Svoboda, M.; Fuchs, B.; Poulsen, C.; Nothwehr, J.; Owen, S.
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
Hannaford, Jamie; Barker, Lucy; Svensson, Cecilia; Tanguy, Maliko; Laize, Cedric; Bachmair, Sophie; Tijdeman, Erik; Stahl, Kerstin; Collins, Kevin
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.
Zink, Matthias; Samaniego, Luis; Kumar, Rohini; Thober, Stephan; Mai, Juliane; Schäfer, David; Marx, Andreas
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.
Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Cheng, Hongguang
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.
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.
Brown, Jesslyn F.
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.
Marx, Andreas; Zink, Matthias; Pommerencke, Julia; Kumar, Rohini; Thober, Stephan; Samaniego, Luis
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
Svoboda, M. D.; Hayes, M. J.
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.
Limaye, Ashutosh; McNider, Richard; Moss, Donald; Alhamdan, Mohammad
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
Hartfield, K. A.; Van Leeuwen, W. J. D.; Crimmins, M.; Marsh, S. E.; Torrey, Y.; Rahr, M.; Orr, B. J.
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
Svoboda, Mark D.; Fuchs, Brian A.; Poulsen, Chris C.; Nothwehr, Jeff R.
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.
Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian
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
Hao, Z.; Xia, Y.; Hao, F.; Singh, V. P.
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.
Arndt, D. S.; Brewer, M.; Heim, R. R., Jr.
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.
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...
Svoboda, M.; Fuchs, B.; Hayes, M. J.
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
Heim, R. R.; Brewer, M.
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
Wang, H.; Lin, H.; Liu, D.
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.
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.
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
Li, B.; Rodell, M.; Beaudoing, H. K.; Getirana, A.; Zaitchik, B. F.
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.
Hao, Z.; Aghakouchak, A.
Drought monitoring is fundamental to decision-making and reducing drought effect. A variety of variables, such as, precipitation, soil moisture and runoff have been used to characterize different forms of drought. Several indices, such as the standardized precipitation index (SPI), soil moisture anomaly, have been used for drought monitoring. Due to the complexity of drought phenomenon in its causation and impact, drought monitoring based on a single variable may not be sufficient for detecting drought condition promptly and reliably. The recently proposed Multivariate Standardized Drought Index (MSDI; Hao and AghaKouchak, 2012) describes droughts based on the states of multiple variables such as precipitation and soil moisture. In this study, MSDI is employed for drought analysis including detecting drought onsite, recession, severity and spatial extent across the continental United States. The results are cross -validated with the U.S. Drought Monitor data as well as the commonly used standardized indices (e.g., SPI). The results show that MSDI provides attractive properties in drought detection and that it is a useful tool for drought monitoring. Reference: Hao Z., AghaKouchak A., 2012, A multivariate multi-index drought modeling framework, Water Resources Research, under review.
Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza
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
Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza
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.
Heim, R. R.
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.
Mariotti, Annarita; Schubert, Siegfried D.; Mo, Kingtse; Peters-Lidard, Christa; Wood, Andy; Pulwarty, Roger; Huang, Jin; Barrie, Dan
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
Lavado, Waldo; Felipe, Oscar; Caycho, Tania; Sosa, Jesus; Fernandez, Carlos; Endara, Sofia
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 .
Nghiem, S. V.; Brakenridge, G. R.; Neumann, G.
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
Heim, R. R.
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.
AghaKouchak, A.; Hao, Z.; Farahmand, A.; Nakhjiri, N.
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
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...
Close, S.; Simpson, C.
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.
Frevert, D.; Lins, H.; ,
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.
Bachmair, Sophie; Stahl, Kerstin; Hannaford, Jamie; Svoboda, Mark
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
White, Kristopher D.; Case, Jonathan L.
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
Nagy, Attila; Tamás, János; Fehér, János
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
Luo, Lifeng; Wood, Eric F.
Severe droughts developed in the West and Southeast of the U.S. starting early in 2007. The development of the droughts is well monitored and predicted by our model-based Drought Monitoring and Prediction System (DMAPS). Using the North America Land Data Assimilation System (NLDAS) realtime meteorological forcing and the Variable Infiltration Capacity (VIC) land surface model, DMAPS is capable of providing a quantitative assessment of the drought in near realtime. Using seasonal climate forecasts from NCEP's Climate Forecast System (CFS) as one input, DMAPS successfully predicted the evolution of the droughts several months in advance. The realtime monitoring and prediction of drought with the system will provide invaluable information for drought preparation and drought impact assessment at national and local scales.
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
Nijssen, B.; Shukla, S.; Mo, K. C.; Lettenmaier, D. P.
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?
Heim, R. R.; Vose, R. S.; Lawrimore, J. H.; Cook, E. R.
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.
di, L.; Yu, G.; Han, W.; Deng, M.
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
Lawrimore, J. H.; Heim, R. R.
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.
Ly, Vickie; Gao, Michael; Cary, Cheryl; Turnbull-Appell, Sophie; Surunis, Anton
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.
Doesken, N.; Smith, R.; Ryan, W.; Schwalbe, Z.; Verdin, J. P.
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.
Rhee, Jinyoung; Im, Jungho; Park, Seonyoung
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
Liu, Sanchao; Li, Wenbo
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.
Nijssen, B.; Xiao, M.; Lettenmaier, D. P.
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.
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
Perez, G. J.; Macapagal, M.; Olivares, R.; Macapagal, E. M.; Comiso, J. C.
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.
Shen, S.; Dai, Q.; Yin, H.; Howard, A.
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.
Fu, R.; Fernando, D. N.; Pu, B.
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
Brown, J. F.
Droughts originate from precipitation deficiencies resulting in water shortages that affect certain activities or sectors. Since droughts are normal climate phenomena, society has been dealing with their related impacts and consequences for many centuries. Historically, reliable observations of rainfall have been available for about two hundred years, and most meteorological drought indicators incorporate this variable, either alone or combined with other measures. Traditionally, surface observation networks have been the primary sources for drought information. However, common limitations of climate indicators derived from ground-based networks include large gaps in coverage and coarse spatial detail. In addition, decision-makers need information concerning the effects that drought may be having on certain human and natural systems. Specific examples of these effects include declining forage production, lower crop yields, increased wildfire danger, deteriorating soil conditions, diminishing water supplies, and limits on recreation. Droughts differ from other natural hazards in several significant ways. They may be gradual or "creeping" in their development (on the scale of weeks or months, not days). They can last for periods of years and exhibit large variability in both spatial extent and severity. Monitoring and predicting drought conditions are necessary activities of government agencies at State, Federal, and local levels as part of decision support for planning, risk management, and hazard mitigation activities. Satellite remotely-sensed data providing large-area synoptic coverage and finer spatial resolution can fill in the gaps, reinforce, and complement the science framework for characterizing, monitoring, and predicting natural hazards. Earth observations from remote platforms have a unique role to provide information pertinent to all hazards. For drought science, examples of key data sets include satellite rainfall estimates, albedo measurements, soil
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.
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.
Kwok Wong, Wai; Hisdal, Hege
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
Alonge, C. J.; Cosgrove, B. A.
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
Granato, Gregory E.
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
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.
Huang, Jung; Tien, Yu-Chuan; Lin, Hsuan-Te; Liu, Tzu-Ming; Tung, Ching-Pin
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.
Anderson, W. B.; Hain, C.; Zaitchik, B. F.; Anderson, M. C.; Alo, C. A.; Yilmaz, M. T.
East Africa contains a number of highly drought prone regions, and the humanitarian consequences of drought in those regions can be severe. The severity of these drought impacts combined with a paucity of in situ monitoring networks has given rise to numerous efforts to develop reliable remote drought monitoring systems based on satellite data, physically-based models, or a combination of the two. Here we present the results of a cross-comparison and preliminary integration of three soil moisture monitoring methodologies that, combined, offer the potential for a soil moisture based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa. Three independent methods for estimating soil moisture anomalies, the AMSR-E microwave based satellite sensor, the ALEXI thermal infrared based model and the Noah land surface model, are evaluated using triple collocation error analysis (TCEA). TCEA is used to estimate the reliability of each soil moisture anomaly methodology through statistical cross-comparison-a particularly useful approach given the virtual absence of in situ soil moisture data in this region. While AMSR-E, ALEXI, and Noah each appear to produce reliable soil moisture anomaly estimates over some areas within East Africa, many areas posed significant challenges to one or more methods. These challenges include seasonal cloud cover that hinders ALEXI estimates, dense vegetation that impedes AMSR-E retrievals, and complex hydrology that tests the limits of Noah model assumptions. TCEA allows for assessment of the reliability of each method across seasonal and geographic gradients and provides systematic criteria for merging the three methods into an integrated estimate of spatially distributed soil moisture anomalies for all of East Africa. Results for the period 2007-2011 demonstrate the potential and the limitations of this approach in application to real time drought monitoring.
Anderson, W. B.; Zaitchik, B. F.; Hain, C. R.; Anderson, M. C.; Yilmaz, M. T.; Mecikalski, J.; Schultz, L.
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
Anderson, W. B.; Zaitchik, B. F.; Hain, C. R.; Anderson, M. C.; Yilmaz, M. T.; Mecikalski, J.; Schultz, L.
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
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.
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
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...
Xiao, M.; Nijssen, B.; Shukla, S.; Lettenmaier, D. P.
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
Hobbins, M.; McEvoy, D.; Huntington, J. L.; Wood, A. W.; Morton, C.; Verdin, J. P.
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
Sheffield, Justin; Chaney, Nate; Yuan, Xing; Wood, Eric
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
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.
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
Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.
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
Aghakouchak, Amir; Tourian, Mohammad J.
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
Garrote, Luis; Sordo, Alvaro; Iglesias, Ana
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
Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Xia, Youlong; Ouyang, Wei; Shen, Xinyi
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.
Housborg, Rasmus; Rodell, Matthew
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.
Yang, Shiqi; Tang, Yunhui; Gao, Yanghua; Xu, Yongjin
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.
Zhang, J.; Becker-Reshef, I.; Justice, C. O.
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
Huang, He; Zhou, Hongjian; Wang, Ping; Wu, Wei; Yang, Siquan
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.
Cary, C.; Ly, V.; Gao, M.; Surunis, A.; Turnbull-Appell, S.; Sodergren, C.; Brooks, A. N.
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.
Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.
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
Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.
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
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.
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.
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.
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.
Hao, Zengchao; Hong, Yang; Xia, Youlong; Singh, Vijay P.; Hao, Fanghua; Cheng, Hongguang
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.
Feng, Hai-Xia; Qin, Qi-Ming; Li, Bin-Yong; Liu, Fang; Jiang, Hong-Bo; Dong, Heng; Wang, Jin-Liang; Liu, Ming-Chao; Zhang, Ning
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.
Slinski, K.; Hogue, T. S.; McCray, J. E.; Porter, A.
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.
Timmermans, J.; Gokmen, M.; Eden, U.; Abou Ali, M.; Vekerdy, Z.; Su, Z.
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
Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena
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
Zeng, Linglin; Shan, Jie; Xiang, Daxiang
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.
McNally, A.; Yatheendradas, S.; Jayanthi, H.; Funk, C. C.; Peters-Lidard, C. D.
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:  In many regions that are vulnerable to food insecurity ground based measurements of precipitation, evapotranspiration and soil moisture are sparse or non-existent,  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  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
Contreras, Sergio; Hunink, Johannes E.
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.
Nyabeze, W. R.; Dlamini, L.; Lahlou, O.; Imani, Y.; Alaoui, S. B.; Vermooten, J. S. A.
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
Park, S.; Im, J.; Rhee, J.; Park, S.
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
Lynch, A. H.; Adler, C.; Howard, N.
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.
Hao, Cui; Zhang, Jiahua; Yao, Fengmei
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
Drought increasingly threatens the sustainability of regional water resources in many states in the United States. Drought has large economic impacts and significant environmental and societal effects. Although much research on drought at national, regional, and local scales has been conducted to mitigate drought impacts, still drought claims economic losses estimated at about $8.5 billion per year. One possible reason for such huge losses may be a lack of clear understanding of the characteristics of drought at local scales that the end user can relate to the particular water management constraints of their basin. Sustainable water management alternatives are explored and discussed to mitigate climate-induced drought impacts on western agriculture. Current drought monitoring, forecasting, and outlooks efforts are demonstrated along with visualization and research survey. Future direction for Big Drought research is also highlighted.
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...
Yi, Hang; Wen, Lianxing
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.
Yi, Hang; Wen, Lianxing
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
Yi, Hang; Wen, Lianxing
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.
Sui, Xin-Xin; Qin, Qi-Ming; Dong, Heng; Wang, Jin-Liang; Meng, Qing-Ye; Liu, Ming-Chao
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.
Miller, Brian W; Leslie, Paul W; McCabe, J Terrence
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.
Leslie, Paul W.; McCabe, J. Terrence
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
Velpuri, Naga Manohar; Senay, Gabriel B.; Morisette, Jeffrey T.
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.
Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Minh, V. Q.
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.
Sawada, Yohei; Koike, Toshio
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.
Peters-Lidard, Christa D.; Mocko, David; Kumar, Sujay; Ek, Michael; Xia, Youlong; Dong, Jiarui
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.
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.
Enenkel, Markus; Rojas, Oscar; Balint, Zoltan
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
Cammalleri, Carmelo; Micale, Fabio; Vogt, Jürgen
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
Anderson, M. C.; Hain, C.; Mecikalski, J. R.; Kustas, W. P.
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
Yan, Hongxiang; Moradkhani, Hamid
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.
Tucker, Compton J.
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.
El Vilaly, M. M.; Van Leeuwen, W. J.; Didan, K.; Marsh, S. E.; Crimmins, , M. A.
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
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...
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 ...
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...
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...
Zheng, You-Fei; Cheng, Jin-Xin; Wu, Rong-Jun; Guan, Fu-Lai; Yao, Shu-Ran
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.
Reyes Gomez, V. M.; Nunez Lopez, D.
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
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).
Tadesse, T.; Wall, N.; Haigh, T.; Shiferaw, A. S.; Beyene, S.; Demisse, G. B.; Zaitchik, B.
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.
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
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.
Wang, Li-Tao; Wang, Shi-Xin; Zhou, Yi; Liu, Wen-Liang; Wang, Fu-Tao
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.
Hunink, J.; Hoogewoud, J. C.; Prinsen, G.; Veldhuizen, A.
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
Hobbins, M. T.; Wood, A. W.; Werner, K.
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
Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.
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.
Katiraie-Boroujerdy, Pari-Sima; Nasrollahi, Nasrin; Hsu, Kuo-lin; Sorooshian, Soroosh
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.
Klos, Ryan J; Wang, G Geoff; Bauerle, William L; Rieck, James R
Drought frequency and intensity has been predicted to increase under many climate change scenarios. It is therefore critical to understand the response of forests to potential climate change in an effort to mitigate adverse impacts. The purpose of this study was to explore the regional effects of different drought severities on tree growth and mortality. Specifically, we investigated changes in growth and mortality rates across the southeastern United States under various drought and stand conditions using 1991-2005 Forest Health and Monitoring (FHM) plot data from Alabama, Georgia, and Virginia. Drought effects were examined for three species groups (pines, oaks, and mesophytic species) using the Palmer drought severity index (PDSI) as an indicator of drought severity. Stand variables, including total basal area, total tree density, tree species richness, slope, and stand age, were used to account for drought effects under varying stand conditions. The pines and mesophytic species exhibited significant reductions in growth rate with increasing drought severity. However, no significant difference in growth rate was observed within the oak species group. Mean mortality rates within the no-drought class were significantly lower than those within the other three drought classes, among which no significant differences were found, for both pines and mesophytic species. Mean mortality rates were not significantly different among drought classes for oaks. Total basal area, total tree density, and stand age were negatively related to growth and positively related to mortality, which suggests that older and denser stands are more susceptible to drought damage. The effect of basal area on growth increased with drought severity for the oak and mesophytic species groups. Tree species richness was negatively related to mortality for the pine and mesophytic species groups, indicating that stands with more species suffer less mortality. Slope was positively related to mortality
Takeuchi, W.; Oyoshi, K.; Muraki, Y.
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/.
Makokha, Godfrey Ouma; Wang, Lei; Zhou, Jing; Li, Xiuping; Wang, Aihui; Wang, Guangpeng; Kuria, David
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.
Tadesse, T.; Haile, M.; Senay, G.; Wardlow, B.D.; Knutson, C.L.
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.
Liu, Ming; Yang, Siquan; Huang, He; He, Haixia; Li, Suju; Cui, Yan
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.
Pozzi, Will; Sheffield, Justin; Stefanski, Robert; Cripe, Douglas; Pulwarty, Roger; Vogt, Jurgen V.; Heim, Richard R., Jr.; Brewer, Michael J.; Svoboda, Mark; Westerhoff, Rogier; vanDijk, Albert I. J. M.; Lloyd-Hughes, Benjamin; Pappenberger, Florian; Werner, Micha; Dutra, Emanuel; Wetterhall, Fredrik; Wagner, Wolfgang; Schubert, Siegfried; Mo, Kingste; Nicholson, Margaret; Bettio, Lynette; Nunez, Liliana; vanBeek, Rens; Bierkens, Marc; deGoncalves, Luis Gustavo Goncalves; deMattos, Joao Gerd Zell; Lawford, Richard
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
Han, E.; Crow, W. T.; Holmes, T. R.; Bolten, J. D.
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
Teng, William L.
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.
Huang, He; Fan, Yida; Yang, Siquan; Wen, Qi; Pan, Donghua; Fan, Chunbo; He, Haixia
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.
Vivoni, E.; Mascaro, G.; Shupe, J. W.; Hiatt, C.; Potter, C. S.; Miller, R. L.; Stanley, J.; Abraham, T.; Castilla-Rubio, J.
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.
Park, Seonyoung; Im, Jungho; Jang, Eunna; Yoon, Hyunjin; Rhee, Jinyoung
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
Lessel, J.; Ceccato, P.
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.
Aggett, G. R.
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.
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.
Cendrero Mateo, M.; Carmo-Silva, A.; Salvucci, M.; Moran, S. M.; Hernandez, M.
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
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.
Azmi, M.; Rudiger, C.; Walker, J. P.
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.
Liu, Anlin; Li, Xingmin; He, Yanbo; Deng, Fengdong
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.
McNeeley, S.; Ojima, D. S.; Beeton, T.
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.
