scholarly journals Evaluation of SWAT Soil Water Estimation Accuracy Using Data from Indiana, Colorado, and Texas

2020 ◽  
Vol 63 (6) ◽  
pp. 1827-1843
Author(s):  
Ahmed A. Hashem ◽  
Bernard A. Engel ◽  
Gary W. Marek ◽  
Jerry E. Moorhead ◽  
Dennis C. Flanagan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Profile ◽  
Assessment Tool ◽  
Swat Model ◽  
List Type ◽  
Water Assessment ◽  
Soil And Water

HighlightsSWAT soil water assessment was performed using soil water measurements.Dryland SWAT model soil water content was greater than the irrigated SWAT model.Using SWAT soil water estimates for real-time (daily) irrigation management purposes with the existing SWAT soil water subroutines and available soils data is considered risky.The surface layer showed the greatest soil water variability compared to deeper layers.Abstract. Soil water content (SWC) is a challenging measurement at the field, watershed, and regional scales. Soil and Water Assessment Tool (SWAT) soil water estimates were evaluated at three locations: the St. Joseph River watershed (SJRW) in northeast Indiana, the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and the USDA-ARS Limited Irrigation Research Farm (LIFR) at Greeley, Colorado. The soil water estimates were evaluated under two scenarios: (1) for the defined soil profile, and (2) by individual layer. Each site’s soil water assessment was performed based on the existing management conditions during each experiment, whether dryland or irrigated, and for various periods depending on SWC measurement availability at each site. The SWAT soil water was evaluated as follows: the Indiana site was evaluated under dryland conditions using daily soil water observations for one year; the Texas site was evaluated for a ten-year period under irrigated and dryland conditions using weekly soil water observations from four lysimeters; and the Colorado site was evaluated under irrigated conditions for a four-year period. The simulated soil water was evaluated by comparing the model simulations with observed daily and weekly soil water measurements at the three sites. Based on the results, even though all the SWAT models were considered to perform as good models following calibration (streamflow, ET, etc.), the soil water simulations were unacceptable for the defined soil profile and for individual layers at the three sites. Deeper soil layers had observations greater than field capacity values, indicating poor soil parameterization. The dryland model had greater water content than the irrigated model, contradicting the soil water measurements. This greater soil water simulation with the dryland model is a result of SWAT model uncertainties with ET reduction under dryland conditions due to water stress. This study indicated that soil water estimation using the default SWAT soil water equations has many sources of uncertainties. Two apparent sources resulted in the SWAT model’s poor performance: (1) SWAT soil water routines that do not fully represent soil water moving between layers to meet plant demand and (2) uncertainty in soil parameterization. Keywords: Hydrologic modeling, Soil moisture, Soil moisture sensor, Soil water, Soil and Water Assessment Tool.

Evaluation of SWAT Soil Water Estimation Accuracy Using Data from Indiana, Colorado, and Texas

2020 ◽  
Vol 63 (6) ◽  
pp. 1827-1843
Author(s):  
Ahmed A. Hashem ◽  
Bernard A. Engel ◽  
Gary W. Marek ◽  
Jerry E. Moorhead ◽  
Dennis C. Flanagan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Profile ◽  
Assessment Tool ◽  
Swat Model ◽  
List Type ◽  
Water Assessment ◽  
Soil And Water

