Soil Moisture and the Persistence of North American Drought.

NASA Astrophysics Data System (ADS)

Oglesby, Robert J.; Erickson, David J., III

1989-11-01

We describe numerical sensitivity experiments exploring the effects of soil moisture on North American summertime climate using the NCAR CCMI, a 12-layer global atmospheric general circulation model. In particular. the hypothesis that reduced soil moisture may help induce and amplify warm, dry summers over midlatitude continental interiors is examined. Equilibrium climate statistics are computed for the perpetual July model response to imposed soil moisture anomalies over North America between 36° and 49°N. In addition, the persistence of imposed soil moisture anomalies is examined through use of the seasonal cycle mode of operation with use of various initial atmospheric states both equilibrated and nonequilibrated to the initial soil moisture anomaly.The climate statistics generated by thew model simulations resemble in a general way those of the summer of 1988, when extensive heat and drought occurred over much of North America. A reduction in soil moisture in the model leads to an increase in surface temperature, lower surface pressure, increased ridging aloft, and a northward shift of the jet stream. Low-level moisture advection from the Gulf of Mexico is important in determining where persistent soil moisture deficits can be maintained. In seasonal cycle simulations, it lock longer for an initially unequilibrated atmosphere to respond to the imposed soil moisture anomaly, via moisture transport from the Gulf of Mexico, than when initially the atmosphere was in equilibrium with the imposed anomaly., i.e., the initial state was obtained from the appropriate perpetual July simulation. The results demonstrate the important role of soil moisture in prolonging and/or amplifying North American summertime drought.

Simulating soil moisture change in a semiarid rangeland watershed with a process-based water-balance model

Treesearch

Howard Evan Canfield; Vicente L. Lopes

2000-01-01

A process-based, simulation model for evaporation, soil water and streamflow (BROOK903) was used to estimate soil moisture change on a semiarid rangeland watershed in southeastern Arizona. A sensitivity analysis was performed to select parameters affecting ET and soil moisture for calibration. Automatic parameter calibration was performed using a procedure based on a...

Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

NASA Astrophysics Data System (ADS)

Shellito, Peter J.; Small, Eric E.; Livneh, Ben

2018-03-01

Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

Evaluation of a simple, point-scale hydrologic model in simulating soil moisture using the Delaware environmental observing system

NASA Astrophysics Data System (ADS)

Legates, David R.; Junghenn, Katherine T.

2018-04-01

Many local weather station networks that measure a number of meteorological variables (i.e. , mesonetworks) have recently been established, with soil moisture occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from soil surveys, can be used to obtain estimates of soil moisture. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily soil moisture at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes soil moisture measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific soil moisture is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured soil moisture, often near the accuracy of the soil moisture instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained soils after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely affects the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.

Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century

NASA Astrophysics Data System (ADS)

Ruosteenoja, Kimmo; Markkanen, Tiina; Venäläinen, Ari; Räisänen, Petri; Peltola, Heli

2018-02-01

Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.

Evaluation of HCMM data for assessing soil moisture and water table depth. [South Dakota

NASA Technical Reports Server (NTRS)

Moore, D. G.; Heilman, J. L.; Tunheim, J. A.; Westin, F. C.; Heilman, W. E.; Beutler, G. A.; Ness, S. D. (Principal Investigator)

1981-01-01

Soil moisture in the 0-cm to 4-cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the soil temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating soil moisture and soil temperature shows that soils with different moisture profiles differed in soil temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.

Is soil moisture initialization important for seasonal to decadal predictions?

NASA Astrophysics Data System (ADS)

