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HighlightsThis study calibrated a hydrologic model for pre- and postfire conditions and simulated postfire hydrologic response.Postfire rainfall-runoff was more influenced by canopy and soil water factors and less by antecedent soil moisture (ASM).For moderate to low ASM, postfire streamflow responded linearly to precipitation; prefire showed little response.Postfire streamflow increased and shifted from baseflow- to runoff-dominated, and runoff occurred across all ASM.Abstract. Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning. Keywords: Landscape change, PRMS, Runoff change, Wildfire.

HighlightsFilter strip processes for water pollutant reductions were quantified from 74 studies with almost 300 data points.Regression was significant versus width (sediment, N, P), area ratio (sediment, atrazine), and Ks (N, P, atrazine, alachlor).This review discusses ten functional factors affecting FS efficiency as well as FS monitoring recommendations.Cost-effectiveness was assessed and varies considerably by influent load and treatment effectiveness.Abstract. Filter strips (FSs) are edge-of-field conservation practices commonly implemented to reduce flux of sediment, nutrients, and other constituents from agricultural fields. While various aspects of FS effectiveness have been reviewed, this study provides a comprehensive summary of FS efficiency data for sediment, nutrients, pesticides, and pathogens as part of a special collection focused on agricultural conservation practices. This analysis also fills an important gap by assessing performance-based FS costs and cost-effectiveness. Data from 74 U.S. and international studies with 294 different treatments and 3,050 replications were compiled and analyzed. Results showed that runoff reduction tended to increase with increasing FS width up to about 15 m and that sediment reduction was significantly related to the ratio of FS area to drainage area and to FS width, with reduction tending to increase with increasing width up to about 20 m. Total P reduction was significantly related to FS soil saturated hydraulic conductivity, and total N reduction was significantly related to both saturated hydraulic conductivity and width. Total P and total N reductions both tended to increase with increasing FS width up to about 20 m and with increasing FS slope up to about 10%. Annualized FS costs were estimated to range from $314 to $865 ha-1 year-1 for different FS implementations. A major component of the cost is the opportunity cost of taking land out of production. Costs per unit of sediment retained by FS systems ranged from $10.3 to $18.6 Mg-1. A comprehensive assessment of FS cost-effectiveness (cost:benefit) is needed. Monitoring equipment, approaches, and recommendations are discussed, acknowledging the challenges of implementing field-scale FS studies. This information is critical to guide on-farm and programmatic FS decisions and to increase the scientific understanding of this commonly used agricultural conservation practice. Keywords: Best management practice, Buffer strip, Nonpoint-source pollution, Riparian buffer, Vegetated filter strip.

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

Highlights A new DPSIR-ESA Vulnerability Assessment (DEVA) framework for land-resource assessment is presented. We broadly define a land-resource target system to include ecological resources and socioeconomic systems. DEVA operationalizes the process of assessing the vulnerability of a target system to external stressors. Six case studies provide examples of the key DEVA concepts and the seven DEVA steps. Abstract. Land resources are central to understanding the relationship between humans and their environment. We broadly define land resources to include all the ecological resources of climate, water, soil, landforms, flora, and fauna and all the socioeconomic systems that interact with agriculture, forestry, and other land uses within some system boundary. Understanding the vulnerability of land resources to changes in land management or climate forcing is critical to developing sustainable land management strategies. Vulnerability assessments are complex, given the multiple uses of the assessments, the multi-disciplinary nature of the problem, limited understanding, the dynamic structure of vulnerability, scale issues, and problems with identifying effective vulnerability indicators. Here, we propose a novel conceptual framework for vulnerability assessments of land resources that combines the driver-pressure-state-impact-response (DPSIR) framework adopted by the European Environment Agency to describe interactions between society and the environment, and the exposure-sensitivity-adaptive capacity (ESA) framework used by the Intergovernmental Panel on Climate Change to assess impacts of climate change. The DPSIR-ESA Vulnerability Assessment (DEVA) framework operationalizes the process of assessing the vulnerability of a target system to external stressors. The DEVA framework includes seven steps: (1) definition of the target system (land resource), (2) description of internal characteristics of the target system (state), (3) description of target system vulnerability indicators (adaptive capacity, sensitivity), (4) description of stressor characteristics (drivers, pressures), (5) description of stressor vulnerability indicators (exposure), (6) description of target system response to stressors (impacts), and (7) description of modifications to target system or stressors (responses). In stating that they have applied the DEVA framework, analysts acknowledge that they (1) have considered the full breadth of each DEVA element, (2) have made conscious decisions to limit the scope and complexity of certain elements, and (3) can communicate both the rationale for these decisions and the impact of these decisions on the vulnerability assessment results and recommendations. The DEVA framework was refined during invited presentations and follow-up discussions at a series of special sessions with leading experts at two successive ASABE Annual International Meetings. Six case studies drawn from the sessions elaborate on the DEVA framework and provide examples of the key concepts. The DEVA framework gives engineers, planners, and analysts a flexible new approach to apply a broad array of useful tools for vulnerability assessment of land resource systems. Keywords: Driver-pressure-state-impact-response (DPSIR) framework, Exposure-sensitivity-adaptive capacity (ESA) framework, DPSIR-ESA Vulnerability Assessment (DEVA) framework, Land resource, Systems thinking.