HighlightsSWAT soil water assessment was performed using soil water measurements.Dryland SWAT model soil water content was greater than the irrigated SWAT model.Using SWAT soil water estimates for real-time (daily) irrigation management purposes with the existing SWAT soil water subroutines and available soils data is considered risky.The surface layer showed the greatest soil water variability compared to deeper layers.Abstract. Soil water content (SWC) is a challenging measurement at the field, watershed, and regional scales. Soil and Water Assessment Tool (SWAT) soil water estimates were evaluated at three locations: the St. Joseph River watershed (SJRW) in northeast Indiana, the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and the USDA-ARS Limited Irrigation Research Farm (LIFR) at Greeley, Colorado. The soil water estimates were evaluated under two scenarios: (1) for the defined soil profile, and (2) by individual layer. Each site’s soil water assessment was performed based on the existing management conditions during each experiment, whether dryland or irrigated, and for various periods depending on SWC measurement availability at each site. The SWAT soil water was evaluated as follows: the Indiana site was evaluated under dryland conditions using daily soil water observations for one year; the Texas site was evaluated for a ten-year period under irrigated and dryland conditions using weekly soil water observations from four lysimeters; and the Colorado site was evaluated under irrigated conditions for a four-year period. The simulated soil water was evaluated by comparing the model simulations with observed daily and weekly soil water measurements at the three sites. Based on the results, even though all the SWAT models were considered to perform as good models following calibration (streamflow, ET, etc.), the soil water simulations were unacceptable for the defined soil profile and for individual layers at the three sites. Deeper soil layers had observations greater than field capacity values, indicating poor soil parameterization. The dryland model had greater water content than the irrigated model, contradicting the soil water measurements. This greater soil water simulation with the dryland model is a result of SWAT model uncertainties with ET reduction under dryland conditions due to water stress. This study indicated that soil water estimation using the default SWAT soil water equations has many sources of uncertainties. Two apparent sources resulted in the SWAT model’s poor performance: (1) SWAT soil water routines that do not fully represent soil water moving between layers to meet plant demand and (2) uncertainty in soil parameterization. Keywords: Hydrologic modeling, Soil moisture, Soil moisture sensor, Soil water, Soil and Water Assessment Tool.

scholarly journals Soil water content, runoff and soil loss prediction in a small ungauged agricultural basin in the Mediterranean region using the Soil and Water Assessment Tool

2014 ◽  
Vol 153 (3) ◽  
pp. 481-496 ◽  
Author(s):  
M. C. RAMOS ◽  
J. A. MARTÍNEZ-CASASNOVAS
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Loss ◽  
Assessment Tool ◽  
Moderate Intensity ◽  
Daily Weather Data ◽  
Runoff And Soil Loss ◽  
Water Assessment ◽  
Soil And Water

SUMMARYThe aim of the present work was to evaluate the possibilities of using sub-basin data for calibration of the Soil and Water Assessment Tool (SWAT) model in a small (46 ha) ungauged basin (i.e. where the water flow is not systematically measured) and its response. This small basin was located in the viticultural Anoia-Penedès region (North-east Spain), which suffers severe soil erosion. The data sources were: daily weather data from an observatory located close to the basin; a detailed soil map of Catalonia; a 5-m resolution digital elevation model (DEM); a crop/land use map derived from orthophotos taken in 2010 and an additional detailed soil survey (40 points) within the basin, which included properties such as texture, soil organic carbon, electrical conductivity, bulk density and water retention capacity at −33 and −1500 kPa. A sensitivity analysis was performed to identify and rank the sensitive parameters that affect the hydrological response and sediment yield to changes of model input parameters. A 1-year calibration and 1-year validation were carried out on the basis of soil moisture measured at 0·20-m intervals from depths of 0·10 to 0·90 m in two selected sub-basins, and data related to estimations of runoff and sediment concentrations in runoff collected in the same sub-basins. The present paper shows a methodological approach for calibrating SWAT in small ungauged basins using soil water content measurements and runoff samples collected within the basin. The SWAT satisfactorily predicted the average soil water content, runoff and soil loss for moderate intensity events recorded during the study periods. However, it was not satisfactory for high-intensity events which would require exploring the possibilities of using sub-daily information as an input model parameter.

scholarly journals Evaluation of Soil Water Content Using SWAT for Southern Saskatchewan, Canada

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 249
Author(s):  
Mohammad Zare ◽  
Shahid Azam ◽  
David Sauchyn
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Assessment Tool ◽  
Swat Model ◽  
Warm Season ◽  
Decisive Role ◽  
R Value

