Watersheds In Texas Research Articles

Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95% faster than the Central Processing Unit (CPU) benchmark algorithm using 33% less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

Redefining Absentee: Towards Understanding Place Attachment and Stewardship in Non-Residential Landowners in Texas, USA

Approximately 30% of the private land in Texas, USA is under absentee ownership. Understanding who absentee landowners are and their land management behaviors is vital for the protection of privately owned landscapes and the ecosystem services that they support, including surface water quality. By focusing on absentee landowners with properties in five watersheds in Texas, we utilized the theory of place attachment to gain insights into absentee landowners’ land management decisions and their involvement in water quality conservation programs, such as watershed protection plans (WPPs). By conducting a mail-out survey, we obtained 100 responses, which were analyzed using an exploratory factor analysis and a series of nonparametric assessments. The results revealed that, contrary to the term “absentee”, the landowners in our study demonstrated strong feelings of place attachment and heightened land stewardship. Based on these findings, we suggest that instead of considering absentee landowners as obstacles to collaborative conservation initiatives, such as WPPs, natural resource practitioners should recognize and capitalize on the emotional attachment that these landowners have to their properties, thereby fostering their involvement. By demonstrating the owner–land relationship and its behavioral outcomes among absentee landowners, this study provides a novel contribution to the existing literature on place attachment in the context of private land management and conservation.

Precipitation impacts the physicochemical water quality and abundance of microbial source tracking markers in urban Texas watersheds

This study investigated the sources of fecal pollution in surface and groundwaters collected from three urban watersheds in Texas during dry and wet weather and identified the impact of precipitation on water quality. Water samples (n = 316 and 107 for dry and wet events, respectively) were collected biweekly from eight sampling sites (6 sites from creeks and ponds, and 2 well sites) during two-year monitoring and analyzed for six physico-chemical parameters and ten qPCR-based assays targeting general (E. coli, Enterococcus, and universal Bacteroidales), human (BacHum and HF183), animal (Rum2Bac, BacCow, BacCan), and avian (Chicken/Duck-Bac and GFD) fecal markers. Elevated concentrations of NO3-N and NO2-N were observed in ponds and creeks sites during wet weather. Fecal markers analysis indicated higher concentrations of Rum2Bac, BacCow, and BacCan markers in most of pond and creek sites under wet weather, suggesting stormwater runoff contributed to non-point sources of fecal contamination by animal sources. Furthermore, sporadically higher concentrations of these markers were detected at groundwater sampling sites, demonstrating the significant human health risk. Multivariate statistical analysis such as cluster analysis (CA) and principal coordinate analysis (PCoA) was performed to identify relationship between sampling sites; while CA majorly classified ponds, creeks, and well sites separately, PCoA identified similarities in water quality characteristics between waters of wells with ponds and creeks. Overall, results indicate ruminant and dog fecal contamination is a major concern during storm events, consequently impacting surface and groundwater quality of the study.

Evaluating soil erosion and runoff dynamics in a humid subtropic, low stream order, southern plains watershed from cultivation and solar farm development

Much work has been done to understand and improve soil and water conservation where agriculture has driven land use intensification. Less is known about soil- and water-related impacts from intensification driven by solar farming, especially at watershed-scales. Here we employed Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to model Pond Creek, a rural watershed in Texas, USA. Land use is primarily crop cultivation and secondarily pasture for cattle grazing. Presently, several industrial-scale projects are planned to convert ≈15–30% of Pond Creek from agriculture to solar farms. The model was parameterized using public data sources and information from local stakeholders, then calibrated to several historical precipitation events. Experiments were conducted by varying precipitation depth, duration, and land uses: native vegetation pre-cultivation (control), cultivation (current), current conditions with 15% solar farm conversion (solar), and current conditions with 30% solar farm conversion (solar x2). Shifting to solar farming led to significant increases in cumulative sediment load (+12%–30%), with no significant differences in peak discharge rate changes (+0.38%–4%). Comparison to soil loss tolerance values showed current and solar treatment erosion rates exceeded tolerance values between 0.17 and 2.29 tons per hectare and all treatments were significantly different than the native treatment. We discuss high leverage strategies applicable to solar farm development sites as well as watersheds where they reside. Accelerating demand for land for renewable energy such as solar farming warrants greater attention from the soil and water conservation community to anticipate and mitigate impacts across landscapes.

Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA

AbstractUnderstanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from >100 monitoring stations collected over 60 years (1958–2018) across the Colorado River Watershed in Texas (103,000 km2). We paired this river chemistry data with complementary lithology, land use, climate, and stream discharge information. Machine learning techniques were used to produce new insights on controls of stream water chemical behavior, which were validated using traditional multivariate analyses. Studies on stream flow and chemistry in the American west and globally have shown strong relationships between major ion chemical composition, climate, and lithology which hold true for the Colorado River basin in this study. Reactive minerals, including carbonates and evaporites, dominate major ion chemistry across the upper, low‐precipitation regions of the watershed. Upstream and middle reaches of the Colorado River showed shifts from Na‐Cl‐SO4 dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, and agricultural activities, to Ca‐HCO3 water types controlled by carbonate dissolution. In the lower portion of the watershed multiple analyses demonstrate that stream chemistry is more influenced by greater precipitation and the presence of silicate minerals than the middle and upstream reaches. This study demonstrates the power of applying machine learning approaches to publicly available long term water chemistry data sets to improve the understanding of watershed interactions with surficial lithology, salinity sources, and anthropogenic influences of water quality.

Unit Hydrograph Peak Rate Factor Estimation for Texas Watersheds

In many cases, estimation of the peak discharge is the primary goal of hydrologic modeling. We employed a dataset of 1,648 rainfall-runoff events in 104 watersheds in Texas to explore the peak rate factor (PRF) of 2.08 recommended by the Natural Resources Conservation Service (NRCS) for determining peak discharges with the unit hydrograph method, as well as the PRF’s dependency on watershed and storm characteristics. For each event, a unit hydrograph—assumed to follow a two-parameter gamma distribution—was determined by deconvolving the direct runoff hydrograph with the excess rainfall hyetograph. Results showed PRFs reaching up to 9.75 in Houston watersheds with a median of 0.58 and PRFs up to 11.02 with a median of 1.42 for the rest of Texas. It was also found that the recommended PRF of 2.08 falls between the 75th and 90th percentile of the 1,043 events analyzed in all regions except Houston, and is above the 95th percentile in all 605 events analyzed in Houston. Finally, statistical analysis showed that the PRF is primarily dependent on the watershed’s geomorphology but not on its slope, as was suggested earlier. This dependency, however, although statistically significant, explains only marginally the PRF variability.

A multi-decadal analysis of river discharge and suspended sediment load in three Texas coastal rivers in relation to hurricanes, seasonal rainfall, and ENSO

Coastal river discharge and sediment load exert major influence on the sustainability of coastal systems. Controlled by various hydroclimatic/hydrometeorological agents, they exhibit distinct trend/variability at different time scales. Coastal Texas, while being a major target for tropical cyclones over the past 6 decades, has been experiencing drought and flood cycles associated with ENSO in the long term. However, it is still unclear the temporal variability of river discharge and the associated sediment delivery over this area at different time scales, and the controlling factors behind it. In this study, a 58-years (1960–2017) dataset is compiled to analyze the influence of ENSO, seasonal rainfall distribution and hurricanes event on the river discharge and suspended sediment load of three Texas coastal rivers-the San Bernard River, the Brazos River, and the Trinity River, at annual, seasonal and event scales, respectively. In the short-term, all three rivers attained the highest average daily discharge and sediment load during Hurricane Harvey. On a seasonal scale, the precipitation regime exerts more influence on the Texas watersheds than tropical storms and hurricanes. Over a multi-decadal scale, amplified rainstorms during the El Niño phases likely play an important role in the overall discharge and sediment transport in large rivers along the northern Gulf coast. Overall, it is reasonable to conclude that the magnitude of hurricane impacts on the overall discharge and suspended sediment load is regulated by the duration and intensity of the rainfall, as well as the coupled drought-flood cycle in relation to the intensity of ENSO.

