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.     

Comparison of Field Performance of Multiple Soil Moisture Sensors in a Semi‐Arid Rangeland1

Abstract:  Automated electronic soil moisture sensors, such as time domain reflectometry (TDR) and capacitance probes are being used extensively to monitor and measure soil moisture in a variety of scientific and land management applications. These sensors are often used for a wide range of soil moisture applications such as drought forage prediction or validation of large‐scale remote sensing instruments. The convergence of three different research projects facilitated the evaluation and comparison of three commercially available electronic soil moisture probes under field application conditions. The sensors are all installed in shallow soil profiles in a well instrumented small semi‐arid shrub covered subwatershed in Southeastern Arizona. The sensors use either a TDR or a capacitance technique; both of which indirectly measure the soil dielectric constant to determine the soil moisture content. Sensors are evaluated over a range of conditions during three seasons comparing responses to natural wetting and drying sequences and using water balance and infiltration simulation models. Each of the sensors responded to the majority of precipitation events; however, they varied greatly in response time and magnitude from each other. Measured profile soil moisture storage compared better to water balance estimates when soil moisture in deeper layers was accounted for in the calculations. No distinct or consistent trend was detected when comparing the responses from the sensors or the infiltration model to individual precipitation events. The results underscore the need to understand how the sensors respond under field application and recognize the limitations of soil moisture sensors and the factors that can affect their accuracy in predicting soil moisture in situ.     

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.     

Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies1

Anderson, SallyRose, Glenn Tootle, and Henri Grissino‐Mayer, 2012. Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies. Journal of the American Water Resources Association (JAWRA) 48(4): 849‐858. DOI: 10.1111/j.1752‐1688.2012.00651.x Abstract: Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. We used tree‐ring chronologies to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k‐nearest neighbor techniques. We correlated moisture sensitive tree‐ring chronologies in and adjacent to the UCRB with regional soil moisture and tested the relationships for temporal stability. Chronologies that were positively correlated and stable for the calibration period were retained. We used stepwise linear regression to identify the best predictor combinations for each soil moisture region. The regressions explained 42‐78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained more variance in the datasets. Reconstructed soil moisture data was standardized and compared with standardized reconstructed streamflow and snow water equivalent data from the same region. Soil moisture and other hydrologic variables were highly correlated, indicating reconstructions of soil moisture in the UCRB using tree‐ring chronologies successfully represent hydrologic trends.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

EVAPOTRANSPIRATION AND SOIL MOISTURE DYNAMICS ON A SEMIARID PONDEROSA PINE HILLSLOPE1

ABSTRACT: Detailed measurements of soil moisture and ET in semiarid forest environments have not been widely reported in the literature. In this study, soil moisture and water balance components were measured over a four‐year period on a semiarid ponderosa pine hillslope, with evapotranspiration (ET) determined as the residual of measured precipitation, runoff, and change in soil moisture storage. ET accounts for approximately 95 percent of the water budget and has a distinctly bimodal annual pattern, with peaks occurring after spring snowmelt and during the late summer monsoon season, periods that coincide with high soil moisture. Weekly growing season ET rates determined by the hillslope water balance are found to be invariably below calculated potential rates. Normalized ET rates are linearly correlated (r²= 0.62) with soil moisture; therefore, a simple linear relation is proposed. Growing season soil moisture dynamics were modeled based on this relation. Results are in fair agreement (r²= 0.63) with the observed soil moisture data over the four growing seasons; however, for two dry summers with little surface runoff, much better results (r² > 0.90) were obtained.     

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.     

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.     

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.     

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.     

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.     

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.     