HighlightsWe provide context and perspectives on 13 articles in the Global Water Security collection.Limited irrigation, precision irrigation, and sustainable water resources management were critical themes.The collection emphasizes the need for adopting location-specific technologies to achieve global water security.Advances in data acquisition, data analysis, and modeling should be utilized to aid managing water resources. Abstract. This article introduces the Global Water Security collection in this issue of Transactions of the ASABE and issue 36(1) of Applied Engineering in Agriculture. Researchers, educators, industry partners, agricultural producers, and policymakers from 19 countries met at Hyderabad, India, to discuss critical issues and advancements at the Global Water Security Conference for Agriculture and Natural Resources. The conference was organized jointly by ASABE and the Indian Society of Agricultural Engineers (ISAE). This special collection consists of 13 articles selected from the 245 meeting presentations as well as invited articles. A perspectives article in this collection summarizes seven key priorities identified for action at the conference: reduce food waste, increase wastewater reuse, increase agricultural resiliency and efficiency, optimize irrigation efficiency and increase crop water productivity, improve water supply management, improve water resource infrastructure, and enhance water resource decision-making and policy formulation. The remaining 12 articles address a wide range of water security topics grouped by four themes: sustainable management of water resources (3 articles), limited irrigation for water conservation (5 articles), precision irrigation management (2 articles), and water management in hilly regions (2 articles). While these articles are not inclusive of all water security challenges in the agriculture and natural resources sectors, they highlight selected important challenges and potential solutions. The research presented in this special collection emphasizes the importance of developing and using appropriate location-specific technologies that increase water application efficiency and water use efficiency while maintaining adequate water supplies for natural resource functions and ecosystem services to ensure global water security. Keywords: Climate change, Crop water productivity, Food security, Irrigation efficiency, Natural resource policy, Wastewater reuse, Water resource infrastructure, Water scarcity.

This Dataset was compiled from cited literature in support of the review article "Douglas-Mankin KR, Helmers MJ, Harmel RD. 2021. Review of filter strip performance and function for improving water quality from agricultural lands. Trans. ASABE, 64(2). https://doi.org/10.13031/trans.14169", which summarizes, analyzes, and interprets data from 74 published studies to assess filter strip effectiveness in reducing sediment, nutrients, pesticides, and pathogens from agricultural lands. Journal article will be online April 2021.

Uncertainty in simulating hydrologic response to future climate is generally assumed to result from the combined uncertainties of the General Circulation Model (GCM), representative concentration pathway (RCP), downscaling method, and hydrologic model used. However, another source of uncertainty, the observed climate data source used to statistically downscale and bias-correct GCM projections, has largely been overlooked. This study assessed the shifts, variability, and uncertainty in streamflow simulation from three downscaling data sources (NCDC land-based weather stations, NEXRAD spatial grid, and PRISM spatial grid) relative to those introduced by six GCMs and three RCPs in west-central Kansas, U.S. Streamflow simulated by the Soil and Water Assessment Tool (SWAT) hydrologic model was found to be more sensitive to future precipitation than to maximum and minimum temperatures. The greatest uncertainty in simulated streamflow was associated with selection of the GCM. Uncertainty in simulated streamflow associated with the observed bias-correction data source (NCDC, PRISM, NEXRAD) was greater than with RCPs and was primarily related to uncertainty in precipitation. This study highlighted the importance of recognizing uncertainty from bias-correction data sources in representing future climate scenarios in hydrologic simulations.

Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (Ke) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (Kss) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that Ke parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of Kss or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent Kss or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of Kss, and, ultimately, arid rangeland soil erosion simulation. Keywords: Erosion, GIS, Hydrologic model, Rangeland, Runoff.

