydro-climatic variables. The U.S. Drought Monitor (USDM) classifies drought conditions into different drought categories to provide composite drought information by integrating multiple drought indices, which has been commonly used to aid decision making at the federal, state, and local levels. Characterizing drought in categories similar to USDM would be important for decision making for both research and operational purposes. However, drought monitoring, based on a variety of drought indices, is challenged by the classification of drought into categories used by USDM. In this study, an ordinal regression model is proposed to characterize droughts in USDM drought categories based on several drought indices, in which the probability of each drought category can be estimated. The proposed method is assessed by comparing with USDM in Texas and a satisfactory performance for estimating drought categories is revealed.

  15. Monitoring as a partially observable decision problem

    Treesearch

    Paul L. Fackler; Robert G. Haight

    2014-01-01

    Monitoring is an important and costly activity in resource man-agement problems such as containing invasive species, protectingendangered species, preventing soil erosion, and regulating con-tracts for environmental services. Recent studies have viewedoptimal monitoring as a Partially Observable Markov Decision Pro-cess (POMDP), which provides a framework for...

  16. [The new method monitoring agricultural drought based on SWIR-Red spectrum feature space].

    PubMed

    Feng, Hai-Xia; Qin, Qi-Ming; Li, Bin-Yong; Liu, Fang; Jiang, Hong-Bo; Dong, Heng; Wang, Jin-Liang; Liu, Ming-Chao; Zhang, Ning

    2011-11-01

    Drought was a chronic, natural disaster, and Remote sensing drought monitoring had become a potential research field. In the present, short-wave infrared and red bands which sensitive to moisture variation were selected to monitor farmland drought conditions by analyzing the spectral characteristics of vegetation and soil. The goal of this paper was to provide a new method of drought monitoring--normalized drought monitoring index (NPDI), based on new constructed spectrum feature space by the difference of SWIR and Red and the sum of SWIR and Red. Field surveyed soil moisture verified NPDI model, and the result showed that NDPI and MPDI model could effectively monitor agricultural drought, and that had high correlation with soil moisture. The R2 was 0.583 and 0.438 with soil water of 10 cm. The monitoring effect of NPDI model was better than the MPDL. This model was further improvement to PDI and MPDI, and it could monitor the drought condition of different vegetation coverage and whole growing season. It has high application potential and popularization value.

  17. Using Satellite Data to Build Climate Resilience: A Novel East Africa Drought Monitor

    NASA Astrophysics Data System (ADS)

    Slinski, K.; Hogue, T. S.; McCray, J. E.

    2016-12-01

    East Africa is affected by recurrent drought. The 2015-2016 El Niño triggered a severe drought across East Africa causing serious impacts to regional water security, health, and livelihoods. Ethiopia was the hardest hit, with the United Nations Office for the Coordination of Humanitarian Affairs calling the recent drought the worst in 50 years. Resources to monitor the severity and progression of droughts are a critical component to disaster risk reduction, but are challenging to implement in regions with sparse data collection networks such as East Africa. Satellite data is used by the United Nations Food and Agriculture Organization Global Information and Early Warning System, the USAID Famine Early Warning System, and the Africa Drought and Flood Monitor. These systems use remotely sensed vegetation, soil moisture, and meteorological data to develop drought indices. However, they do not directly monitor impacts to water resources, which is necessary to appropriately target drought mitigation efforts. The current study combines new radar data from the European Space Agency's Sentinel-1 mission with satellite imagery to perform a retrospective analysis of the impact of the 2015-2016 drought in East Africa on regional surface water. Inland water body extents during the drought are compared to historical trends to identify the most severely impacted areas. The developed tool has the potential to support on-the-ground humanitarian relief efforts and to refine predictions of water scarcity and crop impacts from existing hydrologic models and famine early warning systems.

  18. Incorporation of GRACE Data into a Bayesian Model for Groundwater Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Slinski, K.; Hogue, T. S.; McCray, J. E.; Porter, A.

    2015-12-01

    Groundwater drought, defined as the sustained occurrence of below average availability of groundwater, is marked by below average water levels in aquifers and reduced flows to groundwater-fed rivers and wetlands. The impact of groundwater drought on ecosystems, agriculture, municipal water supply, and the energy sector is an increasingly important global issue. However, current drought monitors heavily rely on precipitation and vegetative stress indices to characterize the timing, duration, and severity of drought events. The paucity of in situ observations of aquifer levels is a substantial obstacle to the development of systems to monitor groundwater drought in drought-prone areas, particularly in developing countries. Observations from the NASA/German Space Agency's Gravity Recovery and Climate Experiment (GRACE) have been used to estimate changes in groundwater storage over areas with sparse point measurements. This study incorporates GRACE total water storage observations into a Bayesian framework to assess the performance of a probabilistic model for monitoring groundwater drought based on remote sensing data. Overall, it is hoped that these methods will improve global drought preparedness and risk reduction by providing information on groundwater drought necessary to manage its impacts on ecosystems, as well as on the agricultural, municipal, and energy sectors.

  19. Drought monitoring over the Horn of Africa using remotely sensed evapotranspiration, soil moisture and vegetation parameters

    NASA Astrophysics Data System (ADS)

    Timmermans, J.; Gokmen, M.; Eden, U.; Abou Ali, M.; Vekerdy, Z.; Su, Z.

    2012-04-01

    The need to good drought monitoring and management for the Horn of Africa has never been greater. This ongoing drought is the largest in the past sixty years and is effecting the life of around 10 million people, according to the United Nations. The impact of drought is most apparent in food security and health. In addition secondary problems arise related to the drought such as large migration; more than 15000 Somalia have fled to neighboring countries to escape the problems caused by the drought. These problems will only grow in the future to larger areas due to increase in extreme weather patterns due to global climate change. Monitoring drought impact and managing the drought effects are therefore of critical importance. The impact of a drought is hard to characterize as drought depends on several parameters, like precipitation, land use, irrigation. Consequently the effects of the drought vary spatially and range from short-term to long-term. For this reason a drought event can be characterized into four categories: meteorological, agricultural, hydrological and socio-economical. In terms of food production the agricultural drought, or short term dryness near the surface layer, is most important. This drought is usually characterized by low soil moisture content in the root zone, decreased evapotranspiration, and changes in vegetation vigor. All of these parameters can be detected with good accuracy from space. The advantage of remote sensing in Drought monitoring is evident. Drought monitoring is usually performed using drought indices, like the Palmer Index (PDSI), Crop Moisture Index (CMI), Standard Precipitation Index (SPI). With the introduction of remote sensing several indices of these have shown great potential for large scale application. These indices however all incorporate precipitation as the main surface parameter neglecting the response of the surface to the dryness. More recently two agricultural drought indices, the EvapoTranspiration Deficit

  20. Monitoring drought using multi-sensor remote sensing data in cropland of Gansu Province

    NASA Astrophysics Data System (ADS)

    Zeng, Linglin; Shan, Jie; Xiang, Daxiang

    2014-03-01

    Various drought monitoring models have been developed from different perspectives, as drought is impacted by various factors (precipitation, evaporation, runoff) and usually reflected in various aspects (vegetation condition, temperature). Cloud not only plays an important role in the earth's energy balance and climate change, but also directly impacts the regional precipitation and evaporation. As a result, the change of cloud cover and cloud type can be used to monitor drought. This paper proposes a new drought composite index, the Drought Composite Index (DCI), for drought monitoring based on multi-sensor remote sensing data in cropland of Gansu Province. This index combines the cloud classification data (CLS) from FY satellite and Vegetation Condition Index (VCI) which was calculated using the maximum and minimum NDVI values for the same time period from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson correlation was performed to correlate NDVI, VCI, CLS and DCI values to precipitation data and soil moisture (SM) data collected from 20 meteorological stations during the growing season of 2011 and 2012. Better agreement was observed between DCI and precipitation as compared with that between NDVI/VCI and precipitation, especially the one-month precipitation, and there is an obvious time lag in the response of vegetation to precipitation. In addition, the results indicated that DCI well reflected precipitation fluctuations in the study area promising a possibility for early drought awareness necessary and near real-time drought monitoring.

  1. The Crop Risk Zones Monitoring System for resilience to drought in the Sahel

    NASA Astrophysics Data System (ADS)

    Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena

    2016-04-01

    Food security is still one of the major concerns that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. The predominantly rainfed cropping system of Sahel region is dependent on season quality on a year-to-year basis, and susceptible to weather extremes of droughts and extreme temperatures. Low water-storage capacity and high dependence on rainfed agriculture leave the agriculture sector even more vulnerable to climate risks. Crop yields may suffer significantly with either a late onset or early cessation of the rainy season, as well as with a high frequency of damaging dry spells. Early rains at the beginning of the season are frequently followed by dry spells which may last a week or longer. As the amount of water stored in the soil at this time of the year is negligible, early planted crops can suffer water shortage stresses during a prolonged dry spell. Therefore, the choice of the sowing date is of fundamental importance for farmers. The ability to estimate effectively the onset of the season and potentially dangerous dry spells becomes therefore vital for planning rainfed agriculture practices aiming to minimize risks and maximize yields. In this context, advices to farmers are key drivers for prevention allowing a better adaptation of traditional crop calendar to climatic variability. In the Sahel, particularly in CILSS (Permanent Interstates Committee for Drought Control in the Sahel) countries, national Early Warning System (EWS) for food security are underpinned by Multidisciplinary Working Groups (MWGs) lead by National Meteorological Services (NMS). The EWSs are mainly based on tools and models utilizing numeric forecasts and satellite data to outlook and monitor the growing season. This approach is focused on the early identification of risks and on the production of information within the prescribed time period for decision

  2. InfoSequia: the first operational remote sensing-based Drought Monitoring System of Spain

    NASA Astrophysics Data System (ADS)

    Contreras, Sergio; Hunink, Johannes E.

    2016-04-01

    We present a satellite-based Drought Monitoring System that provides weekly updates of maps and bulletins with vegetation drought indices over the Iberian Peninsula. The web portal InfoSequía (http://infosequia.es) aims to complement the current Spanish Drought Monitoring System which relies on a hydrological drought index computed at the basin level using data on river flows and water stored in reservoirs. Drought indices computed by InfoSequia are derived from satellite data provided by MODIS sensors (TERRA and AQUA satellites), and report the relative anomaly observed in the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and in an additive combination of both. Similar to the U.S. Drought Monitoring System by NOAA, the indices include the Vegetation Condition Index (VCI, relative NDVI anomaly), the Temperature Condition Index (TCI, relative LST anomaly) and the Vegetation Health Index (VHI, relative NDVI-LST anomaly). Relative anomalies are codified into four warning levels, and all of them are provided for short periods of time (8-day windows), or longer periods (e.g. 1 year) in order to capture the cumulative effects of droughts in the state variables. Additionally, InfoSequia quantifies the seasonal trajectories of the cumulative deviation of the observed NDVI in relation with the averaged seasonal trajectory observed over a reference period. Through the weekly bulletins, the Drought Monitoring System InfoSequia aims to provide practical information to stakeholders on the sensitivity and resilience of native ecosystems and rainfed agrosystems during drought periods. Also, the remote sensed indices can be used as drought impact indicator to evaluate the skill of seasonal agricultural drought forecasting systems. InfoSequia is partly funded by the Spanish Ministry of Economy and Competiveness through a Torres-Quevedo grant.

  3. Validating modeled soil moisture with in-situ data for agricultural drought monitoring in West Africa

    NASA Astrophysics Data System (ADS)

    McNally, A.; Yatheendradas, S.; Jayanthi, H.; Funk, C. C.; Peters-Lidard, C. D.

    2011-12-01

    The declaration of famine in Somalia on July 21, 2011 highlights the need for regional hydroclimate analysis at a scale that is relevant for agropastoral drought monitoring. A particularly critical and robust component of such a drought monitoring system is a land surface model (LSM). We are currently enhancing the Famine Early Warning Systems Network (FEWS NET) monitoring activities by configuring a custom instance of NASA's Land Information System (LIS) called the FEWS NET Land Data Assimilation System (FLDAS). Using the LIS Noah LSM, in-situ measurements, and remotely sensed data, we focus on the following question: How can Noah be best parameterized to accurately simulate hydroclimate variables associated with crop performance? Parameter value testing and validation is done by comparing modeled soil moisture against fortuitously available in-situ soil moisture observations in the West Africa. Direct testing and application of the FLDAS over African agropastoral locations is subject to some issues: [1] In many regions that are vulnerable to food insecurity ground based measurements of precipitation, evapotranspiration and soil moisture are sparse or non-existent, [2] standard landcover classes (e.g., the University of Maryland 5 km dataset), do not include representations of specific agricultural crops with relevant parameter values, and phenologies representing their growth stages from the planting date and [3] physically based land surface models and remote sensing rain data might still need to be calibrated or bias-corrected for the regions of interest. This research aims to address these issues by focusing on sites in the West African countries of Mali, Niger, and Benin where in-situ rainfall and soil moisture measurements are available from the African Monsoon Multidisciplinary Analysis (AMMA). Preliminary results from model experiments over Southern Malawi, validated with Normalized Difference Vegetation Index (NDVI) and maize yield data, show that the

  4. Assessing existing drought monitoring and forecasting capacities, mitigation and adaptation practices in Africa

    NASA Astrophysics Data System (ADS)

    Nyabeze, W. R.; Dlamini, L.; Lahlou, O.; Imani, Y.; Alaoui, S. B.; Vermooten, J. S. A.

    2012-04-01

    Drought is one of the major natural hazards in many parts of the world, including Africa and some regions in Europe. Drought events have resulted in extensive damages to livelihoods, environment and economy. In 2011, a consortium consisting of 19 organisations from both Africa and Europe started a project (DEWFORA) aimed at developing a framework for the provision of early warning and response through drought impact mitigation for Africa. This framework covers the whole chain from monitoring and vulnerability assessment to forecasting, warning, response and knowledge dissemination. This paper presents the first results of the capacity assessment of drought monitoring and forecasting systems in Africa, the existing institutional frameworks and drought mitigation and adaptation practices. Its focus is particularly on the historical drought mitigation and adaptation actions identified in the North Africa - Maghreb Region (Morocco, Algeria and Tunisia) and in the Southern Africa - Limpopo Basin. This is based on an extensive review of historical drought experiences. From the 1920's to 2009, the study identified 37 drought seasons in the North African - Maghreb Region and 33 drought seasons in the Southern Africa - Limpopo Basin. Existing literature tends to capture the spatial extent of drought at national and administrative scale in great detail. This is driven by the need to map drought impacts (food shortage, communities affected) in order to inform drought relief efforts (short-term drought mitigation measures). However, the mapping of drought at catchment scale (hydrological unit), required for longer-term measures, is not well documented. At regional level, both in North Africa and Southern Africa, two organisations are involved in drought monitoring and forecasting, while at national level 22 organisations are involved in North Africa and 37 in Southern Africa. Regarding drought related mitigation actions, the inventory shows that the most common actions

  5. Predicting the 2015-2016 Ethiopian Drought: Model Skill and Decision-maker Response

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Erkyihun, S. T.; Zhang, Y.; Zaitchik, B. F.

    2016-12-01

    The 2015-2016 Ethiopian drought was one of the worst in decades, causing substantial crop losses and forcing millions of people to seek emergency food aid. Drought is not unfamiliar in Ethiopia; in response to previous droughts in the mid-1980s, agro-climatic forecasts and early warning systems were developed in an effort to avert such crises. In this study we investigate season-ahead predictions made in advance of the main 2015 cropping season by various institutions to understand if the abnormally dry conditions and poor agricultural yields in some parts of the country were accurately predicted or were a surprise. And further, were these probabilistic forecasts certain enough to trigger appropriate action by local and international natural hazard decision-makers?

  6. Drought monitoring using downscaled soil moisture through machine learning approaches over North and South Korea

    NASA Astrophysics Data System (ADS)

    Park, S.; Im, J.; Rhee, J.; Park, S.

    2015-12-01

    Soil moisture is one of the most important key variables for drought monitoring. It reflects hydrological and agricultural processes because soil moisture is a function of precipitation and energy flux and crop yield is highly related to soil moisture. Many satellites including Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity sensor (SMOS), and Soil Moisture Active Passive (SMAP) provide global scale soil moisture products through microwave sensors. However, as the spatial resolution of soil moisture products is typically tens of kilometers, it is difficult to monitor drought using soil moisture at local or regional scale. In this study, AMSR-E and AMSR2 soil moisture were downscaled up to 1 km spatial resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data—Evapotranspiration, Land Surface Temperature, Leaf Area Index, Normalized Difference Vegetation Index, Enhanced Vegetation Index and Albedo—through machine learning approaches over Korean peninsula. To monitor drought from 2003 to 2014, each pixel of the downscaled soil moisture was scaled from 0 to 1 (1 is the wettest and 0 is the driest). The soil moisture based drought maps were validated using Standardized Precipitation Index (SPI) and crop yield data. Spatial distribution of drought status was also compared with other drought indices such as Scaled Drought Condition Index (SDCI). Machine learning approaches were performed well (R=0.905) for downscaling. Downscaled soil moisture was validated using in situ Asia flux data. The Root Mean Square Errors (RMSE) improved from 0.172 (25 km AMSR2) to 0.065 (downscaled soil moisture). The correlation coefficients improved from 0.201 (25 km AMSR2) to 0.341 (downscaled soil moisture). The soil moisture based drought maps and SDCI showed similar spatial distribution that caught both extreme drought and no drought. Since the proposed drought monitoring approach based on the downscaled

  7. Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

    NASA Astrophysics Data System (ADS)

    Hao, Cui; Zhang, Jiahua; Yao, Fengmei

    2015-03-01

    Drought is one of the most frequent climate-related disasters occurring in Southwest China, where the occurrence of drought is complex because of the varied landforms, climates and vegetation types. To monitor the comprehensive information of drought from meteorological to vegetation aspects, this paper intended to propose the optimized meteorological drought index (OMDI) and the optimized vegetation drought index (OVDI) from multi-source satellite data to monitor drought in three bio-climate regions of Southwest China. The OMDI and OVDI were integrated with parameters such as precipitation, temperature, soil moisture and vegetation information, which were derived from Tropical Rainfall Measuring Mission (TRMM), Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST), AMSR-E Soil Moisture (AMSR-E SM), the soil moisture product of China Land Soil Moisture Assimilation System (CLSMAS), and MODIS Normalized Difference Vegetation Index (MODIS NDVI), respectively. Different sources of satellite data for one parameter were compared with in situ drought indices in order to select the best data source to derive the OMDI and OVDI. The Constrained Optimization method was adopted to determine the optimal weights of each satellite-based index generating combined drought indices. The result showed that the highest positive correlation and lowest root mean square error (RMSE) between the OMDI and 1-month standardized precipitation evapotranspiration index (SPEI-1) was found in three regions of Southwest China, suggesting that the OMDI was a good index in monitoring meteorological drought; in contrast, the OVDI was best correlated to 3-month SPEI (SPEI-3), and had similar trend with soil relative water content (RWC) in temporal scale, suggesting it a potential indicator of agricultural drought. The spatial patterns of OMDI and OVDI along with the comparisons of SPEI-1 and SPEI-3 for different months in one year or one month in different years showed

  8. Perceptions of the Decision Process though Drought and Flood in the Murray-Darling Basin, Australia

    NASA Astrophysics Data System (ADS)

    Lynch, A. H.; Adler, C.; Howard, N.

    2012-12-01

    The Murray-Darling Basin incorporates Australia's three longest rivers and spans four States and one Territory. It is important for an agricultural industry worth more than AUS$9 billion per year, but is also the life source and spirit of the Indigenous Yorta Yorta people. Persistent severe drought and extreme flooding episodes have presented new challenges in the region. The exceptionally wet conditions experienced since the break of the "Millenium Drought" beg the question as to whether key drought and flood characteristics are changing due to anthropogenic climate change. Many alternative goals for the management of the Basin answer to the requirement for an evidentiary basis. A choice cannot be made on this basis alone - interests are implicated in any alternative. Here we use Q methodology, an approach that elucidates patterns of subjectivity, to explore the perspectives of Indigenous and non-Indigenous residents, workers and decision-makers in the region. We address the inherent diversity of viewpoints on the risks from and responses to flood and drought, and identify the potential for common ground.

  9. An Effective Statistical-Dynamical Framework for Seasonal Drought Monitoring and Forecasting

    NASA Astrophysics Data System (ADS)

    Moradkhani, Hamid; Yan, Hongxiang; Zarekarizi, Mahkameh

    2017-04-01

    Although research on drought monitoring and prediction has shown some improvement over the past few years, accurate provisions of drought information in a timely manner is still a challenge. Both statistical and dynamical drought prediction methods have been attempted in research and practice. While these approaches have yielded skillful predictions in specific case studies, some limitations still restrict their use. One of the main limitations is the deterministic treatment of the land initial condition. This motivates development of a drought monitoring and prediction system that is based on full characterization of the initial condition. The framework employs a data assimilation (DA) method based on particle filter (PF) to quantify the uncertainties associated with antecedent land surface condition. The initial condition at each forecast step is probabilistically sampled from the ensemble of initial conditions characterized by data assimilation and through a multivariate approach based on copula functions resulting in probabilistic drought prediction. Large computational demands are overcome by developing a modular parallel computing framework which facilitates large ensemble sizes. Usefulness and effectiveness of this hybrid drought estimation framework is demonstrated over the Contiguous United States and the superiority in monitoring and prediction are compared with some current operational systems.

  10. Monitoring drought conditions and their uncertainties in areas with sparse precipitation data. Evaluation of different precipitation datasets in Africa.

    NASA Astrophysics Data System (ADS)

    Naumann, G.; Barbosa, P.; Carrao, H.; Singleton, A.; Vogt, J.

    2012-04-01

    Merged Analysis of Precipitation (CMAP, 2.5°x2.5°). A non-parametric resampling bootstrap approach was used in order to assess the sampling uncertainties associated with SPI estimation in terms of confidence bands. Confidence bands are essential for making a qualified assessment of drought events. The comparative analysis of the four different datasets suggests that is feasible to use short time series of precipitation data with high spatial resolution (0.25°x0.25°) such as the TRMM for reliable drought monitoring over Africa. Furthermore, the bootstrap technique gives an estimate of the SPI uncertainty by providing confidence intervals. The proposed approach for drought monitoring has the potential to be used in support of decision making at continental and sub-continental scales over Africa or other regions that have a sparse distribution of rainfall measurement instruments.

  11. Towards an integrated soil moisture drought monitor for East Africa

    USDA-ARS?s Scientific Manuscript database

    Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived mo...

  12. SERVIR: A Regional Monitoring and Decision Support System for Mesoamerica

    NASA Astrophysics Data System (ADS)

    Irwin, D.; Hardin, D. M.; Sever, T.; Graves, S.

    2008-05-01

    Mesoamerica is a prime example of a multi-national region with natural and human induced stresses that benefits from information provided by observation systems. The region is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability of its population to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, the University of Alabama in Huntsville and numerous SERVIR* partners are developing data products, knowledge extraction methods and decision support tools for environmental monitoring, disaster response and sustainable growth planning in Mesoamerica. The combination of space- based observations from NASA's Earth Observing Satellites with information management and knowledge extraction technologies has yielded a robust system for use by scientists, educators, environmental ministers and policy makers. These resources enhance the ability to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. Now in its fourth year SERVIR has become a partner in the International Space and Major Disasters Charter. In the past year the Charter provided commercial satellite imagery to aid in disaster response to Hurricanes Dean, Felix and Noel. Overcoming roadblocks to coordination and data sharing between countries, organizations and disciplines SERVIR is providing environmental monitoring and decision support products and applications that directly map to several Observation GEOSS societal benefit areas. This paper provides an overview of the ongoing accomplishments of the SERVIR project. *SERVIR is a Spanish verb meaning "to serve" or "be useful" is also an acronym for the Spanish name of the capability: Sistema Regional de Visualizacion y Monitero.

  13. Satellite gravity measurement monitoring terrestrial water storage change and drought in the continental United States.

    PubMed

    Yi, Hang; Wen, Lianxing

    2016-01-27

    We use satellite gravity measurements in the Gravity Recovery and Climate Experiment (GRACE) to estimate terrestrial water storage (TWS) change in the continental United States (US) from 2003 to 2012, and establish a GRACE-based Hydrological Drought Index (GHDI) for drought monitoring. GRACE-inferred TWS exhibits opposite patterns between north and south of the continental US from 2003 to 2012, with the equivalent water thickness increasing from -4.0 to 9.4 cm in the north and decreasing from 4.1 to -6.7 cm in the south. The equivalent water thickness also decreases by -5.1 cm in the middle south in 2006. GHDI is established to represent the extent of GRACE-inferred TWS anomaly departing from its historical average and is calibrated to resemble traditional Palmer Hydrological Drought Index (PHDI) in the continental US. GHDI exhibits good correlations with PHDI in the continental US, indicating its feasibility for drought monitoring. Since GHDI is GRACE-based and has minimal dependence of hydrological parameters on the ground, it can be extended for global drought monitoring, particularly useful for the countries that lack sufficient hydrological monitoring infrastructures on the ground.

  14. Satellite gravity measurement monitoring terrestrial water storage change and drought in the continental United States

    NASA Astrophysics Data System (ADS)

    Yi, Hang; Wen, Lianxing

    2016-01-01

    We use satellite gravity measurements in the Gravity Recovery and Climate Experiment (GRACE) to estimate terrestrial water storage (TWS) change in the continental United States (US) from 2003 to 2012, and establish a GRACE-based Hydrological Drought Index (GHDI) for drought monitoring. GRACE-inferred TWS exhibits opposite patterns between north and south of the continental US from 2003 to 2012, with the equivalent water thickness increasing from -4.0 to 9.4 cm in the north and decreasing from 4.1 to -6.7 cm in the south. The equivalent water thickness also decreases by -5.1 cm in the middle south in 2006. GHDI is established to represent the extent of GRACE-inferred TWS anomaly departing from its historical average and is calibrated to resemble traditional Palmer Hydrological Drought Index (PHDI) in the continental US. GHDI exhibits good correlations with PHDI in the continental US, indicating its feasibility for drought monitoring. Since GHDI is GRACE-based and has minimal dependence of hydrological parameters on the ground, it can be extended for global drought monitoring, particularly useful for the countries that lack sufficient hydrological monitoring infrastructures on the ground.

  15. Satellite gravity measurement monitoring terrestrial water storage change and drought in the continental United States

    PubMed Central

    Yi, Hang; Wen, Lianxing

    2016-01-01

    We use satellite gravity measurements in the Gravity Recovery and Climate Experiment (GRACE) to estimate terrestrial water storage (TWS) change in the continental United States (US) from 2003 to 2012, and establish a GRACE-based Hydrological Drought Index (GHDI) for drought monitoring. GRACE-inferred TWS exhibits opposite patterns between north and south of the continental US from 2003 to 2012, with the equivalent water thickness increasing from −4.0 to 9.4 cm in the north and decreasing from 4.1 to −6.7 cm in the south. The equivalent water thickness also decreases by −5.1 cm in the middle south in 2006. GHDI is established to represent the extent of GRACE-inferred TWS anomaly departing from its historical average and is calibrated to resemble traditional Palmer Hydrological Drought Index (PHDI) in the continental US. GHDI exhibits good correlations with PHDI in the continental US, indicating its feasibility for drought monitoring. Since GHDI is GRACE-based and has minimal dependence of hydrological parameters on the ground, it can be extended for global drought monitoring, particularly useful for the countries that lack sufficient hydrological monitoring infrastructures on the ground. PMID:26813800

  16. Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

    SciTech Connect

    Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai; Leng, Guoyong; Li, Zhe

    2017-01-01

    Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought over SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.

  17. [Monitoring of farmland drought based on LST-LAI spectral feature space].

    PubMed

    Sui, Xin-Xin; Qin, Qi-Ming; Dong, Heng; Wang, Jin-Liang; Meng, Qing-Ye; Liu, Ming-Chao

    2013-01-01

    Farmland drought has the characteristics of wide range and seriously affecting on agricultural production, so real-time dynamic monitored has been a challenging problem. By using MODIS land products, and constructing the spectral space of LST and LAI, the temperature LAI drought index (TLDI) was put forward and validated using ground-measured 0-10 cm averaged soil moisture of Ningxia farmland. The results show that the coefficient of determination (R2) of both them varies from 0.43 to 0.86. Compared to TVDI, the TLDI has higher accuracy for farmland moisture monitoring, and solves the saturation of NDVI during the late development phases of the crop. Furthermore, directly using MODIS land products LST and LAI and avoiding the complicated process of using the original MODIS data provide a new technical process to the regular operation of farmland drought monitoring.

  18. Predicting US Drought Monitor (USDM) states using precipitation, soil moisture, and evapotranspiration anomalies, Part I: Development of a non-discrete USDM index

    USDA-ARS?s Scientific Manuscript database

    The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a non-discrete U.S. Drought Monitor (USDM) index that simultaneously 1) represents th...

  19. Evaluating new SMAP soil moisture for drought monitoring in the rangelands of the US High Plains

    USGS Publications Warehouse

    Velpuri, Naga Manohar; Senay, Gabriel B.; Morisette, Jeffrey T.

    2016-01-01

    Level 3 soil moisture datasets from the recently launched Soil Moisture Active Passive (SMAP) satellite are evaluated for drought monitoring in rangelands.Validation of SMAP soil moisture (SSM) with in situ and modeled estimates showed high level of agreement.SSM showed the highest correlation with surface soil moisture (0-5 cm) and a strong correlation to depths up to 20 cm.SSM showed a reliable and expected response of capturing seasonal dynamics in relation to precipitation, land surface temperature, and evapotranspiration.Further evaluation using multi-year SMAP datasets is necessary to quantify the full benefits and limitations for drought monitoring in rangelands.

  20. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

    NASA Astrophysics Data System (ADS)

    Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Minh, V. Q.

    2012-08-01

    Drought is a complex natural phenomenon, and its impacts on agriculture are enormous. Drought has been a prevalent concern for farmers in the Lower Mekong Basin (LMB) over the last decades; thus, monitoring drought is important for water planning and management to mitigate impacts on agriculture in the region. This study explored the applicability of monthly MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data for agricultural drought monitoring in LMB in the dry season from November 2001 to April 2010. The data were processed using the temperature vegetation dryness index (TVDI), calculated by parameterizing the relationship between the MODIS NDVI and LST data. The daily volumetric surface soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM) were collected and used for verification of the results. In addition, we compared the efficiency of TVDI with a commonly used drought index, the crop water stress index (CWSI), derived from the MODIS LST alone. The results achieved from comparisons between TVDI and AMSR-E soil moisture data indicated acceptable correlations between the two datasets in most cases. There was close agreement between TVDI and TRMM precipitation data through the season, indicating that TVDI was sensitive to precipitation. The TVDI compared to CWSI also yielded close correlations between both datasets. The TVDI was, however, more sensitive to soil moisture stress than CWSI. The results archived by analysis of TVDI indicated that the moderate and severe droughts were spatially scattered over the region from November to March, but more extensive in northeast Thailand and Cambodia. The larger area of severe drought was especially observed for the 2003-2006 dry seasons compared to other years. The results achieved from this study could be important for drought warnings and irrigation scheduling.

  1. Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010-2011)

    NASA Astrophysics Data System (ADS)

    Sawada, Yohei; Koike, Toshio

    2016-07-01

    Despite the importance of the ecological and agricultural aspects of severe droughts, no drought monitoring and prediction framework based on a land data assimilation system (LDAS) has been developed to monitor and predict vegetation dynamics in the middle of droughts. In this study, we applied a LDAS that can simulate surface soil moisture, root-zone soil moisture, and vegetation dynamics to the Horn of Africa drought in 2010-2011 caused by the precipitation deficit in two consecutive rainy seasons. We successfully simulated the ecohydrological drought quantified by the model-estimated soil moistures and leaf area index (LAI). The root-zone soil moisture and LAI are good indicators of prolonged droughts because they reflect the long-term effects of past precipitation deficit. The precipitation deficit in 2010 significantly affected the land surface condition of the next rainy season in 2011, which indicated the importance of obtaining accurate initial soil moisture and LAI values for prediction of multiseasonal droughts. In addition, the general circulation model-based seasonal meteorological prediction showed good performance in predicting land surface conditions of the Horn of Africa drought.

  2. Drought Monitoring for 3 North American Case Studies Based on the North American Land Data Assimilation System (NLDAS)

    NASA Technical Reports Server (NTRS)

    Peters-Lidard, Christa D.; Mocko, David; Kumar, Sujay; Ek, Michael; Xia, Youlong; Dong, Jiarui

    2012-01-01

    Both NLDAS Phase 1 (1996-2007) and Phase 2 (1979-present) datasets have been evaluated against in situ observational datasets, and NLDAS forcings and outputs are used by a wide variety of users. Drought indices and drought monitoring from NLDAS were recently examined by Mo et al. (2010) and Sheffield et al. (2010). In this poster, we will present results analyzing NLDAS Phase 2 forcings and outputs for 3 North American Case studies being analyzed as part of the NOAA MAPP Drought Task Force: (1) Western US drought (1998- 2004); (2) plains/southeast US drought (2006-2007); and (3) Current Texas-Mexico drought (2011-). We will examine percentiles of soil moisture consistent with the NLDAS drought monitor.

  3. Utility of Satellite Data to monitor drought and floods in India

    NASA Astrophysics Data System (ADS)

    Mishra, V.

    2015-12-01

    Extreme hydrologic events such as droughts and floods pose tremendous pressure on society. The damage due to extreme events has increased during the recent decades and it may increase even further under the projected future climate. Real time monitoring of hydrologic extremes is essential to minimize losses to society and infrastructure. However, in many areas, where gauge based observations are not available in timely manner, real-time monitoring of droughts and floods has been challenging. In the absence of in-situ gauge based observations, satellite data from the various platform may provide an useful information for the real-time monitoring. Using the precipitation data from the Tropical Rainfall Measurement Mission (TRMM) a real-time monitor that updates on daily basis was developed for India. The hydrologic variables (soil moisture, runoff, and Evapotranspiration) were simulated using the Variable Infiltration Capacity (VIC) model. The real-time monitor was successfully evaluated for the drought and flood monitoring in India. The monitor provides soil moisture and total runoff conditions at a high resolution. Moreover, the monitor can provide a valuable information on daily streamflow monitoring at the selected gauge stations in India.

  4. Assessing the Relative Soil Moisture for Agricultural Drought Monitoring in Northeast China

    NASA Astrophysics Data System (ADS)

    An, X.; Wu, J.; Zhou, H.

    2016-12-01

    Soil moisture is an important factor affecting crop growth, development and production. Currently, the presence of a growing number of long-term soil moisture networks has allowed users to obtain precise soil moisture measurement. Therefore, it is reasonable to consider soil moisture observations as a potential approach for monitoring agricultural drought. In Northeast China, soil moisture dataset at China Agro-meteorological Stations is relatively complete. In the present work, the capability of the Relative Soil Moisture (RSM) for agricultural drought monitoring in Northeast China was evaluated through the comparisons between RSM and Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Vegetation Index (SVI), the disaster dataset recorded by the national agro-meteorological stations and crop yield data. Results are as follows: (1) The RSM has good correlations with SPEI and SVI during the growing season. Through the analysis between RSM and lagged SVI, it is found that the RSM is significantly correlated with 10-day lagged SVI under water stress. The RSM is able to depict the influence of different drought intensity on crop growth status. With the decrease of RSM, the effect on crop growth status and the affected probability are all increasing. (2)The monitoring probabilities of RSM on different drought categories recorded by the national agro-meteorological stations are all more than 50%. (3)The impact of RSM on crop yield during the main growing season was also explored using 10-day RSM data, and the result shows that the key period was the first dekad in July. In conclusion, RSM has a good applicability in agricultural drought monitoring in Northeast China, and this study can provide theoretical support for agricultural drought monitoring.

  5. The role of soil moisture in monitoring drought events over Europe

    NASA Astrophysics Data System (ADS)

    Cammalleri, Carmelo; Micale, Fabio; Vogt, Jürgen

    2015-04-01

    Drought is a complex phenomenon that manifests at different spatial and temporal scales. Within the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu) an integrated monitoring approach is embraced, attempting at combining various sources of drought information at European level in order to provide a set of drought monitoring tools that encompasses continental, national, regional and local scales. Each tool, ranging from precipitation-based to remotely sensed greenness indicators, aims at capturing different aspect of the heterogeneous nature of drought events. An accurate measure of the effects of drought on vegetated lands can be achieved by exploiting the capability of soil moisture to quantify plant water stress. This is commonly accomplished by either accounting for the level of the current soil moisture compared to the past history or by computing a water deficit index, based on the on the critical values of the soil water retention curve. Under the definition that a vegetated area can be considered affected by drought condition only when the soil moisture status in the root zone is simultaneously: i) unusually dry compared to the "normal" state and ii) causing severe water stress to the vegetation, it is an obvious consequence that a soil moisture-based drought indicator should capture both features. Here we describe a novel drought severity index. DSI, that accounts for the mutual occurrence of these two conditions by means of a weighted average of a water deficit factor and a dryness probability factor. The former quantifies the actual plant water stress level, whereas the latter verifies that the current water deficit condition is unusual for the specific site and period. The reliability of the estimates made by DSI is evaluated by analyzing the performance during some well-known drought events that occurred over Europe between 1995 and 2012. Overall, DSI seems to correctly distinguish the main drought events recognized in the dedicated

  6. Use of satellite-derived soil moisture to improve drought monitoring

    NASA Astrophysics Data System (ADS)

    Enenkel, Markus; Rojas, Oscar; Balint, Zoltan

    2013-04-01

    From all natural disasters droughts rank first regarding the number of people affected, severity, length of event, spatial extent, loss of life and economic consequences. Drought impacts depend not only on the severity of the impact. Regional exposure and vulnerability play a crucial role that is often hard to assess. Industrialized countries, such as the United States, have measures to mitigate consequences. In contrast, developing countries often suffer from long-term impacts on people's livelihoods due to recurring events. Decreasing uncertainties in decision-making by state-of-the-art technologies seems to be the most promising approach. Several drought indices were developed during the last decades for different applications. However, there is an obvious lack of indices that consider drought creating factors and actual user requirements in data-scarce regions. FAO SWALIM (The Somalia Water and Land Information Management Group of the UN Food and Agriculture Organization) developed the Combined Drought Index (CDI) in 2011. It originally consisted of three weighted sub-indices: rainfall and temperature (both from point measurements) and NDVI as a substitute for soil moisture. At least ten years of data are required for each sub-index to detect anomalies reliably. The CDI is calculated as a decadal or monthly product, whereas drought levels range from values >1 (no drought) to <4 (extreme drought). In order to improve operational decision-making in the long run the CDI was revised to substitute point measurements by spatial data. Precipitation and temperature were obtained from modelled (and gauge-corrected) data as well as from satellite-derived datasets. The MODIS instrument onboard Terra provided NDVI data. Soil moisture was integrated from a merged active and passive microwave remote sensing dataset that had been created within the Climate Change Initiative (CCI) of the European Space Agency (ESA) This study aims at illustrating the performance of a satellite

  7. A Decision Support System For Assisting With Stocking Rate Decisions During And Following Drought

    USDA-ARS?s Scientific Manuscript database

    Ranchers and range managers in the West are at the mercy of climatic conditions that determine the amount of annual forge available on rangeland. Typically, stocking or de-stocking decisions need to be made before the final forage production level is known. Erroneous stocking rate decisions can have...

  8. Coping with Natural Hazards in a Conservation Context: Resource-Use Decisions of Maasai Households During Recent and Historical Droughts.

    PubMed

    Miller, Brian W; Leslie, Paul W; McCabe, J Terrence

    2014-10-01

    Analyzing people's decisions can reveal key variables that affect their behaviors. Despite the demonstrated utility of this approach, it has not been applied to livelihood decisions in the context of conservation initiatives. We used ethnographic decision modeling in combination with qualitative comparative analysis (QCA) to examine the herding decisions of Maasai households living near Tarangire National Park (TNP) during recent and historical droughts. The effects of the establishment of TNP on herding practices during drought were different than anticipated based on the size and reliability of several prominent resource areas that are now within the park. We found little evidence of people relying on these swamps and rivers for watering cattle during historical droughts; rather, these sites were more commonly used as grazing areas for small stock and wet-season grazing areas for cattle to avoid disease carried by calving wildebeest. Yet during the 2009 drought, many herders moved their livestock - especially cattle from outside of the study area - toward TNP in search of grazing. Our analysis of herding decisions demonstrates that resource-use decisions are complex and incorporate a variety of information beyond the size or reliability of a given resource area, including contextual factors (e.g., disease, conflict, grazing) and household factors (e.g., social capital, labor, herd size). More broadly, this research illustrates that pairing decision modeling with QCA is a structured approach to identifying these factors and understanding how opportunities, constraints, and perceptions influence how people respond to changes in resource access.

  9. Coping with Natural Hazards in a Conservation Context: Resource-Use Decisions of Maasai Households During Recent and Historical Droughts

    PubMed Central

    Leslie, Paul W.; McCabe, J. Terrence

    2014-01-01

    Analyzing people’s decisions can reveal key variables that affect their behaviors. Despite the demonstrated utility of this approach, it has not been applied to livelihood decisions in the context of conservation initiatives. We used ethnographic decision modeling in combination with qualitative comparative analysis (QCA) to examine the herding decisions of Maasai households living near Tarangire National Park (TNP) during recent and historical droughts. The effects of the establishment of TNP on herding practices during drought were different than anticipated based on the size and reliability of several prominent resource areas that are now within the park. We found little evidence of people relying on these swamps and rivers for watering cattle during historical droughts; rather, these sites were more commonly used as grazing areas for small stock and wet-season grazing areas for cattle to avoid disease carried by calving wildebeest. Yet during the 2009 drought, many herders moved their livestock – especially cattle from outside of the study area – toward TNP in search of grazing. Our analysis of herding decisions demonstrates that resource-use decisions are complex and incorporate a variety of information beyond the size or reliability of a given resource area, including contextual factors (e.g., disease, conflict, grazing) and household factors (e.g., social capital, labor, herd size). More broadly, this research illustrates that pairing decision modeling with QCA is a structured approach to identifying these factors and understanding how opportunities, constraints, and perceptions influence how people respond to changes in resource access. PMID:25506101

  10. Comparing SMMR and AVHRR data for drought monitoring

    NASA Technical Reports Server (NTRS)

    Tucker, Compton J.