SEDIMENT OXYGEN DEMAND IN THE ARROYO COLORADO RIVER1

ABSTRACT: The lower reaches of the Arroyo Colorado have historically failed to meet their use under subsection 303(b) of the U.S. Clean Water Act due to fecal coliform bacteria and low dissolved oxygen (DO). Fish kills, especially at the tidal confluence at the Port of Harlingen, Texas, have been reported. Oxygen demand from sediment (SOD) for a river typically has two states‐diffusion limited SOD (SOD) and potential SOD (pSOD), expressed when sediment is resuspended through increased flow or other disturbances. The objective of this research was to measure SOD in the Arroyo Colorado River in situ, estimate pSOD ex situ, and evaluate the relationship between SOD and the depositional environment. We measured SOD and pSOD in the Arroyo Colorado River at up to eight sites over three sampling events. We identified the sample sites based on a modified Rosgen geomorphic index for streambed stabilization. Sites with high sediment deposition potential had high SOD. The average values of SOD between sites were 0.62 g/m²/day (standard deviation 0.38 g/m²/day) and ranged from 0.13 to 1.2 g/m²/day. Potential SOD values ranged from as low as 19.2 to as high as 2,779 g/m³ sediment/ day. Potential SOD can serve as an indicator of the possible impact of SOD from resuspended sediment in stream systems.     

Integrating Seasonal Information on Nutrients and Benthic Algal Biomass into Stream Water Quality Monitoring

Benthic chlorophyll a (BChl a) and environmental factors that influence algal biomass were measured monthly from February through October in 22 streams from three agricultural regions of the United States. At‐site maximum BChl a ranged from 14 to 406 mg/m² and generally varied with dissolved inorganic nitrogen (DIN): 8 out of 9 sites with at‐site median DIN >0.5 mg/L had maximum BChl a >100 mg/m². BChl a accrued and persisted at levels within 50% of at‐site maximum for only one to three months. No dominant seasonal pattern for algal biomass accrual was observed in any region. A linear model with DIN, water surface gradient, and velocity accounted for most of the cross‐site variation in maximum chlorophyll a (adjusted R² = 0.7), but was no better than a single value of DIN = 0.5 mg/L for distinguishing between low and high‐biomass sites. Studies of nutrient enrichment require multiple samples to estimate algal biomass with sufficient precision given the magnitude of temporal variability of algal biomass. An effective strategy for regional stream assessment of nutrient enrichment could be based on a relation between maximum BChl a and DIN based on repeat sampling at sites selected to represent a gradient in nutrients and application of the relation to a larger number of sites with synoptic nutrient information.     

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.     

Constraints on the in situ density of CO2 within the Utsira formation from time-lapse seafloor gravity measurements

At Sleipner, CO₂ is being separated from natural gas and injected into an underground saline aquifer for environmental purposes. Uncertainty in the aquifer temperature leads to uncertainty in the in situ density of CO₂. In this study, gravity measurements were made over the injection site in 2002 and 2005 on top of 30 concrete benchmarks on the seafloor in order to constrain the in situ CO₂ density. The gravity measurements have a repeatability of 4.3 μGal for 2003 and 3.5 μGal for 2005. The resulting time-lapse uncertainty is 5.3 μGal. Unexpected benchmark motions due to local sediment scouring contribute to the uncertainty. Forward gravity models are calculated based on both 3D seismic data and reservoir simulation models. The time-lapse gravity observations best fit a high temperature forward model based on the time-lapse 3D seismics, suggesting that the average in situ CO₂ density is about to 530 kg/m³. Uncertainty in determining the average density is estimated to be ±65 kg/m³ (95% confidence), however, this does not include uncertainties in the modeling. Additional seismic surveys and future gravity measurements will put better constraints on the CO₂ density and continue to map out the CO₂ flow.     