HighlightsASABE journals publish impactful research in multiple article types in addition to research articles. Prospective authors should consider a journal’s peer-review quality, readership, metrics, and page charges. An article’s impact should be measured based on citations instead of predicted based on the journal’s impact factor. Always recommend subject matter experts as reviewers so that a manuscript can benefit from their suggestions. Publishing in ASABE journals offers opportunities for contributing to and being recognized by the profession. Keywords: Impact factor, Page charges, Peer-reviewed journals, Review quality, Review time

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

This article introduces a special collection of fourteen articles accepted from among the 140 technical presentations, posters, and meeting papers presented at the 10th International ASABE Drainage Symposium. The symposium continued in the tradition of previous symposia that began in 1965 as a forum for presenting and assessing the progress of drainage research and implementation throughout the world. The articles in this collection address a wide range of topics grouped into five broad categories: (1) crop response, (2) design and management, (3) hydrology and scale, (4) modeling, and (5) water quality. The collection provides valuable information for scientists, engineers, planners, and others working on crop production, water quality, and water quantity issues affected by agricultural drainage. The collection also provides perspectives on the challenges of increasing agricultural production in a changing climate, with ever-greater attention to water quality and quantity concerns that will require integrated technical, economic, and social solutions. Keywords: ASABE Drainage Symposium, crop response, design and management, hydrology and scale, modeling, water quality.

In 2010, the Natural Resources and Environmental Systems Hydrology Committee (NRES-21) of ASABE initiated a long-term process to develop guidelines to improve modeling practice through better understanding of the calibration, validation, and evaluation process across applications and more effective interpretation and communication of model performance. This effort generated a compilation of 23 articles with model-specific descriptions and guidance (2012), a position paper outlining guidance for evaluating, interpreting, and communicating performance of hydrologic and water quality models considering intended use (2014), and a compilation of ten articles addressing key topics related to model calibration and validation (2015). In 2016, the first draft of ASABE Engineering Practice 621 (EP621), “Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality (H/WQ) Models,” was developed, subsequently revised, and ultimately approved by the ASABE Standards Committee in 2017. EP621 provides guidelines, not prescriptive requirements, and as such recommends “good” modeling practices to enhance calibration, validation, evaluation, and communication of H/WQ models through establishment of consistent terminology; model selection; compilation and processing of input data and calibration, validation, and evaluation data; determination of model performance measures; model parameterization and calibration; re-examination of input and calibration data and/or consideration of model refinement; re-evaluation of model performance; and documentation of modeling process and results. EP621 can be obtained from the ASABE Technical Library at https://elibrary.asabe.org/abstract.asp?aid=47804. The objectives of this technical note are to review the process and rationale used to develop EP621 and to briefly summarize its major components. Keywords: Hydrologic processes, Hydrology, Model calibration, Modeling, Model validation, Water quality.

This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals ( and ) in 2017. Context, scope, and key results of the published articles are reviewed and perhaps more importantly areas for increased research attention are recommended. This article describes experimental and modeling advances in hydrology, agroecosystems, climate change effects, soil erosion, agricultural irrigation, agricultural drainage, forest resource management, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling and Decision Support for Optimizing Use of Limited Water, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land, water, and agroecosystems, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to expand its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. With this goal in mind, recommendations for future research needs are proposed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results. Keywords: Agriculture, Climate change, Composting, Crop models, Crop water use, Crop yield, Drainage, Drought, Environment, Field experimentation, Food, Forest, Greenhouse gas emissions, Hydrologic modeling, Hydrology, Irrigation, Livestock, Manure management, Mode

HighlightsWe provide context and perspectives on articles in the Wetland Ecosystem Resilience collection.Insights gained on wetland resilience to sea-level rise and climate change, land use and drainage, and nutrients. Abstract. The objective of this article is to introduce a collection of articles that explore current research and scientific thought on wetland ecosystem resilience. The collection contains articles on wetland resilience to climate change, agricultural land use-driven change, and recreational land use, along with evaluations of wetland resilience through high-resolution monitoring and modeling tools. Wetland settings in the U.S. span tidal marshes and coastal plain non-riverine wetlands in North Carolina, prairie potholes in Iowa, Appalachian floodplain wetlands, and floating treatment wetlands in the Midwest. The studies in this collection found vertical accretion rates of 0.7 to 4.0 mm year-1 in a tidal marsh, a wide range of potential wetland hydroperiod responses to climate change, substantial decreases in inundation period, crop yield, and surface-water nitrate (but increases in phosphorus) in artificially drained potholes, and nitrate removal in carbon-amended floating treatment wetlands. Further work is needed to better understand how to design and enhance wetland systems in agricultural regions, better preserve wetland ecosystem services in areas affected by land use and climate change, and provide technical standards for the wide range of designs currently used for wetland treatment systems. Keywords: Agricultural wetlands, Resiliency, Temporal data, Treatment wetlands, Water chemistry, Water quality, Water treatment