Stacke, Tobias; Hagemann, Stefan

2014-05-01

The state of soil moisture can can have a significant impact on regional climate conditions for short time scales up to several months. However, focusing on seasonal to decadal time scales, it is not clear whether the predictive skill of global a Earth System Model might be enhanced by assimilating soil moisture data or improving the initial soil moisture conditions with respect to observations. As a first attempt to provide answers to this question, we set up an experiment to investigate the life time (memory) of extreme soil moisture states in the coupled land-atmosphere model ECHAM6-JSBACH, which is part of the Max Planck Institute for Meteorology's Earth System Model (MPI-ESM). This experiment consists of an ensemble of 3 years simulations which are initialized with extreme wet and dry soil moisture states for different seasons and years. Instead of using common thresholds like wilting point or critical soil moisture, the extreme states were extracted from a reference simulation to ensure that they are within the range of simulated climate variability. As a prerequisite for this experiment, the soil hydrology in JSBACH was improved by replacing the bucket-type soil hydrology scheme with a multi-layer scheme. This new scheme is a more realistic representation of the soil, including percolation and diffusion fluxes between up to five separate layers, the limitation of bare soil evaporation to the uppermost soil layer and the addition of a long term water storage below the root zone in regions with deep soil. While the hydrological cycle is not strongly affected by this new scheme, it has some impact on the simulated soil moisture memory which is mostly strengthened due to the additional deep layer water storage. Ensemble statistics of the initialization experiment indicate perturbation lengths between just a few days up to several seasons for some regions. In general, the strongest effects are seen for wet initialization during northern winter over cold and humid regions, while the shortest memory is found during northern spring. For most regions, the soil moisture memory is either sensitive to wet or to dry perturbations, indicating that soil moisture anomalies interact with the respective weather pattern for a given year and might be able to enhance or dampen extreme conditions. To further investigate this effect, the simulations will be repeated using JSBACH with prescribed meteorological forcing to better disentangle the direct effects of soil moisture initialization and the atmospheric response.

Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

NASA Astrophysics Data System (ADS)

Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

2017-12-01

Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture patterns with more realistic statistical properties than the deterministic model. Additionally, the results suggest that the variance and spatial correlation of the stochastic soil-moisture variations do not vary consistently with the spatial-average soil moisture.

Land-atmosphere coupling and soil moisture memory contribute to long-term agricultural drought

NASA Astrophysics Data System (ADS)

Kumar, S.; Newman, M.; Lawrence, D. M.; Livneh, B.; Lombardozzi, D. L.

2017-12-01

We assessed the contribution of land-atmosphere coupling and soil moisture memory on long-term agricultural droughts in the US. We performed an ensemble of climate model simulations to study soil moisture dynamics under two atmospheric forcing scenarios: active and muted land-atmosphere coupling. Land-atmosphere coupling contributes to a 12% increase and 36% decrease in the decorrelation time scale of soil moisture anomalies in the US Great Plains and the Southwest, respectively. These differences in soil moisture memory affect the length and severity of modeled drought. Consequently, long-term droughts are 10% longer and 3% more severe in the Great Plains, and 15% shorter and 21% less severe in the Southwest. An analysis of Coupled Model Intercomparsion Project phase 5 data shows four fold uncertainty in soil moisture memory across models that strongly affects simulated long-term droughts and is potentially attributable to the differences in soil water storage capacity across models.

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.

One-dimensional simulation of temperature and moisture in atmospheric and soil boundary layers

NASA Technical Reports Server (NTRS)

Bornstein, R. D.; Santhanam, K.

1981-01-01

Meteorologists are interested in modeling the vertical flow of heat and moisture through the soil in order to better simulate the vertical and temporal variations of the atmospheric boundary layer. The one dimensional planetary boundary layer model of is modified by the addition of transport equations to be solved by a finite difference technique to predict soil moisture.

Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble

NASA Astrophysics Data System (ADS)

Lorenz, Ruth; Argüeso, Daniel; Donat, Markus G.; Pitman, Andrew J.; van den Hurk, Bart; Berg, Alexis; Lawrence, David M.; Chéruy, Frédérique; Ducharne, Agnès.; Hagemann, Stefan; Meier, Arndt; Milly, P. C. D.; Seneviratne, Sonia I.

2016-01-01

We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.

Improving Water Level and Soil Moisture Over Peatlands in a Global Land Modeling System

NASA Technical Reports Server (NTRS)

Bechtold, M.; De Lannoy, G. J. M.; Roose, D.; Reichle, R. H.; Koster, R. D.; Mahanama, S. P.

2017-01-01

New model structure for peatlands results in improved skill metrics (without any parameter calibration) Simulated surface soil moisture strongly affected by new model, but reliable soil moisture data lacking for validation.

NASA Soil Moisture Data Products and Their Incorporation in DREAM

NASA Technical Reports Server (NTRS)

Blonski, Slawomir; Holland, Donald; Henderson, Vaneshette

2005-01-01

NASA provides soil moisture data products that include observations from the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite, field measurements from the Soil Moisture Experiment campaigns, and model predictions from the Land Information System and the Goddard Earth Observing System Data Assimilation System. Incorporation of the NASA soil moisture products in the Dust Regional Atmospheric Model is possible through use of the satellite observations of soil moisture to set initial conditions for the dust simulations. An additional comparison of satellite soil moisture observations with mesoscale atmospheric dynamics modeling is recommended. Such a comparison would validate the use of NASA soil moisture data in applications and support acceptance of satellite soil moisture data assimilation in weather and climate modeling.