ENERGY BALANCE OF A CORN RESIDUE-COVERED FIELD DURING SNOWMELT1

ABSTRACT: Transport of agricultural chemicals in runoff and recharge waters from snowmelt and soil thawing may represent a significant event in terms of annual contaminant loadings in temperate regions. Improved understanding of the melt dynamics of shallow snowpacks is necessary to fully assess the implications for water quality. The objective of this study was to measure the energy balance components of a corn (Zea mays L.) stubble field during the melting of its snowcover. Net radiation (Rn), soil (G), sensible (H), and latent (Q) heat fluxes were measured in a field near Ames, Iowa, during the winter of 1994–1995. Energy consumed by melting including change in energy storage of the snowpack was determined as the residual of the measured energy balance. There was continuous snowcover at the field site for 71 days (maximum depth = 222 mm) followed by an open period of 11 days before additional snowfall and a second melt period. The net radiation and snow melt/energy storage change (5) terms dominated the energy balance during both measurement intervals. Peak daily sensible and latent heat fluxes were below 100 W m^?2 on all days except the last day of the second melt period. There was good agreement between predicted and measured values of H and Q during the melting of an aged snow layer but poorer agreement during the melt of fresh snow. Both snowpacks melted rapidly and coincident changes in soil moisture storage were observed. Improved estimates of Q and H, especially for partially open surfaces, will require better characterization of the surface aerodynamic properties and spatially-representative surface temperature measurements.     

Experimental investigation of groundwater contamination by surface sources: Determination of adsorption capacity,diffusion, and sorption potential of selected anions in different soils

The present study investigated the fate and transport of two significant anions through soil to explore their potential as groundwater contaminants. The retention properties of chloride and sulfate in soils having several significantly different characteristics (soil‐1 and soil‐2) were determined using adsorption test and adsorption‐diffusion column experiments. The maximum adsorption capacity of chloride was 3.7 and 1.16 mg/g, respectively, in soil‐1 and soil‐2, with organic matter (OM) content of 3.92% and 4.69%, respectively. The sulfate adsorption obtained was 24.09% and 13.83%, respectively, in the two soils. The anions exhibited monolayer adsorption in the soils with replacement of hydroxyl ions from soils as the major mechanism of adsorption. On the other hand, the adsorption capacities obtained from the adsorption‐diffusion column experiment were about 100 times lower compared to that of the column tests of both of the soils. The maximum adsorption capacity of chloride was 0.03 mg/g and 0.01 mg/g, respectively, in soil‐1 and soil‐2, whereas that of sulfate was 0.04 mg/g and 0.03 mg/g. The empirical relation for depth of penetration (d) from a known spillage onto the soil surface was determined as a function of sorption capacity (S) and initial anion concentration (C) as d = 0.0073e^(?57S)C and d = 0.0038e^(?35S)C for chloride and sulfate, respectively.     

Hydrogeomorphic Reference Condition and Its Relationship with Macroinvertebrate Assemblages in Southeastern U.S. Sand Hills Streams

Defining stream reference conditions is integral to providing benchmarks to ecological perturbation. We quantified channel geometry, hydrologic and environmental variables, and macroinvertebrates in 62 low‐gradient, SE United States (U.S.) Sand Hills (Level IV ecoregion) sand‐bed streams. To identify hydrogeomorphic reference condition (HGM), we clustered channel geometry deviation from expectations given watershed area (Aws), resulting in two HGM groups discriminated by area at the top of bank (Atob) residuals <0.6 m² and >0.6 m² predicted to be HGM reference/nonreference streams, respectively. Two independent partial least squares discriminate analyses used (1) hydrologic/environmental variables and (2) macroinvertebrate mean trait values (mT) on 10 reference/nonreference stream pairs of similar Aws for classification validation. Nonreference streams had flashier hydrographs and altered flow magnitudes, lower organic matter, coarser substrate, higher pH/specific conductivity compared with reference streams. Macroinvertebrate assemblages corresponded to HGM groupings, with mT indicative of multivoltinism, collector‐gatherer functional feeding groups, fast current‐preference taxa, and lower Ephemeroptera, Plecoptera, and Trichoptera richness and biotic integrity in nonreference streams. HGM classifications in Sand Hills, sand‐bed streams were determined from channel geometry. This easily implemented classification is indicative of contemporary hydrologic disturbance resulting in contrasting macroinvertebrate assemblages.