Microplastic Pollution in Surface Waters of Urban Watersheds in Central Texas, United States: A Comparison of Sites With and Without Treated Wastewater Effluent

Most studies of microplastics in freshwater have investigated presence in creeks and rivers below sewage effluents and storm drains. This study examines microplastic distribution in surface waters, including springs and primary streams, located within small, urban Karst watersheds, with and without sources of wastewater treatment discharge. Study locales were in Texas, United States, either in Waco on the Brazos River or in or downstream from San Marcos on the San Marcos River. Research teams collected 800 ml surface water from four different small watersheds and an urban pond (n = 779) and filtered them through 53 μm Nitex mesh. Teams collected samples from springs or primary streams to the lower end of creeks and across stream transects based on distance from the bank and the presence of vegetation and debris. Teams also replicated samples seasonally. Stereo microscopy examined each filter for microplastic particles and subsequently color and type (i.e., fragment, fiber, or sphere). Additionally, we analyzed the influence of urbanization and land use on the origin and transport of the microplastics. Overall, the filters recovered 1,198 microplastic fibers and fragments. On average, 56.7% of all samples at each study locale contained microplastics. Particle abundance was the highest at Proctor Springs (x¯=3.38) and lowest at the pond (x¯=0.98), both headwaters. Local human use and runoff were thus potentially important factors in microplastic presence, while sewage discharge was not unilaterally the primary determinant of microplastic abundance. Peak pollution events occurred in June, September, and October, indicating seasonality of rainfall and recreation affected microplastic frequency and type.

Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach

AbstractThe use of polynomial chaos expansion (PCE) has gained a lot of attention due to its ability to efficiently estimate the effects of parameter uncertainty on model outputs. The traditional PCE technique requires the studied parameters to be independent. In hydrological modeling, although model parameters are often assumed to be independent for simplicity of computation, such an assumption is not always valid. Neglecting parameter correlations could significantly affect the analysis of uncertainty, leading to distorted modeling results. In this study, an improved PCE approach is proposed to address this issue and support the uncertainty analysis for hydrological models with correlated parameters. The proposed approach is based on the integration of principle component analysis (PCA) and PCE, where PCA is used to transform correlated parameters into orthogonal independent components. To demonstrate the applicability of this approach, the Soil & Water Assessment Tool (SWAT) model is applied to the Guadalupe River Watershed in Texas, US, and the integrated PCA‐PCE framework is used to assess the propagation of uncertainty of SWAT's interdependent parameters. A traditional Monte‐Carlo (MC) simulation is also used to address the uncertainty in the developed SWAT model. The results show that PCA‐PCE could generate similar probabilistic flow results compared to MC while maintaining a very high computational efficiency. The coefficients of determination (R2) for the mean and variance are 0.998 and 0.973, respectively, and the computational requirement is reduced by 99% using the developed PCA‐PCE approach. It is shown that the PCA‐PCE approach is reliable and efficient in assessing uncertainties in hydrological models with interdependent parameters.

Watershed-Scale, Probabilistic Risk Assessment of Water Resources Impacts from Climate Change

A framework for the assessment of relative risk to watershed-scale water resources from systemic changes is presented. It is composed of two experiments, or pathways, within a Monte Carlo structure and provides quantification of prediction uncertainty. One simulation pathway is the no change, or null hypothesis, experiment, and the other provides simulation of the hypothesized system change. Each pathway uses a stochastic weather generator and a deterministic water balance model. For climate change impact analysis, the framework is calibrated so that the differences between thirty-year average precipitation and temperature pathway values reproduce climate trends. Simulated weather provides forcing for identical water balance models. Probabilistic time histories of differences in actual evapotranspiration, runoff, and recharge provide likelihood per magnitude change to water resources availability. The framework is applied to a semi-arid watershed in Texas. Projected climate trends for the site are a 3 °C increase in average temperature and corresponding increase in potential evapotranspiration, no significant change in average annual precipitation, and a semi-arid classification from 2011–2100. Two types of water balance model are used in separate applications: (1) monthly water balance and (2) daily distributed parameter. Both implementations predict no significant change, on average, to actual evapotranspiration, runoff, or recharge from 2011–2100 because precipitation is unchanged on average. Increases in extreme event intensity are represented for future conditions producing increased water availability during infrequent events.