Modeling soil moisture memory in savanna ecosystems

NASA Astrophysics Data System (ADS)

Gou, S.; Miller, G. R.

2011-12-01

Antecedent soil conditions create an ecosystem's "memory" of past rainfall events. Such soil moisture memory effects may be observed over a range of timescales, from daily to yearly, and lead to feedbacks between hydrological and ecosystem processes. In this study, we modeled the soil moisture memory effect on savanna ecosystems in California, Arizona, and Africa, using a system dynamics model created to simulate the ecohydrological processes at the plot-scale. The model was carefully calibrated using soil moisture and evapotranspiration data collected at three study sites. The model was then used to simulate scenarios with various initial soil moisture conditions and antecedent precipitation regimes, in order to study the soil moisture memory effects on the evapotranspiration of understory and overstory species. Based on the model results, soil texture and antecedent precipitation regime impact the redistribution of water within soil layers, potentially causing deeper soil layers to influence the ecosystem for a longer time. Of all the study areas modeled, soil moisture memory of California savanna ecosystem site is replenished and dries out most rapidly. Thus soil moisture memory could not maintain the high rate evapotranspiration for more than a few days without incoming rainfall event. On the contrary, soil moisture memory of Arizona savanna ecosystem site lasts the longest time. The plants with different root depths respond to different memory effects; shallow-rooted species mainly respond to the soil moisture memory in the shallow soil. The growing season of grass is largely depended on the soil moisture memory of the top 25cm soil layer. Grass transpiration is sensitive to the antecedent precipitation events within daily to weekly timescale. Deep-rooted plants have different responses since these species can access to the deeper soil moisture memory with longer time duration Soil moisture memory does not have obvious impacts on the phenology of woody plants, as these can maintain transpiration for a longer time even through the top soil layer dries out.

Documentation for Program SOILSIM: A computer program for the simulation of heat and moisture flow in soils and between soils, canopy and atmosphere

NASA Technical Reports Server (NTRS)

Field, Richard T.

1990-01-01

SOILSIM, a digital model of energy and moisture fluxes in the soil and above the soil surface, is presented. It simulates the time evolution of soil temperature and moisture, temperature of the soil surface and plant canopy the above surface, and the fluxes of sensible and latent heat into the atmosphere in response to surface weather conditions. The model is driven by simple weather observations including wind speed, air temperature, air humidity, and incident radiation. The model intended to be useful in conjunction with remotely sensed information of the land surface state, such as surface brightness temperature and soil moisture, for computing wide area evapotranspiration.

A spatial scaling relationship for soil moisture in a semiarid landscape, using spatial scaling relationships for pedology

NASA Astrophysics Data System (ADS)

Willgoose, G. R.; Chen, M.; Cohen, S.; Saco, P. M.; Hancock, G. R.

2013-12-01

In humid areas it is generally considered that soil moisture scales spatially according to the wetness index of the landscape. This scaling arises from lateral flow downslope of ground water within the soil zone. However, in semi-arid and drier regions, this lateral flow is small and fluxes are dominated by vertical flows driven by infiltration and evapotranspiration. Thus, in the absence of runon processes, soil moisture at a location is more driven by local factors such as soil and vegetation properties at that location rather than upstream processes draining to that point. The 'apparent' spatial randomness of soil and vegetation properties generally suggests that soil moisture for semi-arid regions is spatially random. In this presentation a new analysis of neutron probe data during summer from the Tarrawarra site near Melbourne, Australia shows persistent spatial organisation of soil moisture over several years. This suggests a link between permanent features of the catchment (e.g. soil properties) and soil moisture distribution, even though the spatial pattern of soil moisture during the 4 summers monitored appears spatially random. This and other data establishes a prima facie case that soil variations drive spatial variation in soil moisture. Accordingly, we used a previously published spatial scaling relationship for soil properties derived using the mARM pedogenesis model to simulate the spatial variation of soil grading. This soil grading distribution was used in the Rosetta pedotransfer model to derive a spatial distribution of soil functional properties (e.g. saturated hydraulic conductivity, porosity). These functional properties were then input into the HYDRUS-1D soil moisture model and soil moisture simulated for 3 years at daily resolution. The HYDRUS model used had previously been calibrated to field observed soil moisture data at our SASMAS field site. The scaling behaviour of soil moisture derived from this modelling will be discussed and compared with observed data from our SASMAS field sites.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

PubMed

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

PubMed Central

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert. PMID:27764203

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

NASA Technical Reports Server (NTRS)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Evaluating the spatial distribution of water balance in a small watershed, Pennsylvania

NASA Astrophysics Data System (ADS)

Yu, Zhongbo; Gburek, W. J.; Schwartz, F. W.

