IMPACT OF VARIED DATA RESOLUTION ON HYDRAULIC MODELING AND FLOODPLAIN DELINEATION1

ABSTRACT: Current data collection technologies such as light detection and ranging (LIDAR) produce dense digital terrain data that result in more accurate digital terrain models (DTMs) for engineering applications. However, such data are redundant and often cumbersome for hydrologic and hydraulic modeling purposes. Data filtering provides a means of eliminating redundant points and facilitates model preparation. This paper demonstrates the impact of varied data resolution on a case study completed for a 2.3 mi² area with mild slopes (about 001 ft/ft) along Leith Creek near Laurinburg, North Carolina. For the original data set and seven filtered data sets, filtering induced changes in elevation, area, and hydraulic radius were determined for 10 water depths at 23 cross sections. Water surface elevations resulting from HEC‐RAS (Hydrologic Engineering Center‐River Analysis System) models for each data set were then compared. A hydraulic model sensitivity analysis was also conducted to compare filtering error to error introduced by variation in flow rates and roughness values. Finally, automated floodplain delineation was performed for each filter level based on the computed hydraulic model results and the filtered LIDAR elevations. Data filtering results indicate that significant time savings are achieved throughout the modeling process and that filtering to four degrees can be performed without compromising cross‐sectional geometry, hydraulic model results, or floodplain delineation results.     

Spatial Dynamics of Soil Moisture in a Complex Terrain in the Semi‐Arid Loess Plateau Region,China1

Abstract: Soil moisture is an important hydrological variable in reforestation practices in a water‐limited region of the Loess Plateau of northwestern China. The objective of this study was to quantify the spatial dynamics of soil moisture on a complex terrain. During 2004‐2006, a total of 313 sample points in two kinds of grid (2 × 2 m and 20 × 20 m) were arranged for soil moisture measurements (two soil layers: 0‐30 and 30‐60 cm) with Time Domain Reflectometry. The geostatistical properties of soil moisture patterns, the variance and correlation structure of the soil moisture, and the effects of terrain factors on soil moisture were analyzed. The results suggested that our sampling grid captured the spatial variability of soil moisture distributions for this complex terrain. Principal Component Analysis and Cluster Analysis statistics showed that soil moisture decreased as slope gradient increased; that sunny aspects (112.5°‐292.5°) had relatively lower soil moisture than did shady aspects (292.5°‐112.5°); that soil moisture was lowest in the SWW direction and highest in the NWN direction; and that hillslope aspect was the main factor affecting soil moisture in the 0‐ to 30‐cm soil layer, whereas the main factor for the 30‐ to 60‐cm layer was slope gradient. It was found that the relative values of soil moisture for steep slopes (>36%) with shady aspect (292.5°‐112.5°), gentle slopes (<36%) with sunny aspect (112.5°‐292.5°), and steep slopes with sunny aspect were 99, 82, and 80, respectively – assuming a soil moisture value of 100 for gentle slopes with shady aspect. The results of this study are expected to be relevant to and useful for reforestation planning and design, parameterization of distributed hydrology models, and land productivity assessment in the study region.     

Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation1

Greenberg, Jonathan Asher, Erin L. Hestir, David Riano, George J. Scheer, and Susan L. Ustin, 2012. Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation. Journal of the American Water Resources Association (JAWRA) 48(5): 939‐948. DOI: 10.1111/j.1752‐1688.2012.00664.x Abstract: Riparian vegetation provides shade from insolation to stream channels. A consequence of removing vegetation may be an increase in insolation that can increase water temperatures and negatively impact ecosystem health. Although the mechanisms of riparian shading are well understood, spatially explicit, mechanistic models of shading have been limited by the data requirements of precisely describing the three‐dimensional structure of a riparian corridor. Remotely acquired, high spatial resolution LiDAR data provide detailed three‐dimensional vegetation structure and terrain topography over large regions. By parameterizing solar radiation models that incorporate terrain shadowing with LiDAR data, we can produce spatially explicit estimates of insolation. As a case study, we modeled the relative change in insolation on channels in the Sacramento‐San Joaquin River Delta under current conditions and under a hypothesized deforested Delta using classified LiDAR, rasterized at a 1‐m resolution. Our results suggest that the removal of levee vegetation could result in a 9% increase in solar radiation incident on Delta waters, and may lead to water temperature increases. General, coarse‐scale channel characteristics (reach width, azimuth, levee vegetation cover, and height) only accounted for 72% of the variation in the insolation. This indicates that the detailed information derived from LiDAR data has greater explanatory power than coarser reach‐scale metrics often used for insolation estimates.     

Downward shortwave radiation estimation and spatial assessment on sites over complex terrain applying integrative approach of MTCLIM-XL,interpolation, RS and GIS

Downward shortwave radiation (DSR) is a highly variable solar source on spatiotemporal basis and essential for energy and agriculture systems, while its calculations are helpful in the environment-related studies, climatology, and monitoring fire risk. Statistical methods developed to extrapolate values of climatic variables and radiation could fail to generate reliable findings of DSR over a complex terrain without considering local topographic factors. In the present study, we proposed an integrative approach of MTCLIM-XL extrapolation with remote sensing (RS) and geographic information system (GIS) to estimate real-time DSR and its spatial potential over surfaces of contrasting elevated sites on a mountainous terrain of Quetta (Pakistan).Based on methodological approach, remote sensing data product of high-resolution DEM (SRTM 30m) was processed to extract topographic data, and meteorological data were obtained from a base site, Subsequently, MTCLIM-XL executed the simulation to calculate the daily-based DSR (W/m²).Spatial distribution of DSR was generated by applying deterministic interpolation with complementing quantification of Hillshade analysis for spatially obstructive surfaces, and resultant spatial hotspot-based potential was assessed on basis of specified threshold level (above 250 W/m² = 2 kW h/m²) over the specified area. We observed usable potential of DSR at target sites and its spatial distribution during the study period of 2015 to April 2016. Using EUMETSAT CMSAF data as a standard, the validation demonstrates agreeable results of low RMSE and high correlation coefficient values for selected sites, except some sites with relatively high elevations and irregular gradients. Analysis of solar zenith angle to evaluate its inverse relation with increment in DSR values shows agreeable high inverse relation, while the negative trend for only some sites features relatively high rugged topography. In conclusion, MTCLIM-XL with RS and GIS integration manifests as a reliable approach for estimation and spatial potential assessment-based exploration of DSR over complex terrain having no ground data, while prospectively it will complement to the environment-related studies on local to mesoscale.     

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.     

AN INTEGRATED ONE‐DIMENSIONAL AND TWO‐DIMENSIONAL URBAN STORMWATER FLOOD SIMULATION MODEL1

ABSTRACT: Flash flooding is the rapid flooding of low lying areas caused by the stormwater of intense rainfall associated with thunderstorms. Flash flooding occurs in many urban areas with relatively flat terrain and can result in severe property damage as well as the loss of lives. In this paper, an integrated one‐dimensional (1‐D) and two‐dimensional (2‐D) hydraulic simulation model has been established to simulate stormwater flooding processes in urban areas. With rainfall input, the model simulates 2‐D overland flow and 1‐D flow in underground stormwater pipes and drainage channels. Drainage channels are treated as special flow paths and arranged along one or more sides of a 2‐D computational grid. By using irregular computation grids, the model simulates unsteady flooding and drying processes over urban areas with complex drainage systems. The model results can provide spatial flood risk information (e.g., water depth, inundation time and flow velocity during flooding). The model was applied to the City of Beaumont, Texas, and validated with the recorded rainfall and runoff data from Tropical Storm Allison with good agreement.     

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.     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.     

Mixing Zone Characterization of Two Transition Terrain Streams With Tracers1

Abstract: For most wastewater discharges to streams, the effluent creates a plume that becomes less distinct as it mixes with the receiving water. Constant‐discharge tracer studies were used to characterize the plume or physical mixing zone (PMZ) at two similar transition terrain streams. At both sites, the laterally unmixed PMZs did not extend across the entire stream and mixing occurred relatively quickly. The observed plumes were significantly smaller than the regulatory mixing zone (RMZ) allowed by the State of Colorado. At Site 1 mixing occurred within a much shorter distance due to the presence of a riffle zone located a few meters downstream of the discharge point. Interpretation of field data with an analytical model suggests that the effective transverse dispersion coefficient (k_z) for the riffle zone at Site 1 (～1 m²/s) was significantly higher than the average value over the longer nonriffle section at Site 2 (～0.01 m²/s). These results imply that to achieve the fastest mixing in transition terrain streams, thereby minimizing the size of the PMZ, discharge outfalls should be located upstream and close to riffle zones.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

Evapotranspiration in the Meso‐Scale Forested Watersheds in Minjiang Valley,West China1

Abstract: Information on evapotranspiration (ET) can help us understand water balance, particularly in forested watersheds. Previous studies in China show that ET was relatively low (30‐40% of total precipitation) in the Minjiang Valley located in the upper reach of the Yangtze River Basin. However, this conclusion was derived from research on small‐scale watersheds (<100 km²). The objective of this paper was to present ET information on meso‐scale watersheds in the Minjiang Valley. Four meso‐scale watersheds (1,700‐5,600 km²) located in the Minjiang Valley were used to estimate ET using the water balance approach. We first generated forest vegetation variables (coniferous forest percentage, forest cover percentage, and derived forest vegetation index) using remote sensing data. Landsat 5 TM satellite images, acquired on June 26, 1994, were selected for the vegetation classification. Actual annual ET was calculated based on 11‐year estimated precipitation and measured streamflow data (1992‐2002). We also calculated potential ET (PET) using an improved Thornthwaite model for all four watersheds for the period of 1992‐1998. PET can provide additional information about potential capacity of water flux to atmosphere in the region. Seasonal (dry and rainy) PET and ET for all studied watersheds were also estimated for comparison purposes as the water balance approach, at shorter than annual scales, would likely provide inaccurate estimates of ET. The dominant vegetations in the Minjiang Valley were grasslands, conifer forests, and shrub‐lands. Our results confirmed that both ET and PET for three studied meso‐scale watersheds in the Minjiang Valley is relatively low (39.5‐43.8 and 28.2‐47.7% for ET and PET, respectively), with an exception of ET in the Yuzixi watershed being 71.1%. This result is generally consistent with previous research at small watershed scales. Furthermore, the low ET across various scales in the Minjiang Valley may be related to the unique deeply cut valley environment.     

Vegetation associated with different walking track types in the Kosciuszko alpine area, Australia

Tourism infrastructure such as walking tracks can have negative effects on vegetation including in mountain regions. In the alpine area around continental Australia's highest mountain, Mt Kosciuszko (2228 m), there is a range of walking tracks (paved, gravel and raised steel mesh surfaces) in addition to an extensive network of informal/non-hardened tracks. Vegetation characteristics were compared between track types on/under tracks, on the track verge, and in the adjacent native vegetation. For a raised steel mesh walkway there was no difference in vegetation under the walkway, on the verge, and 3m away. In contrast, for a non-hardened track there was 35% bare ground on the track surface but no other detectable impacts. Gravel and paved tracks had distinct verges largely comprising bare ground and exotic species. For non-hardened tracks there was an estimated 270 m2 of disturbance per km of track. For wide gravel tracks the combined area of bare ground, exotic plants and gravel was estimated as 4290 m2 per km, while for narrow gravel tracks it was estimated as 2940 m2 per km. For paved tracks there was around 2680 m2 per km of damage. In contrast, there was no detectable effect of raised steel mesh walkway on vegetation highlighting some of the benefits of this surface over other track types.     

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.     

Evaluation of Methods for Delineating Riparian Zones in a Semi‐Arid Montane Watershed

Riparian zones in semi‐arid, mountainous regions provide a disproportionate amount of the available wildlife habitat and ecosystem services. Despite their importance, there is little guidance on the best way to map riparian zones for broad spatial extents (e.g., large watersheds) when detailed maps from field data or high‐resolution imagery and terrain data are not available. Using well‐established accuracy metrics (e.g., kappa, precision, computational complexity), we evaluated eight methods commonly used to map riparian zones. Focusing on a semi‐arid, mountainous watershed, we found that the most accurate and robust method for mapping riparian zones combines data on upstream drainage area and valley topography. That method performed best regardless of stream order, and was most effective when implemented with fine resolution topographic and stream line data. Other commonly used methods to model riparian zones, such as those based on fixed‐width buffers, yielded inaccurate results. We recommend that until very‐high resolution (<1 m) elevation data are available at broad extents, models of riparian zones for semi‐arid mountainous regions should incorporate drainage area, valley topography, and quantify uncertainty.     

ROOT BIOMASS IN RELATION TO CHANNEL MORPHOLOGY OF HEADWATER STREAMS1

ABSTRACT: Intact riparian zones are the product of an incredibly complex multitude of linkages between the geomorphic, hydrologic, and biotiè features of the ecosystem. Land‐use activities that sever or alter these linkages result in ecosystem degradation. We examined the relationship between riparian vegetation and channel morphology by sampling species composition and herbaceous root biomass in incised (down‐cut and widened) versus unincised (intact) sections of unconstrained reaches in three headwater streams in northeastern Oregon. Incision resulted in a compositional shift from wetland‐obligate plant species to those adapted to drier environments. Root biomass was approximately two times greater in unincised sections than incised sections and decreased with depth more rapidly in incised sections than in unincised sections. Total root biomass ranged from 2,153 g m^‐2 to 4,759 g m^‐2 in unincised sections and from 1,107 g m^‐2 to 2,215 g m^‐2 in incised sections. In unincised sections less than 50 percent of the total root biomass was found in the top 10 cm, with approximately 20 percent in successive 10‐cm depth increments. In contrast, incised sections had greater than 60 percent of the total root biomass in the top 10 cm, approximately 15 percent in the 10 to 20 cm depth, less than 15 percent in the 20 to 30 cm depth, and less than 10 percent in the 30 to 40 cm depth. This distribution of root biomass suggests a positive feedback between vegetation and channel incision: as incision progresses, there is a loss of hydrologic connectivity, which causes a shift to a drier vegetation assemblage and decreased root structure, resulting in a reduced erosive resistance capacity in the lower zone of the streambank, thereby allowing further incision and widening.     

Interpolating SRTM Elevation Data to Higher Resolution to Improve Hydrologic Analysis

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) has been a valuable resource for hydrological analysis, providing elevation data at a consistent resolution on a near‐global scale. However, its resolution (three arc‐second or 90 m) is sometimes too low to obtain the desired level of accuracy and precision for hydrologic analysis. We evaluated the performance of several methods for interpolating SRTM three arc‐second data to a 30‐m resolution grid to better represent topography and derive terrain characteristics of the landscape. STRM data were interpolated to 30‐m DEMs on a common grid using spline, inverse distance weighting (IDW), kriging (KR), natural neighbor methods, and cubic convolution (CC) resampling. Accuracy of the methods was assessed by comparing interpolated and resampled 30‐m grids with the reference data. Slope, aspect, sinks, and stream networks were derived for the 30‐m grids and compared on a cell‐by‐cell basis to evaluate their performance in reproducing the derivatives. The comparisons identify spline and KR as the most accurate interpolation methods, of which spline is preferred because of its relative simplicity. IDW provided the greatest bias in all methods with artifacts evident in slope and aspect maps. The performance of CC projection directly to a 30‐m resolution was comparable to spline interpolation, thus is recommended as the most convenient method for interpolating SRTM to a higher resolution.     

Estimating Nitrogen Loading to Ground Water and Assessing Vulnerability to Nitrate Contamination in a Large Karstic Springs Basin,Florida1

Abstract: A nitrogen (N) mass‐balance budget was developed to assess the sources of N affecting increasing ground‐water nitrate concentrations in the 960‐km² karstic Ichetucknee Springs basin. This budget included direct measurements of N species in rainfall, ground water, and spring waters, along with estimates of N loading from fertilizers, septic tanks, animal wastes, and the land application of treated municipal wastewater and residual solids. Based on a range of N leaching estimates, N loads to ground water ranged from 262,000 to 1.3 million kg/year; and were similar to N export from the basin in spring waters (266,000 kg/year) when 80‐90% N losses were assumed. Fertilizers applied to cropland, lawns, and pine stands contributed about 51% of the estimated total annual N load to ground water in the basin. Other sources contributed the following percentages of total N load to ground water: animal wastes, 27%; septic tanks, 12%; atmospheric deposition, 8%; and the land application of treated wastewater and biosolids, 2%. Due to below normal rainfall (97.3 cm) during the 12‐month rainfall collection period, N inputs from rainfall likely were about 30% lower than estimates for normal annual rainfall (136 cm). Low N‐isotope values for six spring waters (δ¹⁵N‐NO₃ = 3.3 to 6.3‰) and elevated potassium concentrations in ground water and spring waters were consistent with the large N contribution from fertilizers. Given ground‐water residence times on the order of decades for spring waters, possible sinks for excess N inputs to the basin include N storage in the unsaturated zone and parts of the aquifer with relatively sluggish ground‐water movement and denitrification. A geographical‐based model of spatial loading from fertilizers indicated that areas most vulnerable to nitrate contamination were located in closed depressions containing sinkholes and other dissolution features in the southern half of the basin.     

ESTIMATION OF SOIL MOISTURE FROM SEASAT SAR DATA1

ABSTRACT A study was made to determine if Seasat Synthetic Aperture Radar (SAR) data could be used to make practical estimates of soil moisture. Extensive ground measurements were collected at two primary sites near Guymon, Oklahoma, and Sublette, Kansas. The relative sensitivity of the SAR to differences in soil moisture, tillage roughness, and vegetation was determined. To validate the effects detected in the SAR data, an airborne scatterometer with a similar wavelength was flown repeatedly over the Guymon site. Soil moisture variations in the surface 2 cm and surface 15 cm of fields with bare soil, milo and alfalfa produce similar responses in the scattering coefficient from both systems. Roughness due to tillage in row crops produced as much as 12–15 dB increase in the scatterometer return. Most agricultural vegetation was effectively penetrated by the L-band frequencies; however, corn produced an exceptionally high radar return either standing or after combine harvesting. When corn had ripened, there was some evidence that tillage roughness could be detected through the canopy. Moderate tillage roughness produced by grain drill furrows caused over 12 dB increase in return when row directions changed from parallel to perpendicular with respect to the SAR look direction. Dramatic increases in return occurred when vegetation surfaces were wet. Increased radar returns from tillage roughness, some vegetation and wet vegetation surfaces, all dyanmic in nature, were significant and may limit the practical estimation of soil moisture from the radar data.     

INFLUENCES OF WATERSHED,RIPARIAN‐CORRIDOR,AND REACH‐SCALE CHARACTERISTICS ON AQUATIC BIOTA IN AGRICULTURAL WATERSHEDS1

ABSTRACT: Multivariate analyses and correlations revealed strong relations between watershed and riparian‐corridor land cover, and reach‐scale habitat versus fish and macroinvertebrate assemblages in 38 warmwater streams in eastern Wisconsin. Watersheds were dominated by agricultural use, and ranged in size from 9 to 71 km² Watershed land cover was summarized from satellite‐derived data for the area outside a 30‐m buffer. Riparian land cover was interpreted from digital orthophotos within 10‐, 10‐to 20‐, and 20‐to 30‐m buffers. Reach‐scale habitat, fish, and macroinvertebrates were collected in 1998 and biotic indices calculated. Correlations between land cover, habitat, and stream‐quality indicators revealed significant relations at the watershed, riparian‐corridor, and reach scales. At the watershed scale, fish diversity, intolerant fish and EPT species increased, and Hilsenhoff biotic index (HBI) decreased as percent forest increased. At the riparian‐corridor scale, EPT species decreased and HBI increased as riparian vegetation became more fragmented. For the reach, EPT species decreased with embeddedness. Multivariate analyses further indicated that riparian (percent agriculture, grassland, urban and forest, and fragmentation of vegetation), watershed (percent forest) and reach‐scale characteristics (embeddedness) were the most important variables influencing fish (IBI, density, diversity, number, and percent tolerant and insectivorous species) and macroinvertebrate (HBI and EPT) communities.     

Sediment retention in rangeland riparian buffers

Controlling nonpoint-source sediment pollution is a common goal of riparian management, but there is little quantitative information about factors affecting performance of rangeland riparian buffers. This study evaluated the influence of vegetation characteristics, buffer width, slope, and stubble height on sediment retention in a Montana foothills meadow. Three vegetation types (sedge wetland, rush transition, bunchgrass upland) were compared using twenty-six 6- x 2-m plots spanning 2 to 20% slopes. Plots were clipped moderately (10-15 cm stubble) or severely (2-5 cm stubble). Sediment (silt + fine sand) was added to simulated overland runoff 6, 2, or 1 m above the bottom of each plot. Runoff was sampled at 15-s to > 5-min intervals until sediment concentrations approached background levels. Sediment retention was affected strongly by buffer width and moderately by vegetation type and slope, but was not affected by stubble height. Mean sediment retention ranged from 63 to > 99% for different combinations of buffer width and vegetation type, with 94 to 99% retention in 6-m-wide buffers regardless of vegetation type or slope. Results suggest that rangeland riparian buffers should be at least 6 m wide, with dense vegetation, to be effective and reliable. Narrower widths, steep slopes, and sparse vegetation increase risk of sediment delivery to streams. Vegetation characteristics such as biomass, cover, or density are more appropriate than stubble height for judging capacity to remove sediment from overland runoff, though stubble height may indirectly indicate livestock impacts that can affect buffer performance.