The evaluation of historical water use in the Upper Rio Grande Basin (URGB), United States and Mexico, using Landsat-derived actual evapotranspiration (ETa) from 1986 to 2015 is presented here as the first study of its kind to apply satellite observations to quantify long-term, basin-wide crop consumptive use in a large basin. The rich archive of Landsat imagery combined with the Operational Simplified Surface Energy Balance (SSEBop) model was used to estimate and map ETa across the basin and over irrigated fields for historical characterization of water-use dynamics. Monthly ETa estimates were evaluated using six eddy-covariance (EC) flux towers showing strong correspondence (r2 > 0.80) with reasonable error rates (root mean square error between 6 and 19 mm/month). Detailed spatiotemporal analysis using peak growing season (June−August) ETa over irrigated areas revealed declining regional crop water-use patterns throughout the basin, a trend reinforced through comparisons with gridded ETa from the Max Planck Institute (MPI). The interrelationships among seven agro-hydroclimatic variables (ETa, Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), maximum air temperature (Ta), potential ET (ETo), precipitation, and runoff) are all summarized to support the assessment and context of historical water-use dynamics over 30 years in the URGB.

Farmers are challenged to maintain yield and economic productivity with declining water resources and climatic variability in semi-arid regions worldwide. Growth stage-based deficit irrigation has been suggested as a feasible approach to maintain yields with less water. Experiments were conducted in 2012, 2013, and 2015 in which maize (Zea mays L.) was irrigated under twelve treatments with varied levels of deficit irrigation during the late vegetative (Lveg) and maturation (Mat) growth-stage periods in Northern Colorado. The Root Zone Water Quality Model 2 (RZWQM2)-CERES-Maize model was used to simulate the effects of growth stage-based deficit irrigation on maize production and yield components. The results showed that RZWQM2 could simulate the impact of temperature on maize phenology but did not simulate the impact of water stress on maize maturity. Both simulated and observed aboveground biomass, grain yield, and kernel weight decreased with the decrease of irrigation water amount during Lveg and Mat periods. In general, the simulated aboveground biomass and grain yield showed larger errors in terms of root mean squared error (RMSE), relative RMSE, and Nash–Sutcliffe efficiency, than those reported in the previous modeling studies where deficit irrigation was applied uniformly throughout the growing seasons in the same field. Future efforts to improve the effects of deficit irrigation on kernel development will likely make RZWQM2 a better tool for optimizing irrigation management in semi-arid regions.

This article introduces the fourteen articles that comprise the “Crop Modeling and Decision Support for Optimizing Use of Limited Water” collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimize agricultural water management. The following section titles indicate the topics covered in this collection: (1) evapotranspiration modeling (one article), (2) model development and parameterization (two articles), (3) application of crop models for irrigation scheduling (five articles), (4) coordinated water and nutrient management (one article), (5) soil water management (two articles), (6) risk assessment of water-limited irrigation management (one article), and (7) regional assessments of climate impact (two articles). Changing weather and climate, increasing population, and groundwater depletion will continue to stimulate innovations in agricultural water management, and crop models will play an important role in helping to optimize water use in agriculture. Keywords: Agricultural water management, Crop modeling, Evapotranspiration, Irrigation, Irrigation scheduling.

Accurate modeling of crop growth within watershed hydrological models is essential, yet most studies pay little attention to parameterizing crop-growth sub-models or validating their performance. This study evaluated crop sub-model parameters of Soil and Water Assessment Tool (SWAT), a widely used, physically based, hydrological model. Baseline SWAT crop parameters were calibrated at the model hydrologic-response-unit-scale using 10 years of replicated field-scale data at one site and validated using 5 years at a second site for corn and grain sorghum, and new parameters were developed and tested for sweet sorghum (bioenergy crop) using 4 years of unreplicated field data. Calibration of crop yields focused on four parameters: lower harvest index (WYSF), harvest index for optimal growing condition (HVSTI), radiation use efficiency (BIO_E), and maximum leaf area index (BLAI). Calibration improved model performance and resulted in slight changes to SWAT default values for four parameters for corn and sorghum. These results provide important preliminary parameters for modeling sweet sorghum in SWAT; both BIO_E and BLAI were greater than default values for grain sorghum. Calibrated parameters improved model performance in validation of corn but not grain sorghum, which was heavily influenced by drought conditions and possibly other management differences at the validation site. Results of this study support use of site-specific, rather than default or off-site, calibration of crop-model parameters to minimize effects on model performance of different soil, water, and nutrient management conditions. Watershed-specific, field-scale crop-yield calibration methods demonstrated in this study are recommended to reduce the plant-growth-related systematic error component of larger-scale hydrological processes (such as streamflow).