2000-04-01

A conceptual water-balance model was modified from a point application to be distributed for evaluating the spatial distribution of watershed water balance based on daily precipitation, temperature and other hydrological parameters. The model was calibrated by comparing simulated daily variation in soil moisture with field observed data and results of another model that simulates the vertical soil moisture flow by numerically solving Richards' equation. The impacts of soil and land use on the hydrological components of the water balance, such as evapotranspiration, soil moisture deficit, runoff and subsurface drainage, were evaluated with the calibrated model in this study. Given the same meteorological conditions and land use, the soil moisture deficit, evapotranspiration and surface runoff increase, and subsurface drainage decreases, as the available water capacity of soil increases. Among various land uses, alfalfa produced high soil moisture deficit and evapotranspiration and lower surface runoff and subsurface drainage, whereas soybeans produced an opposite trend. The simulated distribution of various hydrological components shows the combined effect of soil and land use. Simulated hydrological components compare well with observed data. The study demonstrated that the distributed water balance approach is efficient and has advantages over the use of single average value of hydrological variables and the application at a single point in the traditional practice.

ERT to aid in WSN based early warning system for landslides

NASA Astrophysics Data System (ADS)

T, H.

2017-12-01

Amrita University's landslide monitoring and early warning system using Wireless Sensor Networks (WSN) consists of heterogeneous sensors like rain gauge, moisture sensor, piezometer, geophone, inclinometer, tilt meter etc. The information from the sensors are accurate and limited to that point. In order to monitor a large area, ERT can be used in conjunction with WSN technology. To accomplish the feasibility of ERT in landslide early warning along with WSN technology, we have conducted experiments in Amrita's landslide laboratory setup. The experiment was aimed to simulate landslide, and monitor the changes happening in the soil using moisture sensor and ERT. Simulating moisture values from resistivity measurements to a greater accuracy can help in landslide monitoring for large areas. For accomplishing the same we have adapted two mathematical approaches, 1) Regression analysis between resistivity measurements and actual moisture values from moisture sensor, and 2) Using Waxman Smith model to simulate moisture values from resistivity measurements. The simulated moisture values from Waxman Smith model is compared with the actual moisture values and the Mean Square Error (MSE) is found to be 46.33. Regression curve is drawn for the resistivity vs simulated moisture values from Waxman model, and it is compared with the regression curve of actual model, which is shown in figure-1. From figure-1, it is clear that there the regression curve from actual moisture values and the regression curve from simulated moisture values, follow the similar pattern and there is a small difference between them. Moisture values can be simulated to a greater accuracy using actual regression equation, but the limitation is that, regression curves will differ for different sites and different soils. Regression equation from actual moisture values can be used, if we have conducted experiment in the laboratory for a particular soil sample, otherwise with the knowledge of soil properties, Waxman model can be used to simulate moisture values. The promising results assure that, ERT measurements when used in conjunction with WSN technique, vital paramters triggering landslides like moisture can be simulated for a large area, which will help in providing early warning for large areas.

Uncertainty in Ecohydrological Modeling in an Arid Region Determined with Bayesian Methods

PubMed Central

Yang, Junjun; He, Zhibin; Du, Jun; Chen, Longfei; Zhu, Xi

2016-01-01

In arid regions, water resources are a key forcing factor in ecosystem circulation, and soil moisture is the critical link that constrains plant and animal life on the soil surface and underground. Simulation of soil moisture in arid ecosystems is inherently difficult due to high variability. We assessed the applicability of the process-oriented CoupModel for forecasting of soil water relations in arid regions. We used vertical soil moisture profiling for model calibration. We determined that model-structural uncertainty constituted the largest error; the model did not capture the extremes of low soil moisture in the desert-oasis ecotone (DOE), particularly below 40 cm soil depth. Our results showed that total uncertainty in soil moisture prediction was improved when input and output data, parameter value array, and structure errors were characterized explicitly. Bayesian analysis was applied with prior information to reduce uncertainty. The need to provide independent descriptions of uncertainty analysis (UA) in the input and output data was demonstrated. Application of soil moisture simulation in arid regions will be useful for dune-stabilization and revegetation efforts in the DOE. PMID:26963523

Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble

USGS Publications Warehouse

Lorenz, Ruth; Argueso, Daniel; Donat, Markus G.; Pitman, Andrew J.; van den Hurk, Bart; Berg, Alexis; Lawrence, David M.; Cheruy, Frederique; Ducharne, Agnes; Hagemann, Stefan; Meier, Arndt; Milly, Paul C.D.; Seneviratne, Sonia I

2016-01-01

We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.