Long, Di; Scanlon, Bridget R.; Longuevergne, Laurent; Sun, Alexander Y.; Fernando, D. Nelun; Save, Himanshu
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.
Dennison, P. E.; Coates, A.; Roberts, D. A.; Roth, K. L.
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.
Mohler, Robert R. J.; Amsbury, David L.
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.
Zhang, C., Sr.; Li, L.
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.
Jackson, John K.; Resh, Vincent H.
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.
Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.
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
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.
Yan, Hao; Wang, Shao-Qiang; Lu, Hou-Quan; Yu, Qin; Zhu, Zai-Chun; Myneni, Ranga B.; Liu, Qiang; Shugart, Herman H.
Vegetation effects are currently disregarded in Palmer Drought Severity Index (PDSI), and the sensitivity of PDSI to the choice of potential evaporation (EP) parameterization is often a concern. We developed a revised self-calibrating PDSI model that replaces EP with leaf area index-based total evapotranspiration (ARTS E0). It also included a simple snowmelt module. Using a unique satellite leaf area index data set and climate data, we calculated and compared ARTS E0, three other types of EP (i.e., Thornthwaite EP_Th, Allen EP_Al, and Penman-Monteith EP_PM), and corresponding PDSI values (i.e., PDSI_ARTS, PDSI_Th, PDSI_Al, and PDSI_PM) for the period 1982-2011. The results of PDSI_ARTS, PDSI_Al, and PDSI_PM show that global land became wetter mainly due to increased precipitation and El Niño-Southern Oscillation (ENSO) effect for the period, which confirms the ongoing intensification of global hydrologic cycle with global temperature increase. However, only PDSI_Th gave a trend of global drying, which confirms that PDSI_Th overestimates the global drying in response to global warming; i.e., PDSI values are sensitive to the parameterizations for Ep. Thus, ARTS E0, EP_Al, and EP_PM are preferred to EP_Th in global drought monitoring. In short, global warming affects global drought condition in two opposite ways. One is to contribute to the increases of EP and hence drought; the other is to increase global precipitation that contributes to global wetting. These results suggest that precipitation trend and its interaction with global warming and ENSO should be given much attention to correctly quantify past and future trends of drought.
Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye
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
Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio
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
KOCH, G. W.; Williams, C.; Ambrose, A.
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.
Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John
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.
Hardin, D.; Graves, S.; Sever, T.; Irwin, D.
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
Ozelkan, Emre; Chen, Gang; Ustundag, Burak Berk
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.
Husak, Gregory J.; Michaelsen, Joel C.; Funk, Christopher C.
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.
Han, E.; Crow, W. T.; Holmes, T. R.; Bolten, J. D.
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
Graham, Angus; Hunter, Morag A.; Pennington, Benjamin T.; Strutt, Kristian D.
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
Shofiyati, Rizatus; Takeuchi, Wataru; Sofan, Parwati; Darmawan, Soni; Awaluddin; Supriatna, Wahyu
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.
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
Castle, Jessica R.; Jacobs, Peter G.
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
Castle, Jessica R; Jacobs, Peter G
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.
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
Mishra, Ashok K.; Singh, Vijay P.
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.
Wang, Mingzhi; Huang, He; Liu, Suihua; Yan, Lei
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.
Fan, Bi; Li, Han-Xiong; Hu, Yong
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.
Takeuchi, W.; Darmawan, S.; Oyoshi, K.
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/
Cook, E. R.; Vose, R. S.; Heim, R. R.; Lawrimore, J. H.
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.
Irwin, Daniel E.; Sever, Tom; Graves, Sara; Hardin, Danny
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
Irwin, D. E.; Sever, T. L.; Graves, S.; Hardin, Dan
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
Bodkin, J.L.; Dean, T.A.
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.
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...
Robinson, E. S.; Lee, J. E.; Yang, X.
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.
Hong, Y.; Gourley, J. J.; Xue, X.; Flamig, Z.
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.
Apps, Matthew A J; Lockwood, Patricia L; Balsters, Joshua H
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.
Frost, C. R.; Enomoto, F. Y.; D'Ortenzio, M. V.; Nguyen, Q. B.
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
Busciolano, Ronald J.
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
Gouveia, Célia M.; Trigo, Ricardo M.
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
Svoboda, M. D.; Hayes, M. J.; Knutson, C. L.; Wardlow, B. D.
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
Bonetto, Sabrina; Facello, Anna; Camaro, Walther; Isotta Cristofori, Elena; Demarchi, Alessandro
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
Angeluccetti, Irene; Perez, Francesca; Cámaro, Walther; Demarchi, Alessandro
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
Bathke, D. J.; Wall, N.; Haigh, T.; Smith, K. H.; Bernadt, T.
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.
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.
Simpson, Mike; Ives, Matthew; Hall, Jim
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
Husak, G. J.; Budde, M. E.; Rowland, J.; Verdin, J. P.; Funk, C. C.; Landsfeld, M. F.
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.
McDaniels, T.; Steyn, D. G.; Johnson, M. S.; Small, M.; Leclerc, G.; Vignola, R.; Chan, K.; Grossmann, I.; Wong-Parodi, G.
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
Zhao, Junfang; Xu, Jingwen; Xie, Xingmei; Lu, Houquan
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.
Mu, Q.; Zhao, M.; Kimball, J. S.; McDowell, N. G.; Running, S. W.
Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse eco-social impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. We developed a method to generate a near-real-time remotely sensed Drought Severity Index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly and annual frequencies. The new DSI integrates and exploits information from current operational satellite based terrestrial evapotranspiration (ET) and Vegetation greenness Index (NDVI) products, which are sensitive to vegetation water stress. Specifically, our approach determines the annual DSI departure from its normal (2000-2011) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (r=0.43) with the precipitation based Palmer Drought Severity Index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely-sensed global terrestrial DSI enhances capabilities for near-real-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.
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 ...
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...
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, ...
Villarreal, Miguel; Norman, Laura M.; Buckley, Steven; Wallace, Cynthia S.A.; Coe, Michelle A.
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.
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
Klisch, A.; Atzberger, C.; Luminari, L.
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.
White, Kristopher D.; Case, Jonathan L.
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.
Hayes, M. J.; Pulwarty, R. S.; Svoboda, M.
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
Zappa, M.; Bernhard, L.; Spirig, C.; Pfaundler, M.; Stahl, K.; Kruse, S.; Seidl, I.; Stähli, M.
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.
Vogt, J.; Sepulcre, G.; De Jager, A.; Magni, D.; Valentini, L.; Russo, S.; Micale, F.; Barbosa, P.
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
Wood, E. F.; Sheffield, J.; Fisher, C. K.; Chaney, N.; Wanders, N.
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
Vogt, Jürgen; de Jager, Alfred; Carrão, Hugo; Magni, Diego; Mazzeschi, Marco; Barbosa, Paulo
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
Hain, C.; Crow, W. T.; Anderson, M. C.; Zhan, X.; Wardlow, B.; Svoboda, M. D.; Mecikalski, J. R.
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
Song, Youngkeun; Njoroge, John B; Morimoto, Yukihiro
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.
Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt; Wardlow, Brian D.; Anderson, Martha C.; Verdin, James P.
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
Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt
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
Dutta, D.; Gupta, A.D.; Ramnarong, V.
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.
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
Okin, G. S.
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.
Vannest, Kimberly J.; Parker, Richard I.; Davis, John L.; Soares, Denise A.; Smith, Stacey L.
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…
Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.; Clemens, Nathan H.
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…
Karamihalaki, Maria; Stagakis, Stavros; Sykioti, Olga; Kyparissis, Aris; Parcharidis, Issaak
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.
González-Dugo, Maria P.; Carpintero, Elisabet; Andreu, Ana
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
Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.
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.
... for agency monitoring to assure that mitigation measures and other commitments associated with the... Administrator for Management to determine the need for additional mitigation measures and whether to prepare...
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.
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...
Ashouri, H.; Hsu, K.; Sorooshian, S.; Braithwaite, D.; Knapp, K. R.; Cecil, L. D.
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.
Rochon, Gilbert L.
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
Bowman, K. W.; Liu, J.; Parazoo, N.; Jiang, Z.; Bloom, A. A.; Lee, M.; Menemenlis, D.; Gierach, M.; Collatz, G. J.; Gurney, K. R.
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.
Saarela, Olli; Lehtonen, Mikko; Halme, Jari; Aikala, Antti; Raivio, Kimmo
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.
Gu, Y.; Hunt, E.; Wardlow, B.; Basara, J.B.; Brown, J.F.; Verdin, J.P.
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.
Kuwayama, Y.; Bernknopf, R.; Macauley, M.; Brookshire, D.; Zaitchik, B. F.; Rodell, M.
Water storage anomalies derived from the Gravity Recovery and Climate Experiment Data Assimilation System (GRACE-DAS) have been used to enhance the information contained in drought indicators. The potential value of this information is to inform local and regional decisions to improve economic welfare in the face of drought. Based on a characterization of current drought evaluations, a modeling framework has been structured to analyze the contributed value of the Earth observations in the assessment of the onset and duration of droughts and their regional impacts. The analysis focuses on (1) characterizing how GRACE-DAS provides Earth observation information for a drought warning, (2) assessing how a GRACE-DAS-enhanced U.S. Drought Monitor would improve economic outcomes in a region, and (3) applying this enhancement process in a decision framework to illustrate the potential role of GRACE data products in a recent drought and response scenario for a value-of-information (VOI) analysis. The VOI analysis quantifies the relative contribution of enhanced understanding and communication of the societal benefits associated with GRACE Earth observation science. Our emphasis is to illustrate the role of an enhanced National Integrated Drought Information System outlook on three key societal outcomes: effects on particular economic sectors, changes in land management decisions, and reductions in damages to ecosystem services.
Lyons, J.E.; Runge, M.C.; Laskowski, H.P.; Kendall, W.L.
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
Mcnutt, C. A.; Pulwarty, R. S.; Darby, L. S.; Verdin, J. P.; Webb, R. S.
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
Muralidharan, R.; Baron, S.
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.
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...
Manos, B; Bournaris, Th; Silleos, N; Antonopoulos, V; Papathanasiou, J
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).
Granger, S. L.; Behrangi, A.
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
Barber, Nancy L.; Stamey, Timothy C.
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.
Neckles, Hilary A.; Lyons, James E.; Guntenspergen, Glenn R.; Shriver, W. Gregory; Adamowicz, Susan C.
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.
Zhang, H.-w.; Chen, H.-l.
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.
Tene, A; Tobin, B; Dyckmans, J; Ray, D; Black, K; Nieuwenhuis, M
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).
Hund, S. V.; Johnson, M. S.; Steyn, D. G.; Keddie, T.; Morillas, L.
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.
Li, Bailing; Rodell, Matthew; Zaitchik, Benjamin F.; Reichle, Rolf H.; Koster, Randal D.; van Dam, Tonie M.
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.
Neri, C.; Magaña Rueda, V.
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
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.
Rossato, Luciana; Antônio Marengo, José; Bassi Marinho Pires, Luciana
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.
Dalecios, Nicolas; Spyropoulos, Nicos V.; Tarquis, Ana M.
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
Yang Wang; Zhiwen Liu; Bin Dong
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).
Deo, Ravinesh C.; Byun, Hi-Ryong; Adamowski, Jan F.; Begum, Khaleda
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
Peterson, James T; Freeman, Mary C
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.
Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin
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
Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin
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.
Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt
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.
Sanvido, Olivier; Romeis, Jörg; Bigler, Franz
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.
Pulwarty, R. S.; Schubert, S. D.
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
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
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
Keum, Jongho; Kaluarachchi, Jagath J
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.
Deng, M.; Di, L.; Han, W.; Yagci, A.; Peng, C.
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
Bhuiyan, C.; Singh, R. P.; Kogan, F. N.
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.
Leelaruban, N.; Akyuz, A.; Padmanabhan, G.; Shaik, S.
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
Bachmair, Sophie; Svensson, Cecilia; Prosdocimi, Ilaria; Hannaford, Jamie; Helm Smith, Kelly; Svoboda, Mark; Stahl, Kerstin
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.
Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.; Etingov, Pavel V.; Zhou, Ning; Dagle, Jeffery E.
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.
Vesselinov, V. V.; Harp, D. R.; Mishra, P. K.; Katzman, D.
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
Balderama, Orlando F
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.
Nam, W.; Wardlow, B.; Hayes, M. J.; Tadesse, T.; Svoboda, M.; Fuchs, B.; Wilhite, D. A.
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.
Wokke, Martijn E; Cleeremans, Axel; Ridderinkhof, K Richard
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.
Mahaffee, Walter F; Stoll, Rob
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.
Robertson, Samuel; Bartlett, Jonathan D; Gastin, Paul B
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.
De Bievre, B.; Célleri, R.; Crespo, P.; Ochoa, B.; Buytaert, W.; Tobón, C.; Villacís, M.; Villazon, M. F.; Llerena, C.; Rodriguez, M.; Viñas, P.
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.
di, L.; Yang, Z.
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
Qamer, F. M.; Shah, S. N. Pd.; Murthy, M. S. R.; Baidar, T.; Dhonju, K.; Hari, B. G.
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.
Winslow, M.; Akhtar-Schuster, M.; Cherlet, M.; Martius, C.; Sommer, S.; Thomas, R.; Vogt, J.
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
Hain, C.; Anderson, M. C.; Otkin, J.; Semmens, K. A.; Zhan, X.; Fang, L.; Li, Z.
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
Jawahar, I M; Mattsson, Jonny
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.
Santos, Maria J.; Whitham, Thomas G.
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.
Oroza, C.; Zheng, Z.; Zhang, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.
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.
Orhan, Osman; Ekercin, Semih; Dadaser-Celik, Filiz
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
Bartel, B.; Mothes, P. A.
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.
Bartel, B. A.; Mothes, P. A.
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.
Heasler, Patrick G.; Wood, Thomas W.
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.
Uhrig, R.E. Oak Ridge National Lab., TN )
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.
Mao, Zhu; Todd, Michael; Mascareñas, David
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.
Seibert, M.; Apel, H.
Drought impacts can be mitigated by enhancing preparedness. For better disaster management an effective drought forecasting and monitoring system has to be combined with detailed knowledge of drought variability and local impact. In regions with low data availability remotely sensed data and global reanalysis data and forecasts play an important role to supplement low density monitoring systems. Many sources of data are available to the public and represent most of the complex aspects of drought. To investigate drought variability - temporal and spatial - several statistical methods have to be combined to characterize a region. Here, we analyze drought variability in the Limpopo basin (southeastern Africa). We combine reanalysis data and drought indices to investigate meteorological drought. Vegetation indices provide an indicator of drought impact. We use empirical orthogonal function analysis and cluster analysis to find spatiotemporal patterns in drought conditions and identify homogeneous regions. For these homogeneous regions we extract a meteorological drought signal. Then, we employ wavelet analysis to investigate major characteristics of the drought signal. We seek to identify external climate factors such as ENSO having a major influence on drought occurrence. These factors can serve as a starting point for the investigation of predictors in a drought foreshadowing system enabling estimation of drought likelihood and severity with several months lead times. The foreshadowing based on statistical analysis and large scale climatic factors can complement physical model based forecasts for enhanced drought preparedness.
Wang, Zhaoguo; Du, Xishihui
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.
Irving, D. H.; Rasheed, M.; Hillman, C.; O'Doherty, N.
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
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.
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.
Vesselinov, V. V.; Harp, D.
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
Enzenhöfer, R.; Geiges, A.; Nowak, W.
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
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 ...
Quaife, T. L.; Black, E.; Brown, M.; Greatrex, H.; Maidment, R.; Mookerjee, A.; Tarnavsky, E.
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.
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...
Turnipseed, D. Phil; Long, Loyd G.
Accurate and reliable water-resources data collected during drought conditions are critical to regulatory agencies such as the Mississippi Department of Environmental Quality (MDEQ). Droughts have affected Mississippi during 1940-44, 1951-57, 1962-71, 1980-82 and 1983-88. In late summer and early autumn 1999, many areas of Mississippi experienced near record drought conditions causing concern to many private and public interests. Personnel from the U.S. Geological Survey (USGS), in cooperation with the MDEQ Office of Land and Water Resources (MDEQ-OLWR) measured water levels and streamflows throughout the State of Mississippi during drought conditions in August through October 1999. Droughts are normal, recurring hydrological events caused by deficiency of precipitation over an extended period of time that can have adverse effects on anthropogenic use of water. Much of the State of Mississippi has continued to experience drought conditions through late winter of 2000. Data on minimum streamflows are an important factor for determining the regulation of flow control structures, effluent discharge, and surface water withdrawals and other water-management decisions during droughts. Data on minimum streamflows become paramount during drought conditions. This report presents information related to the legal aspects of drought conditions and includes selected data collected at streamgages affected by severe drought conditions in Mississippi during the late summer and early autumn of 1999. Comparisons of low-flow characteristics at selected streamgages to other period-of-record low-flows at selected gages in the State are also presented.
Peterson, P.; Funk, C. C.; Landsfeld, M. F.; Pedreros, D. H.; Shukla, S.; Husak, G. J.; Harrison, L.; Verdin, J. P.
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.
Low, Dennis J.; Conger, Randall W.
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
Ma, Mingwei; Ren, Liliang; Singh, Vijay P.; Tu, Xinjun; Jiang, Shanhu; Liu, Yi
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.
Oshorov, A V; Popugaev, K A; Savin, I A; Gavrilov, A G; Kravchuk, A D; Potapov, A A
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.
Dercas, Nicholas; Dalezios, Nicolas
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
Merz, Steffen; Pohlmeier, Andreas; Seidler, Christina; van Dusschoten, Dagmar; Vereecken, Harry
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
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.
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.
Wildy, Helen; Forster, Pat; Louden, William; Wallace, John
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,…
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.
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.
Khader, A.; Rosenberg, D.; McKee, M.
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
Khader, A. I.; Rosenberg, D. E.; McKee, M.
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
Kuwayama, Y.; Bernknopf, R.; Brookshire, D.; Macauley, M.; Zaitchik, B. F.; Rodell, M.; Vail, P.; Thompson, A.
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.
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.
Adeyeri, Michael Kanisuru; Mpofu, Khumbulani
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.
Harvey. Craig; Lawhead, Joel
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
Remke, Alexander-André; Wirtz, Stefan; Brings, Christine; Gronz, Oliver; Seeger, Manuel; Ries, Johannes B.
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
Madrigal, J. M.; Lopez, A.; Garatuza, J.
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
Calmanti, Sandro; Bosi, Lorenzo; Fernandez, Jesus; De Felice, Matteo
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.
Van Loon, A.; Laaha, G.; Van Lanen, H.; Parajka, J.; Fleig, A. K.; Ploum, S.
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.
Van Loon, Anne; Laaha, Gregor; Van Lanen, Henny; Parajka, Juraj; Fleig, Anne; Ploum, Stefan
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.
Schubert, S.; Mo, K.; Peters-Lidard, C.; Wood, A.
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.
Wang, Yeqiao; Nemani, Ramakrishna; Dieffenbach, Fred; Stolte, Kenneth; Holcomb, Glenn B.; Robinson, Matt; Reese, Casey C.; McNiff, Marcia; Duhaime, Roland; Tierney, Geri; Mitchell, Brian; August, Peter; Paton, Peter; LaBash, Charles
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 decisionmaking on management of the A.T. by providing a coherent framework for data integration, status reporting and trend analysis. The A.T. MEGA-Transect DSS is to integrate NASA multi-platform sensor data and modeling through the Terrestrial Observation and Prediction System (TOPS) and in situ measurements from A.T. MEGA-Transect partners to address identified natural resource priorities and improve resource management decisions.
Thompson, D. R.
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.
The EPA is developing a multi-level approach that utilizes satellite and airborne remote sensing to locate and monitor genetically modified corn in the agricultural landscape and pest infestation. The current status of the EPA IRM monitoring program based on remote sensed imager...
The Mesoamerican Biological Corridor (MBC)-a network of managed and protected areas extending from Mexico to Columbia-is a crucial initiative for the Mesoamerican region, with a central development concept of integrating conservation and sustainable use of biodiversity within the framework of sustainable economic development. The MBC is of particular importance to the Central American Commission for Environment and Development (CCAD), which is comprised of the environmental ministers from the seven Central American countries. Responsible for determining priority areas for action in the corridor, CCAD decision makers require current and accurate information, and access to the dynamic knowledge of the changes in the MBC such as deforestation hotspots, fires, and the effects of natural disasters. Currently this information is not integrated and in disparate locations throughout the region and the world. Leveraging NASA technology, satellite data, and capability, we propose to team with the World Bank and the CCAD to develop a regional monitoring and visualization system-with central nodes at the NASA/Marshall Space Flight Center and at CCAD headquarters. This system will assimilate NASA spatial datasets (e.g. MODIS, Landsat, etc.), spatial data from other sources (commercial and public-domain), and ancillary data developed in each of the seven Central American countries (soils, transportation networks, biodiversity indicator maps, etc.). The system will function as a "virtual dashboard" for monitoring the MBC and provide the critical decision support tools for CCAD decision makers. The CCAD central node will also serve as a high-tech showcase for the corridor among the international community, other decision-makers, the media, and students.
Towler, E.; Lazrus, H.; Paimazumder, D.
Traditional drought research has mainly focused on physical drought risks and less on the cultural processes that also contribute to how drought risks are perceived and managed. However, as society becomes more vulnerable to drought and climate change threatens to increase water scarcity, it is clear that drought research would benefit from a more interdisciplinary approach. To assess avoided drought impacts from reduced climate change, drought risks need to be assessed in the context of both climate prediction as well as improved understanding of socio-cultural processes. To this end, this study explores a risk-based framework to combine physical drought likelihoods with perceived risks from stakeholder interviews. Results are presented from a case study on how stakeholders in south-central Oklahoma perceive drought risks given diverse cultural beliefs, water uses, and uncertainties in future drought prediction. Stakeholder interviews (n=38) were conducted in 2012 to understand drought risks to various uses of water, as well as to measure worldviews from the cultural theory of risk - a theory that explains why people perceive risks differently, potentially leading to conflict over management decisions. For physical drought risk, drought projections are derived from a large ensemble of future climates generated from two RCPs that represent higher and lower emissions trajectories (i.e., RCP8.5 and RCP4.5). These are used to develop a Combined Drought Risk Matrix (CDRM) that characterizes drought risks for different water uses as the products of both physical likelihood (from the climate ensemble) and risk perception (from the interviews). We use the CRDM to explore the avoided drought risks posed to various water uses, as well as to investigate the potential for reduction of conflict over water management.
Fan, Chung-Hau; Hansmann, Paul R.
Language in the Individuals With Disabilities Education Improvement Act (IDEIA) allows the use of response-to-intervention (RTI) methodology in the identification of specific learning disabilities. However, there is no consensus on decision rules using curriculum-based measurement of oral reading fluency (CBM-R) for defining responsiveness. The…
Fournet, S.; Aich, V.; Liersch, S.; Hattermann, F. F.
From 1970 to 2002, the Sahel experienced a fairly abrupt, severe and continuous dry episode. The main reason is the oceanic forcing ruling the West African monsoon dynamic. Also, a combinative effect of climate and anthropogenic changes (demographic pressure on land associated to inappropriate land-use practices) initiates and supports the interactive processes of drying and land cover degradation forming a complex land atmosphere feedback convection. The Great Drought in Mali largely affected the regional food security, the human societies and economic development and the conservation of wet and semi-arid ecosystems. It results in an increasing competition and conflicts for water access between vulnerable local stakeholders (rainfed and controlled irrigation farming, nomad pastoralism, traditional fishing) and steers national investments with the construction of dams and diversion channels for development of hydropower energy and fully governed irrigated agriculture. To support drought adaptations in regional development strategies, climate and hydrological forecasting are thus of paramount importance. Whilst climate change is typically associated with an increase in mean global surface temperature, what matters regionally and still remains uncertain is the change in rainfall, discharge and drought patterns from daily intensity to large inter-annual and multi-decadal variability. Different climate data sources exist for investigation of climate variability and change: daily measurements, reanalysis data and climate scenarios using Global and Regional Circulation Models (GCMs and RCMs). This study aims at analyzing the suitability of the different data sources for drought investigation in the target area, the Upper Niger Basin. First, the performance of meteorological data sets based on climate reanalysis is assessed in comparison of data of synoptic stations. Second, one statistical (STAR) and two dynamical regional RCMs (CCLM, REMO) are compared to IPCC-GCM data
Briciu-Burghina, Ciprian; Sullivan, Timothy; Chapman, James; Regan, Fiona
High-frequency, continuous monitoring using in situ sensors offers a comprehensive and improved insight into the temporal and spatial variability of any water body. In this paper, we describe a 7-month exploratory monitoring programme in Dublin Port, demonstrating the value of high-frequency data in enhancing knowledge of processes, informing discrete sampling, and ultimately increasing the efficiency of port and environmental management. Kruskal-Wallis and Mann-Whitney tests were used to show that shipping operating in Dublin Port has a small-medium effect on turbidity readings collected by in situ sensors. Turbidity events are largely related to vessel activity in Dublin Port, caused by re-suspension of sediments by vessel propulsion systems. The magnitudes of such events are strongly related to water level and tidal state at vessel arrival times. Crucially, measurements of Escherichia coli and enterococci contamination from discrete samples taken at key periods related to detected turbidity events were up to nine times higher after vessel arrival than prior to disturbance. Daily in situ turbidity patterns revealed time-dependent water quality "hot spots" during a 24-h period. We demonstrate conclusively that if representative environmental assessment of water quality is to be performed at such sites, sampling times, informed by continous monitoring data, should take into account these daily variations. This work outlines the potential of sensor technologies and continuous monitoring, to act as a decision support tool in both environmental and port management.
Lennard, Amy; Macdonald, Neil; Hooke, Janet
Drought indicators are an under-used metric in UK drought management. Standardised drought indicators offer a potential monitoring and management tool for operational water resource management. However, the use of these metrics needs further investigation. This work uses statistical analysis of the climatological drought signal based on meteorological drought indicators and observed streamflow data to explore the link between meteorological drought and hydrological drought to inform water resource management for a single water resource region. The region, covering 21,000 km2 of the English Midlands and central Wales, includes a variety of landscapes and climatological conditions. Analysis of the links between meteorological drought and hydrological drought performed using streamflow data from 'natural' catchments indicates a close positive relationship between meteorological drought indicators and streamflow, enhancing confidence in the application of drought indicators for monitoring and management. However, many of the catchments in the region are subject to modification through impoundments, abstractions and discharge. Therefore, it is beneficial to explore how climatological drought signal propagates into managed hydrological systems. Using a longitudinal study of catchments and sub-catchments that include natural and modified river reaches the relationship between meteorological and hydrological drought is explored. Initial statistical analysis of meteorological drought indicators and streamflow data from modified catchments shows a significantly weakened statistical relationship and reveals how anthropogenic activities may alter hydrological drought characteristics in modified catchments. Exploring how meteorological drought indicators link to streamflow across the water supply region helps build an understanding of their utility for operational water resource management.
Dalezios, N. R.; Blanta, A.; Spyropoulos, N. V.
Hazard may be defined as a potential threat to humans and their welfare and risk (or consequence) as the probability of a hazard occurring and creating loss. Drought is considered as one of the major natural hazards with significant impact to agriculture, environment, economy and society. This paper deals with drought risk assessment, which the first step designed to find out what the problems are and comprises three distinct steps, namely risk identification, risk management which is not covered in this paper, there should be a fourth step to address the need for feedback and to take post-audits of all risk assessment exercises. In particular, quantitative drought risk assessment is attempted by using statistical methods. For the qualification of drought, the Reconnaissance Drought Index (RDI) is employed, which is a new index based on hydrometeorological parameters, such as precipitation and potential evapotranspiration. The remotely sensed estimation of RDI is based on NOA-AVHRR satellite data for a period of 20 years (1981-2001). The study area is Thessaly, central Greece, which is a drought-prone agricultural region characterized by vulnerable agriculture. Specifically, the undertaken drought risk assessment processes are specified as follows: 1. Risk identification: This step involves drought quantification and monitoring based on remotely sensed RDI and extraction of several features such as severity, duration, areal extent, onset and end time. Moreover, it involves a drought early warning system based on the above parameters. 2. Risk estimation: This step includes an analysis of drought severity, frequency and their relationships. 3. Risk evaluation: This step covers drought evaluation based on analysis of RDI images before and after each drought episode, which usually lasts one hydrological year (12month). The results of these three-step drought assessment processes are considered quite satisfactory in a drought-prone region such as Thessaly in central
Stevenson, M A; Sanson, R L; Miranda, A O; Lawrence, K A; Morris, R S
To mitigate the effects of risks to food safety and infectious disease outbreaks in farmed animals, animal health authorities need to have systems in place to identify and trace the source of identified problems in a timely manner. In the event of emergencies, these systems will allow infected or contaminated premises (and/or animals) to be identified and contained, and will allow the extent of problems to be communicated to consumers and trading partners in a clear and unambiguous manner. The key to achieving these goals is the presence of an effective animal health decision support system that will provide the facilities to record and store detailed information about cases and the population at risk, allowing information to be reported back to decision makers when it is required. Described here are the components of an animal health decision support system, and the ways these components can be used to enhance food safety, responses to infectious disease incursions, and animal health and productivity. Examples are provided to illustrate the benefit these systems can return, using data derived from countries that have such systems (or parts of systems) in place. Emphasis is placed on the features that make particular system components effective, and strategies to ensure that these are kept up to date.
Li, Q.; Zeng, M.; Wang, H.; Li, P.; Wang, K.; Yu, M.
The Huaihe River Basin having China's highest population density (662 persons per km2) lies in a transition zone between the climates of North and South China, and is thus prone to drought. Therefore, the paper aims to develop an appropriate drought assessment approach for drought assessment in the Huaihe River basin, China. Based on the Principal Component Analysis of precipitation, evapotranspiration, soil moisture and runoff, the three latter variables of which were obtained by use of the Xin'anjiang model, a new multivariate drought index (MDI) was formulated, and its thresholds were determined by use of cumulative distribution function. The MDI, the Standardized Precipitation Index (SPI) and the self-calibrating Palmer Drought Severity Index (sc-PDSI) time series on a monthly scale were computed and compared during 1988, 1999/2000 and 2001 drought events. The results show that the MDI exhibited certain advantages over the sc-PDSI and the SPI in monitoring drought evolution. The MDI formulated by this paper could provide a scientific basis for drought mitigation and management, and references for drought assessment elsewhere in China.
Bachmair, Sophie; Tanguy, Maliko; Hannaford, Jamie; Stahl, Kerstin
Drought monitoring and early warning (M&EW) is an important component of agricultural and silvicultural risk management. Meteorological indicators such as the Standardized Precipitation Index (SPI) are widely used in operational M&EW systems and for drought hazard assessment. Meteorological drought yet does not necessarily equate to agricultural drought given differences in drought susceptibility, e.g. crop-specific vulnerability, soil water holding capacity, irrigation and other management practices. How useful are meteorological indicators such as SPI to assess agricultural drought? Would the inclusion of vegetation indicators into drought M&EW systems add value for the agricultural sector? To answer these questions, it is necessary to investigate the link between meteorological indicators and agricultural impacts of drought. Crop yield or loss data is one source of information for drought impacts, yet mostly available as aggregated data at the annual scale. Remotely sensed vegetation stress data offer another possibility to directly assess agricultural impacts with high spatial and temporal resolution and are already used by some M&EW systems. At the same time, reduced crop yield and satellite-based vegetation stress potentially suffer from multi-causality. The aim of this study is therefore to investigate the relation between meteorological drought indicators and agricultural drought impacts for Europe, and to intercompare different agricultural impact variables. As drought indicators we used SPI and the Standardized Precipitation Evaporation Index (SPEI) for different accumulation periods. The focus regarding drought impact variables was on remotely sensed vegetation stress derived from MODIS NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) data, but the analysis was complemented with crop yield data and text-based information from the European Drought Impact report Inventory (EDII) for selected countries. A correlation analysis
Kruse, S.; Seidl, I.
This paper analyses the social capacities for drought risk management from the perspective of national and regional water users and policy- and decision-makers in Switzerland. The analysis follows five dimensions of social capacities as prerequisites for drought risk management. Regarding information and knowledge (1), basic data is available, however not assembled for an integrated drought information system. As for technology and infrastructure (2), limited proactive capacities are available with the exception of a few of the drought-prone regions; in emergency response to drought however, provisional capacities are put together. Regarding organisation and management (3) most regions have enough personnel and effective cooperation in the case of acute and sporadic drought; long-term strategies though are largely missing. Economic resources (4) are sufficient if droughts remain rare. Finally, institutions and policies (5) are not sufficient for proactive drought risk management, but have been suitable in the drought of 2003. Starting points for building social capacities are first, to draw on the extensive experiences with the management of other natural hazards, second to build an integrated drought information system, including social and economic impacts, and third to improve the institutional framework through consistent regulations and coordination for proactive drought risk management.
Russo, Ana; Gouveia, Célia; Trigo, Ricardo; Jerez, Sonia
human resources. The understanding of the present-day underlying mechanisms together with the necessary contextualization within a wider past, is essential to understand future projections, and should lastly rebound on the adequacy of the management decision making. Acknowledgments: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project QSECA (PTDC/AAG-GLO/4155/2012) Gouveia C., Trigo R.M., DaCamara C.C. (2009) Drought and Vegetation Stress Monitoring in Portugal using Satellite Data, Natural Hazards and Earth System Sciences, 9, 1-11. Giorgi, F. and Lionello, P.; Climate change projections for the Mediterranean region. Global and Planetary Change, 63 (2-3): 90-104, 2008. Vicente-Serrano, Sergio M., Santiago Beguería, Juan I. López-Moreno, 2010: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Climate, 23, 1696-1718. Jerez, S., R.M. Trigo, S.M. Vicente-Serrano, D. Pozo-Vázquez, R. Lorente-Plazas, J. Lorenzo-Lacruz, F. Santos-Alamillos and J.P. Montávez (2013). The impact of the North Atlantic Oscillation on the renewable energy resources in south-western Europe. Journal of Applied Meteorology and Climatology, DOI 10.1175/JAMC-D-12-0257.1.
Bruce, Courtenay R.
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…
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 scoutin...
Adlassnig, Klaus-Peter; Fehre, Karsten; Rappelsberger, Andrea
This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning.
A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.; Colliander, A.; Njoku, E. G.
We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE, in-situ groundwater measurements and atmospheric moisture data to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depth in relation to satellite based vegetation metrics, including vegetation greenness (NDVI) measures from MODIS and related higher order productivity (GPP) before, during and following the major drought events observed in the continental US for the past 14 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, NDVI and GPP. We study how changes in atmosphere moisture stress and coupling of water storage components at different depth impact on the spatial and temporal correlation between TWS, SM and vegetation metrics. In Texas, we find that surface SM and GRACE TWS agree with each other in general, and both capture the underlying water supply constraints to vegetation growth. Triggered by a transit increase in precipitation following the 2011 hydrological drought, vegetation productivity in Texas shows more sensitivity to surface SM than TWS. In the Great Plains, the correspondence between TWS and vegetation productivity is modulated by temperature-induced atmosphere moisture stress and by the coupling between surface soil moisture and groundwater through irrigation.
The southwestern United States pined for water in late March and early April 2007. This image is based on data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite from March 22 through April 6, 2007, and it shows the Normalized Difference Vegetation Index, or NDVI, for the period. In this NDVI color scale, green indicates areas of healthier-than-usual vegetation, and only small patches of green appear in this image, near the California-Nevada border and in Utah. Larger areas of below-normal vegetation are more common, especially throughout California. Pale yellow indicates areas with generally average vegetation. Gray areas appear where no data were available, likely due to persistent clouds or snow cover. According to the April 10, 2007, update from the U.S. Drought Monitor, most of the southwestern United Sates, including Utah, Nevada, California, and Arizona, experienced moderate to extreme drought. The hardest hit areas were southeastern California and southwestern Arizona. Writing for the Drought Monitor, David Miskus of the Joint Agricultural Weather Facility reported that March 2007 had been unusually dry for the southwestern United States. While California's and Utah's reservoir storage was only slightly below normal, reservoir storage was well below normal for New Mexico and Arizona. In early April, an international research team published an online paper in Science noting that droughts could become more common for the southwestern United States and northern Mexico, as these areas were already showing signs of drying. Relying on the same computer models used in the Intergovernmental Panel on Climate Change (IPCC) report released in early 2007, the researchers who published in Science concluded that global warming could make droughts more common, not just in the American Southwest, but also in semiarid regions of southern Europe, Mediterranean northern Africa, and the Middle East.
