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.     

THE FLEXIBLE TOLERANCE METHOD FOR ESTIMATING HYDROLOGIC PARAMETERS IN THE ROOT ZONE1

In this study, a constrained minimization method, the flexible tolerance method, was used to solve the optimization problems for determining hydrologic parameters in the root zone: water uptake rate, spatial root distribution, infiltration rate, and evaporation. Synthetic soil moisture data were first generated using the Richards' equation and its associated initial and boundary conditions, and these data were then used for the inverse analyses. The results of inverse simulation indicate the following. If the soil moisture data contain no noise, the rate of estimated water uptake and spatial root distribution parameters are equal to the true values without using constraints. If there is noise in the observed data, constraints must be used to improve the quality of the estimate results. In the estimation of rainfall infiltration and surface evaporation, interpolation methods should be used to reduce the number of unknowns. A fewer number of variables can improve the quality of inversely estimated parameters. Simultaneous estimation of spatial root distribution and water uptake rate or estimation of evaporation and water uptake rate is possible. The method was used to estimate the water uptake rate, spatial root distribution, infiltration rate, and evaporation using long‐term soil moisture data collected from Nebraska's Sand Hills.     

Enhancing the Ability of a Soil Moisture‐based Index for Agricultural Drought Monitoring by Incorporating Root Distribution

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices (e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture‐based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang‐Huai‐Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index (CMI), and the improved root‐weighted soil moisture index (RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution.     

EVAPOTRANSPIRATION CONCEPTUALIZATION IN THE HSPF‐MODFLOW INTEGRATED MODELS1

In 1988, the Florida Institute of Phosphate Research (FIPR) funded project to develop an advanced hydrologic model for shallow water table systems. The FIPR hydrologic model (FHM) was developed to provide an improved predictive capability of the interactions of surface water and ground water using its component models, HSPF and MODFLOW. The Integrated Surface and Ground Water (ISGW) model was developed from an early version of FHM and the two models were developed relatively independently in the late 1990s. Hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, and base flow are explicitly accounted for in both models. Considerable review of FHM and ISGW and their applications occurred through a series of projects. One model evolved, known as the Integrated Hydrological Model IHM. This model more appropriately describes hydrologic processes, including evapotranspiration fluxes within small distributed land‐based discretization. There is a significant departure of many IHM algorithms from FHM and ISGW, especially for soil water and evapotranspiration (ET). In this paper, the ET concepts in FHM, ISGW, and IHM will be presented. The paper also identifies the advantages and data costs of the improved methods. In FHM and IHM, ground water ET algorithms of the MODFLOW ET package replace those of HSPF (ISGW used a different model for ground water ET). However, IHM builds on an improved understanding and characterization of ET partitioning between surface storages, vadose zone storage, and saturated ground water storage. The IHM considers evaporative flux from surface sources, proximity of the water table to land surface, relative moisture condition of the unsaturated zone, thickness of the capillary zone, thickness of the root zone, and relative plant cover density. The improvements provide a smooth transition to satisfy ET demand between the vadose zone and deeper saturated ground water. While the IHM approach provides a more sound representation of the actual soil profile than FHM, and has shown promise at reproducing soil moisture and water table fluctuations as well as field measured ET rates, more rigorous testing is necessary to understand the robustness and/or limitations of this methodology.     

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.     

Evaluation of the European Space Agency Climate Change Initiative Soil Moisture Product over China Using Variance Reduction Factor

In this study, we evaluated the European Space Agency Climate Change Initiative soil moisture product v02.1 (ESA CCI SM v02.1) using in situ observations collected at 547 stations in China from 1991 to 2013. A conventional validation was first conducted, and the triple collocation errors of ESA CCI SM and the European Centre for Medium Range Weather Forecasting reanalysis data were approximately 0.053 and 0.050 m³/m³, respectively. To obtain more reliable validation results, the average soil moisture of the in situ observations per ESA CCI SM pixel was also used as the validation sites. Variance reduction factor (VRF) was adopted to quantify the accuracy of the soil moisture validation sites, and the average VRF was estimated at 4.88%. The validation results were enhanced by excluding validation sites with VRF errors greater than 5% from the statistical analysis. Although the ESA CCI SM underestimated the in situ observations with a Bias of 0.05 m³/m³, a moderately high correlation coefficient of 0.44 and a relatively small unbiased root‐mean‐square difference of 0.05 m³/m³ were observed. This study provides information on the utilization of ESA CCI SM for ecological protection, climate change, and hydrological forecasting. It also suggests the adoption of VRF for future error corrections of satellite‐based products.     

EQUILIBRIUM EVAPORATION AND POSITWE EVAPORATION-SURFACE TEMPERATURE RELATIONSHIPS ACROSS A GRASSLAND1

Sensible (H) and latent (LE) heat fluxes, soil moisture (SM) and surface temperatures (T_s) were analyzed from seven sites at FIFE to evaluate relationships among the spatial variability of evaporative fraction, EF, SM, and the diurnal surface temperature range (T_dr). Intersite correlations between EF and T_dr were significantly negative for regional average soil moisture SM_r < 20 percent, insignificant for 20 < SM_r < 27 percent, and slightly positive for SM_r > 27 percent. Statistical analysis of the pooled correlation coefficient between EF and T_dr for SM_r < 20 percent indicates that it is less than zero at a very high level of significance, while the pooled correlation coefficient for regional SM_r > 27 percent is greater than zero at the 10 percent level. The positive EF:T_dr correlations are attributed to increased surface vapor pressure at warmer sites under nearly potential conditions. These results suggest that to characterize the spatial variability of the energy budget partitioning, a variable representing the thermal response of the site should be included. An important application of these findings relates to modeling the subgrid variability of a region by subdividing the region into a few classes within which surface variables and parameters are assumed invariant. The thermal response of the surface should be included as a variable in defining these classes.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

SATELLITE MICROWAVE OBSERVATIONS OF SOIL MOISTURE VARIATIONS1

ABSTRACT: Results from studies in the Illinois-Indiana and Texas-Oklahoma areas indicate that satellite microwave observations at the 1.55 cm wavelength are responsive to relative moisture variations in the near surface layer of the soil. Because significant vegetation cover absorbs the 1.55 cm microwave emission from the soil, the target area must be predominately bare soil or low density vegetation cover for meaningful measurements to result. The 25 km resolution of the satellite sensor limits application of the microwave techniques to large areas such as watersheds or agricultural districts rather than individual fields. In general, at 1.55 cm. there is an inverse relationship between microwave brightness temperature and changes in soil moisture levels (as indicated by antecedent rainfall) in agricultural regions before the planting of crops or during the early growing season when vegetation cover is sparse. Even early season observations should be of great value in deciding on the time and type of crop planting and for initial irrigation scheduling when the root zone is still in close proximity to the surface.     

SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1

ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.     

On the Relationship Between Mean and Variance of Soil Moisture Fields1

Abstract: The objective of this work was to explain an apparent contradiction in the literature related to the relationship between mean and variance (or standard deviation) of soil moisture fields. Some studies found an increase in soil moisture variance with decreasing mean soil moisture, while others showed a decrease. The evidence of maximum variance in the mid‐range of mean soil moisture was also reported in the literature. In this paper, we focus on the effects of spatial variability of soil texture on the relationship between variance and mean of soil moisture during soil dry‐down processes. Soil texture influences soil moisture mean and variance through its direct effects on evaporation and drainage, which are two main factors controlling soil drying. A differential equation describing soil moisture dry down is proposed and studied. Our study shows that as mean soil moisture is greater than a threshold, variance increases with decreasing mean soil moisture. If mean soil moisture is less than the threshold, variance decreases with decreasing mean soil moisture. The threshold depends on soil texture and is between the field capacity and the wilting point. The soil moisture dry‐down equation is also applied to explain the apparent contradiction with regard to the relationship between mean and variance of soil moisture fields reported in the literature.     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

Linking river, floodplain, and vadose zone hydrology to improve restoration of a coastal river affected by saltwater intrusion

Floodplain forests provide unique ecological structure and function, which are often degraded or lost when watershed hydrology is modified. Restoration of damaged ecosystems requires an understanding of surface water, groundwater, and vadose (unsaturated) zone hydrology in the floodplain. Soil moisture and porewater salinity are of particular importance for seed germination and seedling survival in systems affected by saltwater intrusion but are difficult to monitor and often overlooked. This study contributes to the understanding of floodplain hydrology in one of the last bald cypress [Taxodium distichum (L.) Rich.] floodplain swamps in southeast Florida. We investigated soil moisture and porewater salinity dynamics in the floodplain of the Loxahatchee River, where reduced freshwater flow has led to saltwater intrusion and a transition to salt-tolerant, mangrove-dominated communities. Twenty-four dielectric probes measuring soil moisture and porewater salinity every 30 min were installed along two transects-one in an upstream, freshwater location and one in a downstream tidal area. Complemented by surface water, groundwater, and meteorological data, these unique 4-yr datasets quantified the spatial variability and temporal dynamics of vadose zone hydrology. Results showed that soil moisture can be closely predicted based on river stage and topographic elevation (overall Nash-Sutcliffe coefficient of efficiency = 0.83). Porewater salinity rarely exceeded tolerance thresholds (0.3125 S m(-1)) for bald cypress upstream but did so in some downstream areas. This provided an explanation for observed vegetation changes that both surface water and groundwater salinity failed to explain. The results offer a methodological and analytical framework for floodplain monitoring in locations where restoration success depends on vadose zone hydrology and provide relationships for evaluating proposed restoration and management scenarios for the Loxahatchee River.     

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.     

Upland Controls on the Hydrological Functioning of Riparian Zones in Glacial Till Valleys of the Midwest1

Abstract: Identifying relationships between landscape hydrogeological setting, riparian hydrological functioning and riparian zone sensitivity to climate and water quality changes is critical in order to best use riparian zones as best management practices in the future. In this study, we investigate water table dynamics, water flow path and the relative importance of precipitation, deep ground water (DG) and seep water as sources of water to a riparian zone in a deeply incised glacial till valley of the Midwest. Data indicate that water table fluctuations are strongly influenced by soil texture and to a lesser extent by upland sediment stratigraphy producing seeps near the slope bottom. The occurrence of till in the upland and at 1.7‐2 m in the riparian zone contributes to maintaining flow parallel to the ground surface at this site. Lateral ground‐water fluxes at this site with a steep topography in the upland (16%) and loam soil near the slope bottom are small (<10 l/d/m stream length) and intermittent. A shift in flow path from a lateral direction to a down valley direction is observed in the summer despite the steep concave topography and the occurrence of seeps at the slope bottom. Principal component and discriminant analysis indicate that riparian water is most similar to seep water throughout the year and that DG originating from imbedded sand and gravel layers in the lower till unit is not a major source of water to riparian zones in this setting. Water quality data and the dependence of the riparian zone for recharge on seep water suggest that sites in this setting may be highly sensitive to changes in precipitation and water quality in the upland in the future. A conceptual framework describing the hydrological functioning of riparian zones on this setting is presented to generalize the finding of this study.     

POTENTIAL,ACTUAL, AND EQUILIBRIUM EVAPOTRANSPIRATION IN A WHEAT FIELD 1

ABSTRACT: This paper examines the relationship between both potential (E*) and nonpotential evapotranspiration and equilibrium evapotranspiration (EQ) in an irrigated wheat field in southcentral Alberta, Canada. The control exercised by surface wetness and root reservoir moisture content in determining the value of the Priestley-Taylor constant a is explored. Also investigated is the relationship between a and the vapor flux fraction ET/(R-G) where ET is the actual evapotranspiration, R the net radiation, and G the soil heat flux. It is shown that evapotranspiration occurred at the potential rate (E*) when the available soil moisture (ASM) within the root zone was ≥3 percent. a varied from 0.84 for a dry soil to 1.49 for a saturated soil. The mean a for E* was 1.24. Surface wetness sustained evapotranspiration at the potential rate when such wetting exceeded 2mm d^?1 following a period of prolonged drawdown of soil moisture, α and ET/(R-G) were positively correlated and this correlation strengthened with increasing soil moisture for constant values of the energy partitioning factor s+γ/s where s is the slope of the saturation humidity-temperature curve and γ is the psychrometric constant. ET=EQ when ETI(R-G) lay within the range of 0.59 to 0.82 corresponding to Bowen ratio (β) values of 0.22 and 0.69, respectively.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Changes in soil characteristics and plant species composition along a moisture gradient in a Mediterranean pasture

Soil physicochemical characteristics, total aboveground biomass, number of species and relative abundance of groups and individual species were measured along a moisture gradient in a pasture, flooded in part during winter through early summer, adjacent to Pamvotis lake in Ioannina, Greece. Soil and vegetation measurements were conducted in 39 quadrats arranged in four zones perpendicular to the moisture gradient. The zone closest to the lake, recently separated from the lake, became part of the pasture and its soil texture was quite different from that of the other zones with a substrate containing 91% sand. Except for pH, this zone had the lowest values in the other five soil physicochemical characteristics measured (organic matter, total and extracted inorganic nitrogen, Olsen extracted phosphorus and extractable potassium); in the other zones organic matter, total nitrogen, phosphorus and potassium tended to increase from the driest to the wettest zone. Total aboveground biomass, ranging from 280 to 840 gm-2, is high for herbaceous pastures in the conditions of Mediterranean climate and it was not related to distance from the lake's shoreline, although the highest values were measured at intermediate distances, or to any of the various soil characteristics measured. Also, the number of species/0.25 m2 was not related to any of the various soil characteristics, but it was highest at the intermediate distances from the lake's shoreline. Species composition varied along the moisture gradient. Forbs as well as annual grasses and legumes declined in abundance from the driest to the wettest places; the reverse was the case for sedges and perennial grasses and legumes. These results indicate that the soil moisture gradient was the principal factor affecting soil characteristics and plant species composition. Since most species were recorded in all the four zones of the pasture, indicating that these can tolerate all variations in abiotic conditions of pasture, the vegetation zonation seems to be influenced by competition. Each functional group of species tends to dominate in a particular range of the soil moisture gradient where it is better suited and tends to exclude competitively other species. Management practices (mowing and grazing) affect the kinds of processes which maintain the observed community structure either by preventing the establishment of later successional species, like reeds and woody species, or by moderating the shoot competition, especially in the wetter zones, and thus permitting the creeping species to grow successfully.     

Riparian Ecosystem Management Model: Sensitivity to Soil,Vegetation, and Weather Input Parameters1

Abstract: The Riparian Ecosystem Management Model (REMM) was developed by the U.S. Department of Agriculture‐Agriculture Research Service (USDA‐ARS) and its cooperators to design and evaluate the efficiency of riparian buffer ecosystems for nonpoint source pollution reduction. REMM requires numerous inputs to simulate water movement, sediment transport, and nutrient cycling in the buffer system. In order to identify critical model inputs and their uncertainties, a univariate sensitivity analysis was conducted for nine REMM output variables. The magnitude of each input parameter was changed from ?50% to +50% from the baseline data in 12 intervals or, in some cases, the complete range of an input was tested. Baseline model inputs for the sensitivity analysis were taken from Gibbs Farm, Georgia, where REMM was tested using a 5‐year field dataset. Results of the sensitivity analysis indicate that REMM responses were most sensitive to weather inputs, with minimum daily temperature having the greatest impact on the nitrogen‐related outputs. For example, the 100% change (?50% to +50%) in minimum daily temperature input values yielded a 164.4% change in total nitrogen (N), a 109.3% change in total nitrate (NO₃), and a 127.1% change in denitrification. REMM was most sensitive to precipitation with regard to total flow leaving the riparian vegetative buffer zone (199.8%) and sediment yield (138.2%). Deep seepage (12.2%), volumetric water content (24.8%), and pore size index (6.5%) in the buffer soil profile were the most influential inputs for the output water movement. Sediment yield was most sensitive to Manning’s coefficient (46.6%), bare soil percent (40.7%), and soil permeability (6.1%). For vegetation, specific leaf area, growing degree day coefficients, and maximum root depth influenced the nitrogen related outputs. Overall results suggest that because of the high sensitivity to weather parameters, on‐site weather data is needed for model calibration and validation. The model’s relatively low sensitivity to vegetation parameters also appears to support the use of regional vegetation datasets that would simplify model implementation without compromising results.