Computer simulation of a space SAR using a range-sequential processor for soil moisture mapping

NASA Technical Reports Server (NTRS)

Fujita, M.; Ulaby, F. (Principal Investigator)

1982-01-01

The ability of a spaceborne synthetic aperture radar (SAR) to detect soil moisture was evaluated by means of a computer simulation technique. The computer simulation package includes coherent processing of the SAR data using a range-sequential processor, which can be set up through hardware implementations, thereby reducing the amount of telemetry involved. With such a processing approach, it is possible to monitor the earth's surface on a continuous basis, since data storage requirements can be easily met through the use of currently available technology. The Development of the simulation package is described, followed by an examination of the application of the technique to actual environments. The results indicate that in estimating soil moisture content with a four-look processor, the difference between the assumed and estimated values of soil moisture is within + or - 20% of field capacity for 62% of the pixels for agricultural terrain and for 53% of the pixels for hilly terrain. The estimation accuracy for soil moisture may be improved by reducing the effect of fading through non-coherent averaging.

Evaluation of SMAP Level 2 Soil Moisture Algorithms Using SMOS Data

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann; Shi, J. C.

2011-01-01

The objectives of the SMAP (Soil Moisture Active Passive) mission are global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide soil moisture with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 soil moisture algorithm that is based on passive microwave observations by exploiting Soil Moisture Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only soil moisture algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS soil moisture products. The investigation will result in enhanced SMAP pre-launch algorithms for soil moisture.

The Temporal Dynamics of Spatial Patterns of Observed Soil Moisture Interpreted Using the Hydrus 1-D Model

NASA Astrophysics Data System (ADS)

Chen, M.; Willgoose, G. R.; Saco, P. M.

2009-12-01

This paper investigates the soil moisture dynamics over two subcatchments (Stanley and Krui) in the Goulburn River in NSW during a three year period (2005-2007) using the Hydrus 1-D unsaturated soil water flow model. The model was calibrated to the seven Stanley microcatchment sites (1 sqkm site) using continuous time surface 30cm and full profile soil moisture measurements. Soil type, leaf area index and soil depth were found to be the key parameters changing model fit to the soil moisture time series. They either shifted the time series up or down, changed the steepness of dry-down recessions or determined the lowest point of soil moisture dry-down respectively. Good correlations were obtained between observed and simulated soil water storage (R=0.8-0.9) when calibrated parameters for one site were applied to the other sites. Soil type was also found to be the main determinant (after rainfall) of the mean of modelled soil moisture time series. Simulations of top 30cm were better than those of the whole soil profile. Within the Stanley microcatchment excellent soil moisture matches could be generated simply by adjusting the mean of soil moisture up or down slightly. Only minor modification of soil properties from site to site enable good fits for all of the Stanley sites. We extended the predictions of soil moisture to a larger spatial scale of the Krui catchment (sites up to 30km distant from Stanley) using soil and vegetation parameters from Stanley but the locally recorded rainfall at the soil moisture measurement site. The results were encouraging (R=0.7~0.8). These results show that it is possible to use a calibrated soil moisture model to extrapolate the soil moisture to other sites for a catchment with an area of up to 1000km2. This paper demonstrates the potential usefulness of continuous time, point scale soil moisture (typical of that measured by permanently installed TDR probes) in predicting the soil wetness status over a catchment of significant size.

Uncertain soil moisture feedbacks in model projections of Sahel precipitation

NASA Astrophysics Data System (ADS)

Berg, Alexis; Lintner, Benjamin R.; Findell, Kirsten; Giannini, Alessandra

2017-06-01

Given the uncertainties in climate model projections of Sahel precipitation, at the northern edge of the West African Monsoon, understanding the factors governing projected precipitation changes in this semiarid region is crucial. This study investigates how long-term soil moisture changes projected under climate change may feedback on projected changes of Sahel rainfall, using simulations with and without soil moisture change from five climate models participating in the Global Land Atmosphere Coupling Experiment-Coupled Model Intercomparison Project phase 5 experiment. In four out of five models analyzed, soil moisture feedbacks significantly influence the projected West African precipitation response to warming; however, the sign of these feedbacks differs across the models. These results demonstrate that reducing uncertainties across model projections of the West African Monsoon requires, among other factors, improved mechanistic understanding and constraint of simulated land-atmosphere feedbacks, even at the large spatial scales considered here.