Soil water content (SWC) is one of the most important hydrologic variables; it plays a decisive role in the control of various land surface processes. We simulated SWC using a Soil and Water Assessment Tool (SWAT) model in southern Saskatchewan. SWC was calibrated using measured data and Soil Moisture Active Passive (SMAP) Level-4 for the surface (0–5 cm) SWC for hydrological response units (HRU) at daily and monthly (warm season) intervals for the years 2015 to 2020. We used the SUFI-2 technique in SWAT-CUP, and observed daily instrumented streamflow records, for calibration (1995 to 2004) and validation (2005–2010). The results reveal that the SWAT model performs well with a monthly PBIAS < 10% and Nash–Sutcliffe efficiency (NS) and R2 ≥ 0.8 for calibration and validation. The correlation coefficient between ground measurement with SMAP and SWAT products are 0.698 and 0.633, respectively. Moreover, SMAP data of surface SWC coincides well with measurements in terms of both amount and trend compared with the SWAT product. The highest r value occurred in July when the mean r value in SWAT and SMAP were 0.87 to 0.84, and then in June for r value of 0.75. In contrast, the lowest values were in April and May (0.07 and 0.04, respectively) at the beginning of the growing season in southern Saskatchewan. Furthermore, calibration in the SWAT model is based on a batch form whereby parameters are adjusted to corresponding input by modifying simulations with observations. SWAT underestimates the abrupt increase in streamflow during the snowmelt months (April and May). This study achieved the objective of developing a SWAT model that simulates SWC in a prairie watershed, and, therefore, can be used in a subsequent phase of research to estimate future soil moisture conditions under projected climate changes.

scholarly journals Optimizing the Positioning of Soil Moisture Monitoring Sensors in Winter Wheat Fields

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1707
Author(s):  
Xiaojun Shen ◽  
Jing Liang ◽  
Ketema Zeleke ◽  
Yueping Liang ◽  
Guangshuai Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Profile ◽  
Root Distribution ◽  
Soil Surface ◽  
Soil Moisture Sensors ◽  
Total Average

Collecting accurate real-time soil moisture data in crop root zones is the foundation of automated precision irrigation systems. Soil moisture sensors (SMSs) have been used to monitor soil water content (SWC) in crop fields for a long time; however, there is no generally accepted guideline for determining optimal number and placement of soil moisture sensors in the soil profile. In order to study adequate positioning for the installation of soil moisture sensors in the soil profile, six years of field experiments were carried out in North China Plain (NCP). Soil water content was measured using the gravimetric method every 7 to 10 days during six growing seasons of winter wheat (Triticum aestivum L), and root distribution was measured using a soil core method during the key periods of winter wheat growth. The results from the experimental data analysis show that SWC at different depths had a high linear correlation. In addition, the values of correlation coefficients decreased with increasing soil depth; the coefficient of variation (CV) of SWC was higher in the surface layers than in the deeper layers (depths were 0–40 cm, 0–60 cm, and 0–100 cm during the early, middle, and last stages of winter wheat, respectively); wheat roots were mainly distributed in the surface layer. According to an analysis of CV for SWC and root distribution, the depths of planned wetted layers were determined to be 0–40 cm, 0–60 cm, and 0–100 cm during the sowing to reviving stages (the early stage of winter wheat), returning green and jointing stages (the middle stage of winter wheat), and heading to maturity stage (the last stage of winter wheat), respectively. The correlation and R-cluster analyses of SWC at different layers in the soil profile showed that SMSs should be installed 10 and 30 cm below the soil surface during the winter wheat growing season. The linear regression model can be built using SWC at depths of 10 and 30 cm to predict total average SWC in the soil profile. The results of validation showed that the developed model provided reliable estimates of total average SWC in the planned wetted layer. In brief, this study suggests that suitable positioning of soil moisture sensors is at depths of 10 and 30 cm below the soil surface.

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

2020 ◽  
Vol 36 (3) ◽  
pp. 375-386
Author(s):  
Ruixiu Sui ◽  
Earl D. Vories
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
List Type ◽  
Soil Moisture Sensors

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

Watershed Scale Nitrate-N Abatement of Instream Wetlands: An Appraisal Using the Soil and Water Assessment Tool

2020 ◽  
Vol 36 (3) ◽  
pp. 387-397
Author(s):  
Dagbegnon Clement Sohoulande Djebou ◽  
Ariel A. Szogi ◽  
Ken C. Stone ◽  
Jeffery M. Novak
Keyword(s):  
Water Quality ◽  
Assessment Tool ◽  
Swat Model ◽  
Point Sources ◽  
List Type ◽  
Watershed Scale ◽  
Stream Network ◽  
Nitrate N ◽  
Water Assessment ◽  
Soil And Water

HighlightsSWAT used to address watershed scale nitrate-N abatement of instream wetlands (ISWs).Experimental ISW results were incorporated into the watershed modeling framework.SWAT successfully captured and reproduced ISW impact on nitrate-N at sub-basin level.Scenarios of ISWs implementation were simulated, effects on nitrate-N export were evaluated.Results show ISWs can be used as conservation structures aimed at enhancing water quality.Abstract. In watersheds under high agricultural production, nitrate nitrogen (nitrate-N) pollution often originates from intensive application of fertilizers and animal manure to croplands. Surface runoff and nitrate-N export from farmlands contributes to the pollution of nearby reaches which flow into the watershed stream network. Experimental studies reported significant nitrate removal capacities of constructed instream wetlands (ISWs). However, cases of large-scale implementations of ISWs are uncommon, probably due to a paucity of watershed-scale studies which highlight the influence of ISWs on riverine water quality. To elucidate the ISWs nitrate-N abatement potential at the watershed scale, the Soil and Water Assessment Tool (SWAT) was used to model nitrate-N export in a highly agricultural watershed located in the Coastal Plain of North Carolina. SWAT was first calibrated and validated for streamflow and for nitrate-N export using data collected from the inlet and outlet of an experimental instream wetland. The validated SWAT model was used to simulate a decade of nitrate-N export under two scenarios: 1) watershed with ISWs implemented; and 2) watershed without ISWs. The results of the case study indicated that a watershed-wide implementation of ISWs is likely to curtail annual nitrate-N export by 49%. The study also evaluated cases where ISWs are implemented in selected percentage of sub-basins across the watershed. The outcomes show higher increments of nitrate-N curtailment when ISWs are implemented in the first top agricultural sub-basins. Hence, implementation of ISWs on selected sub-basins can mitigate nitrate-N from non-point sources and enhance water quality in the watershed’s stream network. Keywords: Runoff, Croplands, Instream wetland, Nitrate-N export, Denitrification, SWAT model, Watershed.

scholarly journals Simulasi Arahan Penggunaan Lahan di DAS Limboto dalam Rangka Pengendalian Kekeringan

2019 ◽  
Vol 33 (2) ◽  
pp. 87
Author(s):  
Sri Rahayu Ayuba ◽  
Munajat Nursaputra ◽  
Tisen Tisen
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Input Data ◽  
Assessment Tool ◽  
Soil Moisture Deficit ◽  
Moisture Deficit ◽  
Water Assessment ◽  
Soil And Water

Perubahan penggunaan lahan bisa dibilang kekuatan sosioekonomi yang paling meluas mendorong perubahan dan degradasi ekosistem (Wu, 2008). (Kodoatie, 2010) menyatakan bahwa, terganggunya siklus hidrologi telah menimbulkan “3 T” masalah klasik air “too much (yang menimbulkan banjir), “too little (yang menimbulkan kekeringan) dan “too dirty (yang menimbulkan pencemaran air). Berdasarkan data BNPB tahun 1979-2009 terdapat 8 kejadian kekeringan di Provinsi Gorontalo. Penelitian ini bertujuan (1) mengetahui tingkat kerentanan DAS Limboto terhadap kekeringan. (2) menyusun arahan penggunaan lahan pada DAS Limboto berdasarkan penentuan tingkat kerentanan kekeringan. (3) mengsimulasikan arahan penggunaan lahan dalam rangka pengendalian kekeringan di DAS Limboto. Penelitian ini dilaksanakan pada Daerah Aliran Sungai (DAS) Limboto dengan luas DAS 86412,6 ha. Metode yang digunakan adalah Metode SWAT (Soil and Water Assessment Tool) dengan menggunakan software ArcSwat yang terintegrasi SIG. Penelitian ini termasuk dalam penellitian non-eksperimen yakni dengan menggunakan pengamatan langsung di lapangan. Input data SWAT antara lain lereng, jenis tutupan lahan, iklim, dan jenis tanah. Analisis yang digunakan dalam menentukan kerentanan DAS terhadap kekeringan adalah dengan menggunakan Soil Moisture Deficit Index (SMDI) melalui parameter Soil Water (SW). Pada penelitian ini penggunaan output model SWAT melalui ArcSwat, telah mampu menggambarkan kondisi pasokan air pada DAS Limboto, yang secara keseluruhan telah termasuk dalam kategori “Rentan”. Dengan membandingkan luas area yang mengalami kekeringan pada sebelum dan setelah dilakukan simulasi/running arahan penggunaan lahan maka dapat disimpulkan bahwa selisih luas area DAS yang mengalami kekeringan dengan klasifikasi “Rentan” diperoleh 37.513,1 ha atau secara persentasi mengalami penurunan sebesar 43,4 % dari luas DAS.