Basilakis, Jim; Lovell, Nigel H; Redmond, Stephen J; Celler, Branko G
Telehealth is the provision of health services at a distance. Typically, this occurs in unsupervised or remote environments, such as a patient's home. We describe one such telehealth system and the integration of extracted clinical measurement parameters with a decision-support system (DSS). An enterprise application-server framework, combined with a rules engine and statistical analysis tools, is used to analyze the acquired telehealth data, searching for trends and shifts in parameter values, as well as identifying individual measurements that exceed predetermined or adaptive thresholds. An overarching business process engine is used to manage the core DSS knowledge base and coordinate workflow outputs of the DSS. The primary role for such a DSS is to provide an effective means to reduce the data overload and to provide a means of health risk stratification to allow appropriate targeting of clinical resources to best manage the health of the patient. In this way, the system may ultimately influence changes in workflow by targeting scarce clinical resources to patients of most need. A single case study extracted from an initial pilot trial of the system, in patients with chronic obstructive pulmonary disease and chronic heart failure, will be reviewed to illustrate the potential benefit of integrating telehealth and decision support in the management of both acute and chronic disease.
Farmers across America rely on early drought warning systems to manage their crops. Americans everywhere rely on those farmers to provide food. A new drought tracking system called ESI helps by mon...
Blauhut, Veit; Stahl, Kerstin
The effects of drought on the environment and socio- economic sectors are indisputably linked to each other. Nevertheless, past drought research suffered from an evident lack of ground truth in form of past observed impacts. Consequently most drought indicators are missing the link to a drought's effects on natural and human systems, and the majority of drought risk analyses are based on non-sector-specific, epistemic approaches. Hence, the science and application demands an integration of the missing piece: past drought impacts. Furthermore, for the case of Europe, drought risk analyses are mainly done for country scale or smaller units, even though the effects of the drought hazard are transboundary and long term measures are initiated through the European governance mechanism. This contribution faces drought risk on a pan European scale. A spatio-temporal clustering of drought impact occurrences from the European Drought Impact report Inventory divides Europe into macro regions with similar drought impact characteristics. To link drought impacts to indicators and vulnerability factors, multivariate logistic regression is applied to predict the likelihood of impact occurrence as a proxy for drought risk. As predictive variables we used a selection of common drought indicators and drought vulnerability analysis factors. The final results are displayed as drought risk maps, presenting drought risk for different levels of hazard severity, showing distinct differences in drought risk depending on location and impact sector. With this work, we contribute to the understanding of feedbacks between drought and society. The knowledge of this relation is essential for drought impact predictions and will improve resilience to this hazard. Furthermore, this information may become an essential tool for policy and decision makers on a European level.
Fowler, K.D.; Graves, R.D.
This document pertains to underground waste storage tanks at the Hanford Site that have been identified to potentially contain a significant amount of ferrocyanide compounds. This document evaluates the need for continuously monitoring the headspace vapors in Ferrocyanide Watch List tanks to detect flammable gases or gases that could indicate the occurrence of a propagating ferrocyanide-nitrate/nitrite reaction. The results of modeling studies and gas monitoring, and sludge sample analyses of actual ferrocyanide tank wastes have indicated no need to continuously monitor the vapor spaces in ferrocyanide tanks. This conclusion is based in part on the following factors: (1) a study performance on waste aging suggests that the ferrocyanide has degraded in the tanks during the more than 35 years of storage; therefore, the ferrocyanide is not present in concentrations that could support an exothermic reaction, also, the moisture present in the waste is sufficient to preclude a self-sustaining (propagating) ferrocyanide-nitrate reaction; (2) evaluation of core sample results from Tank 241-C-109 and Tank 241-C-112 support laboratory studies showing that ferrocyanide has degraded and the fuel concentration in the tanks is considerably lower than postulated by flowsheet simulants; (3) no gases have been identified that would indicate the occurrence of a ferrocyanide nitrate/nitrite reaction; additionally, a self-sustaining ferrocyanide nitrate/nitrite reaction is not possible under current and future planned storage conditions. After reviewing the available information, it is evident that there would be little safety benefit from continuous in-tank vapor monitoring, and the time and commitment of operations schedule and equipment funds are not justified in the face of competing needs.
Vogt, J.; Singleton, A.; Sepulcre, G.; Micale, F.; Barbosa, P.
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 the European Drought Observatory (EDO) is a portal, including a map server, a metadata catalogue, a media-monitor and analysis tools. The map server presents Europe-wide up-to-date information on the occurrence and severity of droughts, which is complemented by more detailed information provided by regional, national and local observatories through OGC compliant web mapping and web coverage services. In addition, time series of historical maps as well as graphs of the temporal evolution of drought indices for individual grid cells and administrative regions in Europe can be retrieved and analysed. Current work is focusing on validating the available products, improving the functionalities, extending the linkage to additional national and regional drought information systems and improving medium to long-range probabilistic drought forecasting products. Probabilistic forecasts are attractive in that they provide an estimate of the range of uncertainty in a particular forecast. Longer-term goals include the development of long-range drought forecasting products, the analysis of drought hazard and risk, the monitoring of drought impact and the integration of EDO in a global drought information system. The talk will provide an overview on the development and state of EDO, the different products, and the ways to include a wide range of stakeholders (i.e. European, national river basin, and local authorities) in
Technical Abstract: Increased water demands and drought have resulted in a need to indentify crop hybrids that are drought tolerant, requiring less irrigation to sustain yields. This study was conducted to assess differences in drought tolerance among a group of genetically diverse sugarbeet hybrids...
Yacout, A.M.; Orechwa, Y.
The degradation of the material in critical components is shown to be an effective measure which can be used to compute the risk adjusted economic penalty associated with different maintenance decisions. The approach of estimating the probability, with confidence interval, of the time that a prescribed degradation level is exceeded is shown to be practical, as demonstrated in the analysis of irradiated fuel cladding. The methodology for the estimation of the probability is predicated on the existence of a parsimonious and robust mixed-effects model of the evolution of the degradation. This model, in general, relates measured surrogates of the degradation level to computed or measured variables, which characterize the environment during the operating history of the component. We propose and demonstrate the efficacy of using an artificial neural network, constructed via a genetic supervisor, as an aid in developing the requisite mixed-effects model and testing its continued validity as new data are obtained.
Uhrig, R.E. |
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.
Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao
Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems. PMID:25711500
Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao
Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems.
Mega, Nabil; Medjerab, Abderrahmane
The high plateaus of Algeria is a critical region to policymakers in terms of social, economic, and infrastructure development. The main goal of the present work was to monitor the climatic drought and its impact on vegetation health across the Algerian high plateaus using remote sensing techniques. Vegetation health index (VHI) showed a clear drought in the western region of the study area. The results show practically three periods of drought were evident: October to December 2006, November to December 2009, and December 2012. Agreeable correlations among the obtained results using standard precipitation index for 3 months (SPI-3) and other satellite indicators such as temperature vegetation dryness index (TVDI) and VHI were obtained. TVDI and VHI agreed well with the ground-based observations from SPI-3; thus, these may serve as key and easily accessible indicators of drought. The research shows motivating results that decision makers can use to take timely corrective measures to minimize the reduction in agricultural production in drought prone areas.
Obringer, R.; Zhang, X.; Mallick, K.; Alemohammad, S. H.; Niyogi, D.
This study aims to integrate environmental data for drought monitoring to reduce uncertainty in urban drought characterization as part of the smart city framework. Currently, drought monitoring in urban areas is a challenge. This is due, in part, to a lack of knowledge on the subject of urban droughts and urban drought vulnerability. A critical part to assessing urban drought and implementing the necessary policies is determining drought conditions. Often the timing and severity of the drought can leave cities to enforce water restrictions, so accuracy of this determination has socioeconomic implications. To determine drought conditions, we need to know the water balance over the urban landscape, of which evapotranspiration (ET) is a key variable. However, ET data and models have high uncertainty when compared to other hydrological variables (i.e., precipitation). This is largely due to ill-defined empirical models for characterizing the urban surface resistance parameter (rs) that is used in ET calculations. We propose a method to estimate rs values using a combination of the Surface Temperature Initiated Closure (STIC) method that calculates regional evapotranspiration data and an inverted version of the Penman-Monteith equation. We use this approach across the region surrounding Indianapolis, IN (USA) from 2010-2014. We discuss the potential for this method to be integrated in to smart city framework to improve urban drought assessment.
Garriga, Ricard Giné; de Palencia, Alejandro Jiménez Fdez; Foguet, Agustí Pérez
Today, a vast proportion of people still lack a simple pit latrine and a source of safe drinking water. To help end this appalling state of affairs, there is a pressing need to provide policymakers with evidences which may be the basis of effective planning, targeting and prioritization. Two major challenges often hinder this process: i) lack of reliable data to identify which areas are most in need; and ii) inadequate instruments for decision-making support. In tackling previous shortcomings, this paper proposes a monitoring framework to compile, analyze, interpret and disseminate water, sanitation and hygiene information. In an era of decentralization, where decision-making moves to local governments, we apply such framework at the local level. The ultimate goal is to develop appropriate tools for decentralized planning support. To this end, the study first implements a methodology for primary data collection, which combines the household and the waterpoint as information sources. In doing so, we provide a complete picture of the context in which domestic WASH services are delivered. Second, the collected data are analyzed to underline the emerging development challenges. The use of simple planning indicators serves as the basis to i) reveal which areas require policy attention, and to ii) identify the neediest. Third, a classification process is proposed to prioritize among various populations. Three different case studies from East and Southern African countries are presented. Results indicate that accurate and comprehensive data, if adequately exploited through simple instruments, may be the basis of effective targeting and prioritization, which are central to sector planning. The application of the proposed framework in the real world, however, is to a certain extent elusive; and we point out to conclude two specific challenges that remain unaddressed, namely the upgrade of existing decision-making processes to enhance transparency and inclusiveness, and the
van der Veeken, Frida C. A.
Background Rehabilitation in forensic psychiatry is achieved gradually with different leave modules, in line with the Risk Need Responsivity model. A forensic routine outcome monitoring tool should measure treatment progress based on the rehabilitation theory, and it should be predictive of important treatment outcomes in order to be usable in decision-making. Therefore, this study assesses the predictive validity for both positive (i.e., leave) and negative (i.e., inpatient incidents) treatment outcomes with the Instrument for Forensic Treatment Evaluation (IFTE). Methods Two-hundred and twenty-four patients were included in this study. ROC analyses were conducted with the IFTE factors and items for three leave modules: guided, unguided and transmural leave for the whole group of patients. Predictive validity of the IFTE for aggression in general, physical aggression specifically, and urine drug screening (UDS) violations was assessed for patients with the main diagnoses in Dutch forensic psychiatry, patients with personality disorders and the most frequently occurring co-morbid disorders: those with combined personality and substance use disorders. Results and Conclusions Results tentatively imply that the IFTE has a reasonable to good predictive validity for inpatient aggression and a marginal to reasonable predictive value for leave approvals and UDS violations. The IFTE can be used for information purposes in treatment decision-making, but reports should be interpreted with care and acknowledge patients’ personal risk factors, strengths and other information sources. PMID:27517721
Phillips, Oliver L; van der Heijden, Geertje; Lewis, Simon L; López-González, Gabriela; Aragão, Luiz E O C; Lloyd, Jon; Malhi, Yadvinder; Monteagudo, Abel; Almeida, Samuel; Dávila, Esteban Alvarez; Amaral, Iêda; Andelman, Sandy; Andrade, Ana; Arroyo, Luzmila; Aymard, Gerardo; Baker, Tim R; Blanc, Lilian; Bonal, Damien; de Oliveira, Atila Cristina Alves; Chao, Kuo-Jung; Cardozo, Nallaret Dávila; da Costa, Lola; Feldpausch, Ted R; Fisher, Joshua B; Fyllas, Nikolaos M; Freitas, Maria Aparecida; Galbraith, David; Gloor, Emanuel; Higuchi, Niro; Honorio, Eurídice; Jiménez, Eliana; Keeling, Helen; Killeen, Tim J; Lovett, Jon C; Meir, Patrick; Mendoza, Casimiro; Morel, Alexandra; Vargas, Percy Núñez; Patiño, Sandra; Peh, Kelvin S-H; Cruz, Antonio Peña; Prieto, Adriana; Quesada, Carlos A; Ramírez, Fredy; Ramírez, Hirma; Rudas, Agustín; Salamão, Rafael; Schwarz, Michael; Silva, Javier; Silveira, Marcos; Slik, J W Ferry; Sonké, Bonaventure; Thomas, Anne Sota; Stropp, Juliana; Taplin, James R D; Vásquez, Rodolfo; Vilanova, Emilio
*The rich ecology of tropical forests is intimately tied to their moisture status. Multi-site syntheses can provide a macro-scale view of these linkages and their susceptibility to changing climates. Here, we report pan-tropical and regional-scale analyses of tree vulnerability to drought. *We assembled available data on tropical forest tree stem mortality before, during, and after recent drought events, from 119 monitoring plots in 10 countries concentrated in Amazonia and Borneo. *In most sites, larger trees are disproportionately at risk. At least within Amazonia, low wood density trees are also at greater risk of drought-associated mortality, independent of size. For comparable drought intensities, trees in Borneo are more vulnerable than trees in the Amazon. There is some evidence for lagged impacts of drought, with mortality rates remaining elevated 2 yr after the meteorological event is over. *These findings indicate that repeated droughts would shift the functional composition of tropical forests toward smaller, denser-wooded trees. At very high drought intensities, the linear relationship between tree mortality and moisture stress apparently breaks down, suggesting the existence of moisture stress thresholds beyond which some tropical forests would suffer catastrophic tree mortality.
Staudinger, Maria; Stahl, Kerstin; Seibert, Jan
The Standardized Precipitation Index (SPI) is the most widely used index to characterize and monitor droughts that are related to precipitation deficiencies. However, the SPI does not always deliver the relevant information for hydrological drought management when precipitation deficiencies are not the only reason for droughts as it is the case for example in snow influenced catchments. If precipitation is temporarily stored as snow, then there is a significant difference between meteorological and hydrological drought because the delayed release of melt water from the snow accumulation to the stream. In this study we introduce an extension to the SPI, the Standardized Snow Melt and Rain Index (SMRI), that captures both rain and snow melt deficits, which in effect modify streamflow. The SMRI does not require any snow data instead observations of temperature and precipitation are used to model snow. The SMRI is evaluated for seven Swiss catchments with varying degrees of snow influence. In particular for catchments with a larger component of snowmelt in runoff generation, we found the SMRI to be a good complementary index to the SPI to describe streamflow droughts. In a further step, the SPI and the SMRI were compared for the summer drought of 2003 and the spring drought of 2011 for Switzerland, using gridded products of precipitation and temperature including the entire country.
Funk, Christopher C.; Hoell, Andrew; Shukla, Shraddhanand; Blade, Ileana; Liebmann, Brant; Roberts, Jason B.; Robertson, Franklin R.
In southern Ethiopia, Eastern Kenya, and southern Somalia poor boreal spring rains in 1999, 2000, 2004, 2007, 2008, 2009 and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers support disaster risk reduction while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent droughts to a stronger Walker Circulation, warming in the Indo-Pacific warm pool, and an increased western Pacific sea surface temperature (SST) gradient, we explore the dominant modes of East African rainfall variability, links between these modes and sea surface temperatures, and a simple index-based monitoring-prediction system suitable for drought early warning.
Karamouz, M.; Zeynolabedin, A.; Olyaei, M. A.
are ranked in 5 intervals and for each parameter vulnerability maps are prepared in GIS environment. Selection of theses parameters are based on factors such as regional features and availability of data. Considering the fact that the aforementioned parameters have different level of importance in vulnerability maps, different weights are assigned to the parameters considering how critical each parameter is in the overall drought analysis. Expert's opinion is selected in assigning weights. A multi-criteria decision making (MCDM) framework is used to check the consistency of the provided information. Then the weighted maps are overlaid to find the overall vulnerability map. The map shows very low, low, medium, intense and very intense regional vulnerabilities. According to the results, the west part of East Azarbaijan province is the most vulnerable region to drought which is expected due to the vicinity of this part to Urumia Lake that has been lost most of its water during the last decades. The least vulnerable part seems to be the Eastern part of the province with longer lasting resources. Taking into consideration that Caspian Sea is near this part with high precipitation record, the outcome of this study is in line with the general expectations. The result of this study can be used for preparedness planning and for allocating resources for facing droughts in this region.
Lennard, A. T.; Macdonald, N.
Drought is a complex phenomenon, occurring in most climatic zones, including both high and low rainfall regions. Recent drought events (2004-2006 & 2010-2012) in the UK have highlighted a continued vulnerability to this hazard. The period 2010-2012 was characterised by departures from typical seasonal climatic conditions, resulting in a severe drought, which had a significant impact on water resources in parts of the UK. Recent droughts highlight the need for better understanding of extreme drought events, particularly from a water resource perspective. The UK has a wealth of long climate series that are under used for water resource management planning. Standardised drought indicators offer a potential monitoring and management tool for operational water resource management. However, the application of these metrics for operational water resource management needs further investigation, particularly links between meteorological drought indices, streamflow, groundwater and water supply systems. This work uses standardised drought indices (Standardised Precipitation Index and Standardised Precipitation Evaporation Index) to characterise drought from 1858-2013 within a 21,000 km2 water supply region serving 7.4 million people. Meteorological drought characterisation highlights the severity of late 19th Century droughts (1887-1889, 1890-1910) which are rarely considered in water resource modelling and drought management planning; current UK water resource modelling typically uses data from 1920-2012. Drought characterisation is used to extend the water resources modelling period back to 1884, permitting investigation of the impacts of pre-1920s droughts on reservoir yields and exploring how meteorological and hydrological drought characteristics impact the water supply system. Drought characteristics are also used to investigate the propagation from meteorological drought to hydrological drought using observed data from rivers, aquifers and reservoirs to develop a
Kellner, O; Niyogi, D
Weather and climate events and agronomic enterprise are coupled via crop phenology and yield, which is temperature and precipitation dependent. Additional coupling between weather and climate and agronomic enterprise occurs through agricultural practices such as tillage, irrigation, erosion, livestock management, and forage. Thus, the relationship between precipitation, temperature, and yield is coupled to the relationship between temperature, precipitation, and drought. Unraveling the different meteorological and climatological patterns by comparing different growing seasons provides insight into how drought conditions develop and what agricultural producers can do to mitigate and adapt to drought conditions. The 2012 drought in the United States greatly impacted the agricultural sector of the economy. With comparable severity and spatial extent of the droughts of the 1930s, 1950s, and 1980s, the 2012 drought impacted much of the U.S. crop and livestock producers via decreased forage and feed. This brief summary of drought impacts to agricultural production systems includes 1) the basics of drought; 2) the meteorology and climatology involved in forecasting, predicting, and monitoring drought with attribution of the 2012 drought explored in detail; and 3) comparative analysis completed between the 2011 and 2012 growing season. This synthesis highlights the complex nature of drought in agriculture production systems as producers prepare for future climate variability.
Issel, E P; Bollmann, R; Prenzlau, P
The validity of the modern methods of fetal monitoring to decide for the indication of urgent obstetric operations. The reliability of the modern supervision of the fetus is studied in cases of doubtful fetal heart action. Up to the present day we have no method for the exact estimation of the degree of a damage to the fetus. In such a precarious situation we should use all available methods for the diagnosis of the fetal condition, because the results of only one of the methods offer insufficient evidence. By means of the literature the alterations in the ECG of the dying fetus are interpreted in comparison to artefacts. In cases of doubtful fetal heart action we recommend in addition to the clinical findings to record the fetal ECG, to controll the actual fetal pH and attempt an investigation by ultrasonic.
Muthusamy, Muthusamy; Uma, Subbaraya; Backiyarani, Suthanthiram; Saraswathi, Marimuthu Somasundaram; Chandrasekar, Arumugam
In banana, drought responsive gene expression profiles of drought-tolerant and sensitive genotypes remain largely unexplored. In this research, the transcriptome of drought-tolerant banana cultivar (Saba, ABB genome) and sensitive cultivar (Grand Naine, AAA genome) was monitored using mRNA-Seq under control and drought stress condition. A total of 162.36 million reads from tolerant and 126.58 million reads from sensitive libraries were produced and mapped onto the Musa acuminata genome sequence and assembled into 23,096 and 23,079 unigenes. Differential gene expression between two conditions (control and drought) showed that at least 2268 and 2963 statistically significant, functionally known, non-redundant differentially expressed genes (DEGs) from tolerant and sensitive libraries. Drought has up-regulated 991 and 1378 DEGs and down-regulated 1104 and 1585 DEGs respectively in tolerant and sensitive libraries. Among DEGs, 15.9% are coding for transcription factors (TFs) comprising 46 families and 9.5% of DEGs are constituted by protein kinases from 82 families. Most enriched DEGs are mainly involved in protein modifications, lipid metabolism, alkaloid biosynthesis, carbohydrate degradation, glycan metabolism, and biosynthesis of amino acid, cofactor, nucleotide-sugar, hormone, terpenoids and other secondary metabolites. Several, specific genotype-dependent gene expression pattern was observed for drought stress in both cultivars. A subset of 9 DEGs was confirmed using quantitative reverse transcription-PCR. These results will provide necessary information for developing drought-resilient banana plants. PMID:27867388
Loschetter, Annick; Rohmer, Jérémy
Standard and new generation of monitoring observations provide in almost real-time important information about the evolution of the volcanic system. These observations are used to update the model and contribute to a better hazard assessment and to support decision making concerning potential evacuation. The framework BET_EF (based on Bayesian Event Tree) developed by INGV enables dealing with the integration of information from monitoring with the prospect of decision making. Using this framework, the objectives of the present work are i. to propose a method to assess the added value of information (within the Value Of Information (VOI) theory) from monitoring; ii. to perform sensitivity analysis on the different parameters that influence the VOI from monitoring. VOI consists in assessing the possible increase in expected value provided by gathering information, for instance through monitoring. Basically, the VOI is the difference between the value with information and the value without additional information in a Cost-Benefit approach. This theory is well suited to deal with situations that can be represented in the form of a decision tree such as the BET_EF tool. Reference values and ranges of variation (for sensitivity analysis) were defined for input parameters, based on data from the MESIMEX exercise (performed at Vesuvio volcano in 2006). Complementary methods for sensitivity analyses were implemented: local, global using Sobol' indices and regional using Contribution to Sample Mean and Variance plots. The results (specific to the case considered) obtained with the different techniques are in good agreement and enable answering the following questions: i. Which characteristics of monitoring are important for early warning (reliability)? ii. How do experts' opinions influence the hazard assessment and thus the decision? Concerning the characteristics of monitoring, the more influent parameters are the means rather than the variances for the case considered
Dalezios, N. R.; Blanta, A.; Spyropoulos, N. V.; Tarquis, A. M.
Drought is considered as one of the major natural hazards with a significant impact on agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought 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 risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of 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 the National Oceanic and Atmospheric Administration/advanced very high resolution radiometer (NOAA/AVHRR) satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season, with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than
Dalezios, N. R.; Blanta, A.; Spyropoulos, N. V.; Tarquis, A. M.
Drought is considered as one of the major natural hazards with significant impact to agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought 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 risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of 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 drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than those of mild to moderate drought throughout the warm season. Finally, the areas with diachronic
Taylor, I. H.; Burke, E.; McColl, L.; Falloon, P. D.; Harris, G. R.; McNeall, D.
Drought is a cumulative event, often difficult to define and involving wide-reaching consequences for agriculture, ecosystems, water availability, and society. Understanding how the occurrence of drought may change in the future and which sources of uncertainty are dominant can inform appropriate decisions to guide drought impacts assessments. Our study considers both climate model uncertainty associated with future climate projections, and future emissions of greenhouse gases (future scenario uncertainty). Four drought indices (the Standardised Precipitation Index (SPI), Soil Moisture Anomaly (SMA), the Palmer Drought Severity Index (PDSI) and the Standardised Runoff Index (SRI)) are calculated for the A1B and RCP2.6 future emissions scenarios using monthly model output from a 57-member perturbed parameter ensemble of climate simulations of the HadCM3C Earth System model, for the baseline period 1961-1990, and the period 2070-2099 ("the 2080s"). We consider where there are statistically significant increases or decreases in the proportion of time spent in drought in the 2080s compared to the baseline. Despite the large range of uncertainty in drought projections for many regions, projections for some regions have a clear signal, with uncertainty associated with the magnitude of change rather than direction. For instance, a significant increase in time spent in drought is generally projected for the Amazon, Central America and South Africa whilst projections for northern India consistently show significant decreases in time spent in drought. Whilst the patterns of changes in future drought were similar between scenarios, climate mitigation, represented by the RCP2.6 scenario, tended to reduce future changes in drought. In general, climate mitigation reduced the area over which there was a significant increase in drought but had little impact on the area over which there was a significant decrease in time spent in drought.
Water quality data from 55 monitoring wells during drought conditions surrounding Lake Texoma, located on the border of Oklahoma and Texas, was compared to assess the influence of drought on groundwater quality. During the drought month of October, water table levels were three ...
Van Loon, A.; Van Lanen, H.; Gleeson, T.
Drought is commonly defined as a temporary lack of water compared to normal conditions. In the traditional definition used in the natural sciences (climate science, hydrology, earth science) only natural drivers are included and the human effect on water resources is excluded. Drought impact studies, however, using observed crop yields, wildfire data, reservoir information, etc., can hardy make this division. The interdisciplinarity of drought asks for a broader definition that considers the interplay between the hazard, impacts and management. In the IPCC-SREX report definitional issues are mentioned as one of the reasons that no clear conclusions can be drawn about historic and future changes in drought. Human activities related to drought are mentioned by IPCC, but not included in their definition of drought. In the anthropocene the human aspects of drought can no longer be neglected. The IAHS Panta Rhei initiative, for example, urges hydrologists to include the connection with human systems. We propose a paradigm shift in the definition of drought, namely to expand it to include the effects of human action. For attribution we can then distinguish between climate-induced drought and human-induced drought. In this presentation, we will present a conceptual diagram that will do justice to the interdisciplinarity of drought. We will discuss issues of variability and change, scale (both temporal and spatial scales), feedbacks, and direct and indirect anthropogenic effects. The revised definition provides recognition and a common ground to researchers in all fields of research and is better aligned with drought impacts and with stakeholders' and policy maker's views on drought.
Silva, E. A.; Pedrosa, M. M.; Azevedo, S. C.; Cardim, G. P.; Carvalho, F. P. S.
The matrix of energy generation in Brazil is predominantly hydroelectric power. Consequently, the reservoirs need constant monitoring due to the large volume of artificially dammed water. Images from remote sensing can provide reliable information concerning water bodies. In this paper, we use remote sensing imagery to monitor the Sobradinho dam in three different epochs. The objective was to verify quantitatively the area of the dam's surface reduced due to the drought of 2015, which was considered the worst in history. The approach used water surface area estimations from bands of Landsat5 and Landsat8 satellites which highlight water bodies better from other features present on surface of the Earth. Through the techniques of growth region and normalized difference water index (NDWI), we determined the surface area of the reservoir in 2011 and calculated the decrease caused by the drought. By analyzing the numbers provided by the results it is possible to estimate how the Sobradinho reservoir has been affected by the drastic drought. The results show that the Landsat images enable the monitoring of large reservoirs. Bearing in mind that monitoring is a primary and indispensable tool, not only for technical study, but also for economic and environmental research, it can help establish planning projects and water administration strategies for future decisions about the hydrical resource priority.
Niu, Jun; Chen, Ji; Sun, Liqun
The knowledge of drought evolution characteristics may aid the decision making process in mitigating drought impacts. This study uses a macro-scale hydrological model, Variable Infiltration Capacity (VIC) model, to simulate terrestrial hydrological processes over the Xijiang (West River) basin in South China. Three drought indices, namely standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture anomaly index (SMAI), are employed to examine the spatio-temporal and evolution features of drought events. SPI, SRI and SMAI represent meteorological drought, hydrological drought and agricultural drought, respectively. The results reveal that the drought severity depicted by SPI and SRI is similar with increasing timescales; SRI is close to that of SPI in the wet season for the Liu River basin as the high-frequency precipitation is conserved more by runoff; the time lags appear between SPI and SRI due to the delay response of runoff to precipitation variability for the You River basin. The case study in 2010 spring drought further shows that the spatio-temporal evolutions are modulated by the basin-scale topography. There is more consistency between meteorological and hydrological droughts for the fan-like basin with a converged river network. For the west area of the Xijiang basin with the high elevation, the hydrological drought severity is less than meteorological drought during the developing stage. The recovery of hydrological and agricultural droughts is slower than that of meteorological drought for basins with a longer mainstream.
Minati, Ludovico; Grisoli, Marina; Franceschetti, Silvana; Epifani, Francesca; Granvillano, Alice; Medford, Nick; Harrison, Neil A; Piacentini, Sylvie; Critchley, Hugo D
Adaptive behaviour requires an ability to obtain rewards by choosing between different risky options. Financial gambles can be used to study effective decision-making experimentally, and to distinguish processes involved in choice option evaluation from outcome feedback and other contextual factors. Here, we used a paradigm where participants evaluated 'mixed' gambles, each presenting a potential gain and a potential loss and an associated variable outcome probability. We recorded neural responses using autonomic monitoring, electroencephalography (EEG) and functional neuroimaging (fMRI), and used a univariate, parametric design to test for correlations with the eleven economic parameters that varied across gambles, including expected value (EV) and amount magnitude. Consistent with behavioural economic theory, participants were risk-averse. Gamble evaluation generated detectable autonomic responses, but only weak correlations with outcome uncertainty were found, suggesting that peripheral autonomic feedback does not play a major role in this task. Long-latency stimulus-evoked EEG potentials were sensitive to expected gain and expected value, while alpha-band power reflected expected loss and amount magnitude, suggesting parallel representations of distinct economic qualities in cortical activation and central arousal. Neural correlates of expected value representation were localized using fMRI to ventromedial prefrontal cortex, while the processing of other economic parameters was associated with distinct patterns across lateral prefrontal, cingulate, insula and occipital cortices including default-mode network and early visual areas. These multimodal data provide complementary evidence for distributed substrates of choice evaluation across multiple, predominantly cortical, brain systems wherein distinct regions are preferentially attuned to specific economic features. Our findings extend biologically-plausible models of risky decision-making while providing
Lawford, Richard; Toll, David; Doorn, Bradley; Entin, Jared; Mocko, David; Svoboda, Mark; Rodell, Matthew; Koster, Randy; Schubert, Siegried; Liang, Xin-Zhong; Cai, Ximing; Wardlow, Brian; Xia, Youlong; Verdin, Jim; Ek, Michael
As the harvest season approached in August 2012, much of the United States remained in the grip of a major drought. According to the United States Drought Monitor (USDM), 52 percent of the United States and Puerto Rico was in moderate drought conditions or worse by August 7, 2012 (see Figure 1a). Drought areas were concentrated in the agricultural states in the central U.S.A. The drought threatened global food prices and US biofuel feedstocks. Although areas east of the Mississippi River experienced some relief due to Hurricane Isaac, the drought persisted west of the Mississippi River Basin. The USDA Economic Research Service reports about 80 percent of the US agriculture experienced drought in 2012 making it the most extensive drought since the 1950's. The Financial Times reported 2012 losses at roughly $30 billion dollars. NASA maintains satellite and modelling capabilities that enable the assessment of drought severity and extent on a national and global basis.
Vicente-Serrano, S. M.; Aguilar, E.; Martínez, R.; Martín-Hernández, N.; Azorin-Molina, C.; Sanchez-Lorenzo, A.; El Kenawy, A.; Tomás-Burguera, M.; Moran-Tejeda, E.; López-Moreno, J. I.; Revuelto, J.; Beguería, S.; Nieto, J. J.; Drumond, A.; Gimeno, L.; Nieto, R.
In this study, we analyzed the influence of El Niño-Southern Oscillation (ENSO) on the spatio-temporal variability of droughts in Ecuador for a 48-year period (1965-2012). Droughts were quantified from 22 high-quality and homogenized time series of precipitation and air temperature by means of the Standardized Precipitation Evapotranspiration Index. In addition, the propagation of two different ENSO indices (El Niño 3.4 and El Niño 1 + 2 indices) and other atmospheric circulation processes (e.g., vertical velocity) on different time-scales of drought severity were investigated. The results showed a very complex influence of ENSO on drought behavior across Ecuador, with two regional patterns in the evolution of droughts: (1) the Andean chain with no changes in drought severity, and (2) the Western plains with less severe and frequent droughts. We also detected that drought variability in the Andes mountains is explained by the El Niño 3.4 index [sea surface temperature (SST) anomalies in the central Pacific], whereas the Western plains are much more driven by El Niño 1 + 2 index (SST anomalies in the eastern Pacific). Moreover, it was also observed that El Niño and La Niña phases enhance droughts in the Andes and Western plains regions, respectively. The results of this work could be crucial for predicting and monitoring drought variability and intensity in Ecuador.
Dardel, Cecile; Kergoat, Laurent; Hiernaux, Pierre; Mougin, Eric; Grippa, Manuela; Tucker, Compton Jim
The Sahel region is known to be very sensitive to climatic fluctuations. Precipitation interannual variability has immediate and strong consequences on water resources, vegetation production, all affecting human populations. All along its history, Sahel had to face extreme climatic events. In the recent past, a 25 years period of persistent drought jeopardized the ecosystems equilibrium. Indeed, from the 1970's to the mid 1990's, precipitations were strongly and repeatedly below average. A debate has grown for years in the scientific community about the evolving trend of ecosystem in Sahel: is there desertification, or rehabilitation indicated by a "re-greening" taking place since the 1980's, as observed on satellite data by many scientists? To answer these questions, NDVI (Normalized Difference Vegetation Index) time series derived from NOAA/AVHRR are analyzed and compared to field measurements of the herbaceous aboveground mass, tree inventory and crop phytomass collected in Mali and Niger, from 1984 to 2011 and 1994 to 2011 respectively. The GIMMS-3g NDVI trends analysis from 1981 to 2011 show positive and significant slope values over almost every part of the Sahel, except for western Niger and central Sudan, thus reinforcing the "re-greening" hypothesis. Field observations are in good agreement with satellite data. A positive trend is observed over the Gourma in Mali, particularly for periods beginning in the 1980's, showing the ecosystem resilience to drought. A similar recovery is observed in western Niger, but only up to the mid 1990's, then the trend turns negative without being explained by rainfall. While the Gourma is mainly a pastoral land, western Niger is an agro-pastoral region in which cropped surfaces expanded widely over the last decades. For both regions, the re-greening trends are mainly observed on sandy soils, while erosion processes have been observed on shallow soil surfaces, inducing increased run-off and decrease in vegetation cover to
The first 13th years of the 21st century has begun with a series of widespread, long and intensive droughts around the world. Extreme and severe-to-extreme intensity droughts covered 2-6% and 7-16% of the world land, respectively, affecting environment, economies and humans. These droughts reduced agricultural production, leading to food shortages, human health deterioration, poverty, regional disturbances, population migration and death. This presentation is a travelogue of the 21st century global and regional droughts during the warmest years of the past 100 years. These droughts were identified and monitored with the NOAA operational space technology, called Vegetation Health (VH), which has the longest period of observation and provide good data quality. The VH method was used for assessment of vegetation condition or health, including drought early detection and monitoring. The VH method is based on operational satellites data estimating both land surface greenness (NDVI) and thermal conditions. The 21st century droughts in the USA, Russia, Australia Argentina, Brazil, China, India and other principal grain producing countries were intensive, long, covered large areas and caused huge losses in agricultural production, which affected food and environmental security and led to food riots in some countries. This presentation investigate how droughts affect food and environmental security, if they can be detected earlier, how to monitor their area, intensity, duration and impacts and also their dynamics during the climate warming era with satellite-based vegetation health technology.
Given worldwide experience with drought during the past several decades and the magnitude of associated impacts, it is apparent that vulnerability to extended periods of water shortage is escalating. Developing a national or provincial drought policy and preparedness plan is a complicated but essential first step toward reducing societal vulnerability. Until recently, nations had devoted little effort to drought preparedness, preferring instead the reactive or crisis management approach. Presently, an increasing number of nations are pursuing a more proactive approach that emphasizes the principles of risk management and sustainable development. Because of the multitude of impacts associated with drought and the numerous governmental agencies that have responsibility for some aspect of monitoring, assessment, mitigation, and planning, developing a policy and plan must be an integrated process within and between levels of government. This paper outlines a generic process that can be adopted by governments that desire to develop a more comprehensive and long-term approach to drought management and planning. Countries and states or provincial authorities that have adopted this approach is presented as case studies. This process is timely, given the declaration of the 1990s as the International Decade for Natural Disaster Reduction and the recent International Convention to Combat Desertification and Drought (June, 1994), an offshoot of deliberations at the United Nations Conference on Environment and Development.
Drought constitutes one of the major threats for a number of socioeconomic sectors in Mexico. Meteorological drought occurs in various temporal and spatial scales that range from a few weeks in the tropical Mexico to decades in northern Mexico. Historically, these long term droughts have had a negative impact, not only in economic activities, but in the lives of Mexicans as well. In general, short term droughts over central southern Mexico are related to El Niño conditions. However, drought may also occur when an anomalously low number of easterly waves reach the Caribbean and Mexico. El Niño and easterly wave activity may be related by the intensification of the Caribbean Low Level Jet. However, the role of this form of transient activity as a drought trigger in Mexico has not been explored in depth. The present analysis explores the possibility of more than one form of forcing to explain drought in Mexico on various spatial and temporal scales, as well as in various regions. Such approach to the study of drought may prove useful to diagnose why certain general circulation models are unable of reproducing drought patterns over Mexico.
Timmermans, Joris; Asadollahi Dolatabad, Saeid
Currently, the global needs for food and water is at a critical level. It has been estimated that 12.5 % of the global population suffers from malnutrition and 768 million people still do not have access to clean drinking water. This need is increasing because of population growth but also by climate change. Changes in precipitation patterns will result either in flooding or droughts. Consequently availability, usability and affordability of water is becoming challenge and efficient use of water and water management is becoming more important, particularly during severe drought events. Drought monitoring for agricultural purposes is very hard. While meteorological drought can accurately be monitored using precipitation only, estimating agricultural drought is more difficult. This is because agricultural drought is dependent on the meteorological drought, the impacts on the vegetation, and the resilience of the crops. As such not only precipitation estimates are required but also evapotranspiration at plant/plot scale. Evapotranspiration (ET) describes the amount of water evaporated from soil and vegetation. As 65% of precipitation is lost by ET, drought severity is highly linked with this variable. In drought research, the precise quantification of ET and its spatio-temporal variability is therefore essential. In this view, remote sensing based models to estimate ET, such as SEBAL and SEBS, are of high value. However the resolution of current evapotranspiration products are not good enough for monitoring the impact of the droughts on the specific crops. This limitation originates because plot scales are in general smaller than the resolution of the available satellite ET products. As such remote sensing estimates of evapotranspiration are always a combination of different land surface types and cannot be used for plant health and drought resilience studies. The goal of this research is therefore to enable adequate resolutions of daily evapotranspiration estimates
Lardy-Fontan, Sophie; Guigues, Nathalie; Lalere, Béatrice; Vaslin-Reimann, Sophie
In Europe, the implementation of the Water Framework Directive WFD, in 2001, marks a strong standpoint. In addition to its objectives of a return to good chemical and good ecological status by the year 2015, it fixes the achievement of trends over space and time. The new requirements that arise from the WFD put considerable financial pressure on water management authorities. Because the overall decision-making process relies most of the time on acquired data, it puts considerable pressures on the display of high quality biological as well as chemical environmental measurements. However, performing measurements implies that i) the demonstration of their metrological traceability ii) the evidence of their achievement thanks to accurate and sensitive analytical methods and iii) their statement with a reliable estimate of expanded uncertainty is thoroughly addressed. Moreover, the measurement representativeness, especially in highly dynamic environment, is of prime interest in a context where comparability over space and time is needed. As a consequence, considerable challenges are dwelt on metrologists with great emphasis on parameters that are under regulation. This paper will discuss a panorama of the unavoidable metrological questions that have to be addressed: from the definition of the measurand to the final estimation of uncertainty; from the initial performances demonstration of methods to the final demonstration of mastery and capabilities through inter comparison laboratories and reference materials. A focus will be made on upcoming alternative monitoring approaches that are seldom addressed from a metrological point of view.
The risk of the desertification of a part of Romania is increasingly evident, constituting a serious problem for the environment and the society. This article attempts to assess and monitor the risk of desertification in Dobrogea using Landsat Thematic Mapper (TM) satellite images acquired in 1987, 1994, 2000, 2007 and 2011. In order to assess the risk of desertification, we used as indicators the Modified Soil Adjustment Vegetation Index 1 (MSAVI1), the Moving Standard Deviation Index (MSDI) and the albedo, indices relating to the vegetation conditions, the landscape pattern and micrometeorology. The decision tree classifier (DTC) was also used on the basis of pre-established rules, and maps displaying six grades of desertification risk were obtained: non, very low, low, medium, high and severe. Land surface temperature (LST) was also used for the analysis. The results indicate that, according to pre-established rules for the period of 1987-2011, there are two grades of desertification risk that have an ascending trend in Dobrogea, namely very low and medium desertification. An investigation into the causes of the desertification risk revealed that high temperature is the main factor, accompanied by the destruction of forest shelterbelts and of the irrigation system and, to a smaller extent, by the fragmentation of agricultural land and the deforestation in the study area.
Cacas, Joseph; Glaser, John; Copenhaver, Kenneth; May, George; Stephens, Karen
The United States Environmental Protection Agency (EPA) has declared that "significant benefits accrue to growers, the public, and the environment" from the use of transgenic pesticidal crops due to reductions in pesticide usage for crop pest management. Large increases in the global use of transgenic pesticidal crops has reduced the amounts of broad spectrum pesticides used to manage pest populations, improved yield and reduced the environmental impact of crop management. A significant threat to the continued use of this technology is the evolution of resistance in insect pest populations to the insecticidal Bt toxins expressed by the plants. Management of transgenic pesticidal crops with an emphasis on conservation of Bt toxicity in field populations of insect pests is important to the future of sustainable agriculture. A vital component of this transgenic pesticidal crop management is establishing the proof of concept basic understanding, situational awareness, and monitoring and decision support system tools for more than 133650 square kilometers (33 million acres) of bio-engineered corn and cotton for development of insect resistance . Early and recent joint NASA, US EPA and ITD remote imagery flights and ground based field experiments have provided very promising research results that will potentially address future requirements for crop management capabilities.
Waseem, Muhammad; Ajmal, Muhammad; Kim, Tae-Woong
Comprehensibly considering all physical forms of agricultural, hydrological, and meteorological drought is essential to develop reliable monitoring and prediction indices for the proper assessment of drought. This consideration encouraged to develop and evaluate a multivariate composite drought index (CDI) that considers all possible variables related to individual types of drought. The proposed CDI was primarily based on the weighted similarity measure (entropy weighted Euclidian distance) and the anomaly from the possible wettest and driest conditions of the selected study region (sub basin of Han River, South Korea). The CDI time series identified 2008-2009 as the driest year, while May 2008 was the driest month within the selected period (2003-2011). The comparative analysis revealed that the CDI monthly time series had a significant correlation with the aggregate drought index (ADI). In addition, in comparison with the single variable-based indices i.e., the standardized precipitation index (SPI) and the streamflow drought index (SDI), the CDI comprehensively responded to variability embedded in the individual drought attributes. Moreover, it was concluded that the developed CDI provided a physically sound, temporally flexible and unbiased index that can be directly associated with all possible variants and linked to the climate conditions of the study region without considering any feature extraction technique.
Wang, Yongmei; Ren, Fumin; Zhao, Yilei; Li, Yunjie
The composite-drought index (CI), improved weighted average of precipitation index (IWAP), and the objective identification technique for regional extreme events (OITREE) were employed to detect China's regional meteorological drought events (CRMDEs) during 1961-2010. Compared with existing references, CI and IWAP both showed strong ability in identifying CRMDEs. Generally, the results of CI and IWAP were consistent, especially for extreme and severe CRMDEs. During 1961-2010, although the frequencies of extreme and severe CRMDEs based on CI and IWAP both showed weak decreasing trends, the two mean-integrated indices both showed increasing but not significant trends. However, the results of IWAP were more reasonable than CI's in two aspects. Firstly, the monthly frequency of extreme and severe CRMDEs based on IWAP showed a clear seasonal variation, which coincided with the seasonal variation of the East Asian monsoon over central-eastern China, whereas the frequency based on CI presented a much weaker seasonal variation. Secondly, the two sets of results were sometimes inconsistent with respect to the start and end times of a CRMDE, and CRMDEs based on CI generally showed two unreasonable phenomena: (1) under non-drought conditions, a severe drought stage could suddenly occur in a large area; and (2) during the following period, drought could alleviate gradually in cases of non-precipitation. Comparative analysis suggested that the IWAP drought index possesses obvious advantages in detecting and monitoring regional drought events.
García-Valdecasas Ojeda, Matilde; Gámiz-Fortis, Sonia R.; Castro-Díez, Yolanda; Esteban-Parra, María. Jesús
The Weather Research and Forecasting (WRF) model has been used to show the benefits provided by downscaled fields to detect and analyze wet and dry periods over a region with high precipitation variability such as Spain. We have analyzed the spatiotemporal behavior of two widely used drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), computed at 3 and 12 month time scales, which provide important information in an agricultural and water-resource context. These two indices were computed from WRF outputs and compared with those calculated from observational (monthly precipitation and temperature databases of Spain, MOPREDAS and MOTEDAS) and from the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) data sets. This evaluation was made by using a regional scale and a multistep regionalization method and by comparison of individual grid points. In general, results indicate that the drought indices obtained by using WRF outputs provide a noticeable improvement regarding those computed by using ERA-Interim, higher at longer time scales. Although results show no significant differences between drought indices analyzed, the improvement offered by WRF is greater for SPI than for SPEI. In terms of averaged duration, magnitude, and severity of drought, the benefits provided by WRF are not so evident, presenting better agreement with the observational data at 12 month time scale, being clearer for the intensity. These findings evidence the benefit of using WRF climate fields to monitor, analyze, and detect drought events, being a valuable source of knowledge for a suitable decision making, especially for water-resource management.
Funk, Christopher C.; Verdin, James; Adams Chavula,; Gregory J. Husak,; Harikishan Jayanthi,; Tamuka Magadzire,
During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAID, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were upscaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi.
Pei, Fengsong; Li, Xia; Liu, Xiaoping; Lao, Chunhua
Frequency and severity of droughts were projected to increase in many regions. However, their effects of temporal dynamics on the terrestrial carbon cycle remain uncertain, and hence deserve further investigation. In this paper, the droughts that occurred in China during 2001-2010 were identified by using the standardized precipitation index (SPI). Standardized anomaly index (SAI), which has been widely employed in reflecting precipitation, was extended to evaluate the anomalies of net primary productivity (NPP). In addition, influences of the droughts on vegetation were explored by examining the temporal dynamics of SAI-NPP along with area-weighted drought intensity at different time scales (1, 3, 6, 9 and 12 months). Year-to-year variability of NPP with several factors, including droughts, NDVI, radiation and temperature, was analyzed as well. Consequently, the droughts in the years 2001, 2006 and 2009 were well reconstructed. This indicates that SPI could be applied to the monitoring of the droughts in China during the past decade (2001-2010) effectively. Moreover, strongest correlations between droughts and NPP anomalies were found during or after the drought intensities reached their peak values. In addition, some droughts substantially reduced the countrywide NPP, whereas the others did not. These phenomena can be explained by the regional diversities of drought intensity, drought duration, areal extents of the droughts, as well as the cumulative and lag responses of vegetation to the precipitation deficits. Besides the drought conditions, normalized difference vegetation index (NDVI), radiation and temperature also contribute to the interannual variability of NPP.
Drought is a common feature of every landscape and can last from a few months to several years. According to the Federal Emergency Management Agency (FEMA), droughts are the most costly natural hazard affecting the United States costing 6 to 8 billion dollars annually. Mitigating the impacts of dr...
Droughts are common and occur regularly in Oklahoma. They’re the most costly natural hazard to the United States, and estimates show a $6-$8 billion annual loss to the nation’s farmers and rancher. With the current drought impacting Oklahoma, people managing rangelands are concerned with the short...
Prodhan, Zakaria Hossain; Faruq, Golam
Drought is one of the most important phenomena which limit crops' production and yield. Crops demonstrate various morphological, physiological, biochemical, and molecular responses to tackle drought stress. Plants' vegetative and reproductive stages are intensively influenced by drought stress. Drought tolerance is a complicated trait which is controlled by polygenes and their expressions are influenced by various environmental elements. This means that breeding for this trait is so difficult and new molecular methods such as molecular markers, quantitative trait loci (QTL) mapping strategies, and expression patterns of genes should be applied to produce drought tolerant genotypes. In wheat, there are several genes which are responsible for drought stress tolerance and produce different types of enzymes and proteins for instance, late embryogenesis abundant (lea), responsive to abscisic acid (Rab), rubisco, helicase, proline, glutathione-S-transferase (GST), and carbohydrates during drought stress. This review paper has concentrated on the study of water limitation and its effects on morphological, physiological, biochemical, and molecular responses of wheat with the possible losses caused by drought stress. PMID:24319376
Zhu, Ye; Wang, Wen; Singh, Vijay P; Liu, Yi
Prediction of hydrological drought in the absence of hydrological records is of great significance for water resources management and risk assessment. In this study, two meteorological drought indices, including standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) calculated at different time scales (1 to 12months), were analyzed for their capabilities in detecting hydrological droughts. The predictive skills of meteorological drought indices were assessed through correlation analysis, and two skill scores, i.e. probability of detection (POD) and false alarm rate (FAR). When used independently, indices of short time scales generally performed better than did those of long time scales. However, at least 31% of hydrological droughts were still missed in view of the peak POD score (0.69) of a single meteorological drought index. Considering the distinguished roles of different time scales in explaining hydrological droughts with disparate features, an optimization approach of blending SPI/SPEI at multiple time scales was proposed. To examine the robustness of the proposed method, data of 1964-1990 was used to establish the multiscalar index, then validate during 2000-2010. Results showed that POD exhibited a significant increase when more than two time scales were used, and the best performances were found when blending 8 time scales of SPI and 9 for SPEI, with the corresponding values of 0.82 and 0.85 for POD, 0.205 and 0.21 for FAR, in the calibration period, and even better performance in the validation period. These results far exceeded the performance of any single meteorological drought index. This suggests that when there is lack of streamflow measurements, blending climatic information of multiple time scales to jointly monitor hydrological droughts could be an alternative solution.
Chen, Lei; Huang, Jian-Guo; Alam, Syed Ashraful; Zhai, Lihong; Dawson, Andria; Stadt, Kenneth J; Comeau, Philip G
Adequate and advance knowledge of the response of forest ecosystems to temperature-induced drought is critical for a comprehensive understanding of the impacts of global climate change on forest ecosystem structure and function. Recent massive decline in aspen-dominated forests and an increased aspen mortality in boreal forests have been associated with global warming, but it is still uncertain whether the decline and mortality are driven by drought. We used a series of ring-width chronologies from 40 trembling aspen (Populus tremuloides Michx.) sites along a latitudinal gradient (from 52° to 58°N) in western Canada, in an attempt to clarify the impacts of drought on aspen growth by using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Results indicated that prolonged and large-scale droughts had a strong negative impact on trembling aspen growth. Furthermore, the spatiotemporal variability of drought indices is useful for explaining the spatial heterogeneity in the radial growth of trembling aspen. Due to ongoing global warming and rising temperatures, it is likely that severer droughts with a higher frequency will occur in western Canada. As trembling aspen is sensitive to drought, we suggest that drought indices could be applied to monitor the potential effects of increased drought stress on aspen trees growth, achieve classification of eco-regions and develop effective mitigation strategies to maintain western Canadian boreal forests.
Naumann, G.; Dutra, E.; Barbosa, P.; Pappenberger, F.; Wetterhall, F.; Vogt, J.
Drought monitoring is a key component to mitigate impacts of droughts. Lack of reliable and up-to-date datasets is a common challenge across the Globe. This study investigates different datasets and drought indicators on their capability to improve drought monitoring in Africa. The study was performed for four river basins located in different climatic regions (the Oum er-Rbia in Morocco, the Blue Nile in Eastern Africa, the Upper Niger in Western Africa, and the Limpopo in South-Eastern Africa) as well as the Greater Horn of Africa. The five precipitation datasets compared are the ECMWF ERA - Interim reanalysis, the Tropical Rainfall Measuring Mission satellite monthly rainfall product 3B43, the Global Precipitation Climatology Centre gridded precipitation dataset, the Global Precipitation Climatology Project Global Monthly Merged Precipitation Analyses, and the Climate Prediction Center Merged Analysis of Precipitation. The set of drought indicators used includes the Standardized Precipitation Index, the Standardized Precipitation-Evaporation Index, Soil Moisture Anomalies and Potential Evapotranspiration. A comparison of the annual cycle and monthly precipitation time series shows a good agreement in the timing of the rainy seasons. The main differences between the datasets are in the ability to represent the magnitude of the wet seasons and extremes. Moreover, for the areas affected by drought, all the drought indicators agree on the time of drought onset and recovery although there is disagreement on the extent of the affected area. In regions with limited rain gauge data the estimation of the different drought indicators is characterised by a higher uncertainty. Further comparison suggests that the main source of error in the computation of the drought indicators is the uncertainty in the precipitation datasets rather than the estimation of the distribution parameters of the drought indicators.
Urquijo, Julia; Gonzalez Tánago, Itziar; Ballesteros, Mario; De Stefano, Lucia
data from the European Drought Impact Report Inventory. For specific hotpots vulnerability factors are presented also through spider diagrams in order to allow policy and decision makers to identify underlying sources of vulnerability in the European context. This assessment offers an overall picture at a European level that strives to contribute to enhance the understanding of drought vulnerability across Europe.
Bachmair, S.; Svensson, C.; Hannaford, J.; Barker, L. J.; Stahl, K.
Drought monitoring and early warning is an important measure to enhance resilience towards drought. While there are numerous operational systems using different drought indicators, there is no consensus on which indicator best represents drought impact occurrence for any given sector. Furthermore, thresholds are widely applied in these indicators but, to date, little empirical evidence exists as to which indicator thresholds trigger impacts on society, the economy, and ecosystems. The main obstacle for evaluating commonly used drought indicators is a lack of information on drought impacts. Our aim was therefore to exploit text-based data from the European Drought Impact report Inventory (EDII) to identify indicators that are meaningful for region-, sector-, and season-specific impact occurrence, and to empirically determine indicator thresholds. In addition, we tested the predictability of impact occurrence based on the best-performing indicators. To achieve these aims we applied a correlation analysis and an ensemble regression tree approach, using Germany and the UK (the most data-rich countries in the EDII) as test beds. As candidate indicators we chose two meteorological indicators (Standardized Precipitation Index, SPI, and Standardized Precipitation Evaporation Index, SPEI) and two hydrological indicators (streamflow and groundwater level percentiles). The analysis revealed that accumulation periods of SPI and SPEI best linked to impact occurrence are longer for the UK compared with Germany, but there is variability within each country, among impact categories and, to some degree, seasons. The median of regression tree splitting values, which we regard as estimates of thresholds of impact occurrence, was around -1 for SPI and SPEI in the UK; distinct differences between northern/northeastern vs. southern/central regions were found for Germany. Predictions with the ensemble regression tree approach yielded reasonable results for regions with good impact data
Among the key recommendations of a recent WCRP Workshop on Drought Predictability and Prediction in a Changing Climate is the development of an experimental global drought information system (GDIS). The timeliness of such an effort is evidenced by the wide aITay of relevant ongoing national and international (as well as regional and continental scale) efforts to provide drought information, including the US and North American drought monitors, and various integrating activities such as GEO and the Global Drought Portal. The workshop will review current capabilities and needs, and focus on the steps necessary to develop a GDIS that will build upon the extensive worldwide investments that have already been made in developing drought monitoring (including new space-based observations), drought risk management, and climate prediction capahilities.
Zeppel, Melanie J. B.; Harrison, Sandy P.; Adams, Henry D.; ...
Many species have the ability to resprout vegetatively after a substantial loss of biomass induced by environmental stress, including drought. Many of the regions characterised by ecosystems where resprouting is common are projected to experience more frequent and intense drought during the 21st century. However, in assessments of ecosystem response to drought disturbance there has been scant consideration of the resilience and post-drought recovery of resprouting species. Systematic differences in hydraulic and allocation traits suggest that resprouting species are more resilient to drought-stress than nonresprouting species. Evidence suggests that ecosystems dominated by resprouters recover from disturbance more quickly than ecosystemsmore » dominated by nonresprouters. The ability of resprouters to avoid mortality and withstand drought, coupled with their ability to recover rapidly, suggests that the impact of increased drought stress in ecosystems dominated by these species may be small. Furthermore, the strategy of resprouting needs to be modelled explicitly to improve estimates of future climate-change impacts on the carbon cycle, but this will require several important knowledge gaps to be filled before resprouting can be properly implemented.« less
Zeppel, Melanie J. B.; Harrison, Sandy P.; Adams, Henry D.; Kelley, Douglas I.; Li, Guangqi; Tissue, David T.; Dawson, Todd E.; Fensham, Rod; Medlyn, Belinda E.; Palmer, Anthony; West, Adam G.; McDowell, Nate G.
Many species have the ability to resprout vegetatively after a substantial loss of biomass induced by environmental stress, including drought. Many of the regions characterised by ecosystems where resprouting is common are projected to experience more frequent and intense drought during the 21st century. However, in assessments of ecosystem response to drought disturbance there has been scant consideration of the resilience and post-drought recovery of resprouting species. Systematic differences in hydraulic and allocation traits suggest that resprouting species are more resilient to drought-stress than nonresprouting species. Evidence suggests that ecosystems dominated by resprouters recover from disturbance more quickly than ecosystems dominated by nonresprouters. The ability of resprouters to avoid mortality and withstand drought, coupled with their ability to recover rapidly, suggests that the impact of increased drought stress in ecosystems dominated by these species may be small. Furthermore, the strategy of resprouting needs to be modelled explicitly to improve estimates of future climate-change impacts on the carbon cycle, but this will require several important knowledge gaps to be filled before resprouting can be properly implemented.
Zeppel, Melanie J B; Harrison, Sandy P; Adams, Henry D; Kelley, Douglas I; Li, Guangqi; Tissue, David T; Dawson, Todd E; Fensham, Rod; Medlyn, Belinda E; Palmer, Anthony; West, Adam G; McDowell, Nate G
Many species have the ability to resprout vegetatively after a substantial loss of biomass induced by environmental stress, including drought. Many of the regions characterised by ecosystems where resprouting is common are projected to experience more frequent and intense drought during the 21st Century. However, in assessments of ecosystem response to drought disturbance there has been scant consideration of the resilience and post-drought recovery of resprouting species. Systematic differences in hydraulic and allocation traits suggest that resprouting species are more resilient to drought-stress than nonresprouting species. Evidence suggests that ecosystems dominated by resprouters recover from disturbance more quickly than ecosystems dominated by nonresprouters. The ability of resprouters to avoid mortality and withstand drought, coupled with their ability to recover rapidly, suggests that the impact of increased drought stress in ecosystems dominated by these species may be small. The strategy of resprouting needs to be modelled explicitly to improve estimates of future climate-change impacts on the carbon cycle, but this will require several important knowledge gaps to be filled before resprouting can be properly implemented.
Lackner, S.; Barnwal, P.; von der Goltz, J.
We investigate the lasting effects of early childhood exposure to drought on economic and health outcomes in a large multi-country dataset. By pooling all Demographic and Health Survey rounds for which household geocodes are available, we obtain an individual-level dataset covering 47 developing countries. Among other impact measures, we collect infant and child mortality data from 3.3m live births and data on stunting and wasting for 1.2m individuals, along with data on education, employment, wealth, marriage and childbearing later in life for similarly large numbers of respondents. Birth years vary from 1893 to 2012. We seek to improve upon existing work on the socio-economic impact of drought in a number of ways. First, we introduce from the hydrological literature a drought measure, the Standardized Precipitation Index (SPI), that has been shown to closely proxy the Palmer drought index, but has far less demanding data requirements, and can be obtained globally and for long time periods. We estimate the SPI for 110 years on a global 0.5° grid, which allows us to assign drought histories to the geocoded individual data. Additionally, we leverage our large sample size to explicitly investigate both how drought impacts have changed over time as adaptation occurred at a varying pace in different locations, and the role of the regional extent of drought in determining impacts.
Febrina Amalo, Luisa; Hidayat, Rahmat; Haris
Drought is natural hazard which has causing several impacts, such as decreasing of air and water quality, land degradation, forest fire, decreasing of agricultural crops production. Drought assessment using drought indices have widely conducted for drought monitoring. Remote-sensing-based indices defined as an index which using remote sensing data for mapping the drought condition in particular area or region. This research aims to compare remote-sensing-based drought indices, namely TCI, VCI and VHI to obtain a better understanding about the differentiation between each index, and their application for monitoring drought in East Java on El Nino year 2015. LST and EVI data were used to construct the indices. The result showed, each index proved to be useful, quick, sufficient and inexpensive tool for drought monitoring. However, each index has its differences. TCI proved to be detected drought sensitively in dry season or months when high temperature occurred. While VCI detected drought more sensitive in wet season as well (December-January-February to May) than TCI and VHI. Meanwhile, VHI which the enhancement of TCI and VHI has combined two indicators to provide better comprehension about drought occurrence.
Guo, Peiguo; Baum, Michael; Grando, Stefania; Ceccarelli, Salvatore; Bai, Guihua; Li, Ronghua; von Korff, Maria; Varshney, Rajeev K.; Graner, Andreas; Valkoun, Jan
Drought tolerance is a key trait for increasing and stabilizing barley productivity in dry areas worldwide. Identification of the genes responsible for drought tolerance in barley (Hordeum vulgare L.) will facilitate understanding of the molecular mechanisms of drought tolerance, and also facilitate the genetic improvement of barley through marker-assisted selection or gene transformation. To monitor the changes in gene expression at the transcriptional level in barley leaves during the reproductive stage under drought conditions, the 22K Affymetrix Barley 1 microarray was used to screen two drought-tolerant barley genotypes, Martin and Hordeum spontaneum 41-1 (HS41-1), and one drought-sensitive genotype Moroc9-75. Seventeen genes were expressed exclusively in the two drought-tolerant genotypes under drought stress, and their encoded proteins may play significant roles in enhancing drought tolerance through controlling stomatal closure via carbon metabolism (NADP malic enzyme, NADP-ME, and pyruvate dehydrogenase, PDH), synthesizing the osmoprotectant glycine-betaine (C-4 sterol methyl oxidase, CSMO), generating protectants against reactive-oxygen-species scavenging (aldehyde dehydrogenase,ALDH, ascorbate-dependent oxidoreductase, ADOR), and stabilizing membranes and proteins (heat-shock protein 17.8, HSP17.8, and dehydrin 3, DHN3). Moreover, 17 genes were abundantly expressed in Martin and HS41-1 compared with Moroc9-75 under both drought and control conditions. These genes were possibly constitutively expressed in drought-tolerant genotypes. Among them, seven known annotated genes might enhance drought tolerance through signalling [such as calcium-dependent protein kinase (CDPK) and membrane steroid binding protein (MSBP)], anti-senescence (G2 pea dark accumulated protein, GDA2), and detoxification (glutathione S-transferase, GST) pathways. In addition, 18 genes, including those encoding Δl-pyrroline-5-carboxylate synthetase (P5CS), protein phosphatase 2C
Lennard, Amy; Macdonald, Neil
Drought is a complex phenomenon, occurring in most climatic zones, including both high and low rainfall regions. Recent drought events (2004-2006 & 2010-2012) in the UK have highlighted a continued vulnerability to this hazard. The period 2010-2012 was characterised by departures from typical seasonal climatic conditions, resulting in a severe drought, which had a significant impact on water resources in parts of the UK. Recent droughts highlight the need for better understanding of extreme drought events, particularly from a water resource perspective. The UK has a wealth of long climate series that are under used for water resource management planning. Standardised drought indicators offer a potential monitoring and management tool for operational water resource management. However, the application of these metrics for operational water resource management needs further investigation, particularly links between meteorological drought indices, streamflow, groundwater and water supply systems. This work uses standardised drought indices to investigate the propagation from meteorological drought to hydrological drought using observed data from rivers, aquifers and reservoirs 2013 within a 21,000km2 water supply region serving 7.4 million people. In order to develop a better understanding of the links between drought indices and observed drought impacts. Exploring how meteorological drought indicators link to the water supply region helps build an understanding of their utility for water resource management.
Drought is commonly portrayed using images of cracked earth, dry dams and empty river beds. However, these 2-dimensional representations illustrate the impact of drought rather than drought itself and ignore the rich 4-dimensional spatio-temporal variablity which characterises drought as a natural hazard. Improved visualisations of drought are needed to increase public understanding and assist the research community in understanding its drivers. WebGL is explored as a medium for contructing interactive drought graphics that go beyond displaying drought impacts and address the multi-dimensionality of the problem.
Syed, Shahbaz; Gatien, Mathieu; Perry, Jeffrey J.; Chaudry, Hina; Kim, Soo-Min; Kwong, Kenneth; Mukarram, Muhammad; Thiruganasambandamoorthy, Venkatesh
BACKGROUND: Most patients with chest pain in the emergency department are assigned to cardiac monitoring for several hours, blocking access for patients in greater need. We sought to validate a previously derived decision rule for safe removal of patients from cardiac monitoring after initial evaluation in the emergency department. METHODS: We prospectively enrolled adults (age ≥ 18 yr) who presented with chest pain and were assigned to cardiac monitoring at 2 academic emergency departments over 18 months. We collected standardized baseline characteristics, findings from clinical evaluations and predictors for the Ottawa Chest Pain Cardiac Monitoring Rule: whether the patient is currently free of chest pain, and whether the electrocardiogram is normal or shows only nonspecific changes. The outcome was an arrhythmia requiring intervention in the emergency department or within 8 hours of presentation to the emergency department. We calculated diagnostic characteristics for the clinical prediction rule. RESULTS: We included 796 patients (mean age 63.8 yr, 55.8% male, 8.9% admitted to hospital). Fifteen patients (1.9%) had an arrhythmia, and the rule performed with the following characteristics: sensitivity 100% (95% confidence interval [CI] 78.2%–100%) and specificity 36.4% (95% CI 33.0%–39.6%). Application of the Ottawa Chest Pain Cardiac Monitoring Rule would have allowed 284 out of 796 patients (35.7%) to be safely removed from cardiac monitoring. INTERPRETATION: We successfully validated the decision rule for safe removal of a large subset of patients with chest pain from cardiac monitoring after initial evaluation in the emergency department. Implementation of this simple yet highly sensitive rule will allow for improved use of health care resources. PMID:28246315
Barker, Lucy J.; Hannaford, Jamie; Chiverton, Andrew; Svensson, Cecilia
Drought monitoring and early warning (M & EW) systems are a crucial component of drought preparedness. M & EW systems typically make use of drought indicators such as the Standardised Precipitation Index (SPI), but such indicators are not widely used in the UK. More generally, such tools have not been well developed for hydrological (i.e. streamflow) drought. To fill these research gaps, this paper characterises meteorological and hydrological droughts, and the propagation from one to the other, using the SPI and the related Standardised Streamflow Index (SSI), with the objective of improving understanding of the drought hazard in the UK. SPI and SSI time series were calculated for 121 near-natural catchments in the UK for accumulation periods of 1-24 months. From these time series, drought events were identified and for each event, the duration and severity were calculated. The relationship between meteorological and hydrological drought was examined by cross-correlating the 1-month SSI with various SPI accumulation periods. Finally, the influence of climate and catchment properties on the hydrological drought characteristics and propagation was investigated. Results showed that at short accumulation periods meteorological drought characteristics showed little spatial variability, whilst hydrological drought characteristics showed fewer but longer and more severe droughts in the south and east than in the north and west of the UK. Propagation characteristics showed a similar spatial pattern with catchments underlain by productive aquifers, mostly in the south and east, having longer SPI accumulation periods strongly correlated with the 1-month SSI. For catchments in the north and west of the UK, which typically have little catchment storage, standard-period average annual rainfall was strongly correlated with hydrological drought and propagation characteristics. However, in the south and east, catchment properties describing storage (such as base flow
Zhu, Jinfeng; Zhou, Yi; Wang, Shixin; Wang, Litao; Wang, Futao; Liu, Wenliang; Guo, Bing
The Three-River Headwaters (TRH) region in the Qinghai-Tibet Plateau, China, is of key importance to the ecological security of China and Southeast Asia and contains some of the most sensitive and fragile ecosystems. Monitoring and evaluating the ecosystem service function and its changes in the TRH region could support decision-making for regional ecological protection and restoration programs. Referencing the concept of ecosystem service and the Millennium Ecosystem Assessment (MA) framework, this study built a system of indicators for monitoring and evaluating the ecosystem service function. Thus, combining a multicriteria decision analysis (MCDA) and weighted linear combination (WLC) with analytic hierarchy process (AHP), we applied the ecosystem service function index (ESFI) based on remote sensing data at 1-km spatial resolution to spatiotemporally monitor the changes during the period 2005-2010. The study results indicated that ESFI had a good performance for monitoring the ecosystem service function and showed an improving trend in the TRH region over the past 5 years. Ecosystem environment recovery not only reflected the changing trend of warm and wet climate but was also a response of the ecological protection project of the Key Ecological Function Zone in the TRH region.
Droughts in the Southwest and Central Plains of the United States in the second half of the 21st century could be drier and longer than anything humans have seen in those regions in the last 1,000 ...
Gouveia, Célia; Liberato, Margarida L. R.; Russo, Ana; Montero, Irene
Spain in both considered drought events, however slightly less severe for 2012 than for 2005. In conclusion, and from an operational point of view, our results reveal the ability of the developed methodology to monitor droughts' impacts on crops productions and yields in Iberia. Acknowledgments: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project QSECA (PTDC/AAG-GLO/4155/2012) Garcia-Herrera R., Paredes D., Trigo R. M., Trigo I. F., Hernandez E., Barriopedro D. and Mendes M. A., 2007: The Outstanding 2004/05 Drought in the Iberian Peninsula: Associated Atmospheric Circulation, J. Hydrometeorol., 8, 483-498. Vicente-Serrano, Sergio M., Santiago Beguería, Juan I. López-Moreno, 2010: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Climate, 23, 1696-1718. Trigo R.M., Añel J., Barriopedro D., García-Herrera R., Gimeno L., Nieto R., Castillo R., Allen M.R., Massey N. (2013), The record Winter drought of 2011-12 in the Iberian Peninsula [in "Explaining Extreme Events of 2012 from a Climate Perspective". [Peterson, T. C., M. P. Hoerling, P.A. Stott and S. Herring, Eds.] Bulletin of the American Meteorological Society, 94 (9), S41-S45.
Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought, however other climatic factors such as solar radiation, wind speed, and specific humidity can be important drivers in the depletion of soil moisture and evolution and persistence of dro...
Lau, B.; Overby, C. L.; Wirtz, H. S.; Devine, E. B.
Summary Background Stage 2 Meaningful Use criteria require the use of clinical decision support systems (CDSS) on high priority health conditions to improve clinical quality measures. Although CDSS hold great promise, implementation has been fraught with challenges, evidence of their impact is mixed, and the optimal method of content delivery is unknown. Objective The authors investigated whether implementation of a simple clinical decision support (CDS) tool was associated with improved prescriber adherence to national medication-laboratory monitoring guidelines for safety (hepatic function, renal function, myalgias/rhabdomyolysis) and intermediate outcomes for antidiabetic (Hemoglobin A1c; HbA1c) and antihyperlipidemic (low density lipoprotein; LDL) medications prescribed within a diabetes registry. Methods This was a retrospective observational study conducted in three phases of CDS implementation (2008–2009): pre-, transition-, and post-Prescriptions evaluated were ordered from an electronic health record within a multispecialty medical group. Adherence was evaluated within and without applying guideline-imposed time constraints. Results Forty-thousand prescriptions were ordered over three timeframes. For hepatic and renal function, the proportion of prescriptions for which labs were monitored at any time increased from 52% to 65% (p<0.001); those that met time guidelines, from 14% to 21% (p<0.001). Only 6% of required labs were drawn to monitor for myalgias/rhabdomyolysis, regardless of timeframe. Over 90% of safety labs were within normal limits. The proportion of labs monitored at any time for LDL increased from 56% to 64% (p<0.001); those that met time guidelines from 11% to 17% (p<0.001). The proportion of labs monitored at any time for HbA1c remained the same (72%); those that met time guidelines decreased from 45% to 41% (p<0.001). Conclusion A simple CDS tool may be associated with improved adherence to guidelines. Efforts are needed to confirm
Azmi, Mohammad; Rüdiger, Christoph; Walker, Jeffrey P.
Drought and water stress monitoring plays an important role in the management of water resources, especially during periods of extreme climate conditions. Here, a data fusion-based drought index (DFDI) has been developed and analyzed for three different locations of varying land use and climate regimes in Australia. The proposed index comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type. In deriving the proposed index, weekly data from three different data sources (OzFlux Network, Asia-Pacific Water Monitor, and MODIS-Terra satellite) were employed to first derive commonly used individual standardized drought indices (SDIs), which were then grouped using an advanced clustering method. Next, three different multivariate methods (principal component analysis, factor analysis, and independent component analysis) were utilized to aggregate the SDIs located within each group. For the two clusters in which the grouped SDIs best reflected the water availability and vegetation conditions, the variables were aggregated based on an averaging between the standardized first principal components of the different multivariate methods. Then, considering those two aggregated indices as well as the classifications of months (dry/wet months and active/non-active months), the proposed DFDI was developed. Finally, the symbolic regression method was used to derive mathematical equations for the proposed DFDI. The results presented here show that the proposed index has revealed new aspects in water stress monitoring which previous indices were not able to, by simultaneously considering both hydrometeorological and ecological concepts to define the real water stress of the study areas.
Mens, M. J. P.; Gilroy, K.; Williams, D.
Droughts will likely become more frequent, of greater magnitude and of longer duration in the future due to climate change. Already in the present climate, a variety of drought events may occur with different exceedance frequencies. These frequencies are becoming more uncertain due to climate change. Many methods in support of drought risk management focus on providing insight into changing drought frequencies, and use water supply reliability as key decision criterion. In contrast, robustness analysis focuses on providing insight into the full range of drought events and their impact on a system's functioning. This method has been developed for flood risk systems, but applications on drought risk systems are lacking. This paper aims to develop robustness analysis for drought risk systems, and illustrates the approach through a case study with a water supply reservoir and its users. We explore drought characterization and the assessment of a system's ability to deal with drought events, by quantifying the severity and socio-economic impact of a variety of drought events, both frequent and rare ones. Furthermore, we show the effect of three common drought management strategies (increasing supply, reducing demand and implementing hedging rules) on the robustness of the coupled water supply and socio-economic system. The case is inspired by Oologah Lake, a multipurpose reservoir in Oklahoma, United States. Results demonstrate that although demand reduction and supply increase may have a comparable effect on the supply reliability, demand reduction may be preferred from a robustness perspective. To prepare drought management plans for dealing with current and future droughts, it is thus recommended to test how alternative drought strategies contribute to a system's robustness rather than relying solely on water reliability as the decision criterion.
Mens, M. J. P.; Gilroy, K.; Williams, D.
Droughts will likely become more frequent, greater in magnitude and longer in duration in the future due to climate change. Already in the present climate, a variety of drought events may occur with different exceedance frequencies. These frequencies are becoming more uncertain due to climate change. Many methods in support of drought risk management focus on providing insight into changing drought frequencies, and use water supply reliability as a key decision criterion. In contrast, robustness analysis focuses on providing insight into the full range of drought events and their impact on a system's functionality. This method has been developed for flood risk systems, but applications on drought risk systems are lacking. This paper aims to develop robustness analysis for drought risk systems, and illustrates the approach through a case study with a water supply reservoir and its users. We explore drought characterization and the assessment of a system's ability to deal with drought events, by quantifying the severity and socio-economic impact of a variety of drought events, both frequent and rare ones. Furthermore, we show the effect of three common drought management strategies (increasing supply, reducing demand and implementing hedging rules) on the robustness of the coupled water supply and socio-economic system. The case is inspired by Oologah Lake, a multipurpose reservoir in Oklahoma, United States. Results demonstrate that although demand reduction and supply increase may have a comparable effect on the supply reliability, demand reduction may be preferred from a robustness perspective. To prepare drought management plans for dealing with current and future droughts, it is thus recommended to test how alternative drought strategies contribute to a system's robustness rather than relying solely on water reliability as the decision criterion.
Yusa, Anna; Berry, Peter; Cheng, June J.; Ogden, Nicholas; Bonsal, Barrie; Stewart, Ronald; Waldick, Ruth
Droughts have been recorded all across Canada and have had significant impacts on individuals and communities. With climate change, projections suggest an increasing risk of drought in Canada, particularly in the south and interior. However, there has been little research on the impacts of drought on human health and the implications of a changing climate. A review of the Canadian, U.S. and international literature relevant to the Canadian context was conducted to better define these impacts and adaptations available to protect health. Drought can impact respiratory health, mental health, illnesses related to exposure to toxins, food/water security, rates of injury and infectious diseases (including food-, water- and vector-borne diseases). A range of direct and indirect adaptation (e.g., agricultural adaptation) options exist to cope with drought. Many have already been employed by public health officials, such as communicable disease monitoring and surveillance and public education and outreach. However, gaps exist in our understanding of the impacts of short-term vs. prolonged drought on the health of Canadians, projections of drought and its characteristics at the regional level and the effectiveness of current adaptations. Further research will be critical to inform adaptation planning to reduce future drought-related risks to health. PMID:26193300
Yusa, Anna; Berry, Peter; J Cheng, June; Ogden, Nicholas; Bonsal, Barrie; Stewart, Ronald; Waldick, Ruth
Droughts have been recorded all across Canada and have had significant impacts on individuals and communities. With climate change, projections suggest an increasing risk of drought in Canada, particularly in the south and interior. However, there has been little research on the impacts of drought on human health and the implications of a changing climate. A review of the Canadian, U.S. and international literature relevant to the Canadian context was conducted to better define these impacts and adaptations available to protect health. Drought can impact respiratory health, mental health, illnesses related to exposure to toxins, food/water security, rates of injury and infectious diseases (including food-, water- and vector-borne diseases). A range of direct and indirect adaptation (e.g., agricultural adaptation) options exist to cope with drought. Many have already been employed by public health officials, such as communicable disease monitoring and surveillance and public education and outreach. However, gaps exist in our understanding of the impacts of short-term vs. prolonged drought on the health of Canadians, projections of drought and its characteristics at the regional level and the effectiveness of current adaptations. Further research will be critical to inform adaptation planning to reduce future drought-related risks to health.
Implementation of adequate measures to assess and monitor droughts is recognized as a major matter challenging researchers involved in water resources management. The objective of this study is to assess the hydrologic drought characteristics from the historical rainfall records of Kuwait with arid environment by employing the criterion of Standardized Precipitation Index (SPI). A wide range of monthly total precipitation data from January 1967 to December 2009 is used for the assessment. The computation of the SPI series is performed for intermediate- and long-time scales of 3, 6, 12, and 24 months. The drought severity and duration are also estimated. The bivariate probability distribution for these two drought characteristics is constructed by using Clayton copula. It has been shown that the drought SPI series for the time scales examined have no systematic trend component but a seasonal pattern related to rainfall data. The results are used to perform univariate and bivariate frequency analyses for the drought events. The study will help evaluating the risk of future droughts in the region, assessing their consequences on economy, environment, and society, and adopting measures for mitigating the effect of droughts.
Jeyaseelan, A. T.; Kogan, Felix N.
Drought is the major disaster, which occurs in some part of India every year due to monsoon variability. India has established satellite based National Agricultural Drought Assessment and Monitoring System (NADAMS), at National Remote Sensing Agency, Department of Space since 1987. NADAMS provides near real time monitoring and early warning of drought conditions at National level using NOAA AVHRR and at regional level using IRS WiFS and AWiFS data. ISRO-NASA-NOAA science cooperation project has been initiated during 2005 for development of satellite based decision support drought monitor system in India. Initially, the evaluation of GVI based indices for drought early warning in India was taken up. The study was carried out over five small regions each covering part of a district and over five large regions each covering few districts in each state of Gujarat, Maharashtra and Rajasthan states and the result of the study is presented in this paper. The weekly GVI based indices such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) for the period from 1991-2004 over 5 small regions covering part of districts namely Banaskantha district of Gujarat state to represent Bajra crop, Surendra nagar district of Gujarat state to represent Cotton crop, Nasik district of Maharashtra to represent Bajra crop, Bhandara district to represent Rice crop and Akola district of Maharastra to represent Jowar crop was selected. The weekly GVI based indices over 5 large regions with larger database from 1981 to 2004 covering few districts of Rajasthan state to represent winter wheat and few districts of Maharashtra state to represent Jowar, Rice and Cotton crops were selected. The comparison of seasonal average VCI, TCI and VHI with the corresponding crops yield over 5 small regions indicate better regression coefficient for VHI than VCI or TCI. The comparison over 5 large regions covering larger data base from 1982-2004 indicate better
Damberg, L.; AghaKouchak, A.
Numerous studies indicate that frequency of extreme events such as droughts has increased particularly in the past century. Documenting changes in droughts requires long-term records of observations with suitable temporal and spatial coverage. A key variable for drought monitoring is precipitation which is typically used to describe meteorological droughts. Most studies of historical droughts have been based on analysis of long-term gauge (point) measurements of precipitation. However, the spatial distribution of rain gauges is not sufficient to provide reliable estimates of the spatial distributions of draughts in a global scale. In this study, a merged multi-satellite real-time precipitation data (1979-present) is used by combining long-term GPCP data with real-time satellite observations to monitor droughts in the past three decades. The Standardized Precipitation Index (SPI; McKee et al (1993)) is implemented to evaluate changes in the spatial extent of droughts across land, ocean and the entire globe. Trends and spatial patterns are investigated for moderate to severe droughts (SPI between -1 to -2) and extreme droughts (SPI below -2). While no significant trend for extreme droughts (SPI below -2) has been observed over land, moderate to severe droughts (SPI between -1 to -2) show a significant trend in spatial extent across both land and ocean.
Barker, L. J.; Hannaford, J.; Chiverton, A.; Svensson, C.
Drought monitoring and early warning (M&EW) systems are a crucial component of drought preparedness. M&EW systems typically make use of drought indicators such as the Standardised Precipitation Index (SPI), but such indicators are not widely used in the UK. More generally, such tools have not been well developed for hydrological (i.e. streamflow) drought. To fill these research gaps, this paper characterises meteorological and hydrological droughts, and the propagation from one to the other using the SPI and the related Standardised Streamflow Index (SSI), with the objective of improving understanding of the drought hazard in the UK. SPI and SSI time series were calculated for 121 near-natural catchments in the UK for accumulation periods of 1-24 months. From these time series, drought events were identified and for each event, the duration and severity was calculated. The relationship between meteorological and hydrological drought was examined by cross-correlating the one month SSI with various SPI accumulation periods. Finally, the influence of climate and catchment properties on the drought characteristics and propagation were investigated. Results showed that at short accumulation periods meteorological drought characteristics showed little spatial variability, whilst hydrological drought characteristics showed fewer but longer and more severe droughts in the south and east than in the north and west of the UK. Propagation characteristics showed a similar spatial pattern with catchments underlain by productive aquifers, mostly in the south and east, having longer SPI accumulation periods strongly correlated with the one-month SSI. For catchments in the north and west of the UK, which typically have little catchment storage, standard-period annual average rainfall was strongly correlated to drought and propagation characteristics. However, in the south and east, catchment properties describing storage, such as base flow index, percentage of highly productive
Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.
A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.
Berdanier, Aaron B; Clark, James S
Recent forest diebacks, combined with threats of future drought, focus attention on the extent to which tree death is caused by catastrophic events as opposed to chronic declines in health that accumulate over years. While recent attention has focused on large-scale diebacks, there is concern that increasing drought stress and chronic morbidity may have pervasive impacts on forest composition in many regions. Here we use long-term, whole-stand inventory data from southeastern U.S. forests to show that trees exposed to drought experience multiyear declines in growth prior to mortality. Following a severe, multiyear drought, 72% of trees that did not recover their pre-drought growth rates died within 10 yr. This pattern was mediated by local moisture availability. As an index of morbidity prior to death, we calculated the difference in cumulative growth after drought relative to surviving conspecifics. The strength of drought-induced morbidity varied among species and was correlated with drought tolerance. These findings support the ability of trees to avoid death during drought events but indicate shifts that could occur over decades. Tree mortality following drought is predictable in these ecosystems based on growth declines, highlighting an opportunity to address multiyear drought-induced morbidity in models, experiments, and management decisions.
Stahl, K.; Hannaford, J.; Svoboda, M.; Knutson, C. L.; Bachmair, S.; Acreman, M. C.; Crossman, N. D.; Overton, I. C.; Colloff, M.; Collins, K.
Drought events pose a substantial threat to water security in almost every climate zone and water use sector. Many countries have had difficulty maintaining water supplies and mitigating user conflicts during recent droughts, for example during Australia's Millennium Drought (1998-2010) and the recent droughts in the USA (2012), Europe (early 2000s) and UK (2011/12). With climate projections suggesting that droughts will intensify in many regions, the magnitude of this threat is likely to increase and thus vulnerability of society to drought must be reduced through better preparedness. The Belmont Forum project DrIVER (Drought impacts: Vulnerability thresholds in monitoring and Early-warning Research) aims to contribute to better preparedness by improving links between natural (hydrometeorological) drought characterisation and environmental and socio-economic impacts. Better integration of drought characterisation and impacts are expected to lead to enhanced drought monitoring and early-warning systems and other risk management strategies. We present results from a comparison of the monitoring and early-warning capacity and past drought impacts in case studies on three continents, North America, Europe and Australia. All have high institutional capacity but are set within very different hydroclimatic conditions with differing drought vulnerabilities and management frameworks.
van Lanen, Henny A. J.; van Loon, Anne F.; Ploum, Stefan; Laaha, Gregor; Parajka, Juraj; Garnier, Emmanuel
The previous winter (2013-2014) was warm and dry in many regions of Europe, leading to serious problems in water supply in for example Kosovo. This winter was not an exception. Analyses of drought-related impacts on nature and society indicate that not all drought impacts occur in summer, during periods with below-normal rainfall and connected heat waves. We found that several drought impacts are a consequence of anomalies in winter-related processes, such as snow accumulation and melt. The hydrological drought typology (Van Loon & Van Lanen, 2012) was developed based on analysis of the processes underlying drought propagation in catchments in Norway, Czech Republic and Slovakia. In this typology, two drought types were distinguished that are caused by anomalous winter processes: cold snow season drought and warm snow season drought. In a recent study in the Alpine region, however, we found drought events that could not be classified into one of the types of the existing hydrological drought typology. We could reveal the processes underlying these drought events with further detailed analysis of an extensive dataset of observations and simulations of hydrometeorological variables for a large number of catchments in Austria. In this paper we present two new hydrological drought types related to snow and glacier melt. We discuss the processes underlying these drought types and show that besides precipitation, temperature plays an important role. Furthermore, we discuss the differences between hydrological droughts in the Alpine region and the previously studied regions in North and Central Europe, and investigate the relationship with the impacts of hydrological drought, both in the ancient (1500-1950) and in the recent past (1970-2010). Van Loon, A.F., and Van Lanen, H.A.J.: A process-based typology of hydrological drought, Hydrology and Earth System Science, 16, p. 1915-1946, doi: 10.5194/hess-16-1915-2012, 2012
Kim, Dae-Jun; Kim, Soo-Ock; Kim, Jin-Hee; Shim, Kyo-Moon; Yun, Jin I.
A drought index with respect to the spatio-temporal scale was developed in response to the demand from the agricultural sector in South Korea. The new drought index was calculated based on the soil water balance between the supply and demand of water. The water supply was estimated using the cumulative effective precipitation weighted by the precipitation from two months prior. The water demand was derived from the actual evapotranspiration, which was calculated by applying a crop coefficient to the reference evapotranspiration. The amount of surface runoff on a given soil type was used to calculate the residual soil moisture. The presence of drought was determined based on the probability distribution in the given area. In order to assess the reliability of this index, termed the Agricultural Drought Index (ADI), the amount of residual moisture, which represents the severity of a drought, was compared to the measurements of soil moisture at three experimental sites between July 2012 and December 2013. The results showed that the ADI had greater correlation with measured soil moisture than did the Standardized Precipitation Index (SPI), suggesting that the ADI is a useful indicator of drought. While both the SPI and ADI showed similar trends in the temporal variation of drought conditions at all of the sites, the ADI better detected `severe drought' than did the SPI. The daily relief of severe drought due to precipitation was also better represented by the ADI. Using the high-resolution climatic and spatial data of a small watershed, we produced 270m resolution maps of ADI from week 36 through week 41 of 2013, demonstrating the feasibility of the ADI as an operational drought monitor appropriate for the agricultural environment of South Korea.
DeChant, C. M.; Moradkhani, H.
Over the past half century, drought monitoring has seen an array of developments. Primarily performed with indices, drought monitoring has advanced with progress in observing climate/land surface variables, and with our ability to model hydrological and climatic phenomena. Generally, these indices are based on statistical patterns in the historic record of hydrologic states and fluxes. By casting drought from this perspective, indices become more straightforward to formulate, yet carry a severe drawback in a dynamic climate. Climate is readily highlighted as non-stationary by the majority of earth scientists, which violates the key assumption of an index based on historically observed dryness. This fact necessitates the development of physically-based (not statistically-based) indices, which are more applicable in our changing climate. In this presentation, one such drought index will be proposed, and compared to the more common statistical indices. Overall, this will highlight the potential to move towards drought monitoring that can be related to environmental water supply, through a comparison with soil water storage capacity and land cover. Such an analysis requires highly accurate land surface state estimation, and for this reason, land surface states are estimated through a remote sensing data assimilation framework, which ensures accuracy and quantifies soil/snow water storage uncertainty. The applicability of this method to the upper Colorado Basin is explored, and its sensitivity to drought persistence is assessed.
This book is a collection of selected papers from the NATO Advanced Study Institute on Droughts entitled “Drought Impact Control Technology,” held at the National Laboratory of Civil Engineering in Lisbon, Portugal, in June 1980. The editors of the book have chosen a nontraditional but successful approach to presenting the papers. Instead of including a verbatim proceedings of the institute, they assembled 21 papers presented by 14 of the institute's lecturers, reshaped and synthesized them, and supplemented them by five new papers that cover obvious gaps in topics. The result is enlightening reading and a more or less complete presentation of the subject. The edited material in the book was arranged around three central themes related to efforts needed to cope with or manage the droughts. In the process, the identity of individual contributors has been preserved.