Projected climate change is expected to substantially affect crop and livestock production, and water availability and quality. Concomitantly, the agricultural community is faced with a challenge of increasing food production by more than 70% to meet demand from global population increase by the mid-21st century. This article introduces a Special Collection on Climate Change and reviews principal findings from nine articles drawn from papers presented at the 2015 ASABE 1st Climate Change―Adaptation and Mitigation Symposium. Studies present simulations of crop and livestock production-system adaptations to climate change scenarios; impacts of climate change scenarios on water resources, soil erosion and nonpoint-source pollutant source areas; regional assessment of climatic shifts; and mitigation of greenhouse gas emissions from livestock production. Articles span regional issues across the U.S., southern Asia, and Brazil. These research articles clearly indicate that climate variability and change can negatively impact food production and water resources, and that innovative strategies are needed to mitigate those negative impacts.

Evapotranspiration (ET) processes at the leaf to landscape scales in multiple land uses have important controls and feedbacks for local, regional, and global climate and water resource systems. Innovative methods, tools, and technologies for improved understanding and quantification of ET and crop water use are critical for adapting more effective management strategies to cope with increasing demand for freshwater resources under global climate change. This article introduces an ASABE Special Collection of 12 articles on ET monitoring and modeling research for multiple land uses and scales. The collection focuses on recent advances in four critical topical areas: (1) reference ET (REF-ET) method development and applications, including crop management and irrigation scheduling, limitations due to sensor inaccuracies and variability, and sensitivity to climatic drivers (three articles); (2) ET process and pathway characterization, including canopy interception, transpiration, and soil evaporation measured using various state-of-the-art techniques on crop lands and plantation forests, and effects of soil moisture on grassland water balance (three articles); (3) ET simulation within hydrological models (SWAT, MIKE SHE, RZWQM, RZ-SHAW, RegCM-BATS, DRAINMOD-FOREST, and Thornthwaite water balance) as well as related processes, such as crop growth and ET/PET ratios, for grass, crop, and forest lands (four articles); and (4) geospatial technology applications, such as using remote sensing to estimate ET and its components (soil evaporation and transpiration) for various land uses (two articles). Study sites represent a range of spatial scales and ecohydrological settings, including grasslands in Inner Mongolia dry lands in northern China, semiarid high plains in Texas, corn production regions from Iowa to Colorado, forest plantations on the humid Atlantic Coastal Plain, developed coastal areas on the island of Taiwan, and the continental U.S. Results from these studies will help guide current development and assessment of REF-ET, ET, and monitoring and modeling of their components in multiple scales and ecosystems. The studies also establish a platform for addressing potential inaccuracies in data from weather sensors and algorithms used in remote sensing products for estimating ET and its parameters, including uncertainties in REF-ET estimates, for tall forest vegetation in particular. Furthermore, the studies offer insights into the interactions between climatic variability and change and vegetation through the ET process.

Modeling of ephemeral gully (EG) erosion has lagged that of other soil erosion processes despite its major contribution to watershed sediment losses. Several process and semi-empirical based simulation models have been used to assess the occurrence and location of EGs, magnitude of soil losses from EGs, and degradation, aggradation, and transport of sediment through EGs, but no comprehensive EG model exists. This paper reviews these models and presents a thorough discussion of their background, general formulations, key equations, field assessments, assumptions and limitations. Most current EG models evolved from the original formulations used in CREAMS, with incremental improvements by EGEM, WEPP, AnnAGNPS-REGEM, RUSLER-EphGEE models. These models provide process-based estimation of EG processes and field-scale soil-erosion contributions, but all models, except EphGEE, require users to have a priori knowledge of EG locations and all have significant shortcomings. Several topographic index models are discussed that provide simplistic approaches to locate EGs on the landscape using only topographic features. Process-based threshold index models may provide a more robust simulation of EG location and length, though testing has been limited. EG modeling appears to still be in its infancy, with great opportunities for future research, as discussed herein, to improve the understanding and simulation of EG erosion and transport processes.

Many conservation programs have been established to motivate producers to adopt best management practices (BMP) to minimize pasture runoff and nutrient loads, but a process is needed to assess BMP effectiveness to help target implementation efforts. A study was conