PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

VARIABILITY OF SOIL WATER PROPERTIES AND CROP YIELD IN A SLOPED WATERSHED1

ABSTRACT: Spatial distribution of soil and water properties and the correlations between them and crop yield were determined for a natural rainfall environment. Hydraulic conductivity, soil texture, water retention, and soil-water flux were variables used to investigate their relationship to crop yield using multiple regression techniques. Variations in crop yields on a watershed with a 3 to 4 percent slope and moderately erosive soils were related to soil-water characteristics and soil properties along slope and with depth. Climatic conditions to sustain crop growth and yield ranged from inadequate soil water in 1983 to adequate soil water in 1984. Crop yield was predicted with models using both available and measured soil-water content. Available water content provided a better model for the prediction of water yield and does not require field measurements of actual soil-water content. Soil water holding capacity was more significant for predicting crop yield in soils with moderate to high silt content than infiltrability of water into the soil.     

Using Landsat data to estimate evapotranspiration of winter wheat

An evapotranspiration (ET) model that accurately estimates daily water use and soil moisture on a regional basis is required for many agricultural and hydrological studies. The model should use meterological data that are readily available and crop information that is responsive to the changing vigor of the plants.We evaluated an ET model with a weighing lysimeter and then applied it to winter wheatfields at four Kansas locations. Model inputs are solar radiation, temperature, precipitation, and leaf area index (LAI); included in the outputs are estimates of transpiration, evaporation, and soil moisture. An equation was developed to estimate LAI from Landsat data. Because LAI can be estimated from satellites, the ET model can potentially be used on a regional basis.     

DAYCENT national-scale simulations of nitrous oxide emissions from cropped soils in the United States

Until recently, Intergovernmental Panel on Climate Change (IPCC) emission factor methodology, based on simple empirical relationships, has been used to estimate carbon (C) and nitrogen (N) fluxes for regional and national inventories. However, the 2005 USEPA greenhouse gas inventory includes estimates of N2O emissions from cultivated soils derived from simulations using DAYCENT, a process-based biogeochemical model. DAYCENT simulated major U.S. crops at county-level resolution and IPCC emission factor methodology was used to estimate emissions for the approximately 14% of cropped land not simulated by DAYCENT. The methodology used to combine DAYCENT simulations and IPCC methodology to estimate direct and indirect N2O emissions is described in detail. Nitrous oxide emissions from simulations of presettlement native vegetation were subtracted from cropped soil N2O to isolate anthropogenic emissions. Meteorological data required to drive DAYCENT were acquired from DAYMET, an algorithm that uses weather station data and accounts for topography to predict daily temperature and precipitation at 1-km2 resolution. Soils data were acquired from the State Soil Geographic Database (STATSGO). Weather data and dominant soil texture class that lie closest to the geographical center of the largest cluster of cropped land in each county were used to drive DAYCENT. Land management information was implemented at the agricultural-economic region level, as defined by the Agricultural Sector Model. Maps of model-simulated county-level crop yields were compared with yields estimated by the USDA for quality control. Combining results from DAYCENT simulations of major crops and IPCC methodology for remaining cropland yielded estimates of approximately 109 and approximately 70 Tg CO2 equivalents for direct and indirect, respectively, mean annual anthropogenic N2O emissions for 1990-2003.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Annual Estimates of Recharge,Quick‐Flow Runoff,and Evapotranspiration for the Contiguous U.S. Using Empirical Regression Equations

This study presents new data‐driven, annual estimates of the division of precipitation into the recharge, quick‐flow runoff, and evapotranspiration (ET) water budget components for 2000‐2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick‐flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick‐flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick‐flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first‐order estimates of these quantities across the CONUS, even where field measurements are sparse.     

Influence of compost amendment rate and level of compaction on the hydraulic functioning of soils

There has been widespread interest in using compost to improve the hydrologic functions of degraded soils at construction sites for reducing runoff and increasing infiltration. The objective of this study was to determine the effects of compost amendment rate on saturated hydraulic conductivity (K_s) and water retention in order to identify target compost rates for enhancing soil hydrologic functions. Samples were prepared with three soil textures (sandy loam, silt loam, and sandy clay loam), amended with compost at 0%, 10%, 20%, 30%, 40%, and 50%. All soils were tested at a porosity of 0.5 m³/m³, and the sandy loam was further tested at high (0.55 m³/m³) and low (0.4 m³/m³) porosities. The K_s and water retention data were then used to model infiltration with HYDRUS-1D. With increasing compost amendment rate, K_s and water retention of the mixtures generally increased at the medium porosity level, with more compost needed in heavier soils. As porosity decreased in the sandy loam soil, the amount of compost needed to improve K_s rose from 20% to 50%. Water distribution in pore fractions (gravitational, plant-available, and unavailable water) depended on texture, with only the highest compost rates increasing plant-available water in one soil. Results suggest soil texture should be taken into consideration when choosing a compost rate in order to achieve soil improvement goals. Hydrologic benefits may be limited even at a high rate of compost amendment if soil is compacted.     

Managing Uncertainty in Runoff Estimation with the U.S. Environmental Protection Agency National Stormwater Calculator

The U.S. Environmental Protection Agency National Stormwater Calculator (NSWC) simplifies the task of estimating runoff through a straightforward simulation process based on the EPA Stormwater Management Model. The NSWC accesses localized climate and soil hydrology data, and options to experiment with low‐impact development (LID) features for parcels up to 5 ha in size. We discuss how the NSWC treats the urban hydrologic cycle and focus on the estimation uncertainty in soil hydrology and its impact on runoff simulation by comparing field‐measured soil hydrologic data from 12 cities to corresponding NSWC estimates in three case studies. The default NSWC hydraulic conductivity is 10.1 mm/h, which underestimates conductivity measurements for New Orleans, Louisiana (95 ± 27 mm/h) and overestimates that for Omaha, Nebraska (3.0 ± 1.0 mm/h). Across all cities, the NSWC prediction, on average, underestimated hydraulic conductivity by 10.5 mm/h compared to corresponding measured values. In evaluating how LID interact with soil hydrology and runoff response, we found direct hydrologic interaction with pre‐existing soil shows high sensitivity in runoff prediction, whereas LID isolated from soils show less impact. Simulations with LID on higher permeability soils indicate that nearly all of pre‐LID runoff is treated; while features interacting with less‐permeable soils treat only 50%. We highlight the NSWC as a screening‐level tool for site runoff dynamics and its suitability in stormwater management.     

Modeling the width and placement of riparian vegetated buffer strips: a case study on the Chi-Jia-Wang stream,Taiwan

Improper agricultural activities seriously affect water quality. It is very important to control agricultural nonpoint source pollution along the Chi-Jia-Wang Stream to protect the habitat of land-locked salmon (Oncorhynchus masou formosanus), one of the endangered species in Taiwan. Riparian vegetative buffer strips are used to intercept wastewater, surface runoff and groundwater flow to reduce pesticide, nutrient and other organic pollutants before they enter the stream. This study estimates the suitable width for vegetated buffer strips in the riparian zone along the stream using a geographic information system and an index model. The groundwater in the study area is easily contaminated by pollutants due to the high hydraulic conductivity in the riparian zone of the Chi-Jia-Wang Stream. After comparing simulations on selected 46 kinds of selected pesticides, the maximum safety depth of Fenarimol was estimated. A wider Fenarimol vegetated strip is needed as a buffer zone to ensure pollution control effectiveness. Simulation results can provide a preliminary evaluation for the soil or groundwater pollution caused by pesticides, but the actual influences require onsite data verification.     

TARGETING REMEDIAL MEASURES TO CONTROL NONPOINT SOURCE POLLUTION1

ABSTRACT_: The watershed model GAMES is used for the evaluation of a targeting approach to control fluvial sedimentation arising from soil erosion in agricultural areas. The data considered for the analysis consists of output from the application of the model to existing and hypothetical soil and crop management systems in two small watersheds of southern Ontario, one in the rolling uplands and the other in a very flat lowland area. The model output includes estimates of spring sediment yield from field-size cells to the stream outlet for existing agricultural management conditions, and estimates of sediment yield resulting from the successive implementation of two levels of soil erosion controls under four remedial measures strategies. The results reveal that, for the rolling upland watershed exhibiting a wide range of soil erosion and sediment yield rates, targeted control programs can be expected to provide an extremely effective approach to sediment control. For flat lowland watersheds, exhibiting relatively uniform soil erosion and sediment yield rates, the strategy of targeting controls may be somewhat more effective than a random approach to control, but not as efficient as in the case of watersheds in more rolling terrain. It is evident from the study that a screening model such as GAMES provides a very useful tool for the planning and evaluation of erosion and sediment control programs.     

SIMULATING SOIL WATER RECESSION COEFFICIENTS FOR AGRICULTURAL WATERSHEDS1

ABSTRACT: The antecedent precipitation index (API) has been a useful indicator of soil moisture conditions for watershed runoff calculations, and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. The prognostic equation for soil moisture used in some of the atmospheric general circulation models (GCM) together with Thomthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors as contained in potential evapotranspiration and to depend on soil texture as reflected by field capacity and permanent wilting point. A recently developed model for global insolation is used with climatological data for Wisconsin to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trends at Fennimore and Colby watersheds in Wisconsin. This study suggests that API could be a unifying concept for watershed and atmospheric general circulation modeling.     

SPATIAL DISTRIBUTION OF POINT SOIL MOISTURE ESTIMATES USING LANDSAT TM DATA AND FUZZY-C CLASSIFICATION1

ABSTRACT: Many hydrologic models have input data requirements that are difficult to satisfy for all but a few well-instrumented, experimental watersheds. In this study, point soil moisture in a mountain watershed with various types of vegetative cover was modeled using a generalized regression model. Information on sur-ficial characteristics of the watershed was obtained by applying fuzzy set theory to a database consisting of only satellite and a digital elevation model (DEM). The fuzzy-c algorithm separated the watershed into distinguishable classes and provided regression coefficients for each ground pixel. The regression model used the coefficients to estimate distributed soil moisture over the entire watershed. A soil moisture accounting model was used to resolve temporal differences between measurements at prototypical measurement sites and validation sites. The results were reasonably accurate for all classes in the watershed. The spatial distribution of soil moisture estimates corresponded accurately with soil moisture measurements at validation sites on the watershed. It was concluded that use of the regression model to distribute soil moisture from a specified number of points can be combined with satellite and DEM information to provide a reasonable estimation of the spatial distribution of soil moisture for a watershed.     

Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho1

Stratton, Benjamin T., Venakataramana Sridhar, Molly M. Gribb, James P. McNamara, and Balaji Narasimhan, 2009. Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho. Journal of the American Water Resources Association (JAWRA) 45(6):1390‐1408. Abstract: The distributed Soil Water Assessment Tool (SWAT) hydrologic model was applied to a research watershed, the Dry Creek Experimental Watershed, near Boise Idaho to investigate its water balance components both temporally and spatially. Calibrating and validating SWAT is necessary to enable our understanding of the water balance components in this semiarid watershed. Daily streamflow data from four streamflow gages were used for calibration and validation of the model. Monthly estimates of streamflow during the calibration phase by SWAT produced satisfactory results with a Nash Sutcliffe coefficient of model efficiency 0.79. Since it is a continuous simulation model, as opposed to an event‐based model, it demonstrated the limited ability in capturing both streamflow and soil moisture for selected rain‐on‐snow (ROS) events during the validation period between 2005 and 2007. Especially, soil moisture was generally underestimated compared with observations from two monitoring pits. However, our implementation of SWAT showed that seasonal and annual water balance partitioning of precipitation into evapotranspiration, streamflow, soil moisture, and drainage was not only possible but closely followed the trends of a typical semiarid watershed in the intermountain west. This study highlights the necessity for better techniques to precisely identify and drive the model with commonly observed climatic inversion‐related snowmelt or ROS weather events. Estimation of key parameters pertaining to soil (e.g., available water content and saturated hydraulic conductivity), snow (e.g., lapse rates, melting), and vegetation (e.g., leaf area index and maximum canopy index) using additional field observations in the watershed is critical for better prediction.     

MODELING PERCOLATION LOSSES FROM A PONDED FIELD UNDER VARIABLE WATER-TABLE CONDITIONS1

ABSTRACT: A numerical simulation model was developed to predict the vertical and lateral percolation losses from a ponded agricultural field. The two-dimensional steady-state unsaturated/ saturated flow equation was solved using the finite-difference technique. A constant ponding depth was maintained at the soil surface with different water table conditions in an application of the model for rice fields bordered by bunds. Field experiments were conducted for two different water table depths to collect data on the spatial distribution of volumetric soil-moisture content for model verification. The measured soil-moisture content values were found to be in close agreement with those predicted by the model. The sensitivity analysis of the model with selected hydrologic conditions shows that the model is most sensitive to the values of saturated hydraulic conductivity, but relatively less sensitive to water table depth, ponding depth, and evaporation rate from the soil surface. It implies that, in a ponded rice field condition, the lateral and vertical percolation losses are mostly governed by the hydraulic conductivity of the soil. The vertical percolation losses were almost equal to the saturated hydraulic conductivity values and, in most cases, these losses increased with deeper water table depths. The lateral percolation losses also increased with deeper water table depths; however, these losses were relatively small in comparison to the vertical percolation losses. The vertical and lateral percolation losses increased with the increase in ponding depths. The lateral percolation losses through the bund decreased when the evaporation losses increased from the soil surface. The results of this study indicate that the percolation losses from a ponded field may be predicted accurately for a wide range of soil and hydrological conditions when the values of hydraulic conductivity, evaporation rate, depth of ponding, and water table depth are accurately known.     

APPLICATION OF THE RHESSys MODEL TO A CALIFORNIA SEMIARID SHRUBLAND WATERSHED1

ABSTRACT: Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro‐Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKESHE, for a 34 km² watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes.     

Regional assessment of herbicide sorption and degradation in two sampling years

Sorption and degradation of the herbicide 2,4-D [2,4-dichlorophenoxyacetic acid] were determined for 123 surface soils (0 to 15 cm) collected in 2002 and in 2004 between 49 degrees to 60 degrees north longitude and 110 degrees to 120 degrees west latitude in Alberta, Canada. The soils were characterized by soil organic carbon content (SOC), pH, electrical conductivity, soil texture, cation exchange capacity, carbonate content, and total soil microbial activity. The 2,4-D sorption coefficients, Kd and Koc, were highly variable with coefficients of variation of 89 and 59%, respectively, at the provincial scale. Both Kd and Koc were well described by regression models with SOC and soil pH as variables, regardless of scale. Surprisingly, variations in 2,4-D mineralization were much smaller than variations in sorption. Variability in total 2,4-D mineralization was particularly low, with a coefficient of variation of only 7% at the provincial scale. Average 2,4-D half-lives in ecoregions ranged from 1.7 to 3.5 d, much lower than the field dissipation half-life of 10 d reported for 2,4-D in general pesticide property databases. Regression models describing degradation parameters were generally poor or not significant because 2,4-D mineralization was only weakly associated with measured 2,4-D sorption parameters and soil properties. As such, regional variations in herbicide sorption coefficients should be measured or calculated based on soil properties, to assign distinct pesticide fate model input parameters when estimating 2,4-D off-site transport at the provincial scale. Spatial variations in herbicide degradation appear less important for Alberta as 2,4-D half-lives were similar in soils across the province. The rapid mineralization of 2,4-D is noteworthy because 2,4-D is widely used in Alberta and perhaps adaptation of soil microbial communities allowed for accelerated degradation regardless of soil properties or the extent of 2,4-D sorption by soil.     

DEFINING WATERSHED-SCALE EVAPORATION USING A NORMALIZED DIFFERENCE VEGETATION INDEX1

Monthly composites of the Normalized Difference Vegetation Indices (NDVI), derived from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVILRR), were transformed linearly into monthly evaporation rates and compared with detailed hydrologic-model simulation results for five watersheds across the United States. Model-simulated monthly evaporation values showed high correlations (mean R²= .77) with NDVI-derived evaporation estimates. These latter estimates, used in a classical water balance model, resulted in equally accurate simulations of monthly runoff than when the model was run to estimate monthly evaporation via soil moisture accounting. Comparison of NDVI-derived evaporation estimates with pan data showed promise for transforming NDVI values into evaporation estimates under both wet and water-limiting conditions without resorting to the application of any kind of calibrated hydrologic models.     

APPLICABILITY OF THE GREEN AND AMPT INFILTRATION EQUATION TO RANGELANDS1

ABSTRACT: The objective of this study was to investigate the use of Green and Ampt infiltration equation parameters (determined by least squares fitting of field infiltration data or predicted from soil texture properties) to characterize infiltration on spatially varying rangeland sites. It was found that a least squares regression approach reduces the physically based parameters in the Green and Ampt to empirical coefficients since negative coefficients are obtained, particularly on plots with low infiltration rates. Green and Ampt parameters predicted from soil texture data describe infiltration rates less than 3 cm/hr. The applicability of these Green and Ampt parameters appears limited to sites with lower infiltration rates. Results indicate that soil texture predictive triangles, developed to describe infiltration on agricultural soils, need revision to adequately describe infiltration patterns on rangelands.