scholarly journals Application of the SWAT (Soil and Water Assessment Tool) model in the Ronnea catchment of Sweden

2013 ◽  
Vol 7 (3) ◽  
pp. 252-257
Keyword(s):  
Input Data ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Soil Parameters ◽  
Daily Step ◽  
Calibration And Validation ◽  
The Impact ◽  
Water Assessment ◽  
Soil And Water

The subject of this article is the estimation of quantitative (hydrological) and qualitative parameters in the catchment of Ronnea (1800 Km2, located in south western Sweden) through the application of the Soil and Water Assessment Tool (SWAT). SWAT is a river basin model that was developed for the U.S.D.A. Agricultural Research Service, by the Blackland Research Center in Texas. The SWAT model is a widely known tool that has been used in several cases world-wide. It has the ability to predict the impact of land management practices on water, sediment and agricultural chemical yield in large complex watersheds. The present work investigates certain capabilities of the SWAT model which have not identified up to now. More in specific, the main targets of the work carried out are the following: • Identification of the existing hydrological and qualitative conditions • Preparation - Processing of data required to be used as input data of the model • Hydrological calibration - validation of the model, in 7 subbasins of the Catchment of Ronnea • Estimation and evaluation of the simulated qualitative parameters of the model All available data were offered by the relevant Institutes of Sweden, in the framework of the European program EUROHARP. The existing conditions in the catchment of Ronnea, are described in detail including topography, land uses, soil types, pollution sources, agricultural management practices, precipitation, temperature, wind speed, humidity, solar radiation as well as observed discharges and Nitrogen and Phosphorus substances concentrations. Most of the above data were used as input data for the application of SWAT model. Adequate methods were also used to complete missing values in time series and estimate additional parameters (such as soil parameters) required by the model. Hydrological calibration and validation took place for each outlet of the 7 subbasins of Ronnea catchment in an annual, monthly and daily step. The calibration was achieved by estimating parameters related to ground water movement and evaluating convergence between simulated and observed discharges by using mainly the Nash & Sutcliffe coefficient (NTD). Through the sensitivity analysis, main parameters of the hydrological simulation, were detected. According to the outputs of the SWAT model, the water balance of Ronnea catchment was also estimated. Hydrological calibration and validation is generally considered sufficient in an annual and monthly step. Hydrological calibration – validation in daily step, generally does not lead to high values of the NTD indicator. However, when compared to results obtained by the use of SWAT in Greece, a relatively high value of NTD is achieved in one subbasin. Finally, a comparison between the simulated and observed concentrations of total Phosphorus and Nitrogen was carried out.

scholarly journals Monitoring water content changes in a soil profile with TDR-probes at just three depths - How well does it work?

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Jens Hagenau ◽  
Vander Kaufmann ◽  
Heinz Borg
Keyword(s):  
Time Delay ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
High Precision ◽  
Soil Profile ◽  
Reliable Data ◽  
Short Term ◽  
True Values ◽  
Daily Changes

ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

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