Escherichia coli Antimicrobial Resistance Variability in Water Runoff and Soil from a Remnant Native Prairie, an Improved Pasture, and a Cultivated Agricultural Watershed

Although many previous studies have examined patterns of antimicrobial resistance (AMR) and multidrug resistance (MDR) from domestic animals and farm environments, comparatively little is known about the environmental sources and natural reservoirs of AMR and MDR. In this study, we collected stormwater runoff and soil samples from three watersheds in Texas. Escherichia coli (E. coli) were enumerated, isolated, and analyzed for resistance patterns. E. coli from all sites, irrespective of land use, displayed the presence of AMR/MDR. Higher levels of AMR/MDR were observed in water compared to soil. More isolates were resistant to cephalothin than other antibiotics. For water isolates, 94% was resistant to cephalothin, 27% to tetracycline, and 15% to ampicillin. Across all sites, a large percentage of water isolates demonstrated MDR with 34% resistant to ≥2 antibiotics and 11% to ≥3 antibiotics. All AMR soil isolates were resistant to cephalothin (87% of the total soil isolates), but only 8.9% were MDR. High cephalothin resistance observed in both soil and water suggests the presence of native, cephalothin-resistant E. coli. Higher MDR observed within water compared to the soil populations suggests that resistance sources other than soil, such as more recent fecal depositions as opposed to residual AMR in soil, could have contributed to higher antibiotic-resistant E. coli in runoff.

Water quality trends in Texas estuaries

Coastal watersheds in Texas have experienced significant human population growth over the past several decades, yet there have been no comprehensive assessments of water quality trends in Texas estuaries. Here, analysis of historical estuarine water quality data indicates regional “hot spots” of change. Galveston Bay and Oso Bay, which have highly urbanized watersheds, currently exhibit symptoms of eutrophication. Symptoms of eutrophication were also found in the Baffin Bay-Upper Laguna Madre complex, which has a sparsely populated but agriculturally-intensive watershed. Increasing salinity was observed in estuaries of the central Texas coast and are attributed to long-term decreases in freshwater inflow. Another artifact of decreasing freshwater inflow is a reduction in the delivery of carbonate minerals to estuaries, which manifests as decreases in pH. With findings from this study, targeted studies can now be directed at the estuaries that are experiencing water quality degradation in order to guide future management efforts.

The Role of Field-Scale Management on Soil and Surface Runoff C/N/P Stoichiometry.

Agricultural runoff is an important contributor to water quality impairment. This study was conducted to evaluate the potential role of field-scale management on carbon (C), nitrogen (N), and phosphorus (P) stoichiometry in soils and runoff from agricultural fields. Cultivated and pasture fields at the Riesel watersheds in central Texas were used for this analysis, and nutrients were transformed to evaluate relative to the Redfield ratio (106 C/16 N/1 P). Using the Redfield ratio, all soil samples were P depleted relative to C and N. The majority of stormflow and baseflow runoff samples contained 9 to 19% Redfield N relative to C and P. Shifting from inorganic fertilizer application to poultry litter as a fertilizer source resulted in increased absolute C, N, and P concentrations in stormflow and baseflow runoff. Increasing rates of poultry litter application increased the Redfield P relative to Redfield C, whereas Redfield N remained relatively constant at roughly 9 to 11% in stormflow runoff from cultivated fields. This study shows how land use and management can affect C/N/P stoichiometry in stormflow and baseflow runoff.

GeoFlood: Large‐Scale Flood Inundation Mapping Based on High‐Resolution Terrain Analysis

AbstractRecent floods from intense storms in the southern United States and the unusually active 2017 Atlantic hurricane season have highlighted the need for real‐time flood inundation mapping using high‐resolution topography. High‐resolution topographic data derived from lidar technology reveal unprecedented topographic details and are increasingly available, providing extremely valuable information for improving inundation mapping accuracy. The enrichment of terrain details from these data sets, however, also brings challenges to the application of many classic approaches designed for lower‐resolution data. Advanced methods need to be developed to better use lidar‐derived terrain data for inundation mapping. We present a new workflow, GeoFlood, for flood inundation mapping using high‐resolution terrain inputs that is simple and computationally efficient, thus serving the needs of emergency responders to rapidly identify possibly flooded locations. First, GeoNet, a method for automatic channel network extraction from high‐resolution topographic data, is modified to produce a low‐density, high‐fidelity river network. Then, a Height Above Nearest Drainage (HAND) raster is computed to quantify the elevation difference between each land surface cell and the stream bed cell to which it drains, using the network extracted from high‐resolution terrain data. This HAND raster is then used to compute reach‐average channel hydraulic parameters and synthetic stage‐discharge rating curves. Inundation maps are generated from the HAND raster by obtaining a water depth for a given flood discharge from the synthetic rating curve. We evaluate our approach by applying it in the Onion Creek Watershed in Central Texas, comparing the inundation extent results to Federal Emergency Management Agency 100‐yr floodplains obtained with detailed local hydraulic studies. We show that the inundation extent produced by GeoFlood overlaps with 60%~90% of the Federal Emergency Management Agency floodplain coverage demonstrating that it is able to capture the general inundation patterns and shows significant potential for informing real‐time flood disaster preparedness and response.

Enhancing Crop Acreage Estimation within a Semiarid Watershed via Statistical Assessments and Techniques

Core Ideas We assessed methods to effectively reduce error in crop acreage estimates. The study compared statistical methods against National Agricultural Statistics Service data at the state and county level. We determined that improvements in crop estimates are largely affected by the input datasets. The error in crop acreage estimates is dependent on the crop, scope of analysis, and growing season. It is necessary to examine if the statistical methods designed to improve Cropland Data Layer (CDL) acreage estimates at a national level improve estimates at the county level. This study determined whether the calibration of CDL by statistical techniques increased the accuracy of crop estimates on a semiarid West Texas watershed. Since there is a substantial amount of agriculture and farming in areas like West Texas, accurate crop estimates of datasets at smaller scales become important. We compared three methods: the pixel counting method of the CDL, linear regression of the CDL against the MODIS Land Cover Type Product (MLCT), and the Bayesian method (posterior probability) on the National Land Cover Dataset (NLCD) against the USDA National Agricultural Statistical Service (NASS) Quick Stats. Four crops [corn (Zea mays L.), cotton (Gossypium hirsutum L.), sorghum (Sorghum bicolor [L.] Moench), and winter wheat (Triticum aestivum L.)] were analyzed at state and county levels during two growing seasons (2009 and 2011). Prediction errors were estimated by comparing computed acreage with the appropriate NASS Quick Stats values. The total error for each assessment method was estimated for all acreage values in that particular assessment category. We found that the Bayesian method improved the estimates of sorghum and winter wheat and linear regression improved acreage estimates for cotton and corn in 2011 and 2009, respectively. Our results indicate that county‐level adjustments are very sensitive. Therefore, one must carefully select the method that best fits the type of crop, geographical location, and growing season being considered.

A QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models

This article presents QSWATMOD, a QGIS-based graphical user interface for application and evaluation of SWAT-MODFLOW models. QSWATMOD includes: (i) pre-processing modules to prepare input data for model execution, (ii) configuration modules for SWAT-MODFLOW options, and (iii) post-processing modules to view and interpret model results. QSWATMOD, written in Python, creates linkage files between SWAT and MODFLOW models, runs a simulation, and displays results within the open source Quantum Geographic Information System (QGIS) environment. QSWATMOD is equipped with functionalities that assist in storing and retrieving user and default configuration settings and parameter values, performing the linkage and simulation processes, and uses various geo-processing functionalities (e.g., selection, intersection, union) of QGIS. The use of QSWATMOD is demonstrated through an application to the 471 km2 Middle Bosque River Watershed in central Texas. As the number of SWAT-MODFLOW users grows worldwide, QSWATMOD can be a valuable tool to assist in creating and managing SWAT-MODFLOW projects.

Development of a hydrological model for simulation of runoff from catchments unbounded by ridge lines

Watershed hydrological models are effective tools for simulating the hydrological processes in the watershed. Although there are a plethora of hydrological models, none of them can be directly applied to make water conservation decisions in irregularly bounded areas that do not confirm to topographically defined ridge lines. This study proposes a novel hydrological model that can be directly applied to any catchment, with or without ridge line boundaries. The model is based on the water balance concept, and a linear function concept to approximate the cross-boundary flow from upstream areas to the administrative catchment under consideration. The developed model is tested in 2 watersheds – Riesel Experimental Watershed and a sub-basin of Cedar Creek Watershed in Texas, USA. Hypothetical administrative catchments that did not confirm to the location of ridge lines were considered for verifying the efficacy of the model for hydrologic simulations. The linear function concept used to account the cross boundary flow was based on the hypothesis that the flow coming from outside the boundary to administrative area was proportional to the flow generated in the boundary grid cell. The model performance was satisfactory with an NSE and r2 of ≥0.80 and a PBIAS of <25 in all the cases. The simulated hydrographs for the administrative catchments of the watersheds were in good agreement with the observed hydrographs, indicating a satisfactory performance of the model in the administratively bounded areas.

Evaluation of potential E. coli transport from on-site sewage facilities in a Texas watershed

Potential E. coli contamination in surface waters from on-site sewage facilities was investigated in the Dickinson Bayou watershed, Texas. This watershed is listed as impaired due to bacteria by the Texas Commission on Environmental Quality. Two water quality monitoring stations, with flow meters and automatic water samplers, were installed in the watershed to assess E. coli concentrations in surface runoff. One monitoring station was installed in a neighborhood that solely used an on-site sewage facility (OSSF) and the second monitoring station, the control site, was installed in a neighborhood connected to a municipal sewage plant. For 16 runoff events at the OSSF site, the combined geometric mean E. coli concentration was 639 colony forming units (CFU)/100milliters while the geometric mean E. coli concentration for 13 runoff events at the control site was 371 CFU/100milliliters. The E. coli concentrations from the 2 sites were not statistically different, suggesting that OSSFs may not be the major cause of bacterial contamination in the Dickinson Bayou watershed. In addition, a bacterial source tracking method was employed, which concluded that a portion of the E. coli from both sites were of human origin. Citation: Morrison D, Karthikeyan R, Munster C, Jacob J, Gentry T. 2017. Evaluation of potential E. coli transport from on-site sewage facilities in a Texas watershed. Texas Water Journal. 8(1):18-28. Available from: https://doi.org/10.21423/twj.v8i1.7041.

Evaluating the ranch and watershed scale impacts of using traditional and adaptive multi-paddock grazing on runoff, sediment and nutrient losses in North Texas, USA

Grazing management practices have a significant influence on ecosystem services provided by rangelands. An assessment of hydrologic and water quality impacts of traditional and alternate grazing management practices facilitates adoption of best management practices for long-term sustainability of rangelands. A study was conducted to quantify the runoff, sediment and nutrient losses under traditional continuous and adaptive multi-paddock (MP) grazing management practices in the rangeland-dominated (71% rangeland) Clear Creek Watershed (CCW) in north Texas in the United States using the Agricultural Policy/Environmental Extender (APEX) model. The model was calibrated and validated using observed herbaceous plant biomass and daily soil moisture data at four study ranches in the CCW [two under MP grazing and one each under light continuous (LC) and heavy continuous (HC) grazing practices], and using monthly streamflow, sediment, total nitrogen (TN) and total phosphorus (TP) data measured at the watershed outlet. Both ranch- and watershed-scale results indicated a strong influence of the grazing practice on runoff and water quality. When the grazing management was changed from the baseline MP to HC at one of the study ranches, the simulated average (1980–2013) annual surface runoff, sediment, TN and TP losses increased by 148%, 142%, 144% and 158%, respectively. At the watershed-scale, changing grazing management from the baseline HC to adaptive MP reduced the average annual surface runoff, sediment, TN and TP loads at the watershed outlet by 39%, 34%, 33% and 31%, respectively. In addition, implementation of adaptive MP grazing reduced streamflow during the high flow conditions that have ≤10% exceedance probability, by about 20%, and hence reduced the chances of flooding downstream of the watershed. Adaptive MP grazing was therefore found to be an effective conservation practice on grazing lands for enhancing water conservation and protecting water quality.