    1989-01-01

    Coincident Scanning Microwave Multi-channel Radiometer 37 GHz and Advanced Very High Resolution Radiometer normalized difference vegetation index satellite data have been compared from drought-affected regions of sub-Saharan Africa and northeastern Brazil for the time period of 1980-1985. Although the two satellite data types can be highly correlated, differences between them were found for the Sahel zone in 1985 and for northeastern Brazil from 1984-1985. These findings suggest that scattering or surface roughness contributions may be greater than previously assumed for the 37 GHz microwave data.

  11. Monitoring Drought at Continental Scales Using Thermal Remote Sensing of Evapotranspiration (Invited)

    NASA Astrophysics Data System (ADS)

    Anderson, M. C.; Hain, C.; Mecikalski, J. R.; Kustas, W. P.

    2009-12-01

    Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status: soil surface temperature increases with decreasing water content, while moisture depletion in the plant root zone leads to stomatal closure, reduced transpiration, and elevated canopy temperatures that can be effectively detected from space. Empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring drought conditions over large areas, but may provide ambiguous results when vegetation growth is limited by energy (radiation, air temperature) rather than moisture. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. In this approach, moisture stress can be quantified in terms of the reduction of evapotranspiration (ET) from the potential rate (PET) expected under non-moisture limiting conditions. The Atmosphere-Land Exchange Inverse (ALEXI) model couples a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map fluxes across the U.S. continent at 5-10km resolution using thermal band imagery from the Geostationary Operational Environmental Satellites (GOES). Finer resolution flux maps can be generated through spatial disaggregation using TIR data from polar orbiting instruments such as Landsat (60-120m) and MODIS (1km). A derived Evaporative Stress Index (ESI), given by 1-ET/PET, shows good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be produced at significantly higher spatial resolution due to limited reliance on ground observations. Because the ESI does not use precipitation data as input, it provides an independent means for

  12. Evaluating the Potential Use of Remotely-Sensed and Model-Simulated Soil Moisture for Agricultural Drought Risk Monitoring

    NASA Astrophysics Data System (ADS)

    Yan, Hongxiang; Moradkhani, Hamid

    2016-04-01

    Current two datasets provide spatial and temporal resolution of soil moisture at large-scale: the remotely-sensed soil moisture retrievals and the model-simulated soil moisture products. Drought monitoring using remotely-sensed soil moisture is emerging, and the soil moisture simulated using land surface models (LSMs) have been used operationally to monitor agriculture drought in United States. Although these two datasets yield important drought information, their drought monitoring skill still needs further quantification. This study provides a comprehensive assessment of the potential of remotely-sensed and model-simulated soil moisture data in monitoring agricultural drought over the Columbia River Basin (CRB), Pacific Northwest. Two satellite soil moisture datasets were evaluated, the LPRM-AMSR-E (unscaled, 2002-2011) and ESA-CCI (scaled, 1979-2013). The USGS Precipitation Runoff Modeling System (PRMS) is used to simulate the soil moisture from 1979-2011. The drought monitoring skill is quantified with two indices: drought area coverage (the ability of drought detection) and drought severity (according to USDM categories). The effects of satellite sensors (active, passive), multi-satellite combined, length of climatology, climate change effect, and statistical methods are also examined in this study.

  13. Rapid Monitoring of Drought Impacts on Small-Scale Farms in Africa through Integration of Farmer SMS data and Environmental Sensors

    NASA Astrophysics Data System (ADS)

    Evans, T. P.; Caylor, K. K.; Estes, L. D.; Plale, B. A.; Attari, S.; Waldman, K.

    2016-12-01

    We present research using a novel mobile-phone based approach to monitor crop conditions in Kenya and Zambia through high-frequency text message system with smallholder farmers. This methodology has the potential to generate more actionable information than alternative approaches for crop monitoring such as agricultural extension or remote sensing. Through weekly SMS text messaging in Kenya and Zambia we have documented how farmer's harvest expectations vary as a function of changing environmental conditions through the agricultural growing season. These dryland agro-ecosystems are subject to significant inter-annual variability in the onset of rains, prevalence of within season dry spells and duration of the rainy season. In this context farmers make critical decisions such as what date to plant and what cultivars to plant (e.g. local maize vs. higher yielding but higher cost hybrid maize varieties). By linking high frequency data from farmers and sensors, we are able to determine hinge points in the growing season when farmers perceive crop harvest to be impacted by drought, dryspells and other environmental impacts. This analytical approach provides fundamental insight into the analysis of drought impacts. The impact of drought in Sub-Saharan Africa is complex due to the spatial heterogeneity of rainfall coupled with the heterogeneity of decisions made by farmers. This allows us to drill down to farm-specific impacts of drought with greater precision than that possible via remote sensing and agricultural census methods.

  14. Frequencies of decision making and monitoring in adaptive resource management

    USGS Publications Warehouse

    Williams, Byron K.; Johnson, Fred A.

    2017-01-01

    Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management

  15. Frequencies of decision making and monitoring in adaptive resource management

    PubMed Central

    Johnson, Fred A.

    2017-01-01

    Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management

  16. Relative skills of soil moisture and vegetation optical depth retrievals for agricultural drought monitoring

    USDA-ARS?s Scientific Manuscript database

    Soil moisture condition is an important indicator for agricultural drought monitoring. Through the Land Parameter Retrieval Model (LPRM), vegetation optical depth (VOD) as well as surface soil moisture (SM) can be retrieved simultaneously from brightness temperature observations from the Advanced Mi...

  17. Steady-state chlorophyll flourescence (Fs) as a tool to monitor plant heat and drought stress

    USDA-ARS?s Scientific Manuscript database

    Crop yield decreases when photosynthesis is limited by heat or drought conditions. Yet farmers do not monitor crop photosynthesis because it is difficult to measure at the field scale in real time. Steady-state chlorophyll fluorescence (Fs) can be used at the field level as an indirect measure of p...

  18. Benchmarking the performance of a land data assimilation system for agricultural drought monitoring

    USDA-ARS?s Scientific Manuscript database

    The application of land data assimilation systems to operational agricultural drought monitoring requires the development of (at least) three separate system sub-components: 1) a retrieval model to invert satellite-derived observations into soil moisture estimates, 2) a prognostic soil water balance...

  19. [An improved method and its application for agricultural drought monitoring based on remote sensing].

    PubMed

    Zheng, You-Fei; Cheng, Jin-Xin; Wu, Rong-Jun; Guan, Fu-Lai; Yao, Shu-Ran

    2013-09-01

    From the viewpoint of land surface evapotranspiration, and by using the semi-empirical evapotranspiration model based on the Priestley-Taylor equation and the land surface temperature-vegetation index (LST-VI) triangle algorithm, the current monitoring technology of agricultural drought based on remote sensing was improved, and a simplified Evapotranspiration Stress Index (SESI) was derived. With the application of the MODIS land products from March to November in 2008 and 2009, the triangle algorithm modeling with three different schemes was constructed to calculate the SESI to monitor the agricultural drought in the plain areas of Beijing, Tianjin, and Hebei, in comparison with the Temperature Vegetation Dryness Index (TVDI). The results showed that SESI could effectively simplify the remote sensing drought monitoring method, and there was a good agreement between SESI and surface soil (10 and 20 cm depth) moisture content. Moreover, the performance of SESI was better in spring and autumn than in summer, and the SESI during different periods was more comparable than TVDI. It was feasible to apply the SESI to the continuous monitoring of a large area of agricultural drought.

  20. Monitoring soil microbial dynamics in agroecosystems during two years of recovery after record drought

    USDA-ARS?s Scientific Manuscript database

    We monitored soil microbial dynamics in six agroecosystem in the Southern Plains of the U.S. during climatic recovery after four years of record drought. Our previous study provided some of the first information that linked significant reductions in soil enzymatic potential and microbial diversity ...

  1. Remote Sensing Approach to Drought Monitoring to Inform Range Management at the Hopi Tribe and Navajo Nation

    NASA Astrophysics Data System (ADS)

    El Vilaly, M. M.; Van Leeuwen, W. J.; Didan, K.; Marsh, S. E.; Crimmins, , M. A.

    2012-12-01

    The Hopi Tribe and Navajo Nation are situated in the Northeastern corner of Arizona in the Colorado River Plateau. For more than a decade, the area has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. Moreover, these persistent droughts threaten ecosystem services, agriculture, and livestock production activities, and make this region sensitive to inter-annual climate variability and change. The limited hydroclimatic observations, bolstered by numerous anecdotal drought impact reports, indicate that the region has been suffering through an almost 15-year long drought which is threatening its socio-economic development. The objective of this research is to employ remote sensing data to monitor the ongoing drought and inform management and decision-making. The overall goals of this study are to develop a common understanding of the current status of drought across the area in order to understand the existing seasonal and inter-annual relationships between climate variability and vegetation dynamics. To analyze and investigate vegetation responses to climate variability, land use practices, and environmental factors in Hopi and Navajo nation during the last 22 years, a drought assessment framework was developed that integrates climate and topographical data with land surface remote sensing time series data. Multi-sensor Normalized Difference Vegetation Index time series data were acquired from the vegetation index and phenology project (vip.arizona.edu) from 1989 to 2010 at 5.6 km, were analyzed to characterize the intra-annual changes of vegetation, seasonal phenology and inter-annual vegetation response to climate variability and environmental factors. Due to the low number of retrieval obtained from TIMESAT software, we developed a new framework that can maximize the number of retrieval. Four vegetation development stages, annual integrated NDVI (Net Primary

  2. Monitoring drought occurrences using MODIS evapotranspiration data: Direct impacts on agricultural productivity in Southern Brazil

    NASA Astrophysics Data System (ADS)

    Ruhoff, Anderson

    2014-05-01

    regional droughts (2005, 2010 and 2012) occurred in Southern Brazil, with similar wetting and drying patterns based on the Standardized Precipitation Index (SPI) and strong correlation with agricultural productivity. Overall, the MODIS remotely sensed drought indices reveal the efficacy and effectiveness for near-real time monitor land surface drought events. Furthermore, understanding and predicting the consequences of drought events on agricultural productivity is emerging as one of the greatest challenges currently due to the increasing global demand for food. Acknowledgements: This work was made possible through the support of the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS).

  3. Advance of the Monitor of Drought for the Northern Region of Mexico

    NASA Astrophysics Data System (ADS)

    Reyes Gomez, V. M.; Nunez Lopez, D.

    2007-05-01

    In the last 13 years, the State of Chihuahua suffered a lingering drought that caused social, economical and environmental impacts hardly quantifiable. Since 2002, a monitoring system was implemented to watch the evolution of the meteorological drought in Chihuahua, recently being broadened for the states in the North of Mexico. Evaluation of the Meteorological Drought The Monitoring System on the Drought in Chihuahua includes the following steps: missing data gaps were completed basing on the statistical procedures described by Young (1992); the source code, was compiled to create a computer program, with which it can be derived a level of climatic station, historical series of values for the SPI in time scales of 1 to 48 months; under this classification scheme, it is considered that a drought event begins when the values of the SPI are inferior to -0.7 (McKee et al. 1995). The spatial distribution of the SPI was determined through spatial interpolation techniques using a reverse method of the distance between stations included in Arc/Info©. This same procedure was applied for the States of Sonora, Sinaloa, Durango and Zacatecas with the purpose of implementing this tool for the north of Mexico. Advances on the Monitoring System The monitoring system allows an analysis of the frequency, duration and intensity of the drought events that took place in several climatic regions (Núñez-López et al., 2005); un map of spatial distribution of the SPI for the northern region of Mexico, in the States of Sonora, Sinaloa, Durango and Chihuahua. The generated map will be published in a section on the CEISS web page (www.sequia.edu.mx), together with the monthly bulletin available to the public in general; it is monitoring to an annual scale, the tendencies of the deficits or surplus of the runoff volumes on three of the main dams in the State of Chihuahua Conclusions The Drought Monitoring System in Chihuahua complies with the following international rules for the

  4. Drought monitoring with remote sensing based land surface phenology applications and validation

    NASA Astrophysics Data System (ADS)

    El Vilaly, Mohamed Abd salam M.

    Droughts are a recurrent part of our climate, and are still considered to be one of the most complex and least understood of all natural hazards in terms of their impact on the environment. In recent years drought has become more common and more severe across the world. For more than a decade, the US southwest has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources. The focus of this work is achieving a better understanding of the impact of drought on the lands of the Hopi Tribe and Navajo Nation, situated in the Northeastern corner of Arizona. This research explores the application of remote sensing data and geospatial tools in two studies to monitor drought impacts on vegetation productivity. In both studies we used land surface phenometrics as the data tool. In a third related study, I have compared satellite-derived land surface phenology (LSP) to field observations of crop stages at the Maricopa Agricultural Center to achieve a better understanding of the temporal sensitivity of satellite derived phenology of vegetation and understand their accuracy as a tool for monitoring change. The first study explores long-term vegetation productivity responses to drought. The paper develops a framework for drought monitoring and assessment by integrating land cover, climate, and topographical data with LSP. The objective of the framework is to detect long-term vegetation changes and trends in the Normalized Difference Vegetation Index (NDVI) related productivity. The second study examines the major driving forces of vegetation dynamics in order to provide valuable spatial information related to inter-annual variability in vegetation productivity for mitigating drought impacts. The third study tests the accuracy of remote sensing-derived LSP by comparing them to the actual seasonal phases of crop growth. This provides a way to compare and validate the various LSP algorithms, and more crucially, helps to

  5. Building a Framework in Improving Drought Monitoring and Early Warning Systems in Africa

    NASA Astrophysics Data System (ADS)

    Tadesse, T.; Wall, N.; Haigh, T.; Shiferaw, A. S.; Beyene, S.; Demisse, G. B.; Zaitchik, B.

    2015-12-01

    Decision makers need a basic understanding of the prediction models and products of hydro-climatic extremes and their suitability in time and space for strategic resource and development planning to develop mitigation and adaptation strategies. Advances in our ability to assess and predict climate extremes (e.g., droughts and floods) under evolving climate change suggest opportunity to improve management of climatic/hydrologic risk in agriculture and water resources. In the NASA funded project entitled, "Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa (GHA) under Evolving Climate Conditions to Support Adaptation Strategies," we are attempting to develop a framework that uses dialogue between managers and scientists on how to enhance the use of models' outputs and prediction products in the GHA as well as improve the delivery of this information in ways that can be easily utilized by managers. This process is expected to help our multidisciplinary research team obtain feedback on the models and forecast products. In addition, engaging decision makers is essential in evaluating the use of drought and flood prediction models and products for decision-making processes in drought and flood management. Through this study, we plan to assess information requirements to implement a robust Early Warning Systems (EWS) by engaging decision makers in the process. This participatory process could also help the existing EWSs in Africa and to develop new local and regional EWSs. In this presentation, we report the progress made in the past two years of the NASA project.

  6. Early drought detection, monitoring, and assessment of crop losses from space: global approach

    NASA Astrophysics Data System (ADS)

    Kogan, Felix

    2006-12-01

    With nearly 30 years of the accumulated AVHRR data which were collected from NOAA operational polar-orbiting environmental satellites, the area of their applications expanded in the direction of agricultural production modeling, understanding of climate and global change, resource management, and early and more efficient monitoring of the environmental impacts (especially droughts) on economy and society. This becomes possible due to development of Vegetation Health indices (VHI). This paper discusses utility of AVHRR-based VHI for modeling crop and pasture yield with specific emphasis on early drought warning and estimation of losses in agricultural production.

  7. [Vegetation water content retrieval and application of drought monitoring using multi-spectral remote sensing].

    PubMed

    Wang, Li-Tao; Wang, Shi-Xin; Zhou, Yi; Liu, Wen-Liang; Wang, Fu-Tao

    2011-10-01

    The vegetation is one of main drying carriers. The change of Vegetation Water Content (VWC) reflects the spatial-temporal distribution of drought situation and the degree of drought. In the present paper, a method of retrieving the VWC based on remote sensing data is introduced and analyzed, including the monitoring theory, vegetation water content indicator and retrieving model. The application was carried out in the region of Southwest China in the spring, 2010. The VWC data was calculated from MODIS data and spatially-temporally analyzed. Combined with the meteorological data from weather stations, the relationship between the EWT and weather data shows that precipitation has impact on the change in vegetation moisture to a certain extent. However, there is a process of delay during the course of vegetation absorbing water. So precipitation has a delaying impact on VWC. Based on the above analysis, the probability of drought monitoring and evaluation based on multi-spectral VWC data was discussed. Through temporal synthesis and combined with auxiliary data (i. e. historical data), it will help overcome the limitation of data itself and enhance the application of drought monitoring and evaluation based on the multi-spectral remote sensing.

  8. [Evaluating the utility of MODIS vegetation index for monitoring agricultural drought].

    PubMed

    Li, Hua-Peng; Zhang, Shu-Qing; Gao, Zi-Qiang; Sun, Yan

    2013-03-01

    The exclusive shortwave bands provided by MODIS sensors offer new opportunities for agricultural drought monitoring, since they are very sensitive to vegetation moisture. In the present work, we selected Songnen Plain in Northeast China as study area aiming at monitoring agricultural drought of dry farmland here. Four types of vegetation water indices and vegetation greenness indices were calculated from the 8-day composite MODIS product (MODO9A1) in vegetation growing season between 2001 and 2010, respectively. Multi-scale standardized precipitation index (SPI) derived from precipitation data of weather stations was used as reference data to estimate drought sensitivity of various vegetation indices, and a pixel-to-weather station paired correlation approach was used to calculate the Pearson correlation coefficient between vegetation index and SPIs. The result indicated that vegetation water indices established by near infrared and shortwave infrared bands outperformed vegetation greenness indices based on visible and near infrared bands. Of these indices, NDII7 performs the best with highest correlation coefficients across all SPIs. The authors' results demonstrated the potential of MODIS shortwave spectral bands in monitoring agricultural drought, and this provides new insights to future research.

  9. Improving Drought Monitoring and Predictions Using Physically Based Evaporative Demand Estimates

    NASA Astrophysics Data System (ADS)

    Hobbins, M. T.; Wood, A. W.; Werner, K.

    2011-12-01

    Existing drought monitors rely heavily on precipitation (Prcp) and temperature (T) data to derive moisture fluxes at the surface, often using estimates of evaporative demand (Eo) based only on T to derive actual evapotranspiration (ET) from land surface models (LSMs). An example of this is the popular Palmer Drought Severity Index (PDSI). In the analysis of drought trends and dynamics, however, the choice of Eo-driver for LSMs is crucial: it significantly affects both the direction and magnitude of trends in estimated ET and soil moisture, particularly in energy-limited areas (in water-limited areas, ET and soil moisture trends are driven by Prcp trends). All else equal, in the long term, T-based Eo measures result in declining ET estimates (i.e., drying) as T rises, whereas using more appropriate, physically based Eo estimates will more accurately reflect observations of both wetting and drying under warming. With regard to the short-term variabilities more appropriate to monitoring ongoing droughts, we contend that, given that various requirements are met, using an appropriate Eo driver (i) as a drought metric in itself, (ii) to drive drought monitors' LSMs, and (iii) in combination with short-term Eo forecasts will enhance characterization of the evaporative dynamics of ongoing drought and permit more accurate predictions of drought development. The requirements of an appropriate Eo estimate are as follows: that at operationally appropriate time and space scales Eo is diagnostic of ET (i.e., ET and Eo co-vary in a complementary fashion); that the Eo formulation and driving data produce good estimates of Eo (i.e., the model is physically based in that it combines radiative and advective drivers, and produces Eo estimates that are accurate and unbiased with respect to observations from drivers that are available with limited latency on a daily basis) and at operational spatio-temporal resolutions; and that Eo can be forecast at operational time and space scales

  10. Development and assessment of Transpirative Deficit Index (D-TDI) for agricultural drought monitoring

    NASA Astrophysics Data System (ADS)

    Borghi, Anna; Rienzner, Michele; Gandolfi, Claudio; Facchi, Arianna

    2017-04-01

    Drought is a major cause of crop yield loss, both in rainfed and irrigated agroecosystems. In past decades, many approaches have been developed to assess agricultural drought, usually based on the monitoring or modelling of the soil water content condition. All these indices show weaknesses when applied for a real time drought monitoring and management at the local scale, since they do not consider explicitly crops and soil properties at an adequate spatial resolution. This work describes a newly developed agricultural drought index, called Transpirative Deficit Index (D-TDI), and assesses the results of its application over a study area of about 210 km2 within the Po River Plain (northern Italy). The index is based on transforming the interannual distribution of the transpirative deficit (potential crop transpiration minus actual transpiration), calculated daily by means of a spatially distributed conceptual hydrological model and cumulated over user-selected time-steps, to a standard normal distribution (following the approach proposed by the meteorological index SPI - Standard Precipitation Index). For the application to the study area a uniform maize crop cover (maize is the most widespread crop in the area) and 22-year (1993-2014) meteorological data series were considered. Simulation results consist in maps of the index cumulated over 10-day time steps over a mesh with cells of 250 m. A correlation analysis was carried out (1) to study the characteristics and the memory of D-TDI and to assess its intra- and inter-annual variability, (2) to assess the response of the agricultural drought (i.e., the information provided by D-TDI) to the meteorological drought computed through the SPI over different temporal steps. The D-TDI is positively auto-correlated with a persistence of 30 days, and positively cross-correlated to the SPI with a persistence of 40 days, demonstrating that D-TDI responds to meteorological forcing. Correlation analyses demonstrate that soils

  11. Monitoring and forecasting the 2009-2010 severe drought in Southwest China

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

    2015-12-01

    From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

  12. Quantifying the reliability of four global datasets for drought monitoring over a semiarid region

    NASA Astrophysics Data System (ADS)

    Katiraie-Boroujerdy, Pari-Sima; Nasrollahi, Nasrin; Hsu, Kuo-lin; Sorooshian, Soroosh

    2016-01-01

    Drought is one of the most relevant natural disasters, especially in arid regions such as Iran. One of the requirements to access reliable drought monitoring is long-term and continuous high-resolution precipitation data. Different climatic and global databases are being developed and made available in real time or near real time by different agencies and centers; however, for this purpose, these databases must be evaluated regionally and in different local climates. In this paper, a near real-time global climate model, a data assimilation system, and two gridded gauge-based datasets over Iran are evaluated. The ground truth data include 50 gauges from the period of 1980 to 2010. Drought analysis was carried out by means of the Standard Precipitation Index (SPI) at 2-, 3-, 6-, and 12-month timescales. Although the results show spatial variations, overall the two gauge-based datasets perform better than the models. In addition, the results are more reliable for the western portion of the Zagros Range and the eastern region of the country. The analysis of the onsets of the 6-month moderate drought with at least 3 months' persistence indicates that all datasets have a better performance over the western portion of the Zagros Range, but display poor performance over the coast of the Caspian Sea. Base on the results of this study, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset is a preferred alternative for drought analysis in the region when gauge-based datasets are not available.

  13. Development of satellite-based drought monitoring and warning system in Asian Pacific countries

    NASA Astrophysics Data System (ADS)

    Takeuchi, W.; Oyoshi, K.; Muraki, Y.

    2013-12-01

    This research focuses on a development of satellite-based drought monitoring warning system in Asian Pacific countries. Drought condition of cropland is evaluated by using Keeth-Byram Drought Index (KBDI) computed from rainfall measurements with GSMaP product, land surface temperature by MTSAT product and vegetation phenology by MODIS NDVI product at daily basis. The derived information is disseminated as a system for an application of space based technology (SBT) in the implementation of the Core Agriculture Support Program. The benefit of this system are to develop satellite-based drought monitoring and early warning system (DMEWS) for Asian Pacific counties using freely available data, and to develop capacity of policy makers in those countries to apply the developed system in policy making. A series of training program has been carried out in 2013 to officers and researchers of ministry of agriculture and relevant agencies in Greater Mekong Subregion countries including Cambodia, China, Myanmar, Laos, Thailand and Vietnam. This system is running as fully operational and can be accessed at http://webgms.iis.u-tokyo.ac.jp/DMEWS/.

  14. Decision support for dutch drought management and climate change with the Netherland Hydrological Modeling Instrument

    NASA Astrophysics Data System (ADS)

    Hunink, J.; Hoogewoud, J. C.; Prinsen, G.; Veldhuizen, A.

    2012-04-01

    Netherlands Hydrological Modeling Instrument Decision support for dutch drought management and climate change. J. Hunink , J.C.Hoogewoud , A. Veldhuizen , G. Prinsen , The Netherlands Hydrological modeling Instrument (NHI) is the center point of a framework of models, to coherently model the hydrological system and the multitude of functions it supports. Dutch hydrological institutes Deltares, Alterra, Netherlands Environmental Assessment Agency, RWS Waterdienst, STOWA and Vewin are cooperating in enhancing the NHI for adequate decision support. The instrument is used by three different ministries involved in national water policy matters, for instance drought management, manure policy and climate change issues. The basis of the modeling instrument is a state-of-the-art on-line coupling of the groundwater system (MODFLOW), the unsaturated zone (metaSWAP) and the surface water system (MOZART-DM). It brings together hydro(geo)logical processes from the column to the basin scale, ranging from 250x250m plots to the river Rhine and includes salt water flow. The NHI is validated with an eight year run (1998-2006) with dry and wet periods and is updated every year. During periods of water scarcity the NHI is used for operational forecasting and decision support system for the National Board of water Distribution. It provides data on nationwide calculated water demands, development of water levels in reservoirs and possible los of yield in agricultural area's. For the exploration of the future of fresh water supply in the Netherlands an extensive study is set up using the NHI. In this study different climate scenarios are being evalueated. In the first phase the focus is on describing the range of possible effects, the second phase focuses on adaptive measures and preparing for decisions how to alter the hydrological system. Results from the first phase show that in future scenario's fresh water may not be available to current water users. Important decisions about the

  15. Comparative analysis of agricultural drought monitoring based on climate and soil moisture indicators in the Huang-Huai-Hai Plain of China

    NASA Astrophysics Data System (ADS)

    Wu, J.; Zhou, H.; An, X.

    2016-12-01

    Agricultural drought is one of very common agro-meteorological disasters, having a serious threat on crop growth and production. Effective drought monitoring will aid the drought response management to reduce drought loss. Currently there has been many drought indices used for agricultural drought monitoring. Climate-based Standardized Precipitation Evapotranspiration Index (SPEI) was widely used for agricultural drought monitoring in view of its merits of simple calculation, multi-scale and with consideration of evapotranspiration. Besides, soil moisture has been recognized as the most direct indicator for monitoring agricultural drought, which provides a huge potential for monitoring due to the increase of soil moisture accessibility. Given that algorithms of both indices are different and their monitoring results also may exist the discrepancies, it is necessary to analyze their performances of both indices in agricultural drought monitoring. In this study, we chose the climate-based SPEI and soil moisture-based index (SMI) to analyze. Firstly, drought events identified by SPEI and SMI were analyzed from the perspective of drought evolution. Then the performances of SPEI and SMI were assessed through the comparison with observed drought events. Finally, vegetation change since 2000 and impact of drought on vegetation growth in Huang-Huai-Hai (HHH) Plain were explored based on MODIS NDVI data. The results show that SPEI-based drought trend agrees with SMI-based analysis, demonstrating that drought has an alleviating trend in HHH Plain in recent years. The SPEI and SMI could identify most of drought events, whereas the SMI has better performance than SPEI when it comes to drought category. The NDVI in HHH Plain shows an upward trend since 2000 and drought occurred in 2000 causes a decline in vegetation vigor. Both the SPEI and SMI are significantly correlated with NDVI anomaly, and correlation between SMI and NDVI anomaly is slightly higher than that of SPEI.

  16. Quantitative drought monitoring in a typical cold river basin over Tibetan Plateau: An integration of meteorological, agricultural and hydrological droughts

    NASA Astrophysics Data System (ADS)

    Makokha, Godfrey Ouma; Wang, Lei; Zhou, Jing; Li, Xiuping; Wang, Aihui; Wang, Guangpeng; Kuria, David

    2016-12-01

    We introduce a Rainfall, Snow and Glacier melt (RSG) standardized anomaly (SA) index to reflect water availability in cold river basins by taking into account snow and glacier melt that influence seasonal water availability. The study takes advantage of a high-resolution Water and Energy Budget-Based Hydrological Distributed Model with improved snow physics (WEB-DHM-S) at a grid size of 5 km to quantify hydrological regimes in a typical cold river basin in the Tibetan Plateau (Lhasa River basin as a demonstration site) from 1983 to 2012. Standardized anomaly index was utilized as drought Indicator whereby each meteo-hydrological parameter involved in drought quantification was fitted to a distribution pattern on a monthly basis. Akaike Information Criterion and Bayesian Information Criterion were used as selection criteria. Drought indices were computed from the model inputs and outputs, which included RSG for meteorological drought, soil moisture (surface and root-zone) for agricultural drought and discharge and groundwater level for hydrological drought. From spatial and temporal analyses, drought occurred in 1984, 1988, 1995, 1997, 2009 and 2010, with the highest severity in August, September, July, August, June and June, respectively. This study addresses the glacierized cold river basin's dryness by considering the contribution of snow and glacier in drought quantification, an integration of meteorological, agricultural and hydrological was performed to highlight drought hotspots in the Lhasa River Basin. To the best of our knowledge, this is the first drought study in Lhasa River Basin.

  17. Joint use of soil moisture and vegetation growth condition by remote sensing on the agricultural drought monitoring

    NASA Astrophysics Data System (ADS)

    Liu, Ming; Yang, Siquan; Huang, He; He, Haixia; Li, Suju; Cui, Yan

    2015-12-01

    Remote sensing is one of important methods on the agricultural drought monitoring for its long-term and wide-area observations. The detection of soil moisture and vegetation growth condition are two widely used remote sensing methods on that. However, because of the time lag in the impact of water deficit on the crop growth, it is difficulty to indicate the severity of drought by once monitoring. It also cannot distinguish other negative impact on crop growth such as low temperature or solar radiation. In this paper, the joint use of soil moisture and vegetation growth condition detections was applied on the drought management during the summer of 2013 in Liaoning province, China, in which 84 counties were affected by agricultural drought. MODIS vegetation indices and land surface temperature (LST) were used to extract the drought index. Vegetation Condition Index (VCI), which only contain the change in vegetation index, and Vegetation Supply Water Index (VSWI), which combined the information of vegetation index and land surface temperature, were selected to compare the monitoring ability on drought during the drought period in Liaoning, China in 2014. It was found that VCI could be a good method on the loss assessment. VSWI has the information on the change in LST, which can indicate the spatial pattern of drought and can also be used as the early warning method in the study.

  18. The need for integration of drought monitoring tools for proactive food security management in sub-Saharan Africa

    USGS Publications Warehouse

    Tadesse, T.; Haile, M.; Senay, G.; Wardlow, B.D.; Knutson, C.L.

    2008-01-01

    Reducing the impact of drought and famine remains a challenge in sub-Saharan Africa despite ongoing drought relief assistance in recent decades. This is because drought and famine are primarily addressed through a crisis management approach when a disaster occurs, rather than stressing preparedness and risk management. Moreover, drought planning and food security efforts have been hampered by a lack of integrated drought monitoring tools, inadequate early warning systems (EWS), and insufficient information flow within and between levels of government in many sub-Saharan countries. The integration of existing drought monitoring tools for sub-Saharan Africa is essential for improving food security systems to reduce the impacts of drought and famine on society in this region. A proactive approach emphasizing integration requires the collective use of multiple tools, which can be used to detect trends in food availability and provide early indicators at local, national, and regional scales on the likely occurrence of food crises. In addition, improving the ability to monitor and disseminate critical drought-related information using available modern technologies (e.g., satellites, computers, and modern communication techniques) may help trigger timely and appropriate preventive responses and, ultimately, contribute to food security and sustainable development in sub-Saharan Africa. ?? 2008 United Nations.

  19. Toward Global Drought Early Warning Capability - Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

    NASA Technical Reports Server (NTRS)

    Pozzi, Will; Sheffield, Justin; Stefanski, Robert; Cripe, Douglas; Pulwarty, Roger; Vogt, Jurgen V.; Heim, Richard R., Jr.; Brewer, Michael J.; Svoboda, Mark; Westerhoff, Rogier; hide

    2013-01-01

    Drought has had a significant impact on civilization throughout history in terms of reductions in agricultural productivity, potable water supply, and economic activity, and in extreme cases this has led to famine. Every continent has semiarid areas, which are especially vulnerable to drought. The Intergovernmental Panel on Climate Change has noted that average annual river runoff and water availability are projected to decrease by 10 percent-13 percent over some dry and semiarid regions in mid and low latitudes, increasing the frequency, intensity, and duration of drought, along with its associated impacts. The sheer magnitude of the problem demands efforts to reduce vulnerability to drought by moving away from the reactive, crisis management approach of the past toward a more proactive, risk management approach that is centered on reducing vulnerability to drought as much as possible while providing early warning of evolving drought conditions and possible impacts. Many countries, unfortunately, do not have adequate resources to provide early warning, but require outside support to provide the necessary early warning information for risk management. Furthermore, in an interconnected world, the need for information on a global scale is crucial for understanding the prospect of declines in agricultural productivity and associated impacts on food prices, food security, and potential for civil conflict. This paper highlights the recent progress made toward a Global Drought Early Warning Monitoring Framework (GDEWF), an underlying partnership and framework, along with its Global Drought Early Warning System (GDEWS), which is its interoperable information system, and the organizations that have begun working together to make it a reality. The GDEWF aims to improve existing regional and national drought monitoring and forecasting capabilities by adding a global component, facilitating continental monitoring and forecasting (where lacking), and improving these tools at

  20. Relative Skills of Soil Moisutre and Vegetation Optical Depth Retrievals for Agricultural Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Han, E.; Crow, W. T.; Holmes, T. R.; Bolten, J. D.

    2012-12-01

    Soil moisture condition is an important indicator for agricultural drought monitoring. Through the Land Parameter Retrieval Model (LPRM), vegetation optical depth (VOD) as well as surface soil moisture (SM) can be retrieved simultaneously from brightness temperature observations from the Advanced Microwave Scanning Radiometer (AMSR-E). This study aims to investigate added skills of VOD in addition to SM for agricultural drought monitoring using monthly LPRM-SM and VOD products from 2002 to 2011. First, the lagged rank cross-correlation between Normalized Difference Vegetation Index (NDVI) and the SM/VOD retrievals is used to evaluate the skills of the SM and VOD for drought monitoring. Interestingly, the highest rank cross-correlation between NDVI and VOD is found with lag of (+1) month (temporally lagged behind ranks of NDVI by 1 month), while the highest rank cross-correlation coefficient of SM is found with lag (-1) month (temporally precedes the ranks of NDVI by 1 month). Lagged responses of plants to the available water capacity in the root zone may explain this lagged peak of correlation of VOD. In order to understand this finding more systematically, additional analysis on the microwave polarization difference index and vertical/horizontal brightness temperature are conducted. Next, different types of observations (SM, VOD and NDVI) and hydrologic model results (Palmer model) are merged to improve predictive power. We adopt two different merging approaches (simple weighting method and auto-regressive model) to quantify the added skills of those different drought-related indices. The results show that adding more information rather than using solely SM observation increases lag (-1) month cross-correlation coefficient with NDVI. This result indicates that different observations/models have independent information to some degree. Therefore further analysis on error-correlations between the observations/model results is also conducted. This study suggests

  1. AVHRR monitoring of U.S. crops during the 1988 drought

    NASA Technical Reports Server (NTRS)

    Teng, William L.

    1990-01-01

    Effects of the 1988 drought on crops in the U.S. Corn Belt were assessed and monitored by the Foreign Crop Condition Assessment Division (FCCAD), U.S. Department of Agriculture. The primary data were vegetation index numbers (VINs), each of which was calculated as an average vegetation index of a geographically referenced cell of AVHRR pixels. Using VINs, the FCCAD was able to detect the existence of drought early in the season, monitor changing conditions, and provide objective assessments of the drought's extent and severity. Field observations confirmed the image analyses, and underlined the importance of the timing of extreme weather events with respect to crop stages for interpreting VINs. The analyses were conducted in an operational environment, providing a unique test of the AVHRR data for large area, near real-time crop monitoring. Because large area, operational remote sensing of crops is quite different from traditional, controlled, small plot research studies, more work is needed to link the two; this would improve crop assessment capabilities.

  2. AVHRR monitoring of U.S. crops during the 1988 drought

    NASA Technical Reports Server (NTRS)

    Teng, William L.

    1990-01-01

    Effects of the 1988 drought on crops in the U.S. Corn Belt were assessed and monitored by the Foreign Crop Condition Assessment Division (FCCAD), U.S. Department of Agriculture. The primary data were vegetation index numbers (VINs), each of which was calculated as an average vegetation index of a geographically referenced cell of AVHRR pixels. Using VINs, the FCCAD was able to detect the existence of drought early in the season, monitor changing conditions, and provide objective assessments of the drought's extent and severity. Field observations confirmed the image analyses, and underlined the importance of the timing of extreme weather events with respect to crop stages for interpreting VINs. The analyses were conducted in an operational environment, providing a unique test of the AVHRR data for large area, near real-time crop monitoring. Because large area, operational remote sensing of crops is quite different from traditional, controlled, small plot research studies, more work is needed to link the two; this would improve crop assessment capabilities.

  3. Drought and coastal ecosystems: an assessment of decision maker needs for information

    Treesearch

    Kirsten Lackstrom; Amanda Brennan; Kirstin Dow

    2016-01-01

    The National Integrated Drought Information System (NIDIS) is in the process of developing drought early warning systems in areas of the U.S. where the development and coordination of drought information is needed. In summer 2012, NIDIS launched a pilot program in North and South Carolina, addressing the uniqueness of drought impacts on coastal ecosystems.

  4. Application of HJ-1A/B and ZY-3 remote sensing data for drought monitoring in Hubei Province China

    NASA Astrophysics Data System (ADS)

    Huang, He; Fan, Yida; Yang, Siquan; Wen, Qi; Pan, Donghua; Fan, Chunbo; He, Haixia

    2013-10-01

    Drought is one kind of nature disasters in the world. It has characteristics of temporal-spatial inhomogeneity, wide affected areas and periodic happening. The economic loss and affected population caused by different droughts are the largest in all natural disasters. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster risk assessment and monitoring. In this paper, three drought monitoring models, such as Vegetation Condition Index (VCI), Temperature Vegetation Dryness Index (TVDI), and Water Supplying Vegetation Index (WSVI) had been selected to monitor the drought occurred from January 2012 to June 2012 in Hubei province, China. Two kinds of remote sensing data, including HJ-1A/B CCD/IRS and ZY-3, had been employed to assess the integrated risk of Hubei drought based on three drought monitoring models. The results shown that the risk of northwest regions and middle regions in Hubei province were higher than that in the other regions. The results also indicated that the extreme risk regions were located in Shiyan, Xiangyang, Suizhou and Jingmen.

  5. Machine Learning Approaches to Drought Monitoring and Assessment through Blending of Multi-sensor Indices for Different Climate Regions

    NASA Astrophysics Data System (ADS)

    Park, Seonyoung; Im, Jungho; Jang, Eunna; Yoon, Hyunjin; Rhee, Jinyoung

    2014-05-01

    Drought causes a water shortage problem which threats human life as well as affects agricultural resources. Unlike other natural disasters such as floods, earthquakes, and landslides, drought is a slow-moving disaster, which is hard to accurately quantify spatio-temporal starting and ending points of the process. It is also difficult to estimate the damage from drought, because such damage combines social, economic, and environmental components in multi-temporal scales. There are many definitions of drought considering its type, temporal scales and regions. Drought has been actively monitored all over the world using in situ meteorological and climate measurements and satellite remote sensing measurements. There are many drought indices that use in situ measurements collected at weather stations, including z-score, Standard Precipitation Index (SPI), and Palmer Drought Severity Index (PDSI). However, these indices are point-based and limited in covering vast areas to show spatial distribution of drought. Since spatial interpolation is required to estimate spatial distribution of drought from in-situ measurements, uncertainty of drought estimation typically increases where in situ data are limited. Drought monitoring and assessment using satellite products provide an effective way as satellite data cover vast areas at high temporal resolution (e.g., daily). Most of remote sensing-based drought studies have focused on arid regions because satellite products usually well respond to the surface condition of short-term drought in arid regions. While drought often occurs in humid regions, satellite-based drought monitoring of such regions needs further investigation. In this study, remote sensing-based drought monitoring and assessment were evaluated for both arid and humid regions in the United States between 2000 and 2012 focusing on metrological and agricultural drought. Since there is no single indicator that represents complexity and diversity of drought, a total 11

  6. Predicting and Monitoring Drought for a Rice Cultivation Season in the Humid Tropics

    NASA Astrophysics Data System (ADS)

    Fernando, D. N.; Robinson, D. A.

    2010-12-01

    The study presents an operational tool for predicting and monitoring drought applicable to the humid tropics. Using Sri Lanka as a case example, it examines the operational predictability of drought and investigates how moisture stress could be monitored as a season unfurls. Drought occurs frequently in Sri Lanka when rainfall associated with the main cultivation season - the Maha (October to March) - fails. During the period 1951-2008, there were 4 extreme [Standardized Precipitation Index (SPI) <-2.0], 1 severe (-1.9 < SPI < -1.5), 5 moderate (-1.49 < SPI < -1.0) and 4 mild (-0.99 < SPI < -0.5) droughts. All extreme and severe droughts occurred in the post-1976 period. Such droughts have a profound impact on rice production. Maha seasonal droughts can be predicted on an operational basis by predicting the failure of the two rainfall regimes that supply moisture during the season. The contemporaneous westerly zonal wind at 850hPa (U850) over the domain 60°E-105°E and 5°S-15°N controls the strength of the October-November convective rainfall season - with failure of the season associated with anomalously strong U850. The contemporaneous northerly vertical shear of the mean meridional wind (Vs) in the domain 80°E-90°E and 0°N-20°N controls the strength of the December-February northeast monsoon season - with failure of the season associated with an anomalously weak Vs. Drought forecast skill was assessed for the period 1981-2002 using predicted fields of U850 issued in September, and Vs, issued in November from three Global Climate Model ensembles - i.e. the fully coupled Climate Forecast System of the National Centers for Environmental Prediction (NCEP_CFS); the ECHAM4.5 forced with persisted sea surface temperature anomalies (ECHAM4.5_PSST) and the ECMAM4.5 forced with constructed analogues of sea surface temperature anomalies (ECHAM4.5_CA). The failure of October-November rainfall can be predicted with good skill over the rice cultivation regions in the

  7. Agriculture In Uruguay: New Methods For Drought Monitoring and Crop Identification Using Remotely Sensed Data

    NASA Astrophysics Data System (ADS)

    Lessel, J.; Ceccato, P.

    2014-12-01

    Agriculture is a vital resource in the country of Uruguay. Here we propose new methods using remotely sensed data for assisting ranchers, land managers, and policy makers in the country to better manage their crops. Firstly, we created a drought severity index based on the climatological anomalies of land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation data from the Tropical Rainfall Monitoring Mission (TRMM), and normalized difference water index (NDWI) data also using MODIS. The use of the climatological anomalies on the variables has improved the ability of the index to correlate with known drought indices versus previously published indices, which had not used them. We applied various coefficient schemes and vegetation indices in order to choose the model which best correlated with the drought indices across 10 sites throughout Uruguay's rangelands. The model was tested over summer months from 2009-2013. In years where drought had indeed been a problem in the country (such as 2009) the model showed intense signals of drought. Secondly, we used Landsat images to identify winter and summer crops in Uruguay. We first classified them using ENVI and then used the classifications in an ArcMap model to identify specific crop areas. We first created a polygon of the classifications for soils and vegetation for each month (omitting cloud covered images). We then used the crop growing cycle to identify the times during the year for which specific polygons should be soil and which should be vegetation. By intersecting the soil polygons with the vegetation polygons during their respective time periods during the crop growing cycle we were able to create an accurately identify crops. When compared to a shapefile of proposed crops for the year the model obtained a kappa value of 0.60 with a probability of detection of 0.79 and a false alarm ratio of 0.31 for the south-western study area over the 2013-2014 summer.

  8. Cloud Computing-based Platform for Drought Decision-Making using Remote Sensing and Modeling Products: Preliminary Results for Brazil

    NASA Astrophysics Data System (ADS)

    Vivoni, E.; Mascaro, G.; Shupe, J. W.; Hiatt, C.; Potter, C. S.; Miller, R. L.; Stanley, J.; Abraham, T.; Castilla-Rubio, J.

    2012-12-01

    Droughts and their hydrological consequences are a major threat to food security throughout the world. In arid and semiarid regions dependent on irrigated agriculture, prolonged droughts lead to significant and recurring economic and social losses. In this contribution, we present preliminary results on integrating a set of multi-resolution drought indices into a cloud computing-based visualization platform. We focused our initial efforts on Brazil due to a severe, on-going drought in a large agricultural area in the northeastern part of the country. The online platform includes drought products developed from: (1) a MODIS-based water stress index (WSI) based on inferences from normalized difference vegetation index and land surface temperature fields, (2) a volumetric water content (VWC) index obtained from application of the NASA CASA model, and (3) a set of AVHRR-based vegetation health indices obtained from NOAA/NESDIS. The drought indices are also presented in terms of anomalies with respect to a baseline period. Since our main objective is to engage stakeholders and decision-makers in Brazil, we incorporated other relevant geospatial data into the platform, including irrigation areas, dams and reservoirs, administrative units and annual climate information. We will also present a set of use cases developed to help stakeholders explore, query and provide feedback that allowed fine-tuning of the drought product delivery, presentation and analysis tools. Finally, we discuss potential next steps in development of the online platform, including applications at finer resolutions in specific basins and at a coarser global scale.

  9. Steady-state chlorophyll fluorescence (Fs) as a tool to monitor plant heat and drought stress

    NASA Astrophysics Data System (ADS)

    Cendrero Mateo, M.; Carmo-Silva, A.; Salvucci, M.; Moran, S. M.; Hernandez, M.

    2012-12-01

    Crop yield decreases when photosynthesis is limited by heat or drought conditions. Yet farmers do not monitor crop photosynthesis because it is difficult to measure at the field scale in real time. Steady-state chlorophyll fluorescence (Fs) can be used at the field level as an indirect measure of photosynthetic activity in both healthy and physiologically-perturbed vegetation. In addition, Fs can be measured by satellite-based sensors on a regular basis over large agricultural regions. In this study, plants of Camelina sativa grown under controlled conditions were subjected to heat and drought stress. Gas exchange and Fs were measured simultaneously with a portable photosynthesis system under light limiting and saturating conditions. Results showed that Fs was directly correlated with net CO2 assimilation (A) and inversely correlated with non-photochemical quenching (NPQ). Analysis of the relationship between Fs and Photosynthetically Active Radiation (PAR) revealed significant differences between control and stressed plants that could be used to track the status, resilience, and recovery of photochemical processes. In summary, the results provide evidence that Fs measurements, even without normalization, are an easy means to monitor changes in plant photosynthesis, and therefore, provide a rapid assessment of plant stress to guide farmers in resource applications. Figure1. Net CO2 assimilation rate (A) of Camelina sativa plants under control conditions and after heat stress exposure for 1 or 3 days (1d-HS and 3d-HS, respectively) (right) and control, drought and re-watering conditions (left). Conditions for infra-red gas analysis were: reference CO2 = 380 μmol mol-1, PPFD = 500 μmol m-2 s-1 and Tleaf set to 25°C (control, drought and re-water) or 35°C (HS). Different letters denote significant differences at the α=0.05 level. Values are means±SEM (n=10). Figure 2. Stable chlorophyll fluorescence (Fs) of Camelina sativa plants under control conditions and

  10. Water monitoring to support the State of Illinois Governor's Drought Response Task Force -August 7, 2012

    USGS Publications Warehouse

    ,

    2012-01-01

    The U.S. Geological Survey (USGS) collects streamflow, groundwater level, and water-quality data for the State of Illinois and the Nation. Much of these data are collected every 15 minutes (real-time) as a part of the national network, so that water-resource managers can make decisions in a timely and reliable manner. Coupled with modeling and other water-resource investigations, the USGS provides data to the State during droughts and other hydrologic events. The types of data, capabilities, and presentation of these materials are described in this document as USGS Real-Time Data, Supplementary Data Collection and Analysis, and National Resources Available.

  11. Weekly Water Stress Monitoring in a Savannah Environment using a new Data Fusion Drought Index

    NASA Astrophysics Data System (ADS)

    Azmi, M.; Rudiger, C.; Walker, J. P.

    2015-12-01

    Due to the increasing pressure on water resources, water stress monitoring has become one of the most significant issues in water resources planning and management, especially during periods of extreme climate conditions. The present study compares the performance of four currently used data fusion based drought indices (DFDIs) to evaluate the weekly water stress at the Howard Springs OzFlux Tower in Northern Australia, covering a 3-year period from January 2011 to December 2013. In addition, a new DFDI has been developed and applied to address the individual shortcomings of the traditional indices. The proposed DFDI comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type (water, vegetation etc). Here, weekly data from three different data sources (OzFlux Network, Asia-Pacific Water Monitor, and MODIS-Terra satellite) were utilized for the evaluations. To derive the new DFDI, an appropriate set of individual standardized drought indices (SDIs) was derived, that are categorized through an advanced clustering method. For two groups in which the clustered SDIs best reflected the water availability and vegetation conditions, the variables are aggregated based on an averaging between the standardized first principal components of three different multivariate methods of PCA, FA and ICA. Then, considering those aggregated indices as well as the classifications of months into dry/wet and active/non-active, the time series of the proposed DFDI is finalized. A comparison, employing the Spearman correlation coefficient, between the proposed index and the traditional data fusion based indices shows a range of correlations from 0.46 to 0.85. The results underline that the proposed index can be more reliable in compare to the previous indices, due to simultaneously relating hydro-meteorological and ecological concepts to define the actual water stress throughout the study area.

  12. Developing a user-friendly Drought Monitoring and Forecasting Tool for Doctors without Borders

    NASA Astrophysics Data System (ADS)

    Enenkel, Markus

    2015-04-01

    Humanitarian aid organizations that focus on drought-related emergency response and disaster preparedness need to take decisions under high uncertainty. Satellite-derived and modelled information can help to decrease this uncertainty. However, in order to benefit from the provided knowledge it is crucial to adapt datasets and tools to actual user requirements and existing organizational capacities. Furthermore, socio-economic vulnerabilities (e. g. current rates of malnutrition) and coping capacities (e. g. access to drought-resistant seeds) of the affected population need to be assessed to link environmental conditions (drought risk) to potential impacts (food insecurity). Forecasts with lead times up to several months are desirable from a logistic point of view, but naturally less accurate than short-term predictions. As a consequence, careful calibration is required to identify and balance forecasts with an acceptable accuracy and the risk of possible false alarms. Therefore, we calibrate modelled predictions of rainfall, temperature and soil moisture via satellite-derived observations. Field tests with Doctors without Borders in Ethiopia help to define critical thresholds, to interpret the information under real conditions and to collect the necessary additional socio-economic data via a smartphone app. The final risk maps need to be visualized in a way that is easy to interpret, but not oversimplified.

  13. [Simplification of crop shortage water index and its application in drought remote sensing monitoring].

    PubMed

    Liu, Anlin; Li, Xingmin; He, Yanbo; Deng, Fengdong

    2004-02-01

    Based on the principle of energy balance, the method for calculating latent evaporation was simplified, and hence, the construction of the drought remote sensing monitoring model of crop water shortage index was also simplified. Since the modified model involved fewer parameters and reduced computing times, it was more suitable for the operation running in the routine services. After collecting the concerned meteorological elements and the NOAA/AVHRR image data, the new model was applied to monitor the spring drought in Guanzhong, Shanxi Province. The results showed that the monitoring results from the new model, which also took more considerations of the effects of the ground coverage conditions and meteorological elements such as wind speed and the water pressure, were much better than the results from the model of vegetation water supply index. From the view of the computing times, service effects and monitoring results, the simplified crop water shortage index model was more suitable for practical use. In addition, the reasons of the abnormal results of CWSI > 1 in some regions in the case studies were also discussed in this paper.

  14. GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas

    NASA Astrophysics Data System (ADS)

    Long, Di; Scanlon, Bridget R.; Longuevergne, Laurent; Sun, Alexander Y.; Fernando, D. Nelun; Save, Himanshu

    2013-07-01

    Texas experienced the most extreme one-year drought on record in 2011 with precipitation at 40% of long-term mean and agricultural losses of $7.6 billion. We assess the value of Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage (TWS) change as an alternative remote sensing-based drought indicator, independent of traditional drought indicators based on in situ monitoring. GRACE shows depletion in TWS of 62.3 ± 17.7 km3 during the 2011 drought. Large uncertainties in simulated soil moisture storage depletion (14-83 km3) from six land surface models indicate that GRACE TWS is a more reliable drought indicator than disaggregated soil moisture or groundwater storage. Groundwater use and groundwater level data indicate that depletion is dominated by changes in soil moisture storage, consistent with high correlation between GRACE TWS and the Palmer Drought Severity Index. GRACE provides a valuable tool for monitoring statewide water storage depletion, linking meteorological and hydrological droughts.

  15. A Web-Based Climate Change Drought Decision Support System (C2D2S2)

    NASA Astrophysics Data System (ADS)

    Aggett, G. R.

    2008-12-01

    Water managers are increasingly recognizing climate change as a significant issue and are requesting detailed information about potential hydrologic impacts suitable for inclusion in planning. For operational forecasts of streamflow, physically-based hydrologic models that can integrate critical parameters from climate change forecasts are required, as they can be used to directly relate altered temperature regimes to changes in snowpack, streamflow timing, and other effects. Available studies, however, are most often academic in nature and have the added limitation of being incompatible with agency specific water management models or the streamflow period of interest. Commissioning of a study focused on a specific system is generally prohibitively expensive for most municipalities and agencies. This study thus focused on the design and development of a prototype web-based Climate Change Drought Decision Support System (C2D2S2) to enable water managers at various operational- and time-scales to rapidly assess the impact of predicted climate change on natural flows at critical nodes along a river network. Results presented here highlight development of the system, specifically determination of the full range of elements required to build and support C2D2S2 including data, methods, tools and infrastructure necessary to power a full system capable of providing widespread and low-cost access to tools that can be used to generate scenarios of future streamflow over the internet. Results also stress the need for close interaction with, and feedback from stakeholders during development. This participation is critical to ensure potential users can use the tool effectively, and that data products are understandable in the context of operational water management decisions.

  16. Monitoring the Impacts of Severe Drought on Plant Species in Southern California Chaparral

    NASA Astrophysics Data System (ADS)

    Dennison, P. E.; Coates, A.; Roberts, D. A.; Roth, K. L.

    2015-12-01

    Airborne imaging spectrometer and thermal infrared image data acquired for the Hyperspectral Infrared Imager (HyspIRI) preparatory campaign were used to measure changes in green vegetation fraction and land surface temperature for twelve dominant plant species affected by drought in the Santa Barbara region of California. Relative green vegetation fraction was calculated from seasonally-acquired Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data using pre-drought 2011 AVIRIS data as a baseline. Land surface temperature was retrieved from MODIS-ASTER Simulator (MASTER) data. Deeply rooted tree species, tree species found on more mesic north-facing slopes, and tree species found in riparian areas had the least change in relative green vegetation fraction in 2013 and 2014 (e.g. QUAG and UMCA in the figure below). Coastal sage scrub and chaparral shrub species demonstrated greater variability as well as a long-term decline in relative green vegetation fraction. Three Ceanothus species (CECU, CEME, and CESP in the figure below) had more severe reductions in relative green vegetation fraction in comparison to another common chaparral shrub species, Adenostoma fasciculatum (ADFA). Species formed clusters in the space defined by land surface temperature and relative green vegetation fraction. Declining relative green vegetation fraction corresponded with increasing land surface temperature. Combined, routine acquisition of imaging spectrometer and thermal infrared imagery should provide new opportunities for monitoring drought impacts on ecosystems.

  17. Extension of a drought monitoring and vegetation classification methodology to the western Sahel

    NASA Technical Reports Server (NTRS)

    Mohler, Robert R. J.; Amsbury, David L.

    1988-01-01

    Biomass of growing vegetation over large semiarid regions can be estimated by digital manipulation of data from the AVHRR on NOAA polar-orbiting satellites. Here, the African Sahel is classified using a methodology which incorporates both the normalized difference and CAUSE procedures for the monitoring of vegetation during drought conditions. Preliminary analysis of color IR photographs taken on Space Shuttle missions indicates that such photographs can be digitized, registered to maps and other images, and utilized to fill temporal gaps in the historical record of data from unmanned satellites.

  18. Lessons Learned on Effective Co-production of Drought Science and Decision Support Tools with the Wind River Reservation Tribal Water Managers

    NASA Astrophysics Data System (ADS)

    McNeeley, S.; Ojima, D. S.; Beeton, T.

    2015-12-01

    The Wind River Reservation in west-central Wyoming is home of the Eastern Shoshone and Northern Arapaho Tribes. The reservation has experienced severe drought impacts on Tribal livelihoods and cultural activities in recent years. Scientists from the North Central Climate Science Center, the National Drought Mitigation Center, the High Plains Regional Climate Center, and multiple others are working in close partnership with the tribal water managers on a reservation-wide drought preparedness project that includes a technical assessment of drought risk, capacity building to train managers on drought and climate science and indicators, and drought planning. This talk will present project activities to date along with the valuable and transferrable lessons learned on effective co-production of actionable science for decision making in a tribal context.

  19. Development of Drought Monitoring and Seasonal Prediction Systems for the National Integrated Drought Information System (NIDIS) Initiative from NOAA's Climate Prediction Program for the Americas (CPPA)

    NASA Astrophysics Data System (ADS)

    Mitchell, K. E.

    2006-12-01

    The U.S. Congress, in response to persistent multi-year drought in the western continental U.S., has called upon NOAA to spearhead the development of a multi-agency National Integrated Drought Information System (NIDIS). Within the NOAA Climate Program Office, the Climate Prediction Program of the Americas (CPPA) has sponsored a number of investigators to develop and demonstrate various approaches and components of drought monitoring and seasonal drought prediction, with the goal of transitioning these approaches and components to NOAA operational application for drought monitoring and prediction. This presentation will review these approaches and pilot results to date, as well review the contributing investigators and institutions. The approaches involve, for example, use of multiple land/hydrology models in both uncoupled "offline" mode and coupled mode (with coupling to atmospheric models). In uncoupled mode, ensemble predictions of land surface forcing, including precipitation, from multiple coupled global and regional seasonal climate prediction models will be used to force the uncoupled land/hydrology models. Considerable effort is being applied to developing "post-processing" algorithms to bias-correct and downscale the ensemble predictions of land surface forcing from the coupled models. Additionally, several investigations are focusing on development and execution of long multi-decadal retrospective "reanalysis" of U.S. land states such as soil moisture and snowpack. Such land reanalyses are critical for casting the seasonal predictions in terms of anomalies from the land model's climatology -- an essential method by overcoming the systematic errors in the land models. Once such tool for these land reanalyses is the N. American Land Data Assimilation System (NLDAS), whose infrastructure was jointly developed by several collaborating institutions of CPPA forerunner programs, such as GAPP and GCIP.

  20. Calculation of Decision Support Interface Values for FEWS NET Remote Monitoring

    NASA Astrophysics Data System (ADS)

    Husak, G. J.

    2011-12-01

    In an effort to expand the spatial extent of monitoring activities, the Famine Early Warning Systems Network (FEWS NET) is seeking new techniques to leverage remotely sensed data. The Decision Support Interface (DSI) represents a new product that is part of a growing suite of remote monitoring tools using existing data products to assist in assessing crop-growing conditions. The DSI indicates areas where remotely sensed data show that further investigation into conditions may be needed. It is designed to be a first-check of conditions for both core monitoring areas as well as those where standard FEWS NET monitoring information may not be available. Initially developed for Africa, the DSI integrates different products and methods into a single assessment of crop-growing conditions. Two primary data inputs drive the DSI: NOAA produced satellite rainfall estimates and eMODIS Normalized Difference Vegetation Index (NDVI). The temporal monitoring unit is the dekad, defined as an approximately 10-day period encompassing either the first ten days (1st-10th), second ten days (11th-20th), or remainder of the month (21st-end). Rainfall accumulations at durations of 1, 3, 6, 9, and 18-dekads are used to capture rainfall conditions for various intervals of the crop calendar. NDVI data are a composite of greenness values over a 10-day period, smoothed in time to correct for atmospheric contamination. Spatial averages of these input data are extracted for defined agricultural regions. The agricultural areas synthesize the best available data for each country, and contain information about typical start and end of the growing season used to determine the period of monitoring for each polygon. Spatial averages of rainfall and NDVI for each polygon are assigned two percentiles, one - termed empirical - based on the ranking of the amount compared to historical amounts for the interval, and a second - termed theoretical - based on a parametric distribution derived from the

  1. Backscattering characteristics Analyses of winter wheat covered area and Drought Monitoring Based on active microwave

    NASA Astrophysics Data System (ADS)

    Zhang, C., Sr.; Li, L.

    2015-12-01

    The advantage of active microwave remote sensing on the sensitivity of polarization characteristic, backscatter intensity and phase characteristics to soil moisture demonstrates its potential to map and monitor relative soil moisture changes and drought information with high spatial resolution. However, the existence of soil surface condition and vegetation effects confounds the retrieval of soil moisture from active microwave, and therefore limits its applications on soil moisture retrieval and drought monitoring. To research how to reduce the effect of soil roughness and wheat cover with multi- incident angles and multi polarization active microwave remote sensing data, MIMICS and AIEM models were used to simulate the backscattering coefficient of winter wheat covered field. The interaction between winter wheat at main growth stages and microwave was analyzed. The effects of surface roughness and physical parameters of wheat on the backscattering characteristics and the variation of different incident angles and different polarization conditions are simulated and analyzed emphatically. Then scattering coefficient information of winter wheat covered area at different wheat growth stage was measured with a C band ground-based scattering meter. At the same time, biomass, leaf area index and soil rough degree, soil water content and other related parameters are collected. After comparing and analyzing the measured data and the simulated data at different incident angles and different polarization modes, we propose an approach of using multi polarization and multi angle data to eliminate the soil roughness and wheat vegetation effects and performing the inversion of soil moisture. Using the Radarsat2 satellite SAR data and ground-based scatter data gotten at the same period in 2012, soil moisture information of greater area is obtained, and then the drought information is obtained, which is consistent with the measured results.

  2. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  3. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

    NASA Astrophysics Data System (ADS)

    Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio

    2017-04-01

    Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze

  4. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

    NASA Astrophysics Data System (ADS)

    Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye

    2016-10-01

    Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30 years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and evaluate their applicability for agricultural drought evaluation when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in-situ rainfall measurements across Chile were initially compared to the satellite-based precipitation estimates. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to

  5. Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

    NASA Technical Reports Server (NTRS)

    Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John

    2014-01-01

    Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

  6. In Situ Stem Psychrometry: toward a Physiologically-Based Drought Monitoring Network

    NASA Astrophysics Data System (ADS)

    KOCH, G. W.; Williams, C.; Ambrose, A.

    2012-12-01

    Plant water potential is a synoptic variable that integrates soil and atmospheric moisture stress and interacts with plant-internal factors to regulate gas exchange and determine vulnerability to drought-induced hydraulic dysfunction. Despite its importance, methods for measuring water potential are labor intensive. This limitation reduces measurement frequency, likely causes important transient events to be overlooked, and restricts development of a richer understanding of the impacts of integrated water stress on plant and ecosystem function. Recent technological advances have enabled in-situ, automated measurement of branch water potential over periods of weeks to months using stem psychrometers. We evaluated this technology through laboratory and field comparisons to standard pressure chamber measurements and with field installations in temperate forest, semi-arid woodland, and chaparral ecosystems. Performance was highly sensitive to installation procedures. With proper sealing, insulation, and radiation shielding, psychrometers typically differed from pressure chamber measurements by less than 0.2 MPa down to water potentials as low as -7 MPa. Measurements in tall trees reaffirmed the influence of gravity on water potential as previously documented with the pressure chamber. Psychrometer performance in situ was stable for periods of several weeks to months, with tissue wound response degrading sensor operation over time. We conclude that stem psychrometer technology is now suitable to serve as the foundation for a physiologically-based drought monitoring network that can anticipate important ecosystem impacts including changes in whole-system fluxes and mortality events.

  7. Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery

    NASA Astrophysics Data System (ADS)

    Ozelkan, Emre; Chen, Gang; Ustundag, Burak Berk

    2016-02-01

    Drought is a rapidly rising environmental issue that can cause hardly repaired or unrepaired damages to the nature and socio-economy. This is especially true for a region that features arid/semi-arid climate, including the Turkey's most important agricultural district - Southeast Anatolia. In this area, we examined the uncertainties of applying Landsat 8 Operational Land Imager (OLI) NDVI data to estimate meteorological drought - Standardized Precipitation Index (SPI) - measured from 31 in-situ agro-meteorological monitoring stations during spring and summer of 2013 and 2014. Our analysis was designed to address two important, yet under-examined questions: (i) how does the co-existence of rainfed and irrigated agriculture affect remote sensing drought monitoring in an arid/semi-arid region? (ii) What is the role of spatial scale in drought monitoring using a GEOBIA (geographic object-based image analysis) framework? Results show that spatial scale exerted a higher impact on drought monitoring especially in the drier year 2013, during which small scales were found to outperform large scales in general. In addition, consideration of irrigated and rainfed areas separately ensured a better performance in drought analysis. Compared to the positive correlations between SPI and NDVI over the rainfed areas, negative correlations were determined over the irrigated agricultural areas. Finally, the time lag effect was evident in the study, i.e., strong correlations between spring SPI and summer NDVI in both 2013 and 2014. This reflects the fact that spring watering is crucial for the growth and yield of the major crops (i.e., winter wheat, barley and lentil) cultivated in the region.

  8. Sequential decision plans, benthic macroinvertebrates, and biological monitoring programs

    NASA Astrophysics Data System (ADS)

    Jackson, John K.; Resh, Vincent H.

    1989-07-01

    A common obstacle to the inclusion of benthic macroinvertebrates in water quality monitoring programs is that numerous sample units must be examined in order to distinguish between impacted and unimpacted conditions, which can add significantly to the total cost of a monitoring program. Sequential decision plans can be used to reduce this cost because the number of sample units needed to classify a site as impacted or unimpacted is reduced by an average of 50%. A plan is created using definitions of unimpacted and impacted conditions, a description of the mathematical distribution of the data, and definitions of acceptable risks of type I and II errors. The applicability of using sequential decision plans and benthic macroinvertebrates in water quality monitoring programs is illustrated with several examples (e.g., identifying moderate and extreme changes in species richness in response to acid mine drainage; assessing the impact of a crude oil contamination on the density of two benthic populations; monitoring the effect of geothermal effluents on species diversity). These examples use data conforming to the negative binomial, Poisson, and normal distributions and define impact as changes in population density, species richness, or species diversity based on empirical data or the economic feasibility of the sequential decision plan. All mathematical formulae and intermediate values are provided for the step-by-step calculation of each sequential decision plan.

  9. Combining modelled and remote sensing soil moisture anomalies for an operational global drought monitoring

    NASA Astrophysics Data System (ADS)

    Cammalleri, Carmelo; Vogt, Jürgen

    2017-04-01

    Soil moisture anomalies (i.e., deviations from the climatology) are often seen as a reliable tool to monitor and quantify the occurrence of drought events and their potential impacts, especially in agricultural and naturally vegetated lands. Soil moisture datasets (or their proxy) can be derived from a variety of sources, including land-surface models and thermal and microwave satellite remote sensing images. However, each data source has different advantages and drawbacks that prevent to unequivocally prefer one dataset over the others, especially in global applications that encompass a wide range of soil moisture regimes. The analysis of the spatial reliability of the different datasets at global scale is further complicated by the lack of reliable long-term soil moisture records for a ground validation over most regions. To overcome this limitation, in recent years the Triple Collocation (TC) technique has been deployed in order to quantify the likely errors associated to three mutually-independent datasets without assuming that one of them represents the "truth". In this study, three global datasets of soil moisture anomalies are investigated: the first one derived from the runs of the Lisflood hydrological model, the second one obtained from the combined active/passive microwave dataset produced in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), and the last one derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) observations. A preliminary analysis of the three datasets aimed at detecting the areas where the TC technique can be successfully applied, hence the spatial distribution of the random error variance for each model is evaluated. This study allows providing useful advises for a robust combination of the three datasets into a single product for a more reliable global drought monitoring.

  10. Monitoring and decision making by people in man machine systems

    NASA Technical Reports Server (NTRS)

    Johannsen, G.

    1979-01-01

    The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

  11. Monitoring the expression of maize genes in developing kernels under drought stress using oligo-microarray

    USDA-ARS?s Scientific Manuscript database

    Preharvest A. flavus infection is usually exacerbated when maize plants suffer drought stress in the late grain-fill stage. However, the field observation suggests that drought-tolerant maize lines displayed less aflatoxin contamination under the stress in comparison with the drought-sensitive maize...

  12. Evaluating the Performance of a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Han, E.; Crow, W. T.; Holmes, T. R.; Bolten, J. D.

    2013-12-01

    Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, we evaluates a soil moisture assimilation system for agricultural drought monitoring by benchmarking each component of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputs/output) as the full component. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system. First, the non-linear LPRM retrieval algorithm does not appear to add much additional predictive information for future NDVI compared to the simple linear benchmark model comprised of initial AMSR-E observations (horizontally and vertically polarized brightness temperatures and surface temperature). Second, the Palmer model performed worse than the purely linear prognostic model (Antecedent Precipitation Index model) in predicting future vegetation condition. This result points out that the saturation threshold of soil layers in the modern LSMs for runoff generation hinders maximum utilization of meteorological input information for agricultural drought monitoring. As to the assimilation algorithm, better performance of the

  13. Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications

    USGS Publications Warehouse

    Husak, Gregory J.; Michaelsen, Joel C.; Funk, Christopher C.

    2007-01-01

    Evaluating a range of scenarios that accurately reflect precipitation variability is critical for water resource applications. Inputs to these applications can be provided using location- and interval-specific probability distributions. These distributions make it possible to estimate the likelihood of rainfall being within a specified range. In this paper, we demonstrate the feasibility of fitting cell-by-cell probability distributions to grids of monthly interpolated, continent-wide data. Future work will then detail applications of these grids to improved satellite-remote sensing of drought and interpretations of probabilistic climate outlook forum forecasts. The gamma distribution is well suited to these applications because it is fairly familiar to African scientists, and capable of representing a variety of distribution shapes. This study tests the goodness-of-fit using the Kolmogorov–Smirnov (KS) test, and compares these results against another distribution commonly used in rainfall events, the Weibull. The gamma distribution is suitable for roughly 98% of the locations over all months. The techniques and results presented in this study provide a foundation for use of the gamma distribution to generate drivers for various rain-related models. These models are used as decision support tools for the management of water and agricultural resources as well as food reserves by providing decision makers with ways to evaluate the likelihood of various rainfall accumulations and assess different scenarios in Africa. 

  14. Indonesian drought monitoring from space. A report of SAFE activity: Assessment of drought impact on rice production in Indonesia by satellite remote sensing and dissemination with web-GIS

    NASA Astrophysics Data System (ADS)

    Shofiyati, Rizatus; Takeuchi, Wataru; Sofan, Parwati; Darmawan, Soni; Awaluddin; Supriatna, Wahyu

    2014-06-01

    Long droughts experienced in Indonesia in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. Two types of drought, Meteorology and Agriculture, have been assessed. During the last 10 years, daily and monthly rainfall data derived from TRMM and GSMaP. MTSAT and AMSR-E data have been analyzed to identify meteorological drought. Agricultural drought has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS data at a period of 5 years (2009 - 2013). Network for data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, and University of Tokyo (technical supporter). A Web-GIS based Drought Monitoring Information System has been developed to disseminate the information to end users. This paper describes the implementation of remote sensing drought monitoring model and development of Web-GIS and satellite based information system.

  15. Monitoring And Modeling Environmental Water Quality To Support Environmental Water Purchase Decision-making

    NASA Astrophysics Data System (ADS)

    Null, S. E.; Elmore, L.; Mouzon, N. R.; Wood, J. R.

    2016-12-01

    More than 25 million cubic meters (20,000 acre feet) of water has been purchased from willing agricultural sellers for environmental flows in Nevada's Walker River to improve riverine habitat and connectivity with downstream Walker Lake. Reduced instream flows limit native fish populations, like Lahontan cutthroat trout, through warm daily stream temperatures and low dissolved oxygen concentrations. Environmental water purchases maintain instream flows, although effects on water quality are more varied. We use multi-year water quality monitoring and physically-based hydrodynamic and water quality modeling to estimate streamflow, water temperature, and dissolved oxygen concentrations with alternative environmental water purchases. We simulate water temperature and dissolved oxygen changes from increased streamflow to prioritize the time periods and locations that environmental water purchases most enhance trout habitat as a function of water quality. Monitoring results indicate stream temperature and dissolved oxygen limitations generally exist in the 115 kilometers upstream of Walker Lake (about 37% of the study area) from approximately May through September, and this reach acts as a water quality barrier for fish passage. Model results indicate that low streamflows generally coincide with critically warm stream temperatures, water quality refugia exist on a tributary of the Walker River, and environmental water purchases may improve stream temperature and dissolved oxygen conditions for some reaches and seasons, especially in dry years and prolonged droughts. This research supports environmental water purchase decision-making and allows water purchase decisions to be prioritized with other river restoration alternatives.

  16. Visualizing Earth Science Data for Environmental Monitoring and Decision Support in Mesoamerica: The SERVIR Project

    NASA Astrophysics Data System (ADS)

    Hardin, D.; Graves, S.; Sever, T.; Irwin, D.

    2005-05-01

    In 2002 and 2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR). Mesoamerica - composed of the seven Central American countries and the five southernmost states of Mexico - makes up only a small fraction of the world's land surface. However, the region is home to approximately eight percent of the planet's biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Mesoamerica's biological and cultural diversity are severely threatened by human impact and natural disasters including extensive deforestation, illegal logging, water pollution, slash and burn agriculture, earthquakes, hurricanes, drought, and volcanic eruption. NASA Marshall Space Flight Center (NASA/MSFC), together with the University of Alabama in Huntsville (UAH) and the SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters, and better understand both natural and human induced effects. The decision support and environmental monitoring data products are typically formatted as conventional two-dimensional, static and animated imagery. However, in addition to conventional data products and as a major portion of our research, we are employing commercial applications that generate three-dimensional interactive visualizations that allow data products to be viewed from multiple angles and at

  17. Calcareous palaeosols and temples in the floodplain of Thebes, Egypt: droughts and decisions

    NASA Astrophysics Data System (ADS)

    Graham, Angus; Hunter, Morag A.; Pennington, Benjamin T.; Strutt, Kristian D.

    2014-05-01

    The Egypt Exploration Society Theban Harbours and Waterscapes Survey (THaWS) works in the area around modern Luxor (Egypt), and investigates the extent to which the Egyptians manipulated the Nile and floodplain through canal and basin construction. A current focus of the project is to understand the relationship between the floodplain and a series of temples on the West Bank. A longstanding puzzle on the West Bank is why the temple of Amenhotep III (1390-1352 BCE) is not located in the same area as all the others. While 19 kings of the Egyptian New Kingdom (1550-1070 BCE) built their temples on the toe-slope of the limestone cliffs fronting onto the edge of the modern alluvium, Amenhotep's sits entirely on the modern floodplain. Egyptologists have suggested this was done to allow the inundation of the Nile to wash into the temple, symbolising and recreating the essential Egyptian cosmogony of the primeval mound. However, was it possible that a period of low Nile discharge enabled him to build on the alluvium whilst keeping the temple dry from the Nile floods? The project is testing this hypothesis through an interdisciplinary approach which provides focussed information on the development of the floodplain over historic time periods. It combines geophysical survey (Electrical Resistivity Tomography, Ground Penetrating Radar and magnetometry) with geoarchaeology using an Eijkelkamp hand auger and gouge auger with facies being dated using the stratigraphic sequence of ceramic fragments within them. Two fieldwork seasons have been carried out to date (Graham et al. 2012, 2013). Calcareous palaeosols c. 4m below the surface have been identified in three separate augers across a distance of 3 km on the West Bank floodplain, suggesting a period of low inundation levels / drought. At one of the locations an ancient surface appears to lie 0.3-0.4m above the calcisol. Ceramic fragments from this unit tentatively indicate a New Kingdom date. The strontium isotope record from

  18. A Drought Cyberinfrastructure System for Improving Water Resource Management and Policy Making

    NASA Astrophysics Data System (ADS)

    AghaKouchak, Amir

    2015-04-01

    Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness, management, and response decision making. This presentation provides an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using both remote sensing observations and model simulations. Designed as a cyberinfrastructure system, GIDMaPS provides drought information based on a wide range of model simulations and satellite observations from different space agencies. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts, and better management and distribution of water resources among and across different users. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is designed to provide drought information for water resource management, and short-term decision making. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The presentation will highlight how this drought cyberinfrastructure system can be used to improve water

  19. A Multimodel Global Drought Information System (GDIS) for Near Real-Time Monitoring of Surface Water Conditions (Invited)

    NASA Astrophysics Data System (ADS)

    Nijssen, B.

    2013-12-01

    While the absolute magnitude of economic losses associated with weather and climate disasters such as droughts is greatest in the developed world, the relative impact is much larger in the developing world, where agriculture typically constitutes a much larger percentage of the labor force and food insecurity is a major concern. Nonetheless, our ability to monitor and predict the development and occurrence of droughts at a global scale in near real-time is limited and long-term records of soil moisture are essentially non-existent globally The problem is particularly critical given that many of the most damaging droughts occur in parts of the world that are most deficient in terms of in situ precipitation observations. In recent years, a number of near real-time drought monitoring systems have been developed with regional or global extent. While direct observations of key variables such as moisture storage are missing, the evolution of land surface models that are globally applicable provides a means of reconstructing them. The implementation of a multi-model drought monitoring system is described, which provides near real-time estimates of surface moisture storage for the global land areas between 50S and 50N with a time lag of about one day. Near real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system is distinguished from other operational systems in that it uses multiple land surface models to simulate surface moisture storage, which are then combined to derive a multi-model estimate of drought. Previous work has shown that while land surface models agree in broad context, particularly in terms of soil moisture percentiles, important differences remain, which motivates a multi-model ensemble approach. The system is an extension of similar systems developed by at the University of Washington for the Pacific Northwest and for the United States, but global application of the protocols used in the U

  20. Improving agricultural drought monitoring in West Africa using root zone soil moisture estimates derived from NDVI

    NASA Astrophysics Data System (ADS)

    McNally, A.; Funk, C. C.; Yatheendradas, S.; Michaelsen, J.; Cappelarere, B.; Peters-Lidard, C. D.; Verdin, J. P.

    2012-12-01

    The Famine Early Warning Systems Network (FEWS NET) relies heavily on remotely sensed rainfall and vegetation data to monitor agricultural drought in Sub-Saharan Africa and other places around the world. Analysts use satellite rainfall to calculate rainy season statistics and force crop water accounting models that show how the magnitude and timing of rainfall might lead to above or below average harvest. The Normalized Difference Vegetation Index (NDVI) is also an important indicator of growing season progress and is given more weight over regions where, for example, lack of rain gauges increases error in satellite rainfall estimates. Currently, however, near-real time NDVI is not integrated into a modeling framework that informs growing season predictions. To meet this need for our drought monitoring system a land surface model (LSM) is a critical component. We are currently enhancing the FEWS NET monitoring activities by configuring a custom instance of NASA's Land Information System (LIS) called the FEWS NET Land Data Assimilation System. Using the LIS Noah LSM, in-situ measurements, and remotely sensed data, we focus on the following questions: What is the relationship between NDVI and in-situ soil moisture measurements over the West Africa Sahel? How can we use this relationship to improve modeled water and energy fluxes over the West Africa Sahel? We investigate soil moisture and NDVI cross-correlation in the time and frequency domain to develop a transfer function model to predict soil moisture from NDVI. This work compares sites in southwest Niger, Benin, Burkina Faso, and Mali to test the generality of the transfer function. For several sites with fallow and millet vegetation in the Wankama catchment in southwest Niger we developed a non-parametric frequency response model, using NDVI inputs and soil moisture outputs, that accurately estimates root zone soil moisture (40-70cm). We extend this analysis by developing a low order parametric transfer function

  1. Drought monitoring of Shandong province in late 2010 using data acquired by Terra MODIS

    NASA Astrophysics Data System (ADS)

    Wang, Mingzhi; Huang, He; Liu, Suihua; Yan, Lei

    2011-12-01

    Drought has been a frequently happened type of disaster in China, and it has caused massive losses to people's lives. Especially the drought happened in Shandong province in the late 2010, which was recognized as the severest in the past five hundred years in some areas. Evaluation must be done in order to make proper rescue plans. Instead of collecting data site by site, remote sensing is an efficient way to acquire data in a large area, which is very helpful for drought identification. Some normal ways in remote sensing for drought analysis are explained and compared in this paper, and then the VSWI method is chosen to evaluation the drought in Shandong province. Because of its free data policy and wide availability, the data sets acquired by Terra-MODIS are chosen to identify the drought severity in Shandong province. From the drought severity level images we can see that almost the whole area of Shandong province was lack of water except the Weishan Lake and eastern coastline regions where large area of water exists. The southwest region, including Heze and Jining, is in moderate drought condition, where it is used to be an important grain-producing area. This drought condition will inevitably put a negative effect on its grain production. The central and southern areas were in severe drought condition, but fortunately these areas are of hills and mountains, so the drought will only affect the lives of residents. The northern parts, including Dezhou and Bingzhou areas, were also in severe drought condition, and these regions are also important for grain-producing, so the severe drought disaster will lead to a sharp grain output cut. This analysis results will not only shed light on the rescue process, but also give the government some clues on how to maintain the grain supply safety.

  2. Drought modeling - A review

    NASA Astrophysics Data System (ADS)

    Mishra, Ashok K.; Singh, Vijay P.

    2011-06-01

    SummaryIn recent years droughts have been occurring frequently, and their impacts are being aggravated by the rise in water demand and the variability in hydro-meteorological variables due to climate change. As a result, drought hydrology has been receiving much attention. A variety of concepts have been applied to modeling droughts, ranging from simplistic approaches to more complex models. It is important to understand different modeling approaches as well as their advantages and limitations. This paper, supplementing the previous paper ( Mishra and Singh, 2010) where different concepts of droughts were highlighted, reviews different methodologies used for drought modeling, which include drought forecasting, probability based modeling, spatio-temporal analysis, use of Global Climate Models (GCMs) for drought scenarios, land data assimilation systems for drought modeling, and drought planning. It is found that there have been significant improvements in modeling droughts over the past three decades. Hybrid models, incorporating large scale climate indices, seem to be promising for long lead-time drought forecasting. Further research is needed to understand the spatio-temporal complexity of droughts under climate change due to changes in spatio-temporal variability of precipitation. Applications of copula based models for multivariate drought characterization seem to be promising for better drought characterization. Research on decision support systems should be advanced for issuing warnings, assessing risk, and taking precautionary measures, and the effective ways for the flow of information from decision makers to users need to be developed. Finally, some remarks are made regarding the future outlook for drought research.

  3. Towards Developing a Regional Drought Information System for Lower Mekong

    NASA Astrophysics Data System (ADS)

    Dutta, R.; Jayasinghe, S.; Basnayake, S. B.; Apirumanekul, C.; Pudashine, J.; Granger, S. L.; Andreadis, K.; Das, N. N.

    2016-12-01

    With the climate and weather patterns changing over the years, the Lower Mekong Basin have been experiencing frequent and prolonged droughts resulting in severe damage to the agricultural sector affecting food security and livelihoods of the farming community. However, the Regional Drought Information System (RDIS) for Lower Mekong countries would help prepare vulnerable communities from frequent and severe droughts through monitoring, assessing and forecasting of drought conditions and allowing decision makers to take effective decisions in terms of providing early warning, incentives to farmers, and adjustments to cropping calendars and so on. The RDIS is an integrated system that is being designed for drought monitoring, analysis and forecasting based on the need to meet the growing demand of an effective monitoring system for drought by the lower Mekong countries. The RDIS is being built on four major components that includes earth observation component, meteorological data component, database storage and Regional Hydrologic Extreme Assessment System (RHEAS) framework while the outputs from the system will be made open access to the public through a web-based user interface. The system will run on the RHEAS framework that allows both nowcasting and forecasting using hydrological and crop simulation models such as the Variable Infiltration Capacity (VIC) model and the Decision Support System for Agro-Technology Transfer (DSSAT) model respectively. The RHEAS allows for a tightly constrained observation based drought and crop yield information system that can provide customized outputs on drought that includes root zone soil moisture, Standard Precipitation Index (SPI), Standard Runoff Index (SRI), Palmer Drought Severity Index (PDSI) and Crop Yield and can integrate remote sensing products, along with evapotranspiration and soil moisture data. The anticipated outcomes from the RDIS is to improve the operational, technological and institutional capabilities of

  4. Drought monitoring and warning system of rice paddy field in Asia by MTSAT and GSMaP

    NASA Astrophysics Data System (ADS)

    Takeuchi, W.; Darmawan, S.; Oyoshi, K.

    2014-12-01

    This research focuses on a development of satellite-based drought monitoring warning system in Asian Pacific country. Drought condition of cropland is evaluated by using Keeth-Byram Drought Index (KBDI) computed from rainfall measurements with GSMaP product, land surface temperature by MTSAT product and vegetation phenology by MODIS NDVI product at daily basis. The derived information is disseminated as a system for an application of space based technology (SBT) in the implementation of the Core Agriculture Support Program. The benefit of this system are to develop satellite-based drought monitoring and early warning system in Asian counties using freely available data, and to develop capacity of policy makers in those countries to apply the developed system in policy making. A series of training program has been carried out in 2013 and 2014 to officers and researchers of ministry of agriculture and relevant agencies in Greater Mekong Subregion countries including Cambodia, China, Myanmar, Laos, Thailand and Vietnam. This system is running as fully operational and can be accessed at http://webgms.iis.u-tokyo.ac.jp/DMEWS/

  5. Putting Current North America Drought Conditions into a Multi-Century Perspective. Part 1: Constructing the Paleo Drought Dataset

    NASA Astrophysics Data System (ADS)

    Cook, E. R.; Vose, R. S.; Heim, R. R.; Lawrimore, J. H.

    2007-12-01

    Drought is an important climatological phenomenon which has significant socioeconomic and environmental impacts. Several drought indices have been developed to quantify drought, but all of them rely on meteorological observations taken at instrumented in situ weather stations. The instrumental record for drought monitoring in the U.S. extends back only about a hundred years, and the record is even shorter in other countries such as Canada and Mexico. Recurrence intervals and water management compacts (for example, the Colorado River Basin domestic or Rio Grande international compacts) based upon such short records may not be built upon the true climatology of a region. Reliable drought information can be derived from paleoclimatic data such as tree- rings, thus enabling researchers and decision-makers to assess drought variability and impacts over a multi- century period. Previous work has developed research-quality paleoclimatic drought reconstructions which have been used in retrospective analyses but, until now, such data bases have not been used comprehensively in operational monitoring. Part 1 of this paper describes the development of the reconstructed paleoclimatic Palmer drought index gridded dataset for North America from tree-ring data. Part 2 of this paper describes how the reconstructed paleoclimatic data base is blended with a 20th century instrumental-based Palmer drought index gridded dataset for operational drought monitoring applications across North America.

  6. Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions.

    PubMed

    Castle, Jessica R; Jacobs, Peter G

    2016-09-01

    While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care.

  7. Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions

    PubMed Central

    Castle, Jessica R.; Jacobs, Peter G.

    2016-01-01

    While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care. PMID:26880390

  8. Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

    USDA-ARS?s Scientific Manuscript database

    Agricultural drought is defined as a shortage of moisture in the root zone of plants. Recently available satellite-based remote sensing data have accelerated development of drought early warning system by providing spatially continuous soil moisture information repeatedly at short-term interval. Non...

  9. Forest productivity and drought in tropical Africa: observations from the Global Ozone Monitoring Experiment-2

    NASA Astrophysics Data System (ADS)

    Robinson, E. S.; Lee, J. E.; Yang, X.

    2014-12-01

    The impact of seasonal water stress on Africa's tropical regions has yet to be characterized despite drought's potential to cause famine and a reduction of biodiversity across the continent. Through the analysis of a new data set of sun-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2, we demonstrate that fluorescence varies with water availability, particularly over regions with distinctive wet and dry seasons. Water availability was determined via both precipitation (from the Global Precipitation Climatology Project) and daytime canopy water content measurements (from the SeaWinds Scatterometer onboard the QuickSCAT satellite). Variance in SIF values was largely explained by both canopy water content and precipitation, which paralleled one-another. When viewed in the context of the previously defined relationship between fluorescence and gross primary production (GPP) - SIF scales linearly with GPP - our results suggest that photosynthetic activity in tropical Africa is limited by water availability. The characterization of this trend is critical in defining the response of tropical ecosystems to water stress and corroborating similar relationships in other tropical regions (e.g. Amazonia). Ultimately, the viability of Africa's tropical regions amidst a changing climate is rooted in its ecosystem-wide response to water stress; the future of the African tropics is limited by how well plants cope with water stress.

  10. Global and Regional Real-time Systems for Flood and Drought Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Gourley, J. J.; Xue, X.; Flamig, Z.

    2015-12-01

    A Hydrometeorological Extreme Mapping and Prediction System (HyXtreme-MaP), initially built upon the Coupled Routing and Excess STorage (CREST) distributed hydrological model, is driven by real-time quasi-global TRMM/GPM satellites and by the US Multi-Radar Multi-Sensor (MRMS) radar network with dual-polarimetric upgrade to simulate streamflow, actual ET, soil moisture and other hydrologic variables at 1/8th degree resolution quasi-globally (http://eos.ou.edu) and at 250-meter 2.5-mintue resolution over the Continental United States (CONUS: http://flash.ou.edu).­ Multifaceted and collaborative by-design, this end-to-end research framework aims to not only integrate data, models, and applications but also brings people together (i.e., NOAA, NASA, University researchers, and end-users). This presentation will review the progresses, challenges and opportunities of such HyXTREME-MaP System used to monitor global floods and droughts, and also to predict flash floods over the CONUS.

  11. Statistical analysis of long-term hydrologic records for selection of drought-monitoring sites on Long Island, New York

    USGS Publications Warehouse

    Busciolano, Ronald J.

    2005-01-01

    Ground water is the sole source of water supply for more than 3 million people on Long Island, New York. Large-scale ground-water pumpage, sewering systems, and prolonged periods of below-normal precipitation have lowered ground-water levels and decreased stream-discharge in western and central Long Island. No method is currently (2004) available on Long Island that can assess data from the ground-water-monitoring network to enable water managers and suppliers with the ability to give timely warning of severe water-level declines.This report (1) quantifies past drought- and human-induced changes in the ground-water system underlying Long Island by applying statistical and graphical methods to precipitation, stream-discharge, and ground-water-level data from selected monitoring sites; (2) evaluates the relation between water levels in the upper glacial aquifer and those in the underlying Magothy aquifer; (3) defines trends in stream discharge and ground-water levels that might indicate the onset of drought conditions or the effects of excessive pumping; and (4) discusses the long-term records that were used to select sites for a Long Island drought-monitoring network.Long Island’s long-term hydrologic records indicated that the available data provide a basis for development of a drought-monitoring network. The data from 36 stations that were selected as possible drought-monitoring sites—8 precipitation-monitoring stations, 8 streamflow-gaging (discharge) stations, 15 monitoring wells screened in the upper glacial aquifer under water-table (unconfined) conditions, and 5 monitoring wells screened in the underlying Magothy aquifer under semi-confined conditions—indicate that water levels in western parts of Long Island have fallen and risen markedly (more than 15 ft) in response to fluctuations in pumpage, and have declined from the increased use of sanitary- and storm-sewer systems. Water levels in the central and eastern parts, in contrast, remain relatively

  12. The 2005 and 2012 major drought events in Iberia: monitoring vegetation dynamics and crop yields using satellite data.

    NASA Astrophysics Data System (ADS)

    Gouveia, Célia M.; Trigo, Ricardo M.

    2014-05-01

    The Iberian Peninsula is recurrently affected by drought episodes and therefore by the adverse effects associated that range from severe water shortages to economic losses and related social impacts. During the hydrological years of 2004/2005 and 2011/2012, Iberia was hit by two of the worst drought episodes ever recording in this semi-arid region (Garcia-Herrera at al., 2007; Trigo et al., 2013). These two drought episodes were extreme in both its magnitude and spatial extent. A tendency towards a drier Mediterranean for the period 1970-2010 in comparison with 1901-70 has been identified (Hoerling et al., 2012), reinforcing the need for a continuous monitoring of vegetation stress and reliable estimates of the drought impacts. The strong effect of water scarcity on vegetation dynamics is well documented in Mediterranean and other semi-arid regions. Despite the usual link established between the decrease of vegetation greenness and the lack of precipitation during a considerably long period, the impact on vegetation activity may be amplified by other climatic anomalies, such as high temperature, high wind, and low relative humidity. The recent availability of consistent satellite imagery covering large regions over long periods of time has progressively reinforced the role of remote sensing in environmental studies, in particular in those related to drought episodes (e.g. Gouveia et al., 2009). The aim of the present work is to assess and monitor the cumulative impact over time of drought conditions on vegetation over Iberian Peninsula. For this purpose we have used the regional fields of the Normalized Difference Vegetation Index (NDVI) as obtained from the VEGETATION-SPOT5 instrument, from 1999 to 2013. The entire 15-yr long period was analysed, but particular attention was devoted to the two extreme drought episodes of 2004-2005 and 2011-2012. During the hydrological years of 2004-2005 and 2011-2012 drought episodes negative anomalies of NDVI were observed over

  13. An Intelligent Decision System for Intraoperative Somatosensory Evoked Potential Monitoring.

    PubMed

    Fan, Bi; Li, Han-Xiong; Hu, Yong

    2016-02-01

    Somatosensory evoked potential (SEP) is a useful, noninvasive technique widely used for spinal cord monitoring during surgery. One of the main indicators of a spinal cord injury is the drop in amplitude of the SEP signal in comparison to the nominal baseline that is assumed to be constant during the surgery. However, in practice, the real-time baseline is not constant and may vary during the operation due to nonsurgical factors, such as blood pressure, anaesthesia, etc. Thus, a false warning is often generated if the nominal baseline is used for SEP monitoring. In current practice, human experts must be used to prevent this false warning. However, these well-trained human experts are expensive and may not be reliable and consistent due to various reasons like fatigue and emotion. In this paper, an intelligent decision system is proposed to improve SEP monitoring. First, the least squares support vector regression and multi-support vector regression models are trained to construct the dynamic baseline from historical data. Then a control chart is applied to detect abnormalities during surgery. The effectiveness of the intelligent decision system is evaluated by comparing its performance against the nominal baseline model by using the real experimental datasets derived from clinical conditions.

  14. A Look into the National Drought Mitigation Center: Providing 15 Years of Drought Services (Invited)

    NASA Astrophysics Data System (ADS)

    Svoboda, M. D.; Hayes, M. J.; Knutson, C. L.; Wardlow, B. D.

    2009-12-01

    The National Drought Mitigation Center (NDMC) was formed in 1995 at the University of Nebraska-Lincoln. Over the past 15 years, the NDMC has made it a priority to work with various local, state, tribal and federal entities to provide a suite of drought/climate services, with a goal of bringing research to fruition through applications and operations. Through our research and outreach projects, the NDMC has worked to reduce risk to drought by developing several mitigation strategies, monitoring and decision making tools and other services aimed at enhancing our nation’s capacity to cope with drought. Two of the earliest NDMC activities were the creation of a website and assessing drought conditions around the United States. An electronic drought clearinghouse was built in 1995 at drought.unl.edu. The site was designed, and still concentrates, on the concepts of drought monitoring, planning, and mitigation and also serves as a repository of information from around the world. The NDMC’s electronic quarterly newsletter, DroughtScape, disseminates information about all things drought to people across the country. In addition, the NDMC has developed and is home to websites for the U.S. Drought Monitor (USDM), Drought Impact Reporter (DIR), and the Vegetation Drought Response Index (VegDRI). In an effort to inform decision makers, the NDMC continually pursues ways to raise the awareness and visibility of drought as one of the most costly hazards we face. This began in the mid-1990s with the creation of a state-based drought impact assessment map that would help lead to the formation of the USDM in 1999 and the DIR in 2005. The NDMC plays a key role in producing the weekly USDM and the monthly North American Drought Monitor (NADM). The USDM was created out of collaborations between the NDMC, United States Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) and has quickly become one of the most widely used products in assessing

  15. An approach to integrate spatial and climatological data as support to drought monitoring and agricultural management problems in South Sudan

    NASA Astrophysics Data System (ADS)

    Bonetto, Sabrina; Facello, Anna; Camaro, Walther; Isotta Cristofori, Elena; Demarchi, Alessandro

    2016-04-01

    Drought is a natural hazard characterized by an abnormally dry event in the hydrological cycle caused by insufficient precipitation over an extended period of time, which affects more people than any other natural disaster and results in social, economic and environmental costs. In Africa, the economic system is based primarily on natural resources for example farming. For this reason, climate variability and events such as drought are phenomena that can represent significant disturbances and threats in the agricultural systems. In particular, this study concerns the monitoring of environmental changes in the south sector of South Sudan. The climate and environment in the South Sudan have shown localised changes during the course of this century and recurrent wars and droughts in the last years determined a large food-crisis. Actually, the security situation is stabilised with sporadic fighting concentrated in Jonglei, Unity and Upper Nile States. With the stabilisation of the conflict, many refugees have returned to their regions, trying to recover the economic structure based mainly on agriculture. For this reason, it is important to monitoring and analysis the vegetation and drought trend over the last years to support agricultural development and food security, in particular in post-conflict areas. This study focuses on the analysis of the relationship between the temporal variations of state of vegetation and the precipitation patterns. A historical analysis of the vegetation behaviour (NDVI) and the drought during the year is developed. In addition, with the aim to identify the wet and dry seasons, an analysis of precipitation is performed. Based on the vegetation and precipitation trends obtained, it is possible to characterize the best areas to start an agricultural system, giving priority to certain areas in order to plan the land use for agricultural purposes and programming crop (which and where). Consequently, with the aim to identify possible

  16. A global drought monitoring system: insights of an approach integrating remote sensing data and vulnerability to food insecurity

    NASA Astrophysics Data System (ADS)

    Angeluccetti, Irene; Perez, Francesca; Cámaro, Walther; Demarchi, Alessandro

    2015-04-01

    Early Warning Systems (EWS) for drought are currently underdeveloped compared to those related to other natural hazards. Both forecasting and monitoring of drought events are still posing challenges to the scientific community. In fact, the multifaceted nature of drought (i.e. hydrological, meteorological, and agricultural) is source of coexistence for different ways to measure this phenomenon and its effects. Similarly, drought impacts are various and complex thus difficult to be univocally measured. In the present study an approach for monitoring drought in near-real time and for estimating its impacts is presented. The EWS developed runs on a global extent and is mainly based on the early detection and monitoring of vegetation stress. On the one hand the monitoring of vegetation phenological parameters, whose extraction is based on the analysis of the MODIS-derived NDVI function, allows the fortnightly assessment of the vegetation productivity which could be expected at the end of the growing season. On the other hand, the Standardized Precipitation Index (SPI), calculated adapting TRMM-derived precipitation data in a selected distribution is used, before the growing season start, in order to early detect meteorological conditions which could give rise to vegetation stress events. During the growing season the SPI is used as check information for vegetation conditions. The relationships between rainfall and vegetation dynamics have been statistically analyzed considering different types of vegetation, in order to identify the most suitable rainfall cumulating interval to be used for the proposed monitoring procedures in different areas. A simplified vulnerability model, coupled with the above-mentioned hazard data, returns food security conditions, i.e. the estimated impacts over an investigated area. The model includes a set of agricultural indicators that accounts for the diversity of cultivated crops, the percentage of irrigated area and the suitability of

  17. Public Participation, Education, and Engagement in Drought Planning

    NASA Astrophysics Data System (ADS)

    Bathke, D. J.; Wall, N.; Haigh, T.; Smith, K. H.; Bernadt, T.

    2014-12-01

    Drought is a complex problem that typically goes beyond the capacity, resources, and jurisdiction of any single person, program, organization, political boundary, or sector. Thus, by nature, monitoring, planning for, and reducing drought risk must be a collaborative process. The National Drought Mitigation Center, in partnership with the National Integrated Drought Information System (NIDIS) Program Office and others, provides active engagement and education drought professionals, stakeholders, and the general public about managing drought-related risks through resilience planning, monitoring, and education. Using case studies, we discuss recruitment processes, network building, participation techniques, and educational methods as they pertain to a variety of unique audiences with distinct objectives. Examples include collaborative decision-making at a World Meteorological Organization conference; planning, and peer-learning among drought professionals in a community of practice; drought condition monitoring through citizen science networks; research and education dissemination with stakeholder groups; and informal learning activities for all ages. Finally, we conclude with evaluation methods, indicators of success, and lessons learned for increasing the effectiveness of our programs in increasing drought resilience.

  18. SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

    NASA Technical Reports Server (NTRS)

    Irwin, D. E.; Sever, T. L.; Graves, S.; Hardin, Dan

    2004-01-01

    In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica, composed of the seven Central American countries and the five southernmost states of Mexico, make up only a small fraction of the world's land surface. However, the region is home to seven to eight percent of the planet's biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica's biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far in

  19. SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

    NASA Technical Reports Server (NTRS)

    Irwin, Daniel E.; Sever, Tom; Graves, Sara; Hardin, Danny

    2005-01-01

    In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica - composed of the seven Central American countries and the five southernmost states of Mexico - make up only a small fraction of the world s land surface. However, the region is home to seven to eight percent of the planet s biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica s biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far

  20. SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

    NASA Technical Reports Server (NTRS)

    Irwin, D. E.; Sever, T. L.; Graves, S.; Hardin, Dan

    2004-01-01

    In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica, composed of the seven Central American countries and the five southernmost states of Mexico, make up only a small fraction of the world's land surface. However, the region is home to seven to eight percent of the planet's biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica's biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far in

  1. SIAM-SERVIR: An Environmental Monitoring and Decision Support System for Mesoamerica

    NASA Technical Reports Server (NTRS)

    Irwin, Daniel E.; Sever, Tom; Graves, Sara; Hardin, Danny

    2005-01-01

    In 2002/2003 NASA, the World Bank and the United States Agency for International Development (USAID) joined with the Central American Commission for Environment and Development (CCAD) to develop an advanced decision support system for Mesoamerica (named SERVIR) as part of the Mesoamerican Environmental Information System (SIAM). Mesoamerica - composed of the seven Central American countries and the five southernmost states of Mexico - make up only a small fraction of the world s land surface. However, the region is home to seven to eight percent of the planet s biodiversity (14 biosphere reserves, 31 Ramsar sites, 8 world heritage sites, 589 protected areas) and 45 million people including more than 50 different ethnic groups. Today Mesoamerica s biological and cultural diversity is severely threatened by extensive deforestation, illegal logging, water pollution, and uncontrolled slash and burn agriculture. Additionally, Mesoamerica's distinct geology and geography result in disproportionate vulnerability to natural disasters such as earthquakes, hurricanes, drought, and volcanic eruptions. NASA Marshall Space Flight Center, together with the University of Alabama in Huntsville (UAH) and the SIAM-SERVIR partners are developing state-of-the-art decision support tools for environmental monitoring as well as disaster prevention and mitigation in Mesoamerica. These partners are contributing expertise in space-based observation with information management technologies and intimate knowledge of local ecosystems to create a system that is being used by scientists, educators, and policy makers to monitor and forecast ecological changes, respond to natural disasters and better understand both natural and human induced effects. In its first year of development and operation, the SIAM-SERVIR project has already yielded valuable information on Central American fires, weather conditions, and the first ever real-time data on red tides. This paper presents the progress thus far

  2. Monitoring in the nearshore: A process for making reasoned decisions

    USGS Publications Warehouse

    Bodkin, James L.; Dean, T.A.

    2003-01-01

    Over the past several years, a conceptual framework for the GEM nearshore monitoring program has been developed through a series of workshops. However, details of the proposed monitoring program, e.g. what to sample, where to sample, when to sample and at how many sites, have yet to be determined. In FY 03 we were funded under Project 03687 to outline a process whereby specific alternatives to monitoring are developed and presented to the EVOS Trustee Council for consideration. As part of this process, two key elements are required before reasoned decisions can be made. These are: 1) a comprehensive historical perspective of locations and types of past studies conducted in the nearshore marine communities within Gulf of Alaska, and 2) estimates of costs for each element of a proposed monitoring program. We have developed a GIS database that details available information from past studies of selected nearshore habitats and species in the Gulf of Alaska and provide a visual means of selecting sites based (in part) on the locations for which historical data of interest are available. We also provide cost estimates for specific monitoring plan alternatives and outline several alternative plans that can be accomplished within reasonable budgetary constraints. The products that we will provide are: 1) A GIS database and maps showing the location and types of information available from the nearshore in the Gulf of Alaska; 2) A list of several specific monitoring alternatives that can be conducted within reasonable budgetary constraints; and 3) Cost estimates for proposed tasks to be conducted as part of the nearshore program. Because data compilation and management will not be completed until late in FY03 we are requesting support for close-out of this project in FY 04.

  3. The role of the midcingulate cortex in monitoring others' decisions

    PubMed Central

    Apps, Matthew A. J.; Lockwood, Patricia L.; Balsters, Joshua H.

    2013-01-01

    A plethora of research has implicated the cingulate cortex in the processing of social information (i.e., processing elicited by, about, and directed toward others) and reward-related information that guides decision-making. However, it is often overlooked that there is variability in the cytoarchitectonic properties and anatomical connections across the cingulate cortex, which is indicative of functional variability. Here we review evidence from lesion, single-unit recording and functional imaging studies. Taken together, these support the claim that the processing of information that has the greatest influence on social behavior can be localized to the gyral surface of the midcingulate cortex (MCCg). We propose that the MCCg is engaged when predicting and monitoring the outcomes of decisions during social interactions. In particular, the MCCg processes statistical information that tracks the extent to which the outcomes of decisions meet goals when interacting with others. We provide a novel framework for the computational mechanisms that underpin such social information processing in the MCCg. This framework provides testable hypotheses for the social deficits displayed in autism spectrum disorders and psychopathy. PMID:24391534

  4. The role of the midcingulate cortex in monitoring others' decisions.

    PubMed

    Apps, Matthew A J; Lockwood, Patricia L; Balsters, Joshua H

    2013-01-01

    A plethora of research has implicated the cingulate cortex in the processing of social information (i.e., processing elicited by, about, and directed toward others) and reward-related information that guides decision-making. However, it is often overlooked that there is variability in the cytoarchitectonic properties and anatomical connections across the cingulate cortex, which is indicative of functional variability. Here we review evidence from lesion, single-unit recording and functional imaging studies. Taken together, these support the claim that the processing of information that has the greatest influence on social behavior can be localized to the gyral surface of the midcingulate cortex (MCCg). We propose that the MCCg is engaged when predicting and monitoring the outcomes of decisions during social interactions. In particular, the MCCg processes statistical information that tracks the extent to which the outcomes of decisions meet goals when interacting with others. We provide a novel framework for the computational mechanisms that underpin such social information processing in the MCCg. This framework provides testable hypotheses for the social deficits displayed in autism spectrum disorders and psychopathy.

  5. Water monitoring to support the State of Illinois Governor’s Drought Response Task Force – August 24, 2012

    USGS Publications Warehouse

    ,

    2012-01-01

    The U.S. Geological Survey (USGS) collects streamflow, groundwater levels, and water-quality data for the State of Illinois and the Nation. Much of these data are collected every 15 minutes (real-time) as a part of the national network, so that water-resource managers can make decisions in a timely and reliable manner. Coupled with modeling and other water-resource investigations, the USGS provides data to the State during droughts and other hydrologic events. The types of data, capabilities, and presentation of these materials are described in this document as USGS Real-Time Data, Supplementary Data Collection and Analysis, and National Resources Available.

  6. A Collaborative Decision Environment to Support UAV Wildfire Monitoring Missions

    NASA Astrophysics Data System (ADS)

    Frost, C. R.; Enomoto, F. Y.; D'Ortenzio, M. V.; Nguyen, Q. B.

    2006-12-01

    NASA developed the Collaborative Decision Environment (CDE), the ground-based component of its Intelligent Mission Management (IMM) technology for science missions employing long endurance unmanned aerial vehicles (UAVs). The CDE was used to support science mission planning and decision-making for a NASA- and U.S. Forest Service-sponsored mission to monitor wildfires in the western United States using a multi- spectral imager flown onboard the General Atomics Altair UAV in summer of 2006. The CDE is a ground-based system that provides the mission/science team with situational awareness, collaboration, and decision tools. The CDE is used for pre-flight planning, mission monitoring, and visualization of acquired data. It integrates external data products used for planning and executing a mission, such as weather, large wildfire locations, satellite-derived fire detection data, temporarily restricted airspace, and satellite imagery. While a prototype CDE was developed as a Java-based client/server application in 2004-2005, the team investigated the use of Google Earth to take advantage of its 3-D visualization capabilities, friendly user interface, and enhanced graphics performance. External data is acquired via the Internet by leveraging established and emerging Open Geospatial Consortium (OGC) standards and is re-formatted into the Keyhole Markup Language (KML) specification used by Google Earth. Aircraft flight position and sensor data products are relayed from the instrument ground station to CDE servers where they are made available to users. An instant messaging chat server is used to facilitate real-time communication between remote users. This paper will present an overview of the CDE system architecture, and discuss how science user input was crucial to shaping and developing the system. Examples from the UAV mission will be used to illustrate the presentation. Plans for future development work to improve mission operations, such as integration with

  7. Evaluation of strategies for nature-based solutions to drought: a decision support model at the national scale

    NASA Astrophysics Data System (ADS)

    Simpson, Mike; Ives, Matthew; Hall, Jim

    2016-04-01

    There is an increasing body of evidence in support of the use of nature based solutions as a strategy to mitigate drought. Restored or constructed wetlands, grasslands and in some cases forests have been used with success in numerous case studies. Such solutions remain underused in the UK, where they are not considered as part of long-term plans for supply by water companies. An important step is the translation of knowledge on the benefits of nature based solutions at the upland/catchment scale into a model of the impact of these solutions on national water resource planning in terms of financial costs, carbon benefits and robustness to drought. Our project, 'A National Scale Model of Green Infrastructure for Water Resources', addresses this issue through development of a model that can show the costs and benefits associated with a broad roll-out of nature based solutions for water supply. We have developed generalised models of both the hydrological effects of various classes and implementations of nature-based approaches and their economic impacts in terms of construction costs, running costs, time to maturity, land use and carbon benefits. Our next step will be to compare this work with our recent evaluation of conventional water infrastructure, allowing a case to be made in financial terms and in terms of security of water supply. By demonstrating the benefits of nature based solutions under multiple possible climate and population scenarios we aim to demonstrate the potential value of using nature based solutions as a component of future long-term water resource plans. Strategies for decision making regarding the selection of nature based and conventional approaches, developed through discussion with government and industry, will be applied to the final model. Our focus is on keeping our work relevant to the requirements of decision-makers involved in conventional water planning. We propose to present the outcomes of our model for the evaluation of nature

  8. Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin, China.

    PubMed

    Zhao, Junfang; Xu, Jingwen; Xie, Xingmei; Lu, Houquan

    2016-05-15

    Drought assessment is important for developing measures to reduce agricultural vulnerability and thereby secure the livelihoods of those who depend on agriculture. This study uses four global ensemble weather prediction systems: the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centres for Environmental Prediction (NCEP) in the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive from 2006 to 2010. Based on results from the XXT (the first X denotes Xinanjiang, the second X denotes hybrid, and the T denotes TOPMODEL) distributed hydrological model, as well as soil moisture observations and digital elevation model (DEM) data, synthesized drought grades were established in the Huaihe River Basin of China. To filter out the impact of short-term fluctuations on observed soil moisture, a 30-day moving average was calculated. Use of the moving average significantly improves the correlation between observed soil moisture and simulated soil water deficit depth. Finally, a linear regression model describing the relationship between observed soil moisture and simulated soil water deficit depth was constructed. The deterministic regression coefficient was 0.5872, the correlation coefficient was 0.77, and the regression coefficient was -154.23. The trends in drought grades calculated using soil moisture and soil water deficit depth were found to be the same, and the grades agreed to within one level. Our findings highlight the importance of synthesizing drought grading when assessing drought using different soil moisture indicators in order to obtain a more comprehensive forecast of drought conditions.

  9. An Updated Decision Support Interface: A Tool for Remote Monitoring of Crop Growing Conditions

    NASA Astrophysics Data System (ADS)

    Husak, G. J.; Budde, M. E.; Rowland, J.; Verdin, J. P.; Funk, C. C.; Landsfeld, M. F.

    2014-12-01

    Remote sensing of agroclimatological variables to monitor food production conditions is a critical component of the Famine Early Warning Systems Network portfolio of tools for assessing food security in the developing world. The Decision Support Interface (DSI) seeks to integrate a number of remotely sensed and modeled variables to create a single, simplified portal for analysis of crop growing conditions. The DSI has been reformulated to incorporate more variables and give the user more freedom in exploring the available data. This refinement seeks to transition the DSI from a "first glance" agroclimatic indicator to one better suited for the differentiation of drought events. The DSI performs analysis of variables over primary agricultural zones at the first sub-national administrative level. It uses the spatially averaged rainfall, normalized difference vegetation index (NDVI), water requirement satisfaction index (WRSI), and actual evapotranspiration (ETa) to identify potential hazards to food security. Presenting this information in a web-based client gives food security analysts and decision makers a lightweight portal for information on crop growing conditions in the region. The crop zones used for the aggregation contain timing information which is critical to the DSI presentation. Rainfall and ETa are accumulated from different points in the crop phenology to identify season-long deficits in rainfall or transpiration that adversely affect the crop-growing conditions. Furthermore, the NDVI and WRSI serve as their own seasonal accumulated measures of growing conditions by capturing vegetation vigor or actual evapotranspiration deficits. The DSI is currently active for major growing regions of sub-Saharan Africa, with intention of expanding to other areas over the coming years.

  10. Identifiying and evaluating a suitable index for agricultural drought monitoring in the Texas High Plains

    USDA-ARS?s Scientific Manuscript database

    Drought is a highly destructive natural phenomenon that affects portions of the United States almost every year. Severe water deficiencies can become catastrophic for agriculture and crop yields, especially in the Texas High Plains where generally inadequate rainfall is augmented by irrigation for c...

  11. A satellite-based drought index describing anomalies in evapotranspiration for global crop monitoring

    USDA-ARS?s Scientific Manuscript database

    The utility and reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, precipitation-based indices only reflect one component of the surface hydrologic cycle, ...

  12. Multi-index time series monitoring of drought and fire effects on desert grasslands

    USGS Publications Warehouse

    Villarreal, Miguel; Norman, Laura M.; Buckley, Steven; Wallace, Cynthia S.A.; Coe, Michelle A.

    2016-01-01

    The Western United States is expected to undergo both extended periods of drought and longer wildfire seasons under forecasted global climate change and it is important to understand how these disturbances will interact and affect recovery and composition of plant communities in the future. In this research paper we describe the temporal response of grassland communities to drought and fire in southern Arizona, where land managers are using repeated, prescribed fire as a habitat restoration tool. Using a 25-year atlas of fire locations, we paired sites with multiple fires to unburned control areas and compare satellite and field-based estimates of vegetation cover over time. Two hundred and fifty Landsat TM images, dating from 1985–2011, were used to derive estimates of Total Vegetation Fractional Cover (TVFC) of live and senescent grass using the Soil-Adjusted Total Vegetation Index (SATVI) and post-fire vegetation greenness using the Normalized Difference Vegetation Index (NDVI). We also implemented a Greenness to Cover Index that is the difference of time-standardized SATVI-TVFC and NDVI values at a given time and location to identify post-fire shifts in native, non-native, and annual plant cover. The results highlight anomalous greening and browning during drought periods related to amounts of annual and non-native plant cover present. Results suggest that aggressive application of prescribed fire may encourage spread of non-native perennial grasses and annual plants, particularly during droughts.

  13. Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

    PubMed

    Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

    2017-09-26

    Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R (2)), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R (2), MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

  14. Detecting and monitoring large-scale drought effects on forests: toward an integrated approach

    Treesearch

    Steve Norman; Frank H. Koch; William W. Hargrove

    2016-01-01

    Although drought is recognized as an important and overarching driver of ecosystem change, its occurrence and effects have been difficult to describe over large geographic areas (Hogg and others 2008, Panu and Sharma 2002).

  15. Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

    USDA-ARS?s Scientific Manuscript database

    Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this ...

  16. Hyperspectral remote sensing for monitoring species-specific drought impacts in southern California

    NASA Astrophysics Data System (ADS)

    Coates, Austin Reece

    A drought persisting since the winter of 2011-2012 has resulted in severe impacts on shrublands and forests in southern California, USA. Effects of drought on vegetation include leaf wilting, leaf abscission, and potential plant mortality. These impacts vary across plant species, depending on differences in species' adaptations to drought, rooting depth, and edaphic factors. During 2013 and 2014, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data were acquired seasonally over the Santa Ynez Mountains and Santa Ynez Valley north of Santa Barbara, California. To determine the impacts of drought on individual plant species, spectral mixture analysis was used to model a relative green vegetation fraction (RGVF) for each image date in 2013 and 2014. A July 2011 AVIRIS image acquired during the last nondrought year was used to determine a reference green vegetation (GV) endmember for each pixel. For each image date in 2013 and 2014, a three-endmember model using the 2011 pixel spectrum as GV, a lab nonphotosynthetic vegetation (NPV) spectrum, and a photometric shade spectrum was applied. The resulting RGVF provided a change in green vegetation cover relative to 2011. Reference polygons collected for 14 plant species and land cover classes were used to extract the RGVF values from each date. The deeply rooted tree species and tree species found in mesic areas appeared to be the least affected by the drought, whereas the evergreen chaparral showed the most extreme signs of distress. Coastal sage scrub had large seasonal variability; however, each year, it returned to an RGVF value only slightly below the previous year. By binning all the RGVF values together, a general decreasing trend was observed from the spring of 2013 to the fall of 2014. This study intends to lay the groundwork for future research in the area of multitemporal, hyperspectral remote sensing. With proposed plans for a hyperspectral sensor in space (HyspIRI), this type of research will prove to

  17. Satellite-based drought monitoring in Kenya in an operational setting

    NASA Astrophysics Data System (ADS)

    Klisch, A.; Atzberger, C.; Luminari, L.

    2015-04-01

    The University of Natural Resources and Life Sciences (BOKU) in Vienna (Austria) in cooperation with the National Drought Management Authority (NDMA) in Nairobi (Kenya) has setup an operational processing chain for mapping drought occurrence and strength for the territory of Kenya using the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m ground resolution from 2000 onwards. The processing chain employs a modified Whittaker smoother providing consistent NDVI "Mondayimages" in near real-time (NRT) at a 7-daily updating interval. The approach constrains temporally extrapolated NDVI values based on reasonable temporal NDVI paths. Contrary to other competing approaches, the processing chain provides a modelled uncertainty range for each pixel and time step. The uncertainties are calculated by a hindcast analysis of the NRT products against an "optimum" filtering. To detect droughts, the vegetation condition index (VCI) is calculated at pixel level and is spatially aggregated to administrative units. Starting from weekly temporal resolution, the indicator is also aggregated for 1- and 3-monthly intervals considering available uncertainty information. Analysts at NDMA use the spatially/temporally aggregated VCI and basic image products for their monthly bulletins. Based on the provided bio-physical indicators as well as a number of socio-economic indicators, contingency funds are released by NDMA to sustain counties in drought conditions. The paper shows the successful application of the products within NDMA by providing a retrospective analysis applied to droughts in 2006, 2009 and 2011. Some comparisons with alternative products (e.g. FEWS NET, the Famine Early Warning Systems Network) highlight main differences.

  18. Futuragua: Fostering Cross-Scale Knowledge to Inform Social-Environmental Decision Processes for Building Drought Resilience in Highly Seasonal Environments

    NASA Astrophysics Data System (ADS)

    McDaniels, T.; Steyn, D. G.; Johnson, M. S.; Small, M.; Leclerc, G.; Vignola, R.; Chan, K.; Grossmann, I.; Wong-Parodi, G.

    2014-12-01

    Improving resilience to drought in complex social-environmental systems (SES) is extraordinarily important, particularly for rural tropical locations where small changes in climate regimes can have dramatic SES impacts. Efforts to build drought resilience must necessarily be planned and implemented within SES governance systems that involve linkages in water and land use administration from local to national levels. These efforts require knowledge and understanding that links climate and weather forecasts to regional and local hydrology, to social-economic and environmental systems, and to governance processes. In order to provide structure for such complex choices and investments, we argue that a focus on structured decision processes that involve linkages among science, technological perspectives, and public values conducted with agencies and stakeholders will provide a crucial framework for comparing and building insight for pursuing alternative courses of action to build drought resilience. This paper focuses on a regional case study in the seasonally-dry northwest region of Costa Rica, in watersheds rated as most threatened in the country in terms of drought. We present the overall framework guiding the transdisciplinary efforts to link scientific and technical understanding to public values, in order to foster civil society actions that lead to improved drought resilience. Initial efforts to characterize hydrological and climate regimes will be reported along with our approach to linking natural science findings, social inventories in terms of perspectives on SES, and the psychology and patterns of reliance on forecast information that provide the basis for characterizing public understanding. The overall linkage of technical and value information is focused on creating and comparing alternative actions that can potentially build resilience in short and long time frames by building decision making processes involving stakeholders, agencies and interested

  19. Assessing the Utility of 3-km Land Information System Soil Moisture Data for Drought Monitoring and Hydrologic Applications

    NASA Technical Reports Server (NTRS)

    White, Kristopher D.; Case, Jonathan L.

    2014-01-01

    The NASA Short term Prediction Research and Transition (SPoRT) Center in Huntsville, AL has been running a real-time configuration of the Noah land surface model within the NASA Land Information System (LIS) since June 2010. The SPoRT LIS version is run as a stand-alone land surface model over a Southeast Continental U.S. domain with 3-km grid spacing. The LIS contains output variables including soil moisture and temperature at various depths, skin temperature, surface heat fluxes, storm surface runoff, and green vegetation fraction (GVF). The GVF represents another real-time SPoRT product, which is derived from the Moderate Resolution Imaging Spectroradiometer instrument aboard NASA's Aqua and Terra satellites. These data have demonstrated operational utility for drought monitoring and hydrologic applications at the National Weather Service (NWS) office in Huntsville, AL since early 2011. The most relevant data for these applications have proven to be the moisture availability (%) in the 0-10 cm and 0-200 cm layers, and the volumetric soil moisture (%) in the 0-10 cm layer. In an effort to better understand their applicability among locations with different terrain, soil and vegetation types, SPoRT is conducting the first formal assessment of these data at NWS offices in Houston, TX, Huntsville, AL and Raleigh, NC during summer 2014. The goal of this assessment is to evaluate the LIS output in the context of assessing flood risk and determining drought designations for the U.S. Drought Monitor. Forecasters will provide formal feedback via a survey question web portal, in addition to the NASA SPoRT blog. In this presentation, the SPoRT LIS and its applications at NWS offices will be presented, along with information about the summer assessment, including training module development and preliminary results.

  20. Global Drought Services: Collaborations Toward an Information System for Early Warning

    NASA Astrophysics Data System (ADS)

    Hayes, M. J.; Pulwarty, R. S.; Svoboda, M.

    2014-12-01

    Drought is a hazard that lends itself well to diligent, sustained monitoring and early warning. However, unlike most hazards, the fact that droughts typically evolve slowly, can last for months or years and cover vast areas spanning multiple political boundaries/jurisdictions and economic sectors can make it a daunting task to monitor, develop plans for, and identify appropriate, proactive mitigation strategies. The National Drought Mitigation Center (NDMC) and National Integrated Drought Information System (NIDIS) have been working together to reduce societal vulnerability to drought by helping decision makers at all levels to: 1) implement drought early warning/forecasting and decision support systems; 2) support and advocate for better collection of, and understanding of drought impacts; and 3) increase long-term resilience to drought through proactive planning. The NDMC and NIDIS risk management approach has been the basis from which many partners around the world are developing a collaboration and coordination nexus with an ultimate goal of building comprehensive global drought early warning information systems (GDEWIS). The core emphasis of this model is on developing and applying useful and usable information that can be integrated and transferred freely to other regions around the globe. The High-Level Ministerial Declaration on Drought, the Integrated Drought Management Programme (IDMP) co-led by the WMO and the Global Water Partnership (GWP), and the Global Framework for Climate Services are drawing extensively from the integrated NDMC-NIDIS risk management framework. This presentation will describe, in detail, the various drought resources, tools, services, and collaborations already being provided and undertaken at the national and regional scales by the NDMC, NIDIS, and their partners. The presentation will be forward-looking, identifying improvements in existing and proposed mechanisms to help strengthen national and international drought early

  1. A prototype platform for water resources monitoring and early recognition of critical droughts in Switzerland

    NASA Astrophysics Data System (ADS)

    Zappa, M.; Bernhard, L.; Spirig, C.; Pfaundler, M.; Stahl, K.; Kruse, S.; Seidl, I.; Stähli, M.

    2014-09-01

    In recent years Switzerland has experienced some unprecedented drought situations. At a political level solutions have been requested for early recognition of hydrological droughts. A prototype information platform has been developed to guide water resources management during situations where water resources drop below critical levels. The development was steered by stakeholders from national administrations and different economic sectors. Since June 2013 the platform has presented daily updated real-time information on several drought indicators including precipitation, streamflow, lake levels, groundwater levels, soil moisture deficit, snow resources, dryness in forests and stream temperatures. For three basins, ensemble forecasts of runoff, soil moisture, snowpack and groundwater storage have been provided. Furthermore, a nationwide operational hydrological simulation at 600 × 600 m resolution gives indications on local water resources deficits. Information for each variable has been used to create automatic "awareness maps" for nine large regions. Three levels of information with increasing detail and complexity can be accessed by over 180 registered users. The operators of the platform give interpreted comments on the content of the platform each week-day. The test phase of the platform will last until the end of 2014.

  2. Automated Monitoring of Carbon Fluxes in a Northern Rocky Mountain Forest Indicates Above-Average Net Primary Productivity During the 2015 Western U.S. Drought

    NASA Astrophysics Data System (ADS)

    Stenzel, J.; Hudiburg, T. W.

    2016-12-01

    As global temperatures rise in the 21st century, "hotter" droughts will become more intense and persistent, particularly in areas which already experience seasonal drought. Because forests represent a large and persistent terrestrial carbon sink which has previously offset a significant proportion of anthropogenic carbon emissions, forest carbon cycle responses to drought have become a prominent research concern. However, robust mechanistic modeling of carbon balance responses to projected drought effects requires improved observation-driven representations of carbon cycle processes; many such component processes are rarely monitored in complex terrain, are modeled or unrepresented quantities at eddy covariance sites, or are monitored at course temporal scales that are not conducive to elucidating process responses at process time scales. In the present study, we demonstrate the use of newly available and affordable automated dendrometers for the estimation of intra-seasonal Net Primary Productivity (NPP) in a Northern Rocky Mountain conifer forest which is impacted by seasonal drought. Results from our pilot study suggest that NPP was restricted by mid-summer moisture deficit under the extraordinary 2015 Western U.S. drought, with greater than 90% off stand growth occurring prior to August. Examination of growth on an inter-annual scale, however, suggests that the study site experienced above-average NPP during this exceptionally hot year. Taken together, these findings indicate that intensifying mid-summer drought in regional forests has affected the timing but has not diminished the magnitude of this carbon flux. By employing automated instrumentation for the intra-annual assessment of NPP, we reveal that annual NPP in regional forests is largely determined before mid-summer and is therefore surprisingly resilient to intensities of seasonal drought that exceed normal conditions of the 20th century.

  3. In-season Drought Monitoring: Testing Instrumentation and Developing Methods of Measurement Analysis

    NASA Astrophysics Data System (ADS)

    Raper, Tyson B.

    Soil moisture sensor use in crop production systems has the potential to give inference on plant water status for the purpose of irrigation scheduling and site-drought characterization. These processed measurements could serve as the framework on which to compile trial results across locations, thereby more accurately defining varietal yield response to drought. Still, the ability to characterize drought within a given field or initiate irrigations from these data hinge upon the ability of the instrument to characterize soil moisture at the sampled point and extrapolate that information across the landscape and time. Therefore, the objectives of this research were to: (1) test the response of the Watermark 200SS (Irrometer Company, Inc., Riverside, CA) and Decagon 10HS (Decagon Devices, Inc., Pullman, WA) to changes in water content of three dissimilar soils representing common soils in row-crop production under variable environmental conditions; (2) develop a soil moisture-based index to quantify drought stress in dryland cotton cultivar trials; and (3) determine if a limited number of soil moisture sensors deployed into a dryland cultivar trial could accurately characterize the VWC at a given point within the field and if this measurement could be extrapolated out to the field scale from the very small sphere of influence characterizing the utilized soil moisture sensors. During the 2012 and 2013 growing seasons soil moisture sensors were deployed into over 14 cotton cultivar trials across the U.S. Cotton Belt and into a water-input controlled container study. Tested sensors' inability to accurately predict container VWC emphasized the relatively small quantity of soil on which these sensors rely and the variability in soil moisture within a very limited volume. Results from the drought-index studies suggested both the Accumulated Soil Moisture Stress Index (ASMSI) and the relative reduction in evapotranspiration (1-(ETc adj/ETc)) appear to have potential in

  4. The European Drought Observatory (EDO) - A European Contribution to a Global Drought Information System (GDIS)

    NASA Astrophysics Data System (ADS)

    Vogt, J.; Sepulcre, G.; De Jager, A.; Magni, D.; Valentini, L.; Russo, S.; Micale, F.; Barbosa, P.

    2013-12-01

    Europe has repeatedly been affected by droughts, resulting in considerable ecological and economic damage and climate change studies indicate a trend towards increasing climate variability most likely resulting in more frequent drought occurrences also in Europe. Against this background, the European Commission's Joint Research Centre (JRC) is developing methods and tools for assessing, monitoring and forecasting droughts in Europe and develops a European Drought Observatory (EDO) to complement and integrate national activities with a European view. At the core of EDO is a portal, including a map viewer, a metadata catalogue, a media-monitor and analysis tools. Underlying data stem from ground and satellite observations as well as from distributed hydrological models and are stored in a relational database. Through the map viewer Europe-wide up-to-date information on the occurrence and severity of droughts is presented, complemented by more detailed information from regional, national and local observatories through OGC compliant web-mapping services. The continent-wide meteorological, soil moisture-related and vegetation-related indicators are then integrated into a combined indicator showing different alert levels targeted specifically to decision makers in water and land management. Finally, time series of historical maps as well as graphs of the temporal evolution of drought indices for individual grid cells in Europe can be retrieved and analysed. On-going work is focusing on developing reliable medium and long-range probabilistic as well as seasonal drought forecasts, the analysis of climate change impacts on drought occurrence, duration and severity and the assessment of current and future drought hazard and risk. In addition, remote sensing-based water-stress indicators from geostationary satellite data (e.g., MSG SEVIRI) are developed in order to complement the available information. The further development of EDO as part of a Global Drought Information

  5. Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems

    NASA Astrophysics Data System (ADS)

    Zein-Sabatto, Saleh; Mikhail, Maged; Bodruzzaman, Mohammad; DeSimio, Martin; Derriso, Mark; Behbahani, Alireza

    2012-06-01

    It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes

  6. Dual Assimilation of Microwave and Thermal-Infrared Satellite Observations of Soil Moisture into NLDAS for Improved Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Hain, C.; Crow, W. T.; Anderson, M. C.; Zhan, X.; Wardlow, B.; Svoboda, M. D.; Mecikalski, J. R.

    2011-12-01

    Our research group is currently developing an operational data assimilation (DA) system for the optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time green vegetation fraction (GVF) into the Noah land-surface model component of the National Land Data Assimilation System (NLDAS). NLDAS produces the hydrologic products (e.g. soil moisture, evapotranspiration, and runoff) used by NCEP for operational drought monitoring, but these products are sensitive to model input errors in soil texture (affecting infiltration rates) and prescribed precipitation rates. Periodic updates of SM state variables in LSMs achieved by assimilating diagnostic moisture information retrieved using satellite remote sensing have been shown to compensate for model errors and result in improved hydrologic output. The work proposed here will build on a project currently funded under the Climate Test Bed Program entitled "A GOES Thermal-Based Drought Early Warning Index for NIDIS", which is developing an operational TIR SM index (Evaporative Stress Index; ESI) based on maps of the ratio of actual to potential ET (fPET) generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm. The research team has demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can significantly reduce SM drifts in LSMs such as Noah. The two different SM retrievals have been shown to be quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25 to 60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root

  7. A Global Drought Observatory for Emergency Response

    NASA Astrophysics Data System (ADS)

    Vogt, Jürgen; de Jager, Alfred; Carrão, Hugo; Magni, Diego; Mazzeschi, Marco; Barbosa, Paulo

    2016-04-01

    Droughts are occurring on all continents and across all climates. While in developed countries they cause significant economic and environmental damages, in less developed countries they may cause major humanitarian catastrophes. The magnitude of the problem and the expected increase in drought frequency, extent and severity in many, often highly vulnerable regions of the world demand a change from the current reactive, crisis-management approach towards a more pro-active, risk management approach. Such approach needs adequate and timely information from global to local scales as well as adequate drought management plans. Drought information systems are important for continuous monitoring and forecasting of the situation in order to provide timely information on developing drought events and their potential impacts. Against this background, the Joint Research Centre (JRC) is developing a Global Drought Observatory (GDO) for the European Commission's humanitarian services, providing up-to-date information on droughts world-wide and their potential impacts. Drought monitoring is achieved by a combination of meteorological and biophysical indicators, while the societal vulnerability to droughts is assessed through the targeted analysis of a series of social, economic and infrastructural indicators. The combination of the information on the occurrence and severity of a drought, on the assets at risk and on the societal vulnerability in the drought affected areas results in a likelihood of impact, which is expressed by a Likelihood of Drought Impact (LDI) indicator. The location, extent and magnitude of the LDI is then further analyzed against the number of people and land use/land cover types affected in order to provide the decision bodies with information on the potential humanitarian and economic bearings in the affected countries or regions. All information is presented through web-mapping interfaces based on OGC standards and customized reports can be drawn by the

  8. Strengthening Agricultural Decisions in Countries at Risk of Food Insecurity: The GEOGLAM Crop Monitor for Early Warning

    NASA Astrophysics Data System (ADS)

    Becker-Reshef, I.; Barker, B.; McGaughey, K.; Humber, M. L.; Sanchez, A.; Justice, C. O.; Rembold, F.; Verdin, J. P.

    2016-12-01

    Timely, reliable information on crop conditions, and prospects at the subnational scale, is critical for making informed policy and agricultural decisions for ensuring food security, particularly for the most vulnerable countries. However, such information is often incomplete or lacking. As such, the Crop Monitor for Early Warning (CM for EW) was developed with the goal to reduce uncertainty and strengthen decision support by providing actionable information on a monthly basis to national, regional and global food security agencies through timely consensus assessments of crop conditions. This information is especially critical in recent years, given the extreme weather conditions impacting food supplies including the most recent El Nino event. This initiative brings together the main international food security monitoring agencies and organizations to develop monthly crop assessments based on satellite observations, meteorological information, field observations and ground reports, which reflect an international consensus. This activity grew out of the successful Crop Monitor for the G20 Agricultural Market Information System (AMIS), which provides operational monthly crop assessments of the main producing countries of the world. The CM for EW was launched in February 2016 and has already become a trusted source of information internationally and regionally. Its assessments have been featured in a large number of news articles, reports, and press releases, including a joint statement by the USAID's FEWS NET, UN World Food Program, European Commission Joint Research Center, and the UN Food and Agriculture Organziation, on the devastating impacts of the southern African drought due to El Nino. One of the main priorities for this activity going forward is to expand its partnership with regional and national monitoring agencies, and strengthen capacity for national crop condition assessments.

  9. Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region.

    PubMed

    Song, Youngkeun; Njoroge, John B; Morimoto, Yukihiro

    2013-05-01

    Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.

  10. Relative Spectral Mixture Analysis for monitoring natural hazards that impact vegetation cover: the importance of the nonphotosynthetic fraction in understanding landscape response to drought, fire, and hurricane damage

    NASA Astrophysics Data System (ADS)

    Okin, G. S.

    2007-12-01

    Remote sensing provides a unique ability to monitor natural hazards that impact vegetation hydrologically. Here, the use of a new multitemporal remote sensing technique that employs free, coarse multispectral remote sensing data is demonstrated in monitoring short- and long-term drought, fire occurrence and recovery, and damage to hurricane-related mangrove ecosystems and subsequent recovery of these systems. The new technique, relative spectral mixture analysis (RSMA), provides information about the nonphotosynthetic fraction (nonphotosynthetic vegetation plus litter) of ground cover in addition to the green vegetation fraction. In some cases, RSMA even provides an improved ability to monitor changes in the green fraction compared to traditional vegetation indices or standard remote sensing products. In arid and semiarid regions, the nonphotosynthetic fraction can vary on an annual basis significantly more than the green fraction and is thus perfectly suited for monitoring drought in these regions. Mortality of evergreen trees due to long-term drought also shows up strongly in the nonphotosynthetic fraction as green vegetation is replaced by dry needles and bare trunks. The response of the nonphotosynthetic fraction to fire is significantly different from that of drought because of the combustion of nonphotosynthetic material. Finally, damage to mangrove ecosystems from hurricane damage, and their subsequent recovery, is readily observable in both the green and nonphotosynthetic fractions as estimated by RSMA.

  11. Future opportunities and challenges in remote sensing of drought

    USGS Publications Warehouse

    Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt; Wardlow, Brian D.; Anderson, Martha C.; Verdin, James P.

    2012-01-01

    The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and

  12. Future Opportunities and Challenges in Remote Sensing of Drought

    NASA Technical Reports Server (NTRS)

    Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt

    2011-01-01

    The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and

  13. Developing a Climate Service: Using Hydroclimate Monitoring and Forecasting to Aid Decision Making in Africa and Latin America

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Sheffield, J.; Fisher, C. K.; Chaney, N.; Wanders, N.

    2015-12-01

    Hydrological and water scarcity predictions have the potential to provide vital information for a variety of needs including water resources management, agricultural and urban water supply, and flood mitigation. In particular, seasonal forecasts of drought risk can enable farmers to make adaptive choices on crop varieties, labor usage, and technology investments. Forecast skill is generally derived from teleconnections with ocean variability specifically sea surface temperature (SST) anomalies and, equally important persistence in the state of the land in terms of soil moisture, snowpack, or streamflow conditions. Short term precipitation forecasts are critical in flood prediction by extending flood prediction lead times beyond the basin travel time, and thus allows for extended warnings. The Global Framework for Climate Services (GFCS) is a UN-wide initiative in which WMO Members and inter- and non- governmental, regional, national and local stakeholders work in partnership to develop targeted climate services. Thus, GFCS offers the potential for hydroclimatologists to develop products (hydroclimatic forecasts) and information services (i.e. product dissemination) to users with the expectation that GFCS will increase the resilience of the society to weather and climate events and to reduce operational costs for economic sectors and regions dependent on water. This presentation will discuss the development of a nascent climate service system focused on hydroclimatic monitoring and forecasting, and initially developed by the authors for Africa and Latin America. Central to this system is the use of satellite remote sensing and hydroclimate forecasts (from days to seasons) in the development of weather and climate information useful for water management in sectors such as flood protection (precipitation and streamflow forecasting) and agriculture (drought and crop forecasting). The elements of this system will be discussed, including the challenges of monitoring and

  14. Evaluation of the Performance of Multiple Drought Indices for Tunisia

    NASA Astrophysics Data System (ADS)

    Geli, H. M. E.; Jedd, T.; Svoboda, M.; Wardlow, B.; Hayes, M. J.; Neale, C. M. U.; Hain, C.; Anderson, M. C.

    2016-12-01

    The recent and frequent drought events in the Middle East and Northern Africa (MENA) create an urgent need for scientists, stakeholders, and decision makers to improve the understanding of drought in order to mitigate its effects. It is well documented that drought is not caused by meteorological or hydrological conditions alone; social, economic, and political governance factors play a large part in whether the components in a water supply system are balanced. In the MENA region, for example, agricultural production can place a significant burden on water supply systems. Understanding the connection between drought and agricultural production is an important first step in developing a sound drought monitoring and mitigation system that links physical indicators with on-the-ground impacts. Drought affect crop yield, livestock health, and water resources availability, among others. A clear depiction of drought onset, duration and severity is essential to provide valuable information to adapt and mitigate drought impact. Therefore, it is important that to be able to connect and evaluate scientific drought data and informational products with societal impact data to more effectively initiate mitigation actions. This approach will further the development of drought maps that are tailored and responsive to immediate and specific societal needs for a region or country. Within the context of developing and evaluating drought impacts maps for the MENA region, this analysis investigates the use of different drought indices and indicators including the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) anomaly, land surface temperature (LST), and Evaporative Stress Index (ESI) for their ability to characterize historic drought events in Tunisia. Evaluation of a "drought map" product is conducted using data at the county level including crop yield, precipitation, in-country interviews with drought monitoring experts and agricultural

  15. Monitoring Drought Effects on Mediteranean Conifer Forests Using SPOT-Vegetation NDVI and NDWI Timeseries

    NASA Astrophysics Data System (ADS)

    Karamihalaki, Maria; Stagakis, Stavros; Sykioti, Olga; Kyparissis, Aris; Parcharidis, Issaak

    2016-08-01

    The aim of this study focuses in the investigation of vegetation's responses to precipitation variations and water stress conditions in three Pinus sp. (pine) forests in Greece and in the assessment of NDWI and NDVI in terms of drought and water stress detection capacity for this type of ecosystems. For the purpose of this study, 11-year time series of NDVI and NDWI indices, issued from SPOT - Vegetation data, were constructed and correlated with ground measured precipitation data for the same time period, for all three study sites. Results show a strong relationship between the two indices. Furthermore, NDWI shows a stronger correlation with precipitation than NDVI, indicating a better capacity for investigating the vegetation water status. Generally, high seasonal precipitation variations seem to have a strong effect on both NDVI and NDWI levels, while a smoother precipitation distribution results to a weaker relationship with the two indices.

  16. Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

    USGS Publications Warehouse

    Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  17. Monitoring drought impact on Mediterranean oak savanna vegetation using remote sensing

    NASA Astrophysics Data System (ADS)

    González-Dugo, Maria P.; Carpintero, Elisabet; Andreu, Ana

    2015-04-01

    A holm oak savanna, known as dehesa in Spain and montado in Portugal, is the largest agroforest ecosystem in Europe, covering about 3 million hectares in the Iberian Peninsula and Greece (Papanastasis et al., 2004). It is considered an example of sustainable land use, supporting a large number of species and diversity of habitats and for its importance in rural development and economy (Plieninger et al., 2001). It is a combination between an agricultural and a naturally vegetated ecosystem, consisting of widely-spaced oak trees (mostly Quercus Ilex and Quercus suber) combined with a sub-canopy composed by crops, annual grassland and/or shrubs. It has a Mediterranean climate with severe periodic droughts. In the last decades, this system is being exposed to multiple threats derived from socio-economic changes and intensive agricultural use, which have caused environmental degradation, including tree decline, changes in soil properties and hydrological processes, and an increase of soil erosion (Coelho et al., 2004). Soil water dynamics plays a central role in the current decline and reduction of forested areas that jeopardizes the preservation of the system. In this work, a series of remotely sensed images since 1990 to present was used to evaluate the effect of several drought events occurred in the study area (1995, 2009, 2010/2011) on the tree density and water status. Data from satellites Landsat and field measurements have been combined in a spectral mixture model to assess separately the evolution of tree, dry grass and bare soil ground coverage. Only summer images have been used to avoid the influence of the green herbaceous layer on the analysis. Thermal data from the same sensors and meteorological information are integrated in a two source surface energy balance model to compute the Evaporative Stress Index (ESI) and evaluate the vegetation water status. The results have provided insights about the severity of each event and the spatial distribution of

  18. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. drought monitor classifications

    USDA-ARS?s Scientific Manuscript database

    Comparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought – particularly during periods of rapid onset. This paper compares the Evaporative Stress Index (ESI) - a diagnostic fast-response indica...

  19. Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

    NASA Technical Reports Server (NTRS)

    Rochon, Gilbert L.

    1989-01-01

    Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and

  20. 33 Years of Near-Global Daily Precipitation from Multisatellite Observations and its Application to Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Ashouri, H.; Hsu, K.; Sorooshian, S.; Braithwaite, D.; Knapp, K. R.; Cecil, L. D.

    2013-12-01

    PERSIANN Climate Data Record (PERSIANN-CDR) is a new retrospective satellite-based precipitation data set that is constructed for long-term hydrological and climate studies. The PERSIANN-CDR is a near-global (60°S-60°N) long-term (1980-2012), multi-satellite, high-resolution precipitation product that provides rain rate estimates at 0.25° and daily spatiotemporal resolution. PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high resolution precipitation data set for studying the spatial and temporal variations and changes of precipitation patterns, particularly in a scale relevant to climate extremes at the global scale. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data from the International Satellite Cloud Climatology Project (ISCCP). PERSIANN-CDR is adjusted using the Global Precipitation Climatology Project (GPCP) monthly precipitation to maintain consistency of two data sets at 2.5° monthly scale throughout the entire reconstruction period. PERSIANN-CDR daily precipitation data demonstrates considerable consistency with both GPCP monthly and GPCP 1DD precipitation products. Verification studies over Hurricane Katrina show that PERSIANN-CDR has a good agreement with NCEP Stage IV radar data, noting that PERSIANN-CDR has better spatial coverage. In addition, the Probability Density Function (PDF) of PERSIANN-CDR over the contiguous United States was compared with the PDFs extracted from CPC gauge data and the TMPA precipitation product. The experiment also shows good agreement of the PDF of PERSIANN-CDR with the PDFs of TMPA and CPC gauge data. The application of PERSIANN-CDR in regional and global drought monitoring is investigated. Consisting of more than three decades of high-resolution precipitation data, PERSIANN-CDR makes us capable of long-term assessment of droughts at a higher resolution (0.25°) than previously possible. The results will be presented at the meeting.

  1. Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

    NASA Technical Reports Server (NTRS)

    Rochon, Gilbert L.

    1989-01-01

    Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and

  2. Design and optimization of a ground water monitoring system using GIS and multicriteria decision analysis

    SciTech Connect

    Dutta, D.; Gupta, A.D.; Ramnarong, V.

    1998-12-31

    A GIS-based methodology has been developed to design a ground water monitoring system and implemented for a selected area in Mae-Klong River Basin, Thailand. A multicriteria decision-making analysis has been performed to optimize the network system based on major criteria which govern the monitoring network design such as minimization of cost of construction, reduction of kriging standard deviations, etc. The methodology developed in this study is a new approach to designing monitoring networks which can be used for any site considering site-specific aspects. It makes it possible to choose the best monitoring network from various alternatives based on the prioritization of decision factors.

  3. Impact of drought on the CO2 atmospheric growth rate 2010-2012 from the NASA Carbon Monitoring System Flux (CMS-Flux) Project

    NASA Astrophysics Data System (ADS)

    Bowman, K. W.; Liu, J.; Parazoo, N.; Jiang, Z.; Bloom, A. A.; Lee, M.; Menemenlis, D.; Gierach, M.; Collatz, G. J.; Gurney, K. R.

    2015-12-01

    The La Nina between 2011-2012 led to significant droughts in the US and Northeastern Brazil while the historic drought in Amazon in 2010 was caused in part by the historic central Pacific El Nino. In order to investigate the role of drought on the atmospheric CO2 growth rate, we use satellite observations of CO2 and CO to infer spatially resolved carbon fluxes and attribute those fluxes to combustion sources correlated with drought conditions. Solar induced fluorescence in turn is used to estimate the impact of drought on productivity and its relationship to total flux. Preliminary results indicate that carbon losses in Mexico are comparable to the total fossil fuel production for that region. These in turn played an important role in the acceleration of the atmospheric growth rate from 2011-2012. These results were enabled using the NASA Carbon Monitoring System Project (CMS-Flux), which is based upon a 4D-variational assimilation system that incorporates observationally-constrained "bottom-up" estimates from the Fossil Fuel Data Assimilation System (FFDAS), the ECCO2-­Darwin physical and biogeochemical adjoint ocean state estimation system, and CASA-GFED3 land-surface biogeochemical model.

  4. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data

    USGS Publications Warehouse

    Gu, Y.; Hunt, E.; Wardlow, B.; Basara, J.B.; Brown, J.F.; Verdin, J.P.

    2008-01-01

    The evaluation of the relationship between satellite-derived vegetation indices (normalized difference vegetation index and normalized difference water index) and soil moisture improves our understanding of how these indices respond to soil moisture fluctuations. Soil moisture deficits are ultimately tied to drought stress on plants. The diverse terrain and climate of Oklahoma, the extensive soil moisture network of the Oklahoma Mesonet, and satellite-derived indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) provided an opportunity to study correlations between soil moisture and vegetation indices over the 2002-2006 growing seasons. Results showed that the correlation between both indices and the fractional water index (FWI) was highly dependent on land cover heterogeneity and soil type. Sites surrounded by relatively homogeneous vegetation cover with silt loam soils had the highest correlation between the FWI and both vegetation-related indices (r???0.73), while sites with heterogeneous vegetation cover and loam soils had the lowest correlation (r???0.22). Copyright 2008 by the American Geophysical Union.

  5. Irrigated Agriculture in Morocco: An Agent-Based Model of Adaptation and Decision Making Amid Increasingly Frequent Drought Events

    NASA Astrophysics Data System (ADS)

    Norton, M.

    2015-12-01

    In the past 100 years, Morocco has undertaken a heavy investment in developing water infrastructure that has led to a dramatic expansion of irrigated agriculture. Irrigated agriculture is the primary user of water in many arid countries, often accounting for 80-90% of total water usage. Irrigation is adopted by farmers not only because it leads to increased production, but also because it improves resilience to an uncertain climate. However, the Mediterranean region as a whole has also seen an increase in the frequency and severity of drought events. These droughts have had a dramatic impact on farmer livelihoods and have led to a number of coping strategies, including the adoption or disadoption of irrigation. In this study, we use a record of the annual extent of irrigated agriculture in Morocco to model the effect of drought on the extent of irrigated agriculture. Using an agent-based socioeconomic model, we seek to answer the following questions: 1) Do farmers expand irrigated agriculture in response to droughts? 2) Do drought events entail the removal of perennial crops like orchards? 3) Can we detect the retreat of irrigated agriculture in the more fragile watersheds of Morocco? Understanding the determinants of irrigated crop expansion and contractions will help us understand how agro-ecological systems transition from 20th century paradigms of expansion of water supply to a 21st century paradigm of water use efficiency. The answers will become important as countries learn how to manage water in new climate regimes characterized by less reliable and available precipitation.

  6. Development of a Decision Support System for Monitoring, Reporting, Forecasting Ecological Conditions of the Appalachian Trail

    Treesearch

    Y. Wang; R. Nemani; F. Dieffenbach; K. Stolte; G. Holcomb

    2010-01-01

    This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decision-making on management of the A.T. by providing a coherent framework for data integration,...

  7. The Theil-Sen Slope for High-Stakes Decisions from Progress Monitoring

    ERIC Educational Resources Information Center

    Vannest, Kimberly J.; Parker, Richard I.; Davis, John L.; Soares, Denise A.; Smith, Stacey L.

    2012-01-01

    More and more, schools are considering the use of progress monitoring data for high-stakes decisions such as special education eligibility, program changes to more restrictive environments, and major changes in educational goals. Those high-stakes types of data-based decisions will need methodological defensibility. Current practice for…

  8. Defensible Progress Monitoring Data for Medium- and High-Stakes Decisions

    ERIC Educational Resources Information Center

    Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.; Clemens, Nathan H.

    2012-01-01

    Within a response to intervention model, educators increasingly use progress monitoring (PM) to support medium- to high-stakes decisions for individual students. For PM to serve these more demanding decisions requires more careful consideration of measurement error. That error should be calculated within a fixed linear regression model rather than…

  9. The Theil-Sen Slope for High-Stakes Decisions from Progress Monitoring

    ERIC Educational Resources Information Center

    Vannest, Kimberly J.; Parker, Richard I.; Davis, John L.; Soares, Denise A.; Smith, Stacey L.

    2012-01-01

    More and more, schools are considering the use of progress monitoring data for high-stakes decisions such as special education eligibility, program changes to more restrictive environments, and major changes in educational goals. Those high-stakes types of data-based decisions will need methodological defensibility. Current practice for…

  10. Defensible Progress Monitoring Data for Medium- and High-Stakes Decisions

    ERIC Educational Resources Information Center

    Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.; Clemens, Nathan H.

    2012-01-01

    Within a response to intervention model, educators increasingly use progress monitoring (PM) to support medium- to high-stakes decisions for individual students. For PM to serve these more demanding decisions requires more careful consideration of measurement error. That error should be calculated within a fixed linear regression model rather than…

  11. Monitoring of water use, drought and yield impacts using imagery from multiple satellites

    USDA-ARS?s Scientific Manuscript database

    Agricultural monitoring systems require information with continuous spatial and temporal sampling, ideally collected at daily timesteps and at spatial scales from county level down to field scale. While remote sensing data significantly improve on the spatial sampling provided by ground-based obser...

  12. Developing Drought Outlook Forums in Support of a Regional Drought Early Warning Information System

    NASA Astrophysics Data System (ADS)

    Mcnutt, C. A.; Pulwarty, R. S.; Darby, L. S.; Verdin, J. P.; Webb, R. S.

    2011-12-01

    The National Integrated Drought Information System (NIDIS) Act of 2006 (P.L. 109-430) charged NIDIS with developing the leadership and partnerships necessary to implement an integrated national drought monitoring and forecasting system that creates a drought "early warning system". The drought early warning information system should be capable of providing accurate, timely and integrated information on drought conditions at the relevant spatial scale to facilitate proactive decisions aimed at minimizing the economic, social and ecosystem losses associated with drought. As part of this effort, NIDIS has held Regional Drought Outlook Forums in several regions of the U.S. The purpose of the Forums is to inform practices that reduce vulnerability to drought through an interactive and collaborative process that includes the users of the information. The Forums have focused on providing detailed assessments of present conditions and impacts, comparisons with past drought events, and seasonal predictions including discussion of the state and expected evolution of the El Niño Southern Oscillation phenomena. Regional Climate Outlook Forums (RCOFs) that include close interaction between information providers and users are not a new concept, however. RCOFs started in Africa in the 1990s in response to the 1997-98 El Niño and have since expanded to South America, Asia, the Pacific islands, and the Caribbean. As a result of feedback from the RCOFs a large body of research has gone into improving seasonal forecasts and the capacity of the users to apply the information in a way that improves their decision-making. Over time, it has become clear that more is involved than just improving the interaction between the climate forecasters and decision-makers. NIDIS is using the RCOF approach as one component in a larger effort to develop Regional Drought Early Warning Information Systems (RDEWS) around the U.S. Using what has been learned over the past decade in the RCOF process

  13. Droughts in Georgia

    USGS Publications Warehouse

    Barber, Nancy L.; Stamey, Timothy C.

    2000-01-01

    Droughts do not have the immediate effects of floods, but sustained droughts can cause economic stress throughout the State. The word 'drought' has various meanings, depending on a person's perspective. To a farmer, a drought is a period of moisture deficiency that affects the crops under cultivation - even two weeks without rainfall can stress many crops during certain periods of the growing cycle. To a meteorologist, a drought is a prolonged period when precipitation is less than normal. To a water manager, a drought is a deficiency in water supply that affects water availability and water quality. To a hydrologist, a drought is an extended period of decreased precipitation and streamflow. Droughts in Georgia have severely affected municipal and industrial water supplies, agriculture, stream water quality, recreation at major reservoirs, hydropower generation, navigation, and forest resources. In Georgia, droughts have been documented at U.S. Geological Survey (USGS) streamflow gaging stations since the 1890's. From 1910 to 1940, about 20 streamflow gaging stations were in operation. Since the early 1950's through the late 1980's, about 100 streamflow gaging stations were in operation. Currently (2000), the USGS streamflow gaging network consists of more than 135 continuous-recording gages. Ground-water levels are currently monitored at 165 wells equipped with continuous recorders.

  14. Tolkku - a toolbox for decision support from condition monitoring data

    NASA Astrophysics Data System (ADS)

    Saarela, Olli; Lehtonen, Mikko; Halme, Jari; Aikala, Antti; Raivio, Kimmo

    2012-05-01

    This paper describes a software toolbox (a software library) designed for condition monitoring and diagnosis of machines. This toolbox implements both new methods and prior art and is aimed for practical down-to-earth data analysis work. The target is to improve knowledge of the operation and behaviour of machines and processes throughout their entire life-cycles. The toolbox supports different phases of condition based maintenance with tools that extract essential information and automate data processing. The paper discusses principles that have guided toolbox design and the implemented toolbox structure. Case examples are used to illustrate how condition monitoring applications can be built using the toolbox. In the first case study the toolbox is applied to fault detection of industrial centrifuges based on measured electrical current. The second case study outlines an application for centralized monitoring of a fleet of machines that supports organizational learning.

  15. A Simple Drought Product and Indicator Derived from Temperature and Relative Humidity Observed by the Atmospheric InfraRed Sounder (AIRS)

    NASA Astrophysics Data System (ADS)

    Granger, S. L.; Behrangi, A.

    2015-12-01

    In the United States, drought results in agricultural losses, impacts to industry, power and energy production, natural resources, municipal water supplies and human health making it one of the costliest natural hazards in the nation. Monitoring drought is therefore critical to help local governments, resource managers, and other groups make effective decisions, yet there is no single definition of drought, and because of the complex nature of drought there is no universal best drought indicator. Remote sensing applications in drought monitoring are advantageous due to the large spatial and temporal frequency of observations, leading to a better understanding of the spatial extent of drought and its duration, and in detecting the onset of drought and its intensity. NASA Earth Observing System (EOS)-era data have potential for monitoring and assessing drought and many are already used either directly or indirectly for drought monitoring. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor are widely used for agricultural and environmental plant-stress monitoring via the USDM, the VegDRI project and FEWSNet. However there remain underutilized sources of information from NASA satellite observations that may have promise for characterizing and understanding meteorological drought. Once such sensor is NASA's Advanced Infra-Red Sounder (AIRS) aboard the Aqua satellite. AIRS and it's sister sensor the Advanced Microwave Sounding Unit (AMSU) that together provide meteorological information of high relevance to meteorological drought, e.g., profiles of water vapor, surface air temperature, and precipitation. Recent work undertaken to develop simple indicators of drought based on temperature and relative humidity from the AIRS suite of instruments is promising. Although there are more sophisticated indicators developed through the application of a variety of

  16. Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function.

    PubMed

    Botvinick, Mattew M

    2007-12-01

    According to one influential account, the anterior cingulate cortex (ACC) serves to monitor for conflicts in information processing. According to another influential account, the ACC monitors action outcomes and guides decision making. Both of these perspectives are supported by an abundance of data, making it untenable to reject one view in favor of the other. Instead, the apparent challenge is to discover how the two perspectives might fit together within a larger account. In the present article, we consider the prospects for such a reconciliation. Juxtaposing the conflict-monitoring and decision-making accounts suggests an extension of the conflict-monitoring theory, by which conflict would act as a teaching signal driving a form of avoidance learning. The effect of this mechanism would be to bias behavioral decision making toward cognitively efficient tasks and strategies. We discuss evidence favoring this proposal and present an initial computational model, which lays the foundation for further development.

  17. The Application of Modified Normalized Difference Water Index (MNDWI) by Leaf Area Index in the Retrieval of Regional Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Zhang, H.-w.; Chen, H.-l.

    2015-04-01

    The vegetation coverage is one of the important factors that restrict the accuracy of remote sensing retrieval of soil moisture. In order to effectively improve the accuracy of the remote sensing retrieval of soil moisture and to reduce the impact of vegetation coverage variation on the retrieval accuracy, the Leaf Area Index (LAI) is introduced to the Normalized Difference Water Index (NDWI) to greatly improve the accuracy of the soil moisture retrieval. In its application on the regional drought monitoring, the paper uses the relative LAI from two places which locate in the north and south of Henan Province respectively (Xin Xiang and Zhu Ma Dian) as indicators. It uses the days after turned-green stage to conduct difference value correction on the Relative Leaf Area Index (RLAL) of the entire province, so as to acquire the distribution of RLAI of the province's wheat producing area. After this, the local remote sensing NDWI will be Modified (MNDWI = NDWI xRLAI ) to acquire the soil moisture distribution status of the entire province's wheat producing area. The result shows that, the Modified Normalized Difference Water Index of LAI which based on the days after turned-green stage can improve the real time retrieval accuracy of soil moisture under different vegetation coverage.

  18. Monitoring Plant Drought Stress Response Using Terahertz Time-Domain Spectroscopy[C][W

    PubMed Central

    Born, Norman; Behringer, David; Liepelt, Sascha; Beyer, Sarah; Schwerdtfeger, Michael; Ziegenhagen, Birgit; Koch, Martin

    2014-01-01

    We present a novel measurement setup for monitoring changes in leaf water status using nondestructive terahertz time-domain spectroscopy (THz-TDS). Previous studies on a variety of plants showed the principal applicability of THz-TDS. In such setups, decreasing leaf water content directly correlates with increasing THz transmission. Our new system allows for continuous, nondestructive monitoring of the water status of multiple individual plants each at the same constant leaf position. It overcomes previous drawbacks, which were mainly due to the necessity of relocating the plants. Using needles of silver fir (Abies alba) seedlings as test subjects, we show that the transmission varies along the main axis of a single needle due to a variation in thickness. Therefore, the relocation of plants during the measuring period, which was necessary in the previous THz-TDS setups, should be avoided. Furthermore, we show a highly significant correlation between gravimetric water content and respective THz transmission. By monitoring the relative change in transmission, we were able to narrow down the permanent wilting point of the seedlings. Thus, we established groups of plants with well-defined levels of water stress that could not be detected visually. This opens up the possibility for a broad range of genetic and physiological experiments. PMID:24501000

  19. Assessment of tree response to drought: validation of a methodology to identify and test proxies for monitoring past environmental changes in trees.

    PubMed

    Tene, A; Tobin, B; Dyckmans, J; Ray, D; Black, K; Nieuwenhuis, M

    2011-03-01

    A thinning experiment stand at Avoca, Ballinvalley, on the east coast of the Republic of Ireland was used to test a developed methodology aimed at monitoring drought stress, based on the analysis of growth rings obtained by coring. The stand incorporated six plots representing three thinning regimes (light, moderate and heavy) and was planted in the spring of 1943 on a brown earth soil. Radial growth (early- and latewood) was measured for the purpose of this study. A multidisciplinary approach was used to assess historic tree response to climate: specifically, the application of statistical tools such as principal component and canonical correlation analysis to dendrochronology, stable isotopes, ring density proxy, blue reflectance and forest biometrics. Results showed that radial growth was a good proxy for monitoring changes to moisture deficit, while maximum density and blue reflectance were appropriate for assessing changes in accumulated temperature for the growing season. Rainfall also influenced radial growth changes but not significantly, and was a major factor in stable carbon and oxygen discrimination, mostly in the latewood formation phase. Stable oxygen isotope analysis was more accurate than radial growth analysis in drought detection, as it helped detect drought signals in both early- and latewood while radial growth analysis only detected the drought signal in earlywood. Many studies have shown that tree rings provide vital information for marking past climatic events. This work provides a methodology to better identify and understand how commonly measured tree proxies relate to environmental parameters, and can best be used to characterize and pinpoint drought events (variously described using parameters such as like moisture deficit, accumulated temperature, rainfall and potential evaporation).

  20. 14 CFR 1216.313 - Implementing and monitoring the decision.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... provide periodic reports as required by the Associate Administrator for Management. (c) If the monitoring activity indicates that resulting environmental effects differ from those described in the current... ENVIRONMENTAL QUALITY Procedures for Implementing the National Environmental Policy Act (NEPA) Agency Procedures...

  1. USGS integrated drought science

    USGS Publications Warehouse

    Ostroff, Andrea C.; Muhlfeld, Clint C.; Lambert, Patrick M.; Booth, Nathaniel L.; Carter, Shawn L.; Stoker, Jason M.; Focazio, Michael J.

    2017-06-05

    Project Need and OverviewDrought poses a serious threat to the resilience of human communities and ecosystems in the United States (Easterling and others, 2000). Over the past several years, many regions have experienced extreme drought conditions, fueled by prolonged periods of reduced precipitation and exceptionally warm temperatures. Extreme drought has far-reaching impacts on water supplies, ecosystems, agricultural production, critical infrastructure, energy costs, human health, and local economies (Milly and others, 2005; Wihlite, 2005; Vörösmarty and others, 2010; Choat and others, 2012; Ledger and others, 2013). As global temperatures continue to increase, the frequency, severity, extent, and duration of droughts are expected to increase across North America, affecting both humans and natural ecosystems (Parry and others, 2007).The U.S. Geological Survey (USGS) has a long, proven history of delivering science and tools to help decision-makers manage and mitigate effects of drought. That said, there is substantial capacity for improved integration and coordination in the ways that the USGS provides drought science. A USGS Drought Team was formed in August 2016 to work across USGS Mission Areas to identify current USGS drought-related research and core capabilities. This information has been used to initiate the development of an integrated science effort that will bring the full USGS capacity to bear on this national crisis.

  2. Monitoring in the context of structured decision-making and adaptive management

    USGS Publications Warehouse

    Lyons, J.E.; Runge, M.C.; Laskowski, H.P.; Kendall, W.L.

    2008-01-01

    In a natural resource management setting, monitoring is a crucial component of an informed process for making decisions, and monitoring design should be driven by the decision context and associated uncertainties. Monitoring itself can play >3 roles. First, it is important for state-dependent decision-making, as when managers need to know the system state before deciding on the appropriate course of action during the ensuing management cycle. Second, monitoring is critical for evaluating the effectiveness of management actions relative to objectives. Third, in an adaptive management setting, monitoring provides the feedback loop for learning about the system; learning is sought not for its own sake but primarily to better achieve management objectives. In this case, monitoring should be designed to reduce the critical uncertainties in models of the managed system. The United States Geological Survey and United States Fish and Wildlife Service are conducting a large-scale management experiment on 23 National Wildlife Refuges across the Northeast and Midwest Regions. The primary management objective is to provide habitat for migratory waterbirds, particularly during migration, using water-level manipulations in managed wetlands. Key uncertainties are related to the potential trade-offs created by management for a specific waterbird guild (e.g., migratory shorebirds) and the response of waterbirds, plant communities, and invertebrates to specific experimental hydroperiods. We reviewed the monitoring program associated with this study, and the ways that specific observations fill >1 of the roles identified above. We used observations from our monitoring to improve state-dependent decisions to control undesired plants, to evaluate management performance relative to shallow-water habitat objectives, and to evaluate potential trade-offs between waterfowl and shorebird habitat management. With limited staff and budgets, management agencies need efficient monitoring

  3. Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model: Evaluation 1 and Potential Value for Drought Monitoring in Western and Central Europe

    NASA Technical Reports Server (NTRS)

    Li, Bailing; Rodell, Matthew; Zaitchik, Benjamin F.; Reichle, Rolf H.; Koster, Randal D.; van Dam, Tonie M.

    2012-01-01

    A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

  4. Hydrologic monitoring using open-source Arduino logging platforms in a socio-hydrological system of the drought-prone tropics, Guanacaste, Costa Rica

    NASA Astrophysics Data System (ADS)

    Hund, S. V.; Johnson, M. S.; Steyn, D. G.; Keddie, T.; Morillas, L.

    2015-12-01

    Water supply is highly disputed in the tropics of northwestern Costa Rica where rainfall exhibits high seasonal variability and long annual dry seasons. Water shortages are common during the dry season, and water conflicts emerge between domestic water users, intensively irrigated agriculture, the tourism industry, and ecological flows. Climate change may further increase the variability of precipitation and the risk for droughts, and pose challenges for small rural agricultural communities experiencing water stress. To adapt to seasonal droughts and improve resilience of communities to future changes, it is essential to increase understanding of interactions between components of the coupled hydrological-social system. Yet, hydrological monitoring and data on water use within developing countries of the humid tropics is limited. To address these challenges and contribute to extended monitoring networks, low-cost and open-source monitoring platforms were developed based off Arduino microelectronic boards and software and combined with hydrological sensors to monitor river stage and groundwater levels in two watersheds of Guanacaste, Costa Rica. Hydrologic monitoring stations are located in remote locations and powered by solar panels. Monitoring efforts were made possible through collaboration with local rural communities, and complemented with a mix of digitized water extraction data and community water use narratives to increase understanding of water use and challenges. We will present the development of the Arduino logging system, results of water supply in relation to water use for both the wet and dry season, and discuss these results within a socio-hydrological system context.

  5. Drought Risk Identification: Early Warning System of Seasonal Agrometeorological Drought

    NASA Astrophysics Data System (ADS)

    Dalecios, Nicolas; Spyropoulos, Nicos V.; Tarquis, Ana M.

    2014-05-01

    By considering drought as a hazard, drought types are classified into three categories, namely meteorological or climatological, agrometeorological or agricultural and hydrological drought and as a fourth class the socioeconomic impacts can be considered. This paper addresses agrometeorological drought affecting agriculture within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with the quantification and monitoring of agrometeorological drought, which constitute part of risk identification. For the quantitative assessment of agrometeorological or agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the Vegetation Health Index (VHI). The computation of VHI is based on satellite data of temperature and the Normalized Difference Vegetation Index (NDVI). The spatiotemporal features of drought, which are extracted from VHI are: areal extent, onset and end time, duration and severity. In this paper, a 20-year (1981-2001) time series of NOAA/AVHRR satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural region of Greece characterized by vulnerable and drought-prone agriculture. The results show that every year there is a seasonal agrometeorological drought with a gradual increase in the areal extent and severity with peaks appearing usually during the summer. Drought monitoring is conducted by monthly remotely sensed VHI images. Drought early warning is developed using empirical relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought, respectively. The two fitted curves offer a seasonal

  6. Use of climate information for drought risk management in Mexico

    NASA Astrophysics Data System (ADS)

    Neri, C.; Magaña Rueda, V.

    2013-05-01

    The occurrence of meteorological droughts in Mexico has brought to light the large vulnerability of the central-northern part of the country to water shortages. This region is facing current and future water shortages due to the increased demand of water from urban growth in addition to droughts. Assessing droughts requires considering long-term losses and side effects. However, governments generally invest little resources in the creation of drought risk reduction programs, even in regions where droughts have been documented in historical records, such as in the northern region of Mexico. It is not clear until now, what is our capacity to predict droughts on seasonal time scale, and even the Drought Monitor for North America not always reflect the severity of the condition at the regional level. An analysis of strategies that focus on droughts show that one of the principal limits in the management of drought risks and preventive decision making is the use of inadequate definitions of drought predictability. In addition, the means to communicate confidence in seasonal climate forecasts has inhibited the use of climate information in the planning of various socioeconomic activities. Although some sectors such as agriculture have programs to reduce the impacts of drought, their efforts have focused in providing subsidies to get along with dry conditions. In other words, there are no actions to reduce the potential impacts of drought. The characterization of the vulnerability of water user groups, particularly in Sonora as case of study, has been useful to identifying what type of climate information decision makers needed. This information will be included in a proposal of a drought early warming for Mexico. A key element in a drought early warming for Mexico is the development of reliable climate information and the use of indicators to determine of the onset, maximum intensity and duration of the event. The occurrence and severity of drought may be estimated using

  7. Evaluation of Soil Moisture and Palmer Drought Severity Index in Brazil

    NASA Astrophysics Data System (ADS)

    Rossato, L.

    2015-12-01

    Soil moisture is one of the main factors for the study of drought, climate and vegetation. In the case of drought, this is a regional phenomenon and affects food security more than any other natural disaster. Therefore, monitoring of different types of drought has been based on indexes that standardize on temporal and spatial scales. Currently, the monitoring of different types of drought is based on indexes that attempt to encapsulate on temporal and regional levels allowing thereby the comparison of water conditions in different areas. Therefore, in order to assess the impact of soil moisture during periods of drought, the Palmer Drought Severity Index (PDSI) was estimated for the entire Brazilian territory, using meteorological (precipitation and evapotranspiration) and soil (field capacity, permanent wilting point and water storage in the soil) data. The data field capacity and permanent wilting point were obtained from the physical properties of the soil, while the water storage in the soil was calculated considering the water balance model. Analyses were made for the years 2000 through 2014, which includes periods with and without occurrence of drought, respectively. The results showed that the PDSI had higher negative indices for the years 2003, 2012, 2013 and 2014 in Brazil's Northeast region, and this region was strongly affected by drought during those years. These indices can serve as a basis for assessing future drought projections, considering different scenarios. The results also show that soil moisture constitutes one of the limiting factors for obtaining high agricultural productivity, in order to reduce the effects caused by drought. Therefore, these indices can serve as a basis for assessing future drought projections, considering different scenarios. It would be desirable to assist decision makers in action plans with more effective strategies, allowing farmers to live with drought without losing their livelihood.

  8. Evaluation of soil moisture and Palmer Drought Severity Index in Brazil

    NASA Astrophysics Data System (ADS)

    Rossato, Luciana; Antônio Marengo, José; Bassi Marinho Pires, Luciana

    2016-04-01

    Soil moisture is one of the main factors for the study of drought, climate and vegetation. In the case of drought, this is a regional phenomenon and affects food security more than any other natural disaster. Therefore, monitoring of different types of drought has been based on indexes that standardize on temporal and spatial scales. Currently, the monitoring of different types of drought is based on indexes that attempt to encapsulate on temporal and regional levels allowing thereby the comparison of water conditions in different areas. Therefore, in order to assess the impact of soil moisture during periods of drought, the Palmer Drought Severity Index was estimated for the entire Brazilian territory, using meteorological (precipitation and evapotranspiration) and soil (field capacity, permanent wilting point and water storage in the soil) data. The data field capacity and permanent wilting point were obtained from the physical properties of the soil, while the water storage in the soil was calculated considering the water balance model. Analyses were made for the years 2000 through 2014, which includes periods with and without occurrence of drought, respectively. The results showed that the PDSI had higher negative indices for the years 2003 and 2012 in Brazil's Northeast region, and this region was strongly affected by drought during those years. These indices can serve as a basis for assessing future drought projections, considering different scenarios. The results also show that soil moisture constitutes one of the limiting factors for obtaining high agricultural productivity, in order to reduce the effects caused by drought. Therefore, these indices can serve as a basis for assessing future drought projections, considering different scenarios. It would be desirable to assist decision makers in action plans with more effective strategies, allowing farmers to live with drought without losing their livelihood.

  9. Combined monitoring, decision and control model for the human operator in a command and control desk

    NASA Technical Reports Server (NTRS)

    Muralidharan, R.; Baron, S.

    1978-01-01

    A report is given on the ongoing efforts to mode the human operator in the context of the task during the enroute/return phases in the ground based control of multiple flights of remotely piloted vehicles (RPV). The approach employed here uses models that have their analytical bases in control theory and in statistical estimation and decision theory. In particular, it draws heavily on the modes and the concepts of the optimal control model (OCM) of the human operator. The OCM is being extended into a combined monitoring, decision, and control model (DEMON) of the human operator by infusing decision theoretic notions that make it suitable for application to problems in which human control actions are infrequent and in which monitoring and decision-making are the operator's main activities. Some results obtained with a specialized version of DEMON for the RPV control problem are included.

  10. A Decision Support System approach for rivers monitoring and sustainable management.

    PubMed

    Manos, B; Bournaris, Th; Silleos, N; Antonopoulos, V; Papathanasiou, J

    2004-01-01

    This paper presents a Decision Support System (DSS) approach developed in the context of the Copernicus project entitled System for Water Monitoring and Sustainable Management based on Ground Stations and Satellite Images (WATERMAN). The main objective of WATERMAN is the monitoring and management of the Strymon River in the Southern Balkans. The specific DSS integrates the main components of WATERMAN and helps the decision maker to monitor the Strymon region; to control and forecast the quantity and quality of the river water; as well as to make objective decisions about the state of the water based on data provided by radio computers, earth stations and satellite images processed by mathematical and statistical models and Geographical Information Systems (GIS).

  11. Application of effective drought index for quantification of meteorological drought events: a case study in Australia

    NASA Astrophysics Data System (ADS)

    Deo, Ravinesh C.; Byun, Hi-Ryong; Adamowski, Jan F.; Begum, Khaleda

    2016-01-01

    Drought indices (DIs) that quantify drought events by their onset, termination, and subsequent properties such as the severity, duration, and peak intensity are practical stratagems for monitoring and evaluating the impacts of drought. In this study, the effective drought index (EDI) calculated over daily timescales was utilized to quantify short-term (dry spells) and ongoing drought events using drought monitoring data in Australia. EDI was an intensive DI that considered daily water accumulation with a weighting function applied to daily rainfall data with the passage of time. A statistical analysis of the distribution of water deficit period relative to the base period was performed where a run-sum method was adopted to identify drought onset for any day (i) with EDI i < 0 (rainfall below normal). Drought properties were enumerated in terms of (1) severity (AEDI ≡ accumulated sum of EDIi < 0), (2) duration (DS ≡ cumulative number of days with EDIi < 0), (3) peak intensity (EDImin ≡ minimum EDI of a drought event), (4) annual drought severity (YAEDI ≡ yearly accumulated negative EDI), and (5) accumulated severity of ongoing drought using event-accumulated EDI (EAEDI). The analysis of EDI signal enabled the detection and quantification of a number of drought events in Australia: Federation Drought (1897-1903), 1911-1916 Drought, 1925-1929 Drought, World War II Drought (1937-1945), and Millennium Drought (2002-2010). In comparison with the other droughts, Millennium Drought was exemplified as an unprecedented dry period especially in Victoria (EAEDI ≈ -4243, DS = 1946 days, EDImin = -4.05, and YAEDI = -4903). For the weather station tested in Northern Territory, the worst drought was recorded during 1925-1929 period. The results justified the suitability of effective drought index as a useful scientific tool for monitoring of drought progression, onset and termination, and ranking of drought based on severity, duration, and peak intensity, which allows an

  12. Application of effective drought index for quantification of meteorological drought events: a case study in Australia

    NASA Astrophysics Data System (ADS)

    Deo, Ravinesh C.; Byun, Hi-Ryong; Adamowski, Jan F.; Begum, Khaleda

    2017-04-01

    Drought indices (DIs) that quantify drought events by their onset, termination, and subsequent properties such as the severity, duration, and peak intensity are practical stratagems for monitoring and evaluating the impacts of drought. In this study, the effective drought index (EDI) calculated over daily timescales was utilized to quantify short-term (dry spells) and ongoing drought events using drought monitoring data in Australia. EDI was an intensive DI that considered daily water accumulation with a weighting function applied to daily rainfall data with the passage of time. A statistical analysis of the distribution of water deficit period relative to the base period was performed where a run-sum method was adopted to identify drought onset for any day ( i) with EDI i < 0 (rainfall below normal). Drought properties were enumerated in terms of (1) severity (AEDI ≡ accumulated sum of EDIi < 0), (2) duration (DS ≡ cumulative number of days with EDIi < 0), (3) peak intensity (EDImin ≡ minimum EDI of a drought event), (4) annual drought severity (YAEDI ≡ yearly accumulated negative EDI), and (5) accumulated severity of ongoing drought using event-accumulated EDI (EAEDI). The analysis of EDI signal enabled the detection and quantification of a number of drought events in Australia: Federation Drought (1897-1903), 1911-1916 Drought, 1925-1929 Drought, World War II Drought (1937-1945), and Millennium Drought (2002-2010). In comparison with the other droughts, Millennium Drought was exemplified as an unprecedented dry period especially in Victoria (EAEDI ≈ -4243, DS = 1946 days, EDImin = -4.05, and YAEDI = -4903). For the weather station tested in Northern Territory, the worst drought was recorded during 1925-1929 period. The results justified the suitability of effective drought index as a useful scientific tool for monitoring of drought progression, onset and termination, and ranking of drought based on severity, duration, and peak intensity, which allows

  13. Drought Risk Assessment based on Natural and Social Factors

    NASA Astrophysics Data System (ADS)

    Huang, Jing; Wang, Huimin; Han, Dawei

    2015-04-01

    In many parts of the world, drought hazard is becoming more frequent and severe due to climate change and human activities. It is crucial to monitor and assess drought conditions, especially for decision making support in agriculture sector. The vegetation index (VI) decreases, and the land surface temperature (LST) increases when the vegetation is under drought stress. Therefore both of these remotely sensed indices are widely used in drought monitoring and assessment. Temperature-Vegetation Dryness Index (TVDI) is obtained by establishing the feature space of the normalized difference vegetation index (NDVI) and LST, which reflects agriculture dry situation by inverting soil moisture. However, these indices only concern the natural hazard-causing factors. Our society is a complex large-scale system with various natural and social elements. The drought risk is the joint consequence of hazard-causing factors and hazard-affected bodies. For example, as the population increases, the exposure of the hazard-affected bodies also tends to increase. The high GDP enhances the response ability of government, and the irrigation and water conservancy reduces the vulnerability. Such characteristics of hazard-affected bodies should be coupled with natural factors. In this study, the 16-day moderate-resolution imaging spectroradiometer (MODIS) NDVI and LST data are combined to establish NDVI-Ts space according to different land use types in Yunnan Province, China. And then, TVDIs are calculated through dry and wet edges modeled as a linear fit to data for each land cover type. Next, the efforts are turned to establish an integrated drought assessment index of social factors and TVDI through ascertaining attribute weight based on rough sets theory. Thus, the new CDI (comprehensive drought index) recorded during spring of 2010 and the spatial variations in drought are analyzed and compared with TVDI dataset. Moreover, actual drought risk situation in the study area is given to

  14. Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems

    USGS Publications Warehouse

    Neckles, Hilary A.; Lyons, James E.; Guntenspergen, Glenn R.; Shriver, W. Gregory; Adamowicz, Susan C.

    2015-01-01

    Most salt marshes in the USA have been degraded by human activities, and coastal managers are faced with complex choices among possible actions to restore or enhance ecosystem integrity. We applied structured decision making (SDM) to guide selection of monitoring variables and management priorities for salt marshes within the National Wildlife Refuge System in the northeastern USA. In general, SDM is a systematic process for decomposing a decision into its essential elements. We first engaged stakeholders in clarifying regional salt marsh decision problems, defining objectives and attributes to evaluate whether objectives are achieved, and developing a pool of alternative management actions for achieving objectives. Through this process, we identified salt marsh attributes that were applicable to monitoring National Wildlife Refuges on a regional scale and that targeted management needs. We then analyzed management decisions within three salt marsh units at Prime Hook National Wildlife Refuge, coastal Delaware, as a case example of prioritizing management alternatives. Values for salt marsh attributes were estimated from 2 years of baseline monitoring data and expert opinion. We used linear value modeling to aggregate multiple attributes into a single performance score for each alternative, constrained optimization to identify alternatives that maximized total management benefits subject to refuge-wide cost constraints, and used graphical analysis to identify the optimal set of alternatives for the refuge. SDM offers an efficient, transparent approach for integrating monitoring into management practice and improving the quality of management decisions.

  15. Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

    NASA Technical Reports Server (NTRS)

    Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

    2010-01-01

    Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

  16. Intricacies in Drought Management Policy, Crisis Response and Preparedness: Linking the Interface

    NASA Astrophysics Data System (ADS)

    Prakash, P.; Harter, T.

    2016-12-01

    Drought per se is often misrepresented as mere water scarcity issue overlooking the complexities associated with it. In many parts of the world, the drought management policy prescriptions are often driven by crisis management rather than preventive approach. As a result, the economic, social and environmental impact of droughts continues to increase even to this day. To overcome this calamity, nations should encourage coordinated effort at both national and regional scale. An integrated approach on open data sharing, technical advancement in monitoring and robust early warning system to deliver timely information to decision makers, drought projection through high performance mathematical model and effective impact assessment procedure, implementing proactive risk management measures and preparedness with effective emergency response programs plans, will certainly increase the likelihood of drought coping capabilities. The present study focuses on knowledge augmentation for better policy framework and action for all countries that suffer from droughts. A comprehensive database at the global scale has been compiled giving information on existing drought management policies/practices and the major challenges faced by major drought distressed countries. Plausible solution is suggested towards integrating the water management policy, response and preparedness, that has been garnered through the lessons from success/failure stories of nations with effective drought management policies

  17. Monitoring Trends in Productivity to Identify Vulnerable Ecosystems: Measuring Ecosystem Condition and Drought Resistance Across California Ecosystems

    NASA Astrophysics Data System (ADS)

    Malone, S. L.; Tulbure, M. G.; Pérez-Luque, A. J.; Ryan, M. G.; Joyce, L. A.

    2016-12-01

    Terrestrial ecosystems are vital for their role in fixing and storing carbon and recycling water and nutrients. Ecosystems buffer the atmosphere from large changes in carbon dioxide through processes (i.e. photosynthesis, respiration, evapotranspiration, and nutrient cycling) that are both driven by and an important feedback to climate and disturbance regimes. Although we understand the carbon value provided by ecosystems, the persistence of carbon sinks is a concern because the processes promoting carbon storage change overtime, shift with climate, and are heavily influenced by disturbance regimes. Combined with the diversity of natural ecosystems, the recent occurrence of drought make California an important case study to examine variations in productivity and drought resistance. We used a time series (2002-2014) of climate and productivity indices to identify drivers of ecosystem condition and drought resistance. Our results show distinct patterns in water use efficiency (WUE) in resilient and vulnerable ecosystems. Under normal conditions WUE varied across California (0.08 to 3.85 g C mm-1 H2O) and WUE generally increased under severe drought conditions in 2014 (p< 0.001; R2 = 0.83). Only 18% of the study area exhibited a decline in WUE (i.e. vulnerable ecosystems) and <1% of the study area experienced no change under drought conditions. Strong correlations between changes in WUE, precipitation and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e. grasslands) also had greater C uptake rates and higher rates of carbon uptake efficiency (CUE = NPP/ LAI) under severe drought conditions. Drought severity, precipitation and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future.

  18. The Lifecycles of Drought: Informing Responses Across Timescales

    NASA Astrophysics Data System (ADS)

    Pulwarty, R. S.; Schubert, S. D.

    2014-12-01

    Drought is a slow-onset hazard that is a normal part of climate. Drought onset and demise are difficult to determine. Impacts are mostly nonstructural, spread over large geographical areas, and can persist long after precipitation deficits end. These factors hinder development of accurate, timely estimates of drought severity and resultant responses. Drivers of drought range from SST anomalies and global scale atmospheric response, through regional forcing and local land-surface feedbacks. Key climatological questions related to drought risk assessment, perception and management include, "Does a drought end by a return to normal precipitation; how much moisture is required and over what period; can the end of a drought be defined by the diminishing impacts e.g. soil moisture, reservoir volumes; will precipitation patterns on which management systems rely, change in the future?" Effective early warning systems inform strategic responses that anticipate crises and crisis evolution across climate timescales. While such "early information" is critical for defining event onset, it is even more critical for identifying the potential for increases in severity. Many social and economic systems have buffers in place to respond to onset (storage, transfers and purchase of grain) but lack response capabilities as drought intensifies, as buffers are depleted. Throughout the drought lifecycle (and between events), monitoring, research and risk assessments are required to: Map decision-making processes and resource capabilities including degradation of water and ecosystems Place multiple climate and land surface indicators within a consistent triggering framework (e.g. climate and vegetation mapping) before critical thresholds are reached Identify policies and practices that impede or enable the flow of information, through policy gaming and other exercises The presentation will outline the capabilities and framework needed to ensure improved scientific inputs to preparedness

  19. Remote-Sensing and Automated Water Resources Tracking: Near Real-Time Decision Support for Water Managers Facing Drought and Flood

    NASA Astrophysics Data System (ADS)

    Reiter, M. E.; Elliott, N.; Veloz, S.; Love, F.; Moody, D.; Hickey, C.; Fitzgibbon, M.; Reynolds, M.; Esralew, R.

    2016-12-01

    Innovative approaches for tracking the Earth's natural resources, especially water which is essential for all living things, are essential during a time of rapid environmental change. The Central Valley is a nexus for water resources in California, draining the Sacramento and San Joaquin River watersheds. The distribution of water throughout California and the Central Valley, while dynamic, is highly managed through an extensive regional network of canals, levees, and pumps. Water allocation and delivery is determined through a complex set of rules based on water contracts, historic priority, and other California water policies. Furthermore, urban centers, agriculture, and the environment throughout the state are already competing for water, particularly during drought. Competition for water is likely to intensify as California is projected to experience continued increases in demand due to population growth and more arid growing conditions, while also having reduced or modified water supply due to climate change. As a result, it is difficult to understand or predict how water will be used to fulfill wildlife and wetland conservation needs. A better understanding of the spatial distribution of water in near real-time can facilitate adaptation of water resource management to changing conditions on the landscape, both over the near- and long-term. The Landsat satellite mission delivers imagery every 16-days from nearly every place on the earth at a high spatial resolution. We have integrated remote sensing of satellite data, classification modeling, bioinformatics, optimization, and ecological analyses to develop an automated near real-time water resources tracking and decision-support system for the Central Valley of California. Our innovative system has applications for coordinated water management in the Central Valley to support people, places, and wildlife and is being used to understand the factors that drive variation in the distribution and abundance of water

  20. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data

    NASA Astrophysics Data System (ADS)

    Bhuiyan, C.; Singh, R. P.; Kogan, F. N.

    2006-12-01

    The hard-rock hilly Aravalli terrain of Rajasthan province of India suffers with frequent drought due to poor and delayed monsoon, abnormally high summer-temperature and insufficient water resources. In the present study, detailed analysis of meteorological and hydrological data of the Aravalli region has been carried out for the years 1984-2003. Standardised Precipitation Index (SPI) has been used to quantify the precipitation deficit. Standardised Water-Level Index (SWI) has been developed to assess ground-water recharge-deficit. Vegetative drought indices like Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) and Vegetation Health Index (VHI) have been computed using NDVI values obtained from Global Vegetation Index (GVI) and thermal channel data of NOAA AVHRR satellite. Detailed analyses of spatial and temporal drought dynamics during monsoon and non-monsoon seasons have been carried out through drought index maps generated in Geographic Information Systems (GIS) environment. Analysis and interpretation of these maps reveal that negative SPI anomalies not always correspond to drought. In the Aravalli region, aquifer-stress shifts its position time to time, and in certain pockets it is more frequent. In comparison to hydrological stress, vegetative stress in the Aravalli region is found to be slower to begin but quicker to withdraw.

  1. Informed decision making for in-home use of motion sensor-based monitoring technologies.

    PubMed

    Bruce, Courtenay R

    2012-06-01

    Motion sensor-based monitoring technologies are designed to maintain independence and safety of older individuals living alone. These technologies use motion sensors that are placed throughout older individuals' homes in order to derive information about eating, sleeping, and leaving/returning home habits. Deviations from normal behavioral patterns are detected using statistical analysis of activities of daily living. Sensors are linked to mobile devices and secure Web pages in order to transmit information to designated caregivers who live outside the home. It is difficult to make informed decisions about purchasing new technologies. This article describes elements for making informed decisions about purchasing motion sensor-based monitoring technologies and factors that could be used to evaluate these technologies. Case managers, physicians, nurses, and social workers may be asked to help older individuals and their families make informed purchasing decisions. Recommendations and practical tools are provided to best support these professionals in their dialog with older individuals and their families.

  2. Modelling crop yield in Iberia under drought conditions

    NASA Astrophysics Data System (ADS)

    Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

    2017-04-01

    The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining

  3. Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision.

    PubMed

    Yang Wang; Zhiwen Liu; Bin Dong

    2016-08-01

    Heart rate (HR) monitoring is necessary for daily healthcare. Wrist-type photoplethsmography (PPG) is a convenient and non-invasive technique for HR monitoring. However, motion artifacts (MA) caused by subjects' movements can extremely interfere the results of HR monitoring. In this paper, we propose a high accuracy method using motion decision, singular spectrum analysis (SSA) and spectral peak searching for daily HR estimation. The proposed approach was evaluated on 8 subjects under a series of different motion states. Compared with electrocardiogram (ECG) recorded simultaneously, the experimental results indicated that the averaged absolute estimation error was 2.33 beats per minute (BPM).

  4. The Development of a Web-service-based On-demand Global Agriculture Drought Information System

    NASA Astrophysics Data System (ADS)

    Deng, M.; Di, L.; Han, W.; Yagci, A.; Peng, C.

    2011-12-01

    The growing demand on detailed and accurate assessments of agriculture drought from local to global scales has made drought monitoring and forecasting a hot research topic in recent years. However, many challenges in this area still remain. One of such challenges is to how to let world-wide decision makers obtain accurate and timely drought information. Current agriculture drought information systems in the world are limited in many aspects, such as only regional or country level coverage, very coarse spatial and temporal resolutions, no on-demand drought information product generation and download services, no online analysis tools, no interoperability with other systems, and ineffective agriculture drought monitoring and forecasting. Leveraging the latest advances in geospatial Web service, interoperability and cyber-infrastructure technologies and the availability of near real-time global remote sensing data, we aims at providing a solution to those problems by building an open, interoperable, standard-compliant, and Web-service-based global agriculture drought monitoring and forecasting system (GADMFS) (http://gis.csiss.gmu.edu/GADMFS/). GADMFS will provide world-wide users with timely, on-demand, and ready-to-use agricultural drought data and information products as well as improved global agriculture drought monitoring, prediction and analysis services. For the monitoring purpose, the system lively links to near real-time satellite remote sensing data sources from NASA and NOAA and relies on drought related remotely sensed physical and biophysical parameters, such as soil moisture and drought-related vegetation indices (VIs, e.g., NDVI) to provide the current conditions of global agricultural drought at high resolutions (up to 500m spatial and daily temporal) to world-wide users on demand. For drought prediction, the system utilizes a neural network based modeling algorithm, trained with current and historic vegetation-based and climate-based drought index

  5. Integrating modeling, monitoring, and management to reduce critical uncertainties in water resource decision making.

    PubMed

    Peterson, James T; Freeman, Mary C

    2016-12-01

    Stream ecosystems provide multiple, valued services to society, including water supply, waste assimilation, recreation, and habitat for diverse and productive biological communities. Managers striving to sustain these services in the face of changing climate, land uses, and water demands need tools to assess the potential effectiveness of alternative management actions, and often, the resulting tradeoffs between competing objectives. Integrating predictive modeling with monitoring data in an adaptive management framework provides a process by which managers can reduce model uncertainties and thus improve the scientific bases for subsequent decisions. We demonstrate an integration of monitoring data with a dynamic, metapopulation model developed to assess effects of streamflow alteration on fish occupancy in a southeastern US stream system. Although not extensive (collected over three years at nine sites), the monitoring data allowed us to assess and update support for alternative population dynamic models using model probabilities and Bayes rule. We then use the updated model weights to estimate the effects of water withdrawal on stream fish communities and demonstrate how feedback in the form of monitoring data can be used to improve water resource decision making. We conclude that investment in more strategic monitoring, guided by a priori model predictions under alternative hypotheses and an adaptive sampling design, could substantially improve the information available to guide decision-making and management for ecosystem services from lotic systems.

  6. A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality

    PubMed Central

    Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

    2015-01-01

    Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks. PMID:26610496

  7. A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality.

    PubMed

    Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

    2015-11-19

    Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

  8. A County-level Crop Specific Drought Severity-Coverage Index

    NASA Astrophysics Data System (ADS)

    Leelaruban, N.; Akyuz, A.; Padmanabhan, G.; Shaik, S.

    2012-12-01

    Understanding drought severity, frequency, duration and spatial extent is critical in drought mitigation, planning and decision making. A county-level approach to addressing drought is ideal since most agricultural management is best administered at county level in the USA. This study sought to apply spatiotemporal drought characteristics at reduced areal extents, namely, at county level for an entire state (North Dakota, USA) using a derived weekly non-dimensional index, Drought Severity and Coverage Index (Isc) based on a stepwise approach. Isc was calculated from weekly percentages of areal coverage values of drought intensity values published by the "U.S. Drought Monitor", DM. DM is published weekly as a joint project by the National Drought Mitigation Center (NDMC), U.S. Department of Agriculture (USDA), and the National Atmospheric and Oceanic Administration (NOAA). In order to facilitate application at the county level, the variation of the drought based on Isc was mapped in county level for state of North Dakota, and drought events were categorized into classes based on weekly Isc to analyze drought frequency. The number of occurrences of drought events were then determined for each county and climate division based on derived classes. The drought frequency analyses showed clear demarcation of counties in an observable dichotomy. Impact of drought on crop yield was also analyzed using USDA National Agricultural Statistics Service (NASS) county level yield, developed Isc values, drought intensity categories of areal coverage values for selected crops such as barley, corn, durum wheat, hay-alfalfa, hay, oats, and spring wheat. This research uses alternative panel statistical procedures instead of the usual time series analysis procedures to account for temporal and spatial variations to accurately model the relationship between exogenous and endogenous drought variables. In alternative panel procedures, two-way random effects model was used which accounts for

  9. Drought impact functions as intermediate step towards drought damage assessment

    NASA Astrophysics Data System (ADS)

    Bachmair, Sophie; Svensson, Cecilia; Prosdocimi, Ilaria; Hannaford, Jamie; Helm Smith, Kelly; Svoboda, Mark; Stahl, Kerstin

    2016-04-01

    provinces with good data availability. Impact functions representing localized drought impacts are more challenging to construct given that less data is available, yet may provide information that more directly addresses stakeholders' needs. Overall, our study contributes insights into how drought intensity translates into ecological and socioeconomic impacts, and how such information may be used for enhancing drought monitoring and early warning.

  10. Uncertainty in drought monitoring by the Standardized Precipitation Index: the case study of the Abruzzo region (central Italy)

    NASA Astrophysics Data System (ADS)

    Vergni, L.; Di Lena, B.; Todisco, F.; Mannocchi, F.

    2017-04-01

    As shown by several authors, drought monitoring by the Standardized Precipitation Index (SPI) presents some uncertainties, mainly dependent on the choice of the probability distribution used to describe the cumulative precipitation and on the characteristics (e.g., length and variability) of the dataset. In this paper, the uncertainty related to SPI estimates has been quantified and analyzed with regards to the case study of the Abruzzo region (Central Italy), by using monthly precipitation recorded at 75 stations during the period 1951-2009. First, a set of distributions suitable to describe the cumulative precipitation at the 3-, 6-, and 12-month time scales was identified by using L-moments ratio diagrams. The goodness-of-fit was evaluated by applying the Kolmogorov-Smirnov test, and the Normality test on the derived SPI series. Then the confidence intervals of SPI have been calculated by applying a bootstrap procedure. The size of the confidence intervals has been considered as a measure of uncertainty, and its dependence on several factors such as the distribution type, the time scale, the record length, and the season has been examined. Results show that the distributions Pearson type III (PE3), Weibull (WEI), Generalized Normal (GNO), Generalized Extreme Value (GEV), and Gamma (GA2) are all suitable to describe the cumulative precipitation, with a slightly better performance of the PE3 and GNO distributions. As expected, the uncertainty increases as the record length and time scale decrease. The leading source of uncertainty is the record length while the effects due to seasonality and time scale are negligible. Two-parameter distributions make it possible to obtain confidence intervals of SPI (particularly for extreme values) narrower than those obtained by three-parameter distributions. Nevertheless, due to a poorer goodness of fit, two-parameter distributions can provide less reliable estimates of the precipitation probability. In any event, independently

  11. An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring.

    PubMed

    Zini, Elisa M; Lanzola, Giordano; Bossi, Paolo; Quaglini, Silvana

    2017-08-11

    We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient's conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients' needs, in our work the Decision

  12. Environmental change challenges decision-making during post-market environmental monitoring of transgenic crops.

    PubMed

    Sanvido, Olivier; Romeis, Jörg; Bigler, Franz

    2011-12-01

    The ability to decide what kind of environmental changes observed during post-market environmental monitoring of genetically modified (GM) crops represent environmental harm is an essential part of most legal frameworks regulating the commercial release of GM crops into the environment. Among others, such decisions are necessary to initiate remedial measures or to sustain claims of redress linked to environmental liability. Given that consensus on criteria to evaluate 'environmental harm' has not yet been found, there are a number of challenges for risk managers when interpreting GM crop monitoring data for environmental decision-making. In the present paper, we argue that the challenges in decision-making have four main causes. The first three causes relate to scientific data collection and analysis, which have methodological limits. The forth cause concerns scientific data evaluation, which is controversial among the different stakeholders involved in the debate on potential impacts of GM crops on the environment. This results in controversy how the effects of GM crops should be valued and what constitutes environmental harm. This controversy may influence decision-making about triggering corrective actions by regulators. We analyse all four challenges and propose potential strategies for addressing them. We conclude that environmental monitoring has its limits in reducing uncertainties remaining from the environmental risk assessment prior to market approval. We argue that remaining uncertainties related to adverse environmental effects of GM crops would probably be assessed in a more efficient and rigorous way during pre-market risk assessment. Risk managers should acknowledge the limits of environmental monitoring programmes as a tool for decision-making.

  13. A description of nurses' decision-making in managing electrocardiographic monitor alarms.

    PubMed

    Gazarian, Priscilla K; Carrier, Natalie; Cohen, Rachel; Schram, Haley; Shiromani, Samara

    2015-01-01

    To describe the cues and factors that nurses use in their decision-making when responding to clinical alarms. Alarms are designed to be very sensitive, and as a result, they are not very specific. Lack of adherence to the practice standards for electrocardiographic monitoring in hospital settings has been observed, resulting in overuse of the electrocardiographic monitoring. Monitoring without consideration of clinical indicators uses scarce healthcare resources and may even produce untoward circumstances because of alarm fatigue. With so many false alarms, alarm fatigue represents a symptom of a larger problem. It cannot be fixed until all of the factors that contribute to its existence have been examined. This was a qualitative descriptive study. This study was conducted at an academic medical centre located in the Northeast United States. Eight participants were enrolled using purposive sampling. Nurses were observed for two three-hour periods. Following each observation, the nurse was interviewed using the critical decision method to describe the cognitive processes related to the alarm activities. Qualitative data from the conducted interviews were analysed via an a priori framework founded in the critical decision method. This study reveals information, experience, guidance and decision-making as the four prominent categories contributing to nurses' decision-making in relation to alarm management. Managing technology was a category not identified a priori that emerged in the data analysis. Nurses revealed a breadth of information needed to adequately identify and interpret monitor alarms, and how they used that information to put the alarms into the particular context of an individual patient's situations. Understanding the cues and factors nurses use when responding to cardiac alarms will guide the development of learning experiences and inform policies to guide practice. © 2014 John Wiley & Sons Ltd.

  14. Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: State of knowledge

    USGS Publications Warehouse

    Peterson, Thomas C.; Heim, Richard R.; Hirsch, Robert M.; Kaiser, Dale P.; Brooks, Harold; Diffenbaugh, Noah S.; Dole, Randall M.; Giovannettone, Jason P.; Guirguis, Kristen; Karl, Thomas R.; Katz, Richard W.; Kunkel, Kenneth E.; Lettenmaier, Dennis P.; McCabe, Gregory J.; Paciorek, Christopher J.; Ryberg, Karen R.; K Wolter, BS Silva; Schubert, Siegfried; Silva, Viviane B. S.; Stewart, Brooke C.; Vecchia, Aldo V.; Villarini, Gabriele; Vose, Russell S.; Walsh, John; Wehner, Michael; Wolock, David; Wolter, Klaus; Woodhouse, Connie A.; Wuebbles, Donald

    2013-01-01

    Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability. Instrumental data indicate that the Dust Bowl of the 1930s and the drought in the 1950s were the most significant twentieth-century droughts in the United States, while tree ring data indicate that the megadroughts over the twelfth century exceeded anything in the twentieth century in both spatial extent and duration. The state of knowledge of the factors that cause heat waves, cold waves, floods, and drought to change is fairly good with heat waves being the best understood.

  15. Use of monitoring data to support conservation management and policy decisions in Micronesia.

    PubMed

    Montambault, Jensen Reitz; Wongbusarakum, Supin; Leberer, Trina; Joseph, Eugene; Andrew, Wayne; Castro, Fran; Nevitt, Brooke; Golbuu, Yimnang; Oldiais, Noelle W; Groves, Craig R; Kostka, Willy; Houk, Peter

    2015-10-01

    Adaptive management implies a continuous knowledge-based decision-making process in conservation. Yet, the coupling of scientific monitoring and management frameworks remains rare in practice because formal and informal communication pathways are lacking. We examined 4 cases in Micronesia where conservation practitioners are using new knowledge in the form of monitoring data to advance marine conservation. These cases were drawn from projects in Micronesia Challenge jurisdictions that received funding for coupled monitoring-to-management frameworks and encompassed all segments of adaptive management. Monitoring in Helen Reef, Republic of Palau, was catalyzed by coral bleaching and revealed evidence of overfishing that led to increased enforcement and outreach. In Nimpal Channel, Yap, Federated States of Micronesia (FSM), monitoring the recovery of marine food resources after customary restrictions were put in place led to new, more effective enforcement approaches. Monitoring in Laolao Bay, Saipan, Commonwealth of the Northern Mariana Islands, was catalyzed by observable sediment loads from poor land-use practices and resulted in actions that reduced land-based threats, particularly littering and illegal burning, and revealed additional threats from overfishing. Pohnpei (FSM) began monitoring after observed declines in grouper spawning aggregations. This data led to adjusting marine conservation area boundaries and implementing market-based size class restrictions. Two themes emerged from these cases. First, in each case monitoring was conducted in a manner relevant to the social and ecological systems and integrated into the decision-making process. Second, conservation practitioners and scientists in these cases integrated culturally appropriate stakeholder engagement throughout all phases of the adaptive management cycle. More broadly, our study suggests, when describing adaptive management, providing more details on how monitoring and management activities are

  16. Intelligent ship traffic monitoring for oil spill prevention: risk based decision support building on AIS.

    PubMed

    Eide, Magnus S; Endresen, Oyvind; Brett, Per Olaf; Ervik, Jon Leon; Røang, Kjell

    2007-02-01

    The paper describes a model, which estimates the risk levels of individual crude oil tankers. The intended use of the model, which is ready for trial implementation at The Norwegian Coastal Administrations new Vardø VTS (Vessel Traffic Service) centre, is to facilitate the comparison of ships and to support a risk based decision on which ships to focus attention on. For a VTS operator, tasked with monitoring hundreds of ships, this is a valuable decision support tool. The model answers the question, "Which ships are likely to produce an oil spill accident, and how much is it likely to spill?".

  17. Development of a decision-making methodology to design a water quality monitoring network.

    PubMed

    Keum, Jongho; Kaluarachchi, Jagath J

    2015-07-01

    The number of water quality monitoring stations in the USA has decreased over the past few decades. Scarcity of observations can easily produce prediction uncertainty due to unreliable model calibration. An effective water quality monitoring network is important not only for model calibration and water quality prediction but also for resources management. Redundant or improperly located monitoring stations may cause increased monitoring costs without improvement to the understanding of water quality in watersheds. In this work, a decision-making methodology is proposed to design a water quality monitoring network by providing an adequate number of monitoring stations and their approximate locations at the eight-digit hydrologic unit codes (HUC8) scale. The proposed methodology is demonstrated for an example at the Upper Colorado River Basin (UCRB), where salinity is a serious concern. The level of monitoring redundancy or scarcity is defined by an index, station ratio (SR), which represents a monitoring density based on water quality load originated within a subbasin. By comparing the number of stations from a selected target SR with the available number of stations including the actual and the potential stations, the suggested number of stations in each subbasin was decided. If monitoring stations are primarily located in the low salinity loading subbasins, the average actual SR tends to increase, and vice versa. Results indicate that the spatial distribution of monitoring locations in 2011 is concentrated on low salinity loading subbasins, and therefore, additional monitoring is required for the high salinity loading subbasins. The proposed methodology shows that the SR is a simple and a practical indicator for monitoring density.

  18. Building Decision Trees for Characteristic Ellipsoid Method to Monitor Power System Transient Behaviors

    SciTech Connect

    Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.; Etingov, Pavel V.; Zhou, Ning; Dagle, Jeffery E.

    2010-12-01

    The characteristic ellipsoid is a new method to monitor the dynamics of power systems. Decision trees (DTs) play an important role in applying the characteristic ellipsoid method to system operation and analysis. This paper presents the idea and initial results of building DTs for detecting transient dynamic events using the characteristic ellipsoid method. The objective is to determine fault types, fault locations and clearance time in the system using decision trees based on ellipsoids of system transient responses. The New England 10-machine 39-bus system is used for running dynamic simulations to generate a sufficiently large number of transient events in different system configurations. Comprehensive transient simulations considering three fault types, two fault clearance times and different fault locations were conducted in the study. Bus voltage magnitudes and monitored reactive and active power flows are recorded as the phasor measurements to calculate characteristic ellipsoids whose volume, eccentricity, center and projection of the longest axis are used as indices to build decision trees. The DT performances are tested and compared by considering different sets of PMU locations. The proposed method demonstrates that the characteristic ellipsoid method is a very efficient and promising tool to monitor power system dynamic behaviors.

  19. Assessing Aridity, Hydrological Drought, and Recovery Using GRACE and GLDAS: a Case Study in Iraq

    NASA Astrophysics Data System (ADS)

    Moradkhani, H.; Almamalachy, Y. S.; Yan, H.; Ahmadalipour, A.; Irannezhad, M.

    2016-12-01

    Iraq has suffered from several drought events during the period of 2003-2012, which imposed substantial impacts on natural environment and socioeconomic sectors, e.g. lower discharge of Tigris and Euphrates, groundwater depletion and increase in its salinity, population migration, and agricultural degradation. To investigate the aridity and climatology of Iraq, Global Land Data Assimilation System (GLDAS) monthly datasets of precipitation, temperature, and evapotranspiration at 0.25 degree spatial resolution are used. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) deficit is used as the hydrological drought indicator. The data is available globally at 1 degree spatial resolution. This study aims to monitor hydrological drought and assess drought recovery time for the period of August 2002 until December 2015. Two approaches are implemented to derive the GRACE-based TWS deficit. The first approach estimates the TWS deficit based on the difference from its own climatology, while the second approach directly calculates the deficit from TWS anomaly. Severity of drought events are calculated by integrating monthly water deficit over the drought period. The results indicate that both methods are capable of capturing the severe drought events in Iraq, while the second approach quantifies higher deficit and severity. In addition, two methods are employed to assess drought recovery time based on the estimated deficit. Both methods indicate similar drought recovery times, varying from less than a month to 9 months. The results demonstrate that the GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information to decision makers for developing drought adaptation and mitigation strategies over data-sparse regions.

  20. Climate- and remote sensing-based tools for drought management application in North and South Korea

    NASA Astrophysics Data System (ADS)

    Nam, W.; Wardlow, B.; Hayes, M. J.; Tadesse, T.; Svoboda, M.; Fuchs, B.; Wilhite, D. A.

    2015-12-01

    North and South Korea have experienced more frequent and extreme droughts since the late 1990s. In recent years, severe droughts in 2000-2001, 2012, and 2015 have led to widespread agricultural and environmental impacts, and resulted in water shortages and large reductions in crop yields. This has been particularly problematic in the agricultural sector of North Korea, which has a high-level of vulnerability due to variations of climate and this, in turn, results in food security issues. This vulnerability is exacerbated by North Korea's relatively small area of arable land, most of which is not very productive. The objective of this study was to develop a drought management application using climate- and remote sensing-based tools for North and South Korea. These tools are essential for improving drought planning and preparedness in this area. In this study, various drought indicators derived from climate and remote sensing data (SPI, SC-PDSI, SPEI, and VegDRI-Korea) were investigated to monitor the current drought condition and evaluate their ability to characterize agricultural and meteorological drought events and their potential impacts. Results from this study can be used to develop or improve the national-level drought management application for these countries. The goal is to provide improved and more timely information on both the spatial and temporal dimensions of drought conditions and provide a tool to identify both past and present drought events in order to make more informed management decisions and reduce the impacts of current droughts and reduce the risk to future events.

  1. Impact of Drought on Groundwater and Soil Moisture - A Geospatial Tool for Water Resource Management

    NASA Astrophysics Data System (ADS)

    Ziolkowska, J. R.; Reyes, R.

    2016-12-01

    For many decades, recurring droughts in different regions in the US have been negatively impacting ecosystems and economic sectors. Oklahoma and Texas have been suffering from exceptional and extreme droughts in 2011-2014, with almost 95% of the state areas being affected (Drought Monitor, 2015). Accordingly, in 2011 alone, around 1.6 billion were lost in the agricultural sector alone as a result of drought in Oklahoma (Stotts 2011), and 7.6 billion in Texas agriculture (Fannin 2012). While surface water is among the instant indicators of drought conditions, it does not translate directly to groundwater resources that are the main source of irrigation water. Both surface water and groundwater are susceptible to drought, while groundwater depletion is a long-term process and might not show immediately. However, understanding groundwater availability is crucial for designing water management strategies and sustainable water use in the agricultural sector and other economic sectors. This paper presents an interactive geospatially weighted evaluation model and a tool at the same time to analyze groundwater resources that can be used for decision support in water management. The tool combines both groundwater and soil moisture changes in Oklahoma and Texas in 2003-2014, thus representing the most important indicators of agricultural and hydrological drought. The model allows for analyzing temporal and geospatial long-term drought at the county level. It can be expanded to other regions in the US and the world. The model has been validated with the Palmer Drought Index Severity Index to account for other indicators of meteorological drought. It can serve as a basis for an upcoming socio-economic and environmental analysis of drought events in the short and long-term in different geographic regions.

  2. Development of a decision support system for small reservoir irrigation systems in rainfed and drought prone areas.

    PubMed

    Balderama, Orlando F

    2010-01-01

    An integrated computer program called Cropping System and Water Management Model (CSWM) with a three-step feature (expert system-simulation-optimization) was developed to address a range of decision support for rainfed farming, i.e. crop selection, scheduling and optimisation. The system was used for agricultural planning with emphasis on sustainable agriculture in the rainfed areas through the use of small farm reservoirs for increased production and resource conservation and management. The application of the model was carried out using crop, soil, and climate and water resource data from the Philippines. Primarily, four sets of data representing the different rainfall classification of the country were collected, analysed, and used as input in the model. Simulations were also done on date of planting, probabilities of wet and dry period and with various capacities of the water reservoir used for supplemental irrigation. Through the analysis, useful information was obtained to determine suitable crops in the region, cropping schedule and pattern appropriate to the specific climate conditions. In addition, optimisation of the use of the land and water resources can be achieved in areas partly irrigated by small reservoirs.

  3. Sure I'm Sure: Prefrontal Oscillations Support Metacognitive Monitoring of Decision Making.

    PubMed

    Wokke, Martijn E; Cleeremans, Axel; Ridderinkhof, K Richard

    2017-01-25

    Successful decision making critically involves metacognitive processes such as monitoring and control of our decision process. Metacognition enables agents to modify ongoing behavior adaptively and determine what to do next in situations in which external feedback is not (immediately) available. Despite the importance of metacognition for many aspects of life, little is known about how our metacognitive system operates or about what kind of information is used for metacognitive (second-order) judgments. In particular, it remains an open question whether metacognitive judgments are based on the same information as first-order decisions. Here, we investigated the relationship between metacognitive performance and first-order task performance by recording EEG signals while participants were asked to make a "diagnosis" after seeing a sample of fictitious patient data (a complex pattern of colored moving dots of different sizes). To assess metacognitive performance, participants provided an estimate about the quality of their diagnosis on each trial. Results demonstrate that the information that contributes to first-order decisions differs from the information that supports metacognitive judgments. Further, time-frequency analyses of EEG signals reveal that metacognitive performance is associated specifically with prefrontal theta-band activity. Together, our findings are consistent with a hierarchical model of metacognition and suggest a crucial role for prefrontal oscillations in metacognitive performance. Monitoring and control of our decision process (metacognition) is a crucial aspect of adaptive decision making. Crucially, metacognitive skills enable us to adjust ongoing behavior and determine future decision making when immediate feedback is not available. In the present study, we constructed a "diagnosis task" that allowed us to assess in what way first-order task performance and metacognition are related to each other. Results demonstrate that the contribution

  4. Model-driven decision support for monitoring network design: methods and applications

    NASA Astrophysics Data System (ADS)

    Vesselinov, V. V.; Harp, D. R.; Mishra, P. K.; Katzman, D.

    2012-12-01

    A crucial aspect of any decision-making process for environmental management of contaminated sites and protection of groundwater resources is the identification of scientifically defensible remediation scenarios. The selected scenarios are ranked based on both their protective and cost effectiveness. The decision-making process is facilitated by implementation of site-specific data- and model-driven analyses for decision support (DS) taking into account existing uncertainties to evaluate alternative characterization and remedial activities. However, due to lack of data and/or complex interdependent uncertainties (conceptual elements, model parameters, measurement/computational errors, etc.), the DS optimization problem is ill posed (non unique) and the model-prediction uncertainties are difficult to quantify. Recently, we have developed and implemented several novel theoretical approaches and computational algorithms for model-driven decision support. New and existing DS tools have been employed for model analyses of the fate and extent of a chromium plume in the regional aquifer at Sandia Canyon Site, LANL. Since 2007, we have performed three iterations of DS analyses implementing different models, decision-making tools, and data sets providing guidance on design of a subsurface monitoring network for (1) characterization of the flow and transports processes, and (2) protection of the water users. The monitoring network is augmented by new wells at locations where acquired new data can effectively reduce uncertainty in model predicted contaminant concentrations. A key component of the DS analyses is contaminant source identification. Due to data and conceptual uncertainties, subsurface processes controlling the contaminant arrival at the top of the regional aquifer are not well defined. Nevertheless, the model-based analyses of the existing data and conceptual knowledge, including respective uncertainties, provide constrained probabilistic estimates of the

  5. Drought Water Right Curtailment

    NASA Astrophysics Data System (ADS)

    Walker, W.; Tweet, A.; Magnuson-Skeels, B.; Whittington, C.; Arnold, B.; Lund, J. R.

    2016-12-01

    California's water rights system allocates water based on priority, where lower priority, "junior" rights are curtailed first in a drought. The Drought Water Rights Allocation Tool (DWRAT) was developed to integrate water right allocation models with legal objectives to suggest water rights curtailments during drought. DWRAT incorporates water right use and priorities with a flow-forecasting model to mathematically represent water law and hydrology and suggest water allocations among water rights holders. DWRAT is compiled within an Excel workbook, with an interface and an open-source solver. By implementing California water rights law as an algorithm, DWRAT provides a precise and transparent framework for the complicated and often controversial technical aspects of curtailing water rights use during drought. DWRAT models have been developed for use in the Eel, Russian, and Sacramento river basins. In this study, an initial DWRAT model has been developed for the San Joaquin watershed, which incorporates all water rights holders in the basin and reference gage flows for major tributaries. The San Joaquin DWRAT can assess water allocation reliability by determining probability of rights holders' curtailment for a range of hydrologic conditions. Forecasted flow values can be input to the model to provide decision makers with the ability to make curtailment and water supply strategy decisions. Environmental flow allocations will be further integrated into the model to protect and improve ecosystem water reliability.

  6. Use of Land Surface Temperature Observations in a Two-Source Energy Balance Model Towards Improved Monitoring of Evapotranspiration and Drought

    NASA Astrophysics Data System (ADS)

    Hain, C.; Anderson, M. C.; Otkin, J.; Semmens, K. A.; Zhan, X.; Fang, L.; Li, Z.

    2014-12-01

    As the world's water resources come under increasing tension due to the dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. However, direct validation of ET models is challenging due to lack of available observations that are sufficiently representative at the model grid scale (10-100 km). Prognostic land-surface models require accurate information about observed precipitation, soil moisture storage, groundwater, and artificial controls on water supply (e.g., irrigation, dams, etc.) to reliably link rainfall to evaporative fluxes. In contrast, diagnostic estimates of ET can be generated, with no prior knowledge of the surface moisture state, by energy balance models using thermal-infrared remote sensing of land-surface temperature (LST) as a boundary condition. One such method, the Atmosphere Land Exchange Inverse (ALEXI) model provides estimates of surface energy fluxes through the use of mid-morning change in LST and radiation inputs. The LST inputs carry valuable proxy information regarding soil moisture and its effect on soil evaporation and canopy transpiration. Additionally, the Evaporative Stress Index (ESI) representing anomalies in the ratio of actual-to-potential ET has shown to be a reliable indicator of drought. ESI maps over the continental US show good correspondence with standard drought metrics and with patterns of precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Furthermore, ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development. Because precipitation is not used in construction of the ESI, it provides an independent assessment of drought conditions and has particular utility for real-time monitoring in regions with sparse rainfall data or

  7. The Ebb and Flow of Airborne Pathogens: Monitoring and Use in Disease Management Decisions.

    PubMed

    Mahaffee, Walter F; Stoll, Rob

    2016-05-01

    Perhaps the earliest form of monitoring the regional spread of plant disease was a group of growers gathering together at the market and discussing what they see in their crops. This type of reporting continues to this day through regional extension blogs, by crop consultants and more formal scouting of sentential plots in the IPM PIPE network (http://www.ipmpipe.org/). As our knowledge of plant disease epidemiology has increased, we have also increased our ability to detect and monitor the presence of pathogens and use this information to make management decisions in commercial production systems. The advent of phylogenetics, next-generation sequencing, and nucleic acid amplification technologies has allowed for development of sensitive and accurate assays for pathogen inoculum detection and quantification. The application of these tools is beginning to change how we manage diseases with airborne inoculum by allowing for the detection of pathogen movement instead of assuming it and by targeting management strategies to the early phases of the epidemic development when there is the greatest opportunity to reduce the rate of disease development. While there are numerous advantages to using data on inoculum presence to aid management decisions, there are limitations in what the data represent that are often unrecognized. In addition, our understanding of where and how to effectively monitor airborne inoculum is limited. There is a strong need to improve our knowledge of the mechanisms that influence inoculum dispersion across scales as particles move from leaf to leaf, and everything in between.

  8. Accesible hydrological monitoring for better decision making and modelling: a regional initiative in the Andes

    NASA Astrophysics Data System (ADS)

    De Bievre, B.; Célleri, R.; Crespo, P.; Ochoa, B.; Buytaert, W.; Tobón, C.; Villacís, M.; Villazon, M. F.; Llerena, C.; Rodriguez, M.; Viñas, P.

    2013-05-01

    The goal of the Hydrological Monitoring of Andean Ecosystems Initiative is to improve the conservation and management of High-Andean ecosystems by providing information on the hydrological response of these ecosystems and how different land-uses affect their water yield and regulation capacity. The initiative fills a gap left by widespread hydrological modeling exercises that suffer from lack of data, and by glacier monitoring under climate change. The initiative proposes a hydrological monitoring system involving precipitation, discharge and land cover monitoring in paired catchments. The methodology is accessible for non-specialist organizations, and allows for generation of evidence of land use impact on hydrology on the short term (i.e. a few years). Nevertheless, long term monitoring is pursued with the aim of identifying trends in hydrological response (as opposed to trends in climate) under global change. In this way it supports decision making on the preservation of the hydrological services of the catchment. The initiative aims at a high number of paired catchment sites along the Andes, in order to draw regional conclusions and capture variability, and is connected to more detailed hydrological research sites of several Andean universities. We present preliminary results of a dozen of sites from Venezuela to Bolivia, summarized in hydrological performance indicators that were agreed upon among hydrologists, local stakeholders, and water authorities. The success factors, as well as limitations, of the network are discussed.

  9. Operationalizing crop monitoring system for informed decision making related to food security in Nepal

    NASA Astrophysics Data System (ADS)

    Qamer, F. M.; Shah, S. N. Pd.; Murthy, M. S. R.; Baidar, T.; Dhonju, K.; Hari, B. G.

    2014-11-01

    In Nepal, two thirds of the total population depend on agriculture for their livelihoods and more than one third of Gross Domestic Product (GDP) comes from the agriculture sector. However, effective agriculture production across the country remains a serious challenge due to various factors, such as a high degree of spatial and temporal climate variability, irrigated and rain-fed agriculture systems, farmers' fragile social and economic fabric, and unique mountain practices. ICIMOD through SERVIR-Himalaya initiative with collaboration of Ministry of Agricultural Development (MoAD) is working on developing a comprehensive crop monitoring system which aims to provide timely information on crop growth and drought development conditions. This system analyzes historical climate and crop conditions patterns and compares this data with the current growing season to provide timely assessment of crop growth. Using remote sensing data for vegetation indices, temperature and rainfall, the system generated anomaly maps are inferred to predict the increase or shortfall in production. Comparisons can be made both spatially and in graphs and figures at district and Village Developmental Committee (VDC) levels. Timely information on possible anomaly in crop production is later used by the institutions like Ministry of Agricultural Development, Nepal and World Food Programme, Nepal to trigger appropriate management response. Future potential includes integrating data on agricultural inputs, socioeconomics, demographics, and transportation to holistically assess food security in the region served by SERVIR-Himalaya.

  10. In-situ monitoring of California's drought: Impacts on key hydrologic variables in the Southern Sierra Nevada

    NASA Astrophysics Data System (ADS)

    Oroza, C.; Zheng, Z.; Zhang, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

    2015-12-01

    Like many semi-arid regions, California relies on seasonal snowmelt from the Sierra Nevada mountain range to provide freshwater allocations for multiple stakeholders throughout the year. The magnitude and timing of runoff from these regions is being altered by consecutive years of drought, affecting downstream ecosystems, hydropower operations, and deliveries to agriculture and urban water users. Understanding the long-term effect of drought on the montane water balance requires temporally continuous, in-situ measurements of key hydrologic variables across large spatial domains. We discuss a seven-year dataset from the Southern Sierra Critical Zone Observatory, which includes co-located measurements of snowpack, soil moisture, and soil temperature in the Kings River watershed. We investigate how these key hydrologic variables are affected as the region transitions from winters that have nearly continuous snow cover (2008-2011) to winters with extended snow-on, snow-off periods (2012-2014). For water year 2014, we observe a 93% decline in average snowpack, a 35% decline in average soil moisture, and a 25% increase in average soil temperature compared to a wet-year index of each variable. We discuss the effect of physiographic features, including slope, aspect, elevation, and canopy coverage on the changes observed in each variable. Finally, we use sparse inverse covariance estimation to investigate the changing conditional relationships throughout the observatory in wet and dry years.

  11. Predictors of Ips confusus Outbreaks During a Record Drought in Southwestern USA: Implications for Monitoring and Management

    NASA Astrophysics Data System (ADS)

    Santos, Maria J.; Whitham, Thomas G.

    2010-02-01

    In many ecosystems the effects of disturbance can be cryptic and disturbance may vary in subtle spatiotemporal ways. For instance, we know that bark beetle outbreaks are more frequent in temperate forests during droughts; however, we have little idea about why they occur in some locations and not others. Understanding biotic and abiotic factors promoting bark beetle outbreaks can be critical to predicting and responding to pest outbreaks. Here we address the environmental factors which are associated with Ips confusus outbreaks during the 2002 widespread drought within the distribution range of pinyon pine woodlands in Arizona. We used univariate statistics to test if whether tree characteristics, other herbivores, stand properties, soil type, wind, and topography were associated with I. confusus outbreak, and logistic regression to create a predictive model for the outbreaks. We found that I. confusus attacks occur in low elevation stands on steeper slopes, where favorable winds for I. confusus dispersion occur. I. confusus select larger trees, in high density stands with understory shrubs that exhibit phenotypic traits characteristic of resistance to stem-boring moths. The model was highly accurate, and explained 95% of the variability in occurrence (98% of the absences and 95% of the presences). Accurate prediction of the impacts of disturbance allow us to anticipate, minimize or mitigate for and eventually counteract its effects, especially those affecting diversity and ecosystem function. Identification of outbreak risk areas can guide regional and national management towards the reduction of infestation risk and enhancing conservation of pinyon-juniper woodlands.

  12. Towards quantitatively monitoring the hydrological droughts over Tibetan Plateau: reconstruction of historical evapotranspiration at four large basins

    NASA Astrophysics Data System (ADS)

    Li, Xiuping; Wang, Lei

    2017-04-01

    Evapotranspiration (ET) is a critical factor that determines terrestrial water and energy budgets, and thus has direct influences on regional hydrological droughts. Lack of reliable historical basin-scale evapotranspiration (ET) estimates is a bottleneck for water balance analyses as well as drought studies on the Tibetan Plateau (TP). This study looks at four large basins on the TP to develop a general approach suitable for large river basins to estimate historical monthly ET. Five existing global ET products are evaluated against monthly ET estimated by the water balance method as a residual from precipitation (P), terrestrial water storage change (ΔS), and discharge (R). The five ET products exhibit similar seasonal variability, despite of the different amounts among them. A bias correction method, based on the probability distribution mapping between the reference ET and the five products during 2003-2012, effectively removes nearly all biases and significantly increases the reliability of the products. Then, the surface water balance changes for the four basins are analyzed based on the corrected ET products as well as observed P and R during 1983-2006. A trend analysis shows an upward trend for ET in the four basins for all seasons during the past three decades, along with the regional warming, as well as a dominating increasing trend in P and negative trend in R.

  13. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

    PubMed Central

    Orhan, Osman; Ekercin, Semih; Dadaser-Celik, Filiz

    2014-01-01

    The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2°C) in the Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–2°C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin. PMID:24587709

  14. Red, Amber or Green? Athlete Monitoring in Team Sport: The Need for Decision Support Systems.

    PubMed

    Robertson, Samuel; Bartlett, Jonathan D; Gastin, Paul B

    2016-12-14

    Decision support systems are used in team sport for a variety of purposes including evaluating individual performance and informing athlete selection. A particularly common form of decision support is the traffic light system, where colour coding is used to indicate a given status of an athlete with respect to performance or training availability. However despite relatively widespread use, there remains a lack of standardisation with respect to how traffic light systems are operationalised. This paper addresses a range of pertinent issues for practitioners relating to the practice of traffic light monitoring in team sports. Specifically, the types and formats of data incorporated in such systems are discussed, along with the various analysis approaches available. Considerations relating to the visualisation and communication of results to key stakeholders in the team sport environment are also presented. In order for the efficacy of traffic light systems to be improved, future iterations should look to incorporate the recommendations made here.

  15. A National Crop Progress Monitoring and Decision Support System Based on NASA Earth Science Results

    NASA Astrophysics Data System (ADS)

    di, L.; Yang, Z.

    2009-12-01

    Timely and accurate information on weekly crop progress and development is essential to a dynamic agricultural industry in the U. S. and the world. By law, the National Agricultural Statistics Service (NASS) of the U. S. Department of Agriculture’s (USDA) is responsible for monitoring and assessing U.S. agricultural production. Currently NASS compiles and issues weekly state and national crop progress and development reports based on reports from knowledgeable state and county agricultural officials and farmers. Such survey-based reports are subjectively estimated for an entire county, lack spatial coverage, and are labor intensive. There has been limited use of remote sensing data to assess crop conditions. NASS produces weekly 1-km resolution un-calibrated AVHRR-based NDVI static images to represent national vegetation conditions but there is no quantitative crop progress information. This presentation discusses the early result for developing a National Crop Progress Monitoring and Decision Support System. The system will overcome the shortcomings of the existing systems by integrating NASA satellite and model-based land surface and weather products, NASS’ wealth of internal crop progress and condition data and Cropland Data Layers (CDL), and the Farm Service Agency’s (FSA) Common Land Units (CLU). The system, using service-oriented architecture and web service technologies, will automatically produce and disseminate quantitative national crop progress maps and associated decision support data at 250-m resolution, as well as summary reports to support NASS and worldwide users in their decision-making. It will provide overall and specific crop progress for individual crops from the state level down to CLU field level to meet different users’ needs on all known croplands. This will greatly enhance the effectiveness and accuracy of the NASS aggregated crop condition data and charts of and provides objective and scientific evidence and guidance for the

  16. Monitoring and Assessment Science to Support Decision-Making by the United Nations Convention to Combat Desertification (UNCCD)

    NASA Astrophysics Data System (ADS)

    Winslow, M.; Akhtar-Schuster, M.; Cherlet, M.; Martius, C.; Sommer, S.; Thomas, R.; Vogt, J.

    2009-12-01

    The United Nations Convention to Combat Desertification (UNCCD) is a global treaty that emerged from the Rio Earth Summit and formally took force in 1996. It has now been ratified by 193 countries (known as Parties to the Convention). Yet the UNCCD has gained only modest support from donors, largely due to questions about the science base underlying its target issue (desertification) resulting in ambiguous definitions and quantification of the problem. The UNCCD recognizes the need to reform itself and commissioned a scientific conference in Buenos Aires, Argentina in September 2009 to discuss ways to improve the scientific underpinning of monitoring and assessment (M&A) of desertification, land degradation and drought (DLDD). Previous attempts by the UNCCD on M&A focused largely on a search for a common, simple, universal set of indicators that could be reported by country Parties to the Convention Secretariat, which would collate them into a global report. However experience found that no single set of indicators is satisfactory to all countries, because DLDD depends strongly on the local environmental and human/social context. Three preparatory Working Groups analyzed the issue of DLDD M&A and recommended the following. Parties should recognize that M&A methods must integrate human-environment parameters to capture the complexity of DLDD phenomena as defined in the Convention’s text. Traditional tendencies had been to isolate biophysical from social and economic parameters, leading to unrealistic conclusions. Parties should take advantage of a much wider range of analytical techniques than just the coarse-scale indicators that had been their main focus to date. Powerful but underutilized techniques include integrated assessment models, remote sensing, geographic information systems and mapping, participatory stakeholder assessment, hierarchical aggregation of related data, knowledge management and many others. Multiple methods could provide validation checks

  17. The Tree Drought Emission MONitor (Tree DEMON), an innovative system for assessing biogenic volatile organic compounds emission from plants.

    PubMed

    Lüpke, Marvin; Steinbrecher, Rainer; Leuchner, Michael; Menzel, Annette

    2017-01-01

    Biogenic volatile organic compounds (BVOC) emitted by plants play an important role for ecological and physiological processes, for example as response to stressors. These emitted compounds are involved in chemical processes within the atmosphere and contribute to the formation of aerosols and ozone. Direct measurement of BVOC emissions requires a specialized sample system in order to obtain repeatable and comparable results. These systems need to be constructed carefully since BVOC measurements may be disturbed by several side effects, e.g., due to wrong material selection and lacking system stability. In order to assess BVOC emission rates, a four plant chamber system was constructed, implemented and throughout evaluated by synthetic tests and in two case studies on 3-year-old sweet chestnut seedlings. Synthetic system test showed a stable sampling with good repeatability and low memory effects. The first case study demonstrated the capability of the system to screen multiple trees within a few days and revealed three different emission patterns of sweet chestnut trees. The second case study comprised an application of drought stress on two seedlings compared to two in parallel assessed seedlings of a control. Here, a clear reduction of BVOC emissions during drought stress was observed. The developed system allows assessing BVOC as well as CO2 and water vapor gas exchange of four tree specimens automatically and in parallel with repeatable results. A canopy volume of 30 l can be investigated, which constitutes in case of tree seedlings the whole canopy. Longer lasting experiments of e.g., 1-3 weeks can be performed easily without any significant plant interference.

  18. Sexism and beautyism effects in selection as a function of self-monitoring level of decision maker.

    PubMed

    Jawahar, I M; Mattsson, Jonny

    2005-05-01

    The authors, in two experiments, investigated the influence of the sex and attractiveness of applicants for male and female sex-typed jobs on selection decisions made by low and high self-monitors. In both experiments, attractiveness and the congruence between applicants' sex and the sex type of the job influenced selection decisions. In addition, high self-monitors were more influenced by attractiveness and sex of the applicant when hiring for sex-typed jobs than low self-monitors, but this difference in hiring pattern was not evident when the job was gender neutral. Results indicate that job applicants may encounter different employment opportunities as a function of their sex, their physical attractiveness, the sex type of the job, and the self-monitoring level of the decision maker. Implications of results are discussed and suggestions for future research are offered.

  19. Drought in the Anthropocene

    NASA Astrophysics Data System (ADS)

    van Loon, Anne F.; Gleeson, Tom; Clark, Julian; van Dijk, Albert I. J. M.; Stahl, Kerstin; Hannaford, Jamie; di Baldassarre, Giuliano; Teuling, Adriaan J.; Tallaksen, Lena M.; Uijlenhoet, Remko; Hannah, David M.; Sheffield, Justin; Svoboda, Mark; Verbeiren, Boud; Wagener, Thorsten; Rangecroft, Sally; Wanders, Niko; van Lanen, Henny A. J.

    2016-02-01

    Drought management is inefficient because feedbacks between drought and people are not fully understood. In this human-influenced era, we need to rethink the concept of drought to include the human role in mitigating and enhancing drought.

  20. Integrating SAR and derived products into operational volcano monitoring and decision support systems

    NASA Astrophysics Data System (ADS)

    Meyer, F. J.; McAlpin, D. B.; Gong, W.; Ajadi, O.; Arko, S.; Webley, P. W.; Dehn, J.

    2015-02-01

    Remote sensing plays a critical role in operational volcano monitoring due to the often remote locations of volcanic systems and the large spatial extent of potential eruption pre-cursor signals. Despite the all-weather capabilities of radar remote sensing and its high performance in monitoring of change, the contribution of radar data to operational monitoring activities has been limited in the past. This is largely due to: (1) the high costs associated with radar data; (2) traditionally slow data processing and delivery procedures; and (3) the limited temporal sampling provided by spaceborne radars. With this paper, we present new data processing and data integration techniques that mitigate some of these limitations and allow for a meaningful integration of radar data into operational volcano monitoring decision support systems. Specifically, we present fast data access procedures as well as new approaches to multi-track processing that improve near real-time data access and temporal sampling of volcanic systems with SAR data. We introduce phase-based (coherent) and amplitude-based (incoherent) change detection procedures that are able to extract dense time series of hazard information from these data. For a demonstration, we present an integration of our processing system with an operational volcano monitoring system that was developed for use by the Alaska Volcano Observatory (AVO). Through an application to a historic eruption, we show that the integration of SAR into systems such as AVO can significantly improve the ability of operational systems to detect eruptive precursors. Therefore, the developed technology is expected to improve operational hazard detection, alerting, and management capabilities.

  1. Influence of coupled ocean-atmosphere phenomena on the Greater Horn of Africa droughts and their implications.

    PubMed

    Mpelasoka, Freddie; Awange, Joseph L; Zerihun, Ayalsew

    2017-08-17

    Drought-like humanitarian crises in the Greater Horn of Africa (GHA) are increasing despite recent progress in drought monitoring and prediction efforts. Notwithstanding these efforts, there remain challenges stemming from uncertainty in drought prediction, and the inflexibility and limited buffering capacity of the recurrent impacted systems. The complexity of the interactions of ENSO, IOD, IPO and NAO, arguably remains the main source of uncertainty in drought prediction. To develop practical drought risk parameters that potentially can guide investment strategies and risk-informed planning, this study quantifies, drought characteristics that underpin drought impacts management. Drought characteristics that include probability of drought-year occurrences, durations, areal-extent and their trends over 11 decades (1903-2012) were derived from the Standardized Precipitation Index (SPI).Transient probability of drought-year occurrences, modelled on Beta distribution, across the region ranges from 10 to 40%, although most fall within 20-30%. For more than half of the drought events, durations of up to 4, 7, 14 and 24months for the 3-, 6-, 12- and 24-month timescales were evident, while 1 out of 10 events persisted for up to 18months for the short timescales, and up to 36months or more for the long timescales. Apparently, only drought areal-extent showed statistically significant trends of up to 3%, 1%, 3.7%, 2.4%, 0.7%, -0.3% and -0.6% per decade over Sudan, Eritrea, Ethiopia, Somalia, Kenya, Uganda and Tanzania, respectively. Since there is no evidence of significant changes in drought characteristics, the peculiarity of drought-like crises in the GHA can be attributed (at least in part) to unaccounted for systematic rainfall reduction. This highlights the importance of distinguishing drought impacts from those associated with new levels of aridity. In principle drought is a temporary phenomenon while aridity is permanent, a difference that managers and decision

  2. Citizen empowerment in volcano monitoring, communication and decision-making at Tungurahua volcano, Ecuador

    NASA Astrophysics Data System (ADS)

    Bartel, B. A.; Mothes, P. A.

    2013-12-01

    Trained citizen volunteers called vigías have worked to help monitor and communicate warnings about Tungurahua volcano, in Ecuador, since the volcano reawoke in 1999. The network, organized by the scientists of Ecuador's Instituto Geofísico de la Escuela Politécnica Nacional (Geophysical Institute) and the personnel from the Secretaría Nacional de Gestión de Riesgos (Risk Management, initially the Civil Defense), has grown to more than 20 observers living around the volcano who communicate regularly via handheld two-way radios. Interviews with participants conducted in 2010 indicate that the network enables direct communication between communities and authorities; engenders trust in scientists and emergency response personnel; builds community; and empowers communities to make decisions in times of crisis.

  3. Citizen Empowerment in Volcano Monitoring, Communication and Decision-Making at Tungurahua Volcano, Ecuador

    NASA Astrophysics Data System (ADS)

    Bartel, B.; Mothes, P. A.

    2013-05-01

    Trained citizen volunteers called vigías have worked to help monitor and communicate warnings about Tungurahua volcano, in Ecuador, since the volcano reawoke in 1999. The network, organized by the scientists of Ecuacor's Instituto Geofísico de la Escuela Politécnica Nacional (Geophysical Institute) and the personnel from the Secretaría Nacional de Gestión de Riesgos (Risk Management, initially the Civil Defense), has grown to well over 20 observers living around the volcano who communicate regularly via handheld two-way radios. Interviews with participants in 2010 indicate that the network enables direct communication between communities and authorities, engenders trust in scientists and emergency response personnel, builds community, and empowers communities to make decisions in times of crisis.

  4. Applications of neural networks to monitoring and decision making in the operation of nuclear power plants

    SciTech Connect

    Uhrig, R.E. Oak Ridge National Lab., TN )

    1990-01-01

    Application of neural networks to monitoring and decision making in the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (1) diagnosing specific abnormal conditions or problems in nuclear power plants, (2) detection of the change of mode of operation of the plant, (3) validating signals coming from detectors, (4) review of noise'' data from TVA's Sequoyah Nuclear Power Plant, and (5) examination of the NRC's database of Letter Event Reports'' for correlation of sequences of events in the reported incidents. Each of these projects and its status are described briefly in this paper. This broad based program has as its objective the definition of the state-of-the-art in using neural networks to enhance the performance of commercial nuclear power plants.

  5. Quantification of the Deterrent Effect of Radiation Portal Monitors Using a Decision Theory Model

    SciTech Connect

    Heasler, Patrick G.; Wood, Thomas W.

    2005-04-28

    Operation of radiation portal monitors (RPMs) can be modeled as a two-person game, with the RPM operator attempting to detect any nuclear weapon passing through the portal, while the opponent tries to pass it through undetected. A key element in the defender's decision strategy is the detection algorithm he employs, while a key element of the opponent's strategy is the threat density he employs. This article constructs a game-theoretic formulation for RPM operation and calculates the ''best'' strategy for each player, called the Minimax strategy. This solution allows one to quantify the deterrent effect that the inspection system has on the opponent--that is, the reduction in threat density due to use of the system.

  6. A haptic-inspired audio approach for structural health monitoring decision-making

    NASA Astrophysics Data System (ADS)

    Mao, Zhu; Todd, Michael; Mascareñas, David

    2015-03-01

    Haptics is the field at the interface of human touch (tactile sensation) and classification, whereby tactile feedback is used to train and inform a decision-making process. In structural health monitoring (SHM) applications, haptic devices have been introduced and applied in a simplified laboratory scale scenario, in which nonlinearity, representing the presence of damage, was encoded into a vibratory manual interface. In this paper, the "spirit" of haptics is adopted, but here ultrasonic guided wave scattering information is transformed into audio (rather than tactile) range signals. After sufficient training, the structural damage condition, including occurrence and location, can be identified through the encoded audio waveforms. Different algorithms are employed in this paper to generate the transformed audio signals and the performance of each encoding algorithms is compared, and also compared with standard machine learning classifiers. In the long run, the haptic decision-making is aiming to detect and classify structural damages in a more rigorous environment, and approaching a baseline-free fashion with embedded temperature compensation.

  7. Sensor data monitoring and decision level fusion scheme for early fire detection

    NASA Astrophysics Data System (ADS)

    Rizogiannis, Constantinos; Thanos, Konstantinos Georgios; Astyakopoulos, Alkiviadis; Kyriazanos, Dimitris M.; Thomopoulos, Stelios C. A.

    2017-05-01

    The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors such as temperature, humidity and CO2 and then the Rate-of-Rise of each changed parameter is calculated. In the second level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic theory and then the corresponding mass values are combined in a decision level fusion process using Evidential Reasoning theory to estimate the final fire event probability.

  8. Monitoring Natural World Heritage Sites: optimization of the monitoring system in Bogda with GIS-based multi-criteria decision analysis.

    PubMed

    Wang, Zhaoguo; Du, Xishihui

    2016-07-01

    Natural World Heritage Sites (NWHSs) are invaluable treasure due to the uniqueness of each site. Proper monitoring and management can guarantee their protection from multiple threats. In this study, geographic information system (GIS)-based multi-criteria decision analysis (GIS-MCDA) was used to assess criteria layers acquired from the data available in the literature. A conceptual model for determining the priority area for monitoring in Bogda, China, was created based on outstanding universal values (OUV) and expert knowledge. Weights were assigned to each layer using the analytic hierarchy process (AHP) based on group decisions, encompassing three experts: one being a heritage site expert, another a forest ranger, and the other a heritage site manager. Subsequently, evaluation layers and constraint layers were used to generate a priority map and to determine the feasibility of monitoring in Bogda. Finally, a monitoring suitability map of Bogda was obtained by referencing priority and feasibility maps.The high-priority monitoring area is located in the montane forest belt, which exhibits high biodiversity and is the main tourist area of Bogda. The northern buffer zone of Bogda comprises the concentrated feasible monitoring areas, and the area closest to roads and monitoring facilities is highly feasible for NWHS monitoring. The suitability of an area in terms of monitoring is largely determined by the monitoring priority in that particular area. The majority of planned monitoring facilities are well distributed in both suitable and less suitable areas. Analysis results indicate that the protection of Bogda will be more scientifically based due to its effective and all-around planned monitoring system proposed by the declaration text of Xinjiang Tianshan, which is the essential file submitted to World Heritage Centre to inscribe as a NWHS.

  9. A Framework for Drought Risk Management

    NASA Astrophysics Data System (ADS)

    Apurv, T.; Cai, X.

    2016-12-01

    Drought is one of the most expensive natural disasters as it affects many sectors of the economy. The threat posed by droughts is expected to further increase due to increasing water demands fuelled by increasing population and also due to climate change in many regions. Management of the increasing drought risk requires shift from traditional crisis management approaches to long term strategic planning for reduction of drought risk. This study proposes a framework for management of long term drought risk. The framework uses the system based approach proposed by Tsakiris et al. (2013), in which a watershed is considered as a system and different water sources in the watershed (like groundwater, reservoirs, streams etc.) are considered as subsystems associated with certain water requirements of different sectors. Droughts are defined separately for each subsystem considering water availability and requirement. The percentile based drought indicator framework proposed by Steinemann et al. (2015) is used for defining drought for each subsystem, allowing the selection of thresholds, variables of interest, and time scale which are most relevant for stakeholders dependent on a particular subsystem. Future drought risk under different drought management strategies are assessed using hydrologic models that model both hydrologic and human components of a watershed. The robustness of a management strategy is assessed by simulating system response across a wide range of stochastically generated future climate scenarios. The framework is useful for operational drought management as it allows direct management of drought risks with consideration of different water sources and water users. Steinemann, A., Iacobellis, S.F., Cayan, D.R., (2015) "Developing and evaluating drought indicators for decision-making" J. Hydrometeor. 16 (4), 1793-1803 Tsakiris, G, Nalbantis, I, Vangelis, H, Verbeiren, B, Huysmans, M, Tychon, B, Jacquemin, I, Canters, F, Vanderhaegen, S, Engelen, G

  10. Evaluation of corn genotypes for drought and heat stress tolerance using physiological measurements and a microcontroller-based monitoring system

    USDA-ARS?s Scientific Manuscript database

    Moisture deficit accompanied by high temperature are major abiotic stress factors that affect corn production in the southern United States, particularly during the reproductive stage of the plant. In evaluating plants for environmental stress tolerance, it is important to monitor changes in their ...

  11. Big Data Architectures for Operationalized Seismic and Subsurface Monitoring and Decision Support Workflows

    NASA Astrophysics Data System (ADS)

    Irving, D. H.; Rasheed, M.; Hillman, C.; O'Doherty, N.

    2012-12-01

    Oilfield management is moving to a more operational footing with near-realtime seismic and sensor monitoring governing drilling, fluid injection and hydrocarbon extraction workflows within safety, productivity and profitability constraints. To date, the geoscientific analytical architectures employed are configured for large volumes of data, computational power or analytical latency and compromises in system design must be made to achieve all three aspects. These challenges are encapsulated by the phrase 'Big Data' which has been employed for over a decade in the IT industry to describe the challenges presented by data sets that are too large, volatile and diverse for existing computational architectures and paradigms. We present a data-centric architecture developed to support a geoscientific and geotechnical workflow whereby: ●scientific insight is continuously applied to fresh data ●insights and derived information are incorporated into engineering and operational decisions ●data governance and provenance are routine within a broader data management framework Strategic decision support systems in large infrastructure projects such as oilfields are typically relational data environments; data modelling is pervasive across analytical functions. However, subsurface data and models are typically non-relational (i.e. file-based) in the form of large volumes of seismic imaging data or rapid streams of sensor feeds and are analysed and interpreted using niche applications. The key architectural challenge is to move data and insight from a non-relational to a relational, or structured, data environment for faster and more integrated analytics. We describe how a blend of MapReduce and relational database technologies can be applied in geoscientific decision support, and the strengths and weaknesses of each in such an analytical ecosystem. In addition we discuss hybrid technologies that use aspects of both and translational technologies for moving data and analytics

  12. Managing the risk of agricultural drought in Africa

    NASA Astrophysics Data System (ADS)

    Quaife, T. L.; Black, E.; Brown, M.; Greatrex, H.; Maidment, R.; Mookerjee, A.; Tarnavsky, E.

    2015-12-01

    Farmers in Africa are highly vulnerable to variability in the weather - especially to drought. Robust and timely information on drought risk can enable farmers to take action to increase yields. Such information also forms the basis of financial instruments, such as weather index insurance. Monitoring weather conditions is, however, difficult in Africa because of the heteorogeneity of the climate, and the sparcity of the ground-observing network. Remotely sensed data (for example satellite-based rainfall estimates) are an alternative to ground observations - but only if the algorithms have skill and the data are presented in a useful form. A more fundamental issue is that the condition of the land surface is affected by factors other than rainfall. The evolving risk of agricultural drought is thus determined by the properties of the land surface, the contemporaneous soil moisture and the risk of rainfall deficits. We present a prototype agricultural decision support tool, based on the JULES land-surface model, driven with ensembles of meteorological driving data, which encompass the uncertainty in rainfall. We discuss the application of the tool for designing and implementing drought insurance in Ghana and Zambia - illustrated with real examples of weather index insurance schemes that are already active.

  13. Decision analyses for optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

    NASA Astrophysics Data System (ADS)

    Vesselinov, V. V.; Harp, D.

    2011-12-01

    Model-based decision making related to environmental management problems is a challenging problem. There has been substantial theoretical research and practical applications related to this problem. However, there are very few cases in which the actual decision analyses have been tested in the field to evaluate their adequacy. Over the last several years, we have performed a series of decision analyses to support optimization of a monitoring network at the Los Alamos National Laboratory (LANL) site. The problem deals with contaminant transport in the regional aquifer beneath the LANL site. At three separate stages, the existing monitoring network was augmented based on analyses of the existing uncertainties; in total, five new monitoring wells were proposed. At each stage, the data collected at the new monitoring wells demonstrated the adequacy of the prior uncertainty and decision analyses. The decision analyses required a detailed estimation of uncertainties in model predictions. Various uncertainties, including measurement errors and uncertainties in the conceptualization and model parameters, contributed to the uncertainties in the model predictions. The decision analyses were computationally intensive requiring on the order of one million model simulations; computational efficiency is achieved using (1) high-performance computing (LANL multiprocessor clusters), (2) novel computational techniques for model analysis, and (3) a simple analytical 3D simulator to simulate contaminant transport. Decision support related to optimal design of monitoring networks required optimization of the proposed new monitoring well locations in order to reduce existing model-prediction uncertainties and environmental risk. An important aspect of the analysis is the application of novel techniques for optimization (SQUADS based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods; Vesselinov & Harp, 2011) and uncertainty quantification (ABAGUS: Agent

  14. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0 Dataset: 35 year Quasi-Global Precipitation Estimates for Drought Monitoring

    NASA Astrophysics Data System (ADS)

    Peterson, P.; Funk, C. C.; Landsfeld, M. F.; Pedreros, D. H.; Shukla, S.; Husak, G. J.; Harrison, L.; Verdin, J. P.

    2015-12-01

    A high quality, long-term, high-resolution precipitation dataset is a key requirement for supporting drought monitoring and long term trend analysis. In this presentation we introduce a new dataset: the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) v2.0, developed by scientists at the University of California, Santa Barbara and the U.S. Geological Survey Earth Resources Observation and Science Center. This new quasi-global precipitation product is available at daily to seasonal time scales, with a spatial resolution of 0.05°, and a 1981 to near real-time period of record. The three main types of information used in the CHIRPS are: (1) global 0.05° precipitation climatologies, (2) gridded precipitation estimates derived from time-varying cold cloud duration, and (3) in situ precipitation observations. The Climate Hazards Group (CHG) has developed an extensive database of in situ daily, pentadal, and monthly precipitation totals with over a billion daily observations worldwide. A screening procedure was developed to flag and remove potential false zeros from the daily GTS and GSOD data. These potentially spurious data can artificially suppress CHIRPS rainfall totals. Using GPCC v7 as the best-available standard, we compare CHIRPS with ARC2, CFS-Reanalysis, CHIRP, CMORPH, CPC-Unified, ECMWF, PERSIANNE, RFE2, TAMSAT, TRMM-RT7, and TRMM-V7. The CHIRPS is shown to have higher correlation, and lower systematic errors (bias) and mean absolute errors with GPCC v7 than the other datasets. Comparison with independent validation data suggests that the CHIRPS performance is similar to research quality products like the GPCC and GPCP, but with higher resolution and lower latency. We conclude by looking at the change in availability of station data within a monitoring time frame, contrasting countries with and without near real time data.

  15. Rational risk-based decision support for drinking water well managers by optimized monitoring designs

    NASA Astrophysics Data System (ADS)

    Enzenhöfer, R.; Geiges, A.; Nowak, W.

    2011-12-01

    Advection-based well-head protection zones are commonly used to manage the contamination risk of drinking water wells. Considering the insufficient knowledge about hazards and transport properties within the catchment, current Water Safety Plans recommend that catchment managers and stakeholders know, control and monitor all possible hazards within the catchments and perform rational risk-based decisions. Our goal is to supply catchment managers with the required probabilistic risk information, and to generate tools that allow for optimal and rational allocation of resources between improved monitoring versus extended safety margins and risk mitigation measures. To support risk managers with the indispensable information, we address the epistemic uncertainty of advective-dispersive solute transport and well vulnerability (Enzenhoefer et al., 2011) within a stochastic simulation framework. Our framework can separate between uncertainty of contaminant location and actual dilution of peak concentrations by resolving heterogeneity with high-resolution Monte-Carlo simulation. To keep computational costs low, we solve the reverse temporal moment transport equation. Only in post-processing, we recover the time-dependent solute breakthrough curves and the deduced well vulnerability criteria from temporal moments by non-linear optimization. Our first step towards optimal risk management is optimal positioning of sampling locations and optimal choice of data types to reduce best the epistemic prediction uncertainty for well-head delineation, using the cross-bred Likelihood Uncertainty Estimator (CLUE, Leube et al., 2011) for optimal sampling design. Better monitoring leads to more reliable and realistic protection zones and thus helps catchment managers to better justify smaller, yet conservative safety margins. In order to allow an optimal choice in sampling strategies, we compare the trade-off in monitoring versus the delineation costs by accounting for ill

  16. Ground-water availability in part of the Borough of Carroll Valley, Adams County, Pennsylvania, and the establishment of a drought-monitor well

    USGS Publications Warehouse

    Low, Dennis J.; Conger, Randall W.

    2002-01-01

    Continued population growth in the Borough of Carroll Valley (Borough) coupled with the drought of 2001 have increased the demand for ground water in the Borough. This demand has led Borough officials to undertake an effort to evaluate the capability of the crystalline-bedrock aquifers to meet future, projected growth and to establish a drought-monitor well within and for the use of the Borough. As part of this effort, this report summarizes ground-water data available from selected sections within the Borough and provides geohydrologic information needed to evaluate ground-water availability and recharge sources within part of the Borough. The availability of ground water in the Borough is limited by the physical characteristics of the underlying bedrock, and its upland topographic setting. The crystalline rocks (metabasalt, metarhyolite, greenstone schist) that underlie most of the study area are among the lowest yielding aquifers in the Commonwealth. More than 25 percent of the wells drilled in the metabasalt, the largest bedrock aquifer in the study area, have driller reported yields less than 1.25 gallons per minute. Driller reports indicate also that water-producing zones are shallow and few in number. In general, 50 percent of the water-producing zones reported by drillers are penetrated at depths of 200 feet or less and 90 percent at depths of 370 feet or less. Borehole geophysical data indicate that most of the water-producing zones are at lithologic contacts, but such contacts are penetrated infrequently and commonly do not intersect areas of ground-water recharge. Single-well aquifer tests and slug tests indicate that the bedrock aquifers also do not readily transmit large amounts of water. The median hydraulic conductivity and transmissivity of the bedrock aquifers are 0.01 foot per dayand 2.75 feet squared per day, respectively. The crystalline and siliciclastic (Weverton and Loudoun Formations) bedrock aquifers are moderately to highly resistant to

  17. Large Aquatic Ecosystem Restoration Monitoring for Decision Makers: Monitoring to Target and Evaluate Success of Ecosystem Restoration

    EPA Science Inventory

    Monitoring ecosystem restoration at various scales in LAEs can be challenging, frustrating and rewarding. Some of the major ecosystem restoration monitoring occurring in LAEs include: seagrass expansion/contraction; dead zone sizes; oyster reefs; sea turtle nesting; toxic and nu...

  18. Large Aquatic Ecosystem Restoration Monitoring for Decision Makers: Monitoring to Target and Evaluate Success of Ecosystem Restoration

    EPA Science Inventory

    Monitoring ecosystem restoration at various scales in LAEs can be challenging, frustrating and rewarding. Some of the major ecosystem restoration monitoring occurring in LAEs include: seagrass expansion/contraction; dead zone sizes; oyster reefs; sea turtle nesting; toxic and nu...

  19. Evaluation and application of the SPDI-JDI for droughts in Texas, USA

    NASA Astrophysics Data System (ADS)

    Ma, Mingwei; Ren, Liliang; Singh, Vijay P.; Tu, Xinjun; Jiang, Shanhu; Liu, Yi

    2015-02-01

    The lack of a system/model for integration of drought-related information has been an important obstacle in efforts for accurate and reliable drought monitoring and prediction. This study proposes an integrated multivariate standardized drought index (i.e. standardized Palmer drought index-based joint drought index, SPDI-JDI), where the Palmer scheme/model is accepted as a multivariate multi-index conceptual framework that integrates multiple drought-related indicators for characterizing drought. Using the meteorological data of ten climate divisions from Texas, the computed SPDI-JDI index is first compared to various ground observations (e.g. streamflow, lake/reservoir water level, soil moisture content and groundwater level) for the reflection of drought/wetness conditions. Moreover, the SPDI-JDI is also evaluated against Palmer drought indices and US Drought Monitor for drought detection. Results indicate that the SPDI-JDI is in good/acceptable agreements with surface and subsurface water anomalies and performs well with respect to the integrated use of Palmer drought severity index, Palmer modified drought index, Palmer hydrologic drought index and Palmer Z index as well as to the US Drought Monitor observations. Potential implications are that the SPDI-JDI allows for insights into different impacts of drought and leads to high probability of drought detection versus multi-source drought information, implying attractive properties originating from its physically inclusive and multivariate joint probabilistic combined nature.

  20. Distributed multisensor processing, decision making, and control under constrained resources for remote health and environmental monitoring

    NASA Astrophysics Data System (ADS)

    Talukder, Ashit; Sheikh, Tanwir; Chandramouli, Lavanya

    2004-04-01

    Previous field-deployable distributed sensing systems for health/biomedical applications and environmental sensing have been designed for data collection and data transmission at pre-set intervals, rather than for on-board processing These previous sensing systems lack autonomous capabilities, and have limited lifespans. We propose the use of an integrated machine learning architecture, with automated planning-scheduling and resource management capabilities that can be used for a variety of autonomous sensing applications with very limited computing, power, and bandwidth resources. We lay out general solutions for efficient processing in a multi-tiered (three-tier) machine learning framework that is suited for remote, mobile sensing systems. Novel dimensionality reduction techniques that are designed for classification are used to compress each individual sensor data and pass only relevant information to the mobile multisensor fusion module (second-tier). Statistical classifiers that are capable of handling missing/partial sensory data due to sensor failure or power loss are used to detect critical events and pass the information to the third tier (central server) for manual analysis and/or analysis by advanced pattern recognition techniques. Genetic optimisation algorithms are used to control the system in the presence of dynamic events, and also ensure that system requirements (i.e. minimum life of the system) are met. This tight integration of control optimisation and machine learning algorithms results in a highly efficient sensor network with intelligent decision making capabilities. The applicability of our technology in remote health monitoring and environmental monitoring is shown. Other uses of our solution are also discussed.

  1. Water uptake patterns and root system architecture of Zea mays in a natural soil under influence of drought stress monitored by MRI

    NASA Astrophysics Data System (ADS)

    Merz, Steffen; Pohlmeier, Andreas; Seidler, Christina; van Dusschoten, Dagmar; Vereecken, Harry

    2012-04-01

    The interface between roots and soil plays a key role in water transport in the Soil-Plant-Atmosphere-Continuum (SPAC). The transport which changes with the degree of dehydration is influenced by both the hydraulic conductivity of roots and the soil. One important factor in plant growth is the amount of available water in the soil, which correlates directly with soil texture. Water uptake of plant roots and water uptake patterns in soil can be monitored using non-invasive 1H Nuclear Magnetic Resonance Imaging (MRI). In a preceding study the effect of root water uptake and uniform desiccation patterns under drought conditions were observed for Ricinus communis grown in a model medium (Pohlmeier et al. 2008). Continuing these studies, the new aspect is the determination of water uptake patterns and root system architecture in a natural soil. The general challenge of MRI in soils are the inherent fast relaxation times T2* and T2 of the soil matrix. With the use of conventional sequences only water in macropores can be determined. The loss of sensitivity can be overcome by MRI sequences with sufficiently short detection times. In this work we employed and assessed two methods: SPI (Single Point Imaging) detects the T2* relaxation with a dead time of < 0.05 ms and SE3D (Spin Echo 3D) probes T2,eff with an echo time of about 0.8 ms. Zea mays, planted in a cylindrical container filled with a natural soil was completely sealed after 4 weeks of growth to avoid evaporation, so water loss took place via transpiration only. The water content of the soil was determined gravimetrically and by means of MRI each 2nd day over a period of 14 days. Furthermore a SEMS (Spin Echo Multi Slice) sequence was used to visualize the growth of root system architecture. This study shows that SPI3D and SE3D are feasible for the determination of water content in a natural soil up to a certain detection limit. We observed quite uniform water uptake patterns during drying of the soil until water

  2. Towards Remotely Sensed Composite Global Drought Risk Modelling

    NASA Astrophysics Data System (ADS)

    Dercas, Nicholas; Dalezios, Nicolas

    2015-04-01

    Drought is a multi-faceted issue and requires a multi-faceted assessment. Droughts may have the origin on precipitation deficits, which sequentially and by considering different time and space scales may impact soil moisture, plant wilting, stream flow, wildfire, ground water levels, famine and social impacts. There is a need to monitor drought even at a global scale. Key variables for monitoring drought include climate data, soil moisture, stream flow, ground water, reservoir and lake levels, snow pack, short-medium-long range forecasts, vegetation health and fire danger. However, there is no single definition of drought and there are different drought indicators and indices even for each drought type. There are already four operational global drought risk monitoring systems, namely the U.S. Drought Monitor, the European Drought Observatory (EDO), the African and the Australian systems, respectively. These systems require further research to improve the level of accuracy, the time and space scales, to consider all types of drought and to achieve operational efficiency, eventually. This paper attempts to contribute to the above mentioned objectives. Based on a similar general methodology, the multi-indicator approach is considered. This has resulted from previous research in the Mediterranean region, an agriculturally vulnerable region, using several drought indices separately, namely RDI and VHI. The proposed scheme attempts to consider different space scaling based on agroclimatic zoning through remotely sensed techniques and several indices. Needless to say, the agroclimatic potential of agricultural areas has to be assessed in order to achieve sustainable and efficient use of natural resources in combination with production maximization. Similarly, the time scale is also considered by addressing drought-related impacts affected by precipitation deficits on time scales ranging from a few days to a few months, such as non-irrigated agriculture, topsoil moisture

  3. Drought in the Rockies

    NASA Technical Reports Server (NTRS)

    2002-01-01

    This image shows the difference between the amount of vegetation in July 2000 and the average July vegetation for North America. Of particular interest are the dry conditions in the western United States. This spring and summer the Rocky Mountains have been relatively dry, and the brown regions stretching from the Canadian to the Mexican border, indicate the effect on the regions' forests. Western Montana and eastern Idaho are particularly parched, and appear darker brown. The dry conditions have contributed to this year's devastating fire season, during which millions of acres have burned in the west. Scientists find that during the growing season, land plants can be used to measure drought. Healthy, thriving plants reflect and absorb visible and near-infrared light differently than plants under stress. These variations in reflectance and absorption can be measured by satellites to produce maps of healthy and stressed vegetation. This image shows Normalized Difference Vegetation Index (NDVI) anomaly, which indicates where vegetation growth was above average (green pixels), below average (brown pixels), or normal (white pixels). For more images and information about measuring vegetation and drought from space visit: Drought and Vegetation Monitoring. Image courtesy NASA Goddard Space Flight Center Biospheric Sciences Branch, based on data from NOAA.

  4. Drought in the Rockies

    NASA Technical Reports Server (NTRS)

    2002-01-01

    This image shows the difference between the amount of vegetation in July 2000 and the average July vegetation for North America. Of particular interest are the dry conditions in the western United States. This spring and summer the Rocky Mountains have been relatively dry, and the brown regions stretching from the Canadian to the Mexican border, indicate the effect on the regions' forests. Western Montana and eastern Idaho are particularly parched, and appear darker brown. The dry conditions have contributed to this year's devastating fire season, during which millions of acres have burned in the west. Scientists find that during the growing season, land plants can be used to measure drought. Healthy, thriving plants reflect and absorb visible and near-infrared light differently than plants under stress. These variations in reflectance and absorption can be measured by satellites to produce maps of healthy and stressed vegetation. This image shows Normalized Difference Vegetation Index (NDVI) anomaly, which indicates where vegetation growth was above average (green pixels), below average (brown pixels), or normal (white pixels). For more images and information about measuring vegetation and drought from space visit: Drought and Vegetation Monitoring. Image courtesy NASA Goddard Space Flight Center Biospheric Sciences Branch, based on data from NOAA.

  5. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend Analysis

    NASA Astrophysics Data System (ADS)

    Peterson, P.; Funk, C. C.; Landsfeld, M. F.; Husak, G. J.; Pedreros, D. H.; Verdin, J. P.; Rowland, J.; Shukla, S.; McNally, A.; Michaelsen, J.; Verdin, A.

    2014-12-01

    A high quality, long-term, high-resolution precipitation dataset is a key requirement for supporting drought monitoring and long term trend analysis. In this presentation we introduce a new dataset: the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), developed by scientists at the University of California, Santa Barbara and the U.S. Geological Survey Earth Resources Observation and Science Center. This new quasi-global precipitation product is available at daily to seasonal time scales, with a spatial resolution of 0.05°, and a 1981 to near real-time period of record. The three main types of information used in the CHIRPS are: (1) global 0.05° precipitation climatologies, (2) time-varying grids of infrared cold cloud duration (CCD) precipitation estimates, and (3) in situ precipitation observations. The CHG has developed an extensive database of in situ daily, pentadal and monthly precipitation totals with over a billion daily observations worldwide. Most of these observations come from four sets: the monthly Global Historical Climate Network version 2, the daily Global Historical Climate Network, the Global Summary of the Day (GSOD), and the daily Global Telecommunication System (GTS) provided by NOAA's Climate Prediction Center (CPC). A screening procedure was developed to remove suspected "false zeros" from the daily GTS and GSOD data, since these data can artificially suppress rainfall totals. We compare CHIRPS and ARC2, CFS-Reanalysis, CHIRP, CMORPH, CPC-Unified, ECMWF, PERSIANNE, RFE2, TAMSAT, TRMM-RT7, TRMM-V7 to GPCC. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. The CHIRPS performance is similar to research quality products like the GPCC and GPCP, but with higher resolution and lower latency. Cross validation results for over 100 countries and comparisons with alternate algorithms will be presented.

  6. Remote Sensing Technologies Mitigate Drought

    NASA Technical Reports Server (NTRS)

    2015-01-01

    Ames Research Center has partnered with the California Department of Water Resources to develop satellite-based technologies to mitigate drought conditions. One project aims to help water managers adjust their irrigation to match the biological needs of each crop, and another involves monitoring areas where land is fallow so emergency relief can more quickly aid affected communities.

  7. Impact of clinical decision support guidelines on therapeutic drug monitoring of gentamicin in newborns.

    PubMed

    Fonzo-Christe, Caroline; Guignard, Bertrand; Zaugg, Claudia; Coehlo, Ana; Posfay-Barbe, Klara M; Gervaix, Alain; Desmeules, Jules; Rollason, Victoria; Combescure, Christophe; Corbelli, Regula; Rimensberger, Peter; Pfister, Riccardo; Bonnabry, Pascal

    2014-10-01

    Our institution's gentamicin dosing and therapeutic drug monitoring (TDM) practices for newborns were suspected to be very heterogeneous. Once-daily dosing (ODD) or extended-interval dosing (EID) and trough concentration measurement were recommended. Clinical decision support guidelines were developed and implemented as clinical decision support in the computerized prescriber order entry system. Impact on dosing, TDM practices, and blood sampling were evaluated. A 1-year retrospective historically controlled study before (April 2008-March 2009) and after the implementation of guidelines (January 2010-December 2010) for newborns (<30 days of life) receiving gentamicin. Blood concentrations (% of peak concentrations sampled, % of patients with zero or one concentration sampled, % of trough concentrations ≤1 mg/L) and dose regimen (ODD/EID) were compared between groups. Factors potentially associated with gentamicin concentration were analyzed (multivariate analysis). One hundred thirty-two (postguidelines) versus 102 (preguidelines) patients were included (median gestational age: 34.3 versus 35.8 weeks, P > 0.05). After implementation of the guidelines, an ODD/EID regimen was almost exclusively used (97.7% versus 61.6%, P < 0.001), the percentage of peak concentrations (0.9% versus 17.2%, P < 0.001) and the number of blood samples per patient (87.1% having 0 or 1 concentration measured versus 48.0, P < 0.001) sharply reduced. A significantly higher percentage of trough concentrations were ≤1 mg/L (68.5% versus 33.0%, P < 0.001). The probability of a trough concentration ≤1 mg/L increased with an ODD/EID regimen (odds ratio, 7.23; 95% confidence interval: 3.48-15.0, P < 0.001) and in the postguidelines group (odds ratio, 2.02; 95% confidence interval: 1.01-4.02, P = 0.045). Guideline implementation generated a sharp reduction in blood sampling. Clinical benefits of better gentamicin dosing and TDM practices were evident. Cost-effectiveness and clinical benefit

  8. Integrated Mapping of Drought-Impacted Areas in the Sierra-Nevada Foothills Region of California Using Landsat Imagery

    NASA Astrophysics Data System (ADS)

    Rao, M.

    2014-12-01

    Drought is a natural disaster with serious implications to environmental, social and economic well-being at local, regional and global scales. In its third year, California's drought condition has seriously impacted not just the agricultural sector, but also the natural resources sector including forestry, wildlife, and fisheries. As of July 15, 2014, the National Weather Service drought monitor shows 81% of California in the category of extreme drought. As future predictions of drought and fire severity become more real in California, there is an increased awareness to pursue innovative and cost-effective solutions that are based on silvicultural treatments and controlled burns to improve forest health and reduce the risk of high-severity wildfires. The main goal of this study is to develop a GIS map of the drought-impacted region of northern and central California using remote sensing data. Specifically, based on a geospatial database for the study region, Landsat imagery in conjunction with field and ancillary data will be analyzed using a combination of supervised and unsupervised classification techniques in addition to spectral indices such as the Modified Perpendicular Drought Index (MPDI). This spectral index basically scales the line perpendicular to the soil line defined in the Red-NIR feature space in conjunction with added information about vegetative fraction derived using NDVI. The image processing will be conducted for two time periods (2001 and 2014) to characterize the severity of the drought. In addition to field data, data collected by state agencies including calforests.org will be used in the classification and accuracy assessment procedures. Visual assessment using high-resolution imagery such as NAIP will be used to further refine the spatial maps. The drought severity maps produced will greatly facilitate site-specific planning efforts aimed at implementing resource management decisions.

  9. [Prх-monitoring based decision-making about decompressive craniectomy in a patient with severe traumatic brain injury. A case report].

    PubMed

    Oshorov, A V; Popugaev, K A; Savin, I A; Gavrilov, A G; Kravchuk, A D; Potapov, A A

    2015-01-01

    The presented case illustrates a new approach to making a decision about decompressive craniectomy in the patient with sever traumatic brain injury and intracranial hypertension. The approach is based on continuous assessment of cerebral autoregulation using Prx-monitoring in addition to monitoring of intracranial pressure and cerebral perfusion pressure. Prx-monitoring enables timely detection of autoregulation failure and provides the opportunity to make a decision about decompressive craniectomy before starting such aggressive methods of intensive care as hypothermia or barbiturate coma.

  10. Characterization of drought patterns through remote sensing over The Chihuahua Desert, Mexico"

    NASA Astrophysics Data System (ADS)

    Madrigal, J. M.; Lopez, A.; Garatuza, J.

    2013-12-01

    Drought is a phenomenon that has intensified during the last few decades in the arid and semi-arid zones of northern Mexico. In the Chihuahua desert, across Chihuahua, Durango and Coahuila states has caused loss of food sustainability (agriculture, livestock), an increase in human health problems, and detriment of ecosystem services as well as important economic losses. In order to understand this phenomenon, it is necessary to create tools that allow monitoring the territory's spatial heterogeneity and multi-temporality. With this purpose we propose the implementation of a drought model which includes the traditional indexes of climatic drought, such as the Palmer Drought Severity Index PDSI, the Standardized Index of Rainfall SPI, data from meteorological stations and biophysical variations obtained from the MODIS sensors product MOD13 NDVI from 2001 to 2010, as well as biophysical variables characteristic of the environment, such as land use and vegetation coverage, Eco-regions, soil moisture, digital elevation model and irrigate agriculture districts. With the MODIS images, a spatially coherent time series was created analyzing the study area's phenology (TIMESAT) created the Seasonal Greenness (SG) and Start of Season Anomaly (SOSA) for the mentioned nine years. Through this, the annual cycles were established. With a decision tree model, all the previously mentioned proposed variables were integrated. The proposed model produces a general map which characterizes the vegetation condition (extreme drought, severe drought, moderate drought, near normal). Even though different techniques have been proposed on the monitoring of droughts, most of them generate drought indexes with a spatial resolution of 1km (Wardlow, B. et. al 2008; Levent T. et al. 2013). One of the main concerns of researchers on the matter is on improving the spatial information content and on having a better representation of the phenomenon. We use the normalized difference vegetation index

  11. The Value of Information from a GRACE-Enhanced Drought Severity Index

    NASA Astrophysics Data System (ADS)

    Kuwayama, Y.; Bernknopf, R.; Brookshire, D.; Macauley, M.; Zaitchik, B. F.; Rodell, M.; Vail, P.; Thompson, A.

    2015-12-01

    In this project, we develop a framework to estimate the economic value of information from the Gravity and Climate Experiment (GRACE) for drought monitoring and to understand how the GRACE Data Assimilation (GRACE-DA) system can inform decision making to improve regional economic outcomes. Specifically, we consider the potential societal value of further incorporating GRACE-DA information into the U.S. Drought Monitor mapmaking process. Research activities include (a) a literature review, (b) a series of listening sessions with experts and stakeholders, (c) the development of a conceptual economic framework based on a Bayesian updating procedure, and (d) an econometric analysis and retrospective case study to understand the GRACE-DA contribution to agricultural policy and production decisions. Taken together, the results from these research activities support our conclusion that GRACE-DA has the potential to lower the variance associated with our understanding of drought and that this improved understanding has the potential to change policy decisions that lead to tangible societal benefits.

  12. Protection of agriculture against drought in Slovenia based on vulnerability and risk assessment

    NASA Astrophysics Data System (ADS)

    Dovžak, M.; Stanič, S.; Bergant, K.; Gregorič, G.

    2012-04-01

    Past and recent extreme events, like earthquakes, extreme droughts, heat waves, flash floods and volcanic eruptions continuously remind us that natural hazards are an integral component of the global environment. Despite rapid improvement of detection techniques many of these events evade long-term or even mid-term prediction and can thus have disastrous impacts on affected communities and environment. Effective mitigation and preparedness strategies will be possible to develop only after gaining the understanding on how and where such hazards may occur, what causes them, what circumstances increase their severity, and what their impacts may be and their study has the recent years emerged as under the common title of natural hazard management. The first step in natural risk management is risk identification, which includes hazard analysis and monitoring, vulnerability analysis and determination of the risk level. The presented research focuses on drought, which is at the present already the most widespread as well as still unpredictable natural hazard. Its primary aim was to assess the frequency and the consequences of droughts in Slovenia based on drought events in the past, to develop methodology for drought vulnerability and risk assessment that can be applied in Slovenia and wider in South-Eastern Europe, to prepare maps of drought risk and crop vulnerability and to guidelines to reduce the vulnerability of the crops. Using the amounts of plant available water in the soil, slope inclination, solar radiation, land use and irrigation infrastructure data sets as inputs, we obtained vulnerability maps for Slovenia using GIS-based multi-criteria decision analysis with a weighted linear combination of the input parameters. The weight configuration was optimized by comparing the modelled crop damage to the assessed actual damage, which was available for the extensive drought case in 2006. Drought risk was obtained quantitatively as a function of hazard and

  13. Assessing the add value of ensemble forecast in a drought early warning

    NASA Astrophysics Data System (ADS)

    Calmanti, Sandro; Bosi, Lorenzo; Fernandez, Jesus; De Felice, Matteo

    2015-04-01

    The EU-FP7 project EUPORIAS is developing a prototype climate service to enhance the existing food security drought early warning system in Ethiopia. The Livelihoods, Early Assessment and Protection (LEAP) system is the Government of Ethiopia's national food security early warning system, established with the support of WFP and the World Bank in 2008. LEAP was designed to increase the predictability and timeliness of response to drought-related food crises in Ethiopia. It combines early warning with contingency planning and contingency funding, to allow the government, WFP and other partners to provide early assistance in anticipation of an impending catastrophes. Currently, LEAP uses satellite based rainfall estimates to monitor drought conditions and to compute needs. The main aim of the prototype is to use seasonal hindcast data to assess the added value of using ensemble climate rainfall forecasts to estimate the cost of assistance of population hit by major droughts. We outline the decision making process that is informed by the prototype climate service, and we discuss the analysis of the expected and skill of the available rainfall forecast data over Ethiopia. One critical outcome of this analysis is the strong dependence of the expected skill on the observational estimate assumed as reference. A preliminary evaluation of the full prototype products (drought indices and needs estimated) using hindcasts data will also be presented.

  14. Drought and Snow: Analysis of Drivers, Processes and Impacts of Streamflow Droughts in Snow-Dominated Regions

    NASA Astrophysics Data System (ADS)

    Van Loon, Anne; Laaha, Gregor; Van Lanen, Henny; Parajka, Juraj; Fleig, Anne; Ploum, Stefan

    2016-04-01

    reservoir levels and, consequently, on drinking water and electricity production. Snow storage therefore, is an important factor to consider in drought monitoring, prediction and management.

  15. Drought and Snow: Analysis of Drivers, Processes and Impacts of Streamflow Droughts in Snow-Dominated Regions

    NASA Astrophysics Data System (ADS)

    Van Loon, A.; Laaha, G.; Van Lanen, H.; Parajka, J.; Fleig, A. K.; Ploum, S.

    2015-12-01

    reservoir levels and, consequently, on drinking water and electricity production. Snow storage therefore, is an important factor to consider in drought monitoring, prediction and management.

  16. The International Study of Leadership in Education: Monitoring Decision Making by School Leaders

    ERIC Educational Resources Information Center

    Wildy, Helen; Forster, Pat; Louden, William; Wallace, John

    2004-01-01

    School principals have difficulty embracing the competing demands of school restructuring. These demands include being accountable for the outcomes of other decision-making groups within, or external to, the school community; having strong views while making decisions collaboratively; and using group processes without wasting the time, commitment,…

  17. An Overview of the NOAA Drought Task Force

    NASA Technical Reports Server (NTRS)

    Schubert, S.; Mo, K.; Peters-Lidard, C.; Wood, A.

    2012-01-01

    The charge of the NOAA Drought Task Force is to coordinate and facilitate the various MAPP-funded research efforts with the overall goal of achieving significant advances in understanding and in the ability to monitor and predict drought over North America. In order to achieve this, the task force has developed a Drought Test-bed that individual research groups can use to test/evaluate methods and ideas. Central to this is a focus on three high profile North American droughts (1998-2004 western US drought, 2006-2007 SE US drought, 2011- current Tex-Mex drought) to facilitate collaboration among projects, including the development of metrics to assess the quality of monitoring and prediction products, and the development of an experimental drought monitoring and prediction system that incorporates and assesses recent advances. This talk will review the progress and plans of the task force, including efforts to help advance official national drought products, and the development of early warning systems by the National Integrated Drought Information System (NIDIS). Coordination with other relevant national and international efforts such as the emerging NMME capabilities and the international effort to develop a Global Drought Information System (GDIS) will be discussed.

  18. Results of Large Area Crop Inventory Experiment (LACIE) drought analysis (South Dakota drought 1976)

    NASA Technical Reports Server (NTRS)

    Thompson, D. R.

    1976-01-01

    LACIE using techniques developed from the southern Great Plains drought analysis indicated the potential for drought damage in South Dakota. This potential was monitored and as it became apparent that a drought was developing, LACIE implemented some of the procedures used in the southern Great Plains drought. The technical approach used in South Dakota involved the normal use of LACIE sample segments (5 x 6 nm) every 18 days. Full frame color transparencies (100 x 100 nm) were used on 9 day intervals to identify the drought area and to track overtime. The green index number (GIN) developed using the Kauth transformation was computed for all South Dakota segments and selected North Dakota segments. A scheme for classifying segments as drought affected or not affected was devised and tested on all available 1976 South Dakota data. Yield model simulations were run for all CRD's Crop Reporting District) in South Dakota.

  19. A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

    NASA Astrophysics Data System (ADS)

    Khader, A.; Rosenberg, D.; McKee, M.

    2012-12-01

    Nitrate pollution poses a health risk for infants whose freshwater drinking source is groundwater. This risk creates a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision maker and the expected outcomes from these alternatives. The alternatives include: (i) ignore the health risk of nitrate contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, pollution transport processes, and climate (Khader and McKee, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine where methemoglobinemia is the main health problem associated with the principal pollutant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not-use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current

  20. A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

    NASA Astrophysics Data System (ADS)

    Khader, A. I.; Rosenberg, D. E.; McKee, M.

    2013-05-01

    Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs

  1. Development of system decision support tools for behavioral trends monitoring of machinery maintenance in a competitive environment

    NASA Astrophysics Data System (ADS)

    Adeyeri, Michael Kanisuru; Mpofu, Khumbulani

    2017-01-01

    The article is centred on software system development for manufacturing company that produces polyethylene bags using mostly conventional machines in a competitive world where each business enterprise desires to stand tall. This is meant to assist in gaining market shares, taking maintenance and production decisions by the dynamism and flexibilities embedded in the package as customers' demand varies under the duress of meeting the set goals. The production and machine condition monitoring software (PMCMS) is programmed in C# and designed in such a way to support hardware integration, real-time machine conditions monitoring, which is based on condition maintenance approach, maintenance decision suggestions and suitable production strategies as the demand for products keeps changing in a highly competitive environment. PMCMS works with an embedded device which feeds it with data from the various machines being monitored at the workstation, and the data are read at the base station through transmission via a wireless transceiver and stored in a database. A case study was used in the implementation of the developed system, and the results show that it can monitor the machine's health condition effectively by displaying machines' health status, gives repair suggestions to probable faults, decides strategy for both production methods and maintenance, and, thus, can enhance maintenance performance obviously.

  2. Development of system decision support tools for behavioral trends monitoring of machinery maintenance in a competitive environment

    NASA Astrophysics Data System (ADS)

    Adeyeri, Michael Kanisuru; Mpofu, Khumbulani

    2017-01-01

    The article is centred on software system development for manufacturing company that produces polyethylene bags using mostly conventional machines in a competitive world where each business enterprise desires to stand tall. This is meant to assist in gaining market shares, taking maintenance and production decisions by the dynamism and flexibilities embedded in the package as customers' demand varies under the duress of meeting the set goals. The production and machine condition monitoring software (PMCMS) is programmed in C# and designed in such a way to support hardware integration, real-time machine conditions monitoring, which is based on condition maintenance approach, maintenance decision suggestions and suitable production strategies as the demand for products keeps changing in a highly competitive environment. PMCMS works with an embedded device which feeds it with data from the various machines being monitored at the workstation, and the data are read at the base station through transmission via a wireless transceiver and stored in a database. A case study was used in the implementation of the developed system, and the results show that it can monitor the machine's health condition effectively by displaying machines' health status, gives repair suggestions to probable faults, decides strategy for both production methods and maintenance, and, thus, can enhance maintenance performance obviously.

  3. The SfM-monitored rill experiment, a tool to detect decisive processes?

    NASA Astrophysics Data System (ADS)

    Remke, Alexander-André; Wirtz, Stefan; Brings, Christine; Gronz, Oliver; Seeger, Manuel; Ries, Johannes B.

    2016-04-01

    The initiation of rill erosion marks the transition from sheet to linear erosion. With this transition, the relevant processes change and therefore, the observation method needs to be changed too: from observing rainfall induced drop impacts to hydraulic observations. For us, the investigation of the decisive processes in eroding rills resulted in a constantly revised and updated rill erosion experiment, that has been used for several years. Within this experiment the sediment transport behavior of rills is simulated and examined. To make the experiment repeatable and replicable, several key-variables have to be kept constant, i.e. water quantity (1000 L), test duration (approx. 4 min.) and the length of the tested rill section (20 m). For each tested rill, the topographic background is determined i.e. catchment area, aspect, slope, position and height of existing knick-points and three cross-sections. After the initial assessment, the rill is flushed with water (250 L min -1) twice in order to determine the modifications of the rill caused by the flowing water. Within these approx. 4 minutes of "controlled destruction" the velocity of the turbulently flowing water at the beginning of the erosional event and after one and two minutes is determined and the corresponding water depth is recorded using three gauges at selected measuring points. At the end of the tested rill segment, the discharge is constantly monitored. Unfortunately, the results of this rill experiment do not directly show the modifications caused by the artificial waterflow. A way out of this knowledge gap is offered by combining this experimental measurement method with a technique already used in different scientific disciplines in more large-scale applications. Structure-from-Motion technology offers the opportunity to get a different, more detailed view inside the erosion rills. A static multi-camera-array and a dynamically moved digital video-frame camera are now used to obtain three

  4. CropEx Web-Based Agricultural Monitoring and Decision Support

    NASA Technical Reports Server (NTRS)

    Harvey. Craig; Lawhead, Joel

    2011-01-01

    CropEx is a Web-based agricultural Decision Support System (DSS) that monitors changes in crop health over time. It is designed to be used by a wide range of both public and private organizations, including individual producers and regional government offices with a vested interest in tracking vegetation health. The database and data management system automatically retrieve and ingest data for the area of interest. Another stores results of the processing and supports the DSS. The processing engine will allow server-side analysis of imagery with support for image sub-setting and a set of core raster operations for image classification, creation of vegetation indices, and change detection. The system includes the Web-based (CropEx) interface, data ingestion system, server-side processing engine, and a database processing engine. It contains a Web-based interface that has multi-tiered security profiles for multiple users. The interface provides the ability to identify areas of interest to specific users, user profiles, and methods of processing and data types for selected or created areas of interest. A compilation of programs is used to ingest available data into the system, classify that data, profile that data for quality, and make data available for the processing engine immediately upon the data s availability to the system (near real time). The processing engine consists of methods and algorithms used to process the data in a real-time fashion without copying, storing, or moving the raw data. The engine makes results available to the database processing engine for storage and further manipulation. The database processing engine ingests data from the image processing engine, distills those results into numerical indices, and stores each index for an area of interest. This process happens each time new data is ingested and processed for the area of interest, and upon subsequent database entries, the database processing engine qualifies each value for each area of

  5. Influence of mathematical and physical background of drought indices on their complementarity and drought recognition ability

    NASA Astrophysics Data System (ADS)

    Frank, Anna; Armenski, Tanja; Gocic, Milan; Popov, Srdjan; Popovic, Ljiljana; Trajkovic, Slavisa

    2017-09-01

    The aim of this study is to test how effective and physically correct are the mathematical approaches of operational indices used by relevant National Agencies across the globe. To do so, the following indices were analysed Standardized Precipitation Index (SPI) -1, 3, 6, 12 and 24, Standardized Precipitation - Evapotranspiration Index (SPEI) - 1, 3, 6, 12 and 24, Effective Drought Index (EDI) and Index of Drying Efficiency of Air (IDEA). To make regions more comparable to each other and follow the spatial development of drought SPI index was advised by World Meteorological Organisation to be used widely by official meteorological services. The SPI and SPEI are used for Drought Early Warning in the USA, National Drought Mitigation Center and NASA, and in the EU by the European Drought Centre (EDC) and in the Balkan Region by National Meteorological Agencies. The EDI Index has wide application in Asia. In this paper four different issues were investigated: 1) how the mathematical method used in a drought indicator's computation influence drought indices' (DI) comparative analyses; 2) the sensitivity of the DIs on any change of the length of observational period; 3) similarities between the DIs time series; 4) and how accurate DIs are when compared to historical drought records. Results suggest that it is necessary to apply a few crucial changes in the Drought Monitoring and Early Warning Systems: 1) reconsider use of SPI and SPEI family indices as a measure of quality of other indices; and for Drought Early Recognition Programs 2) switch to DIs with a solid physical background, such as EDI; 3) Adopt solid physics for modelling drought processes and define the physical measure of drought, e.g. EDI and IDEA indices; 4) investigate further the IDEA index, which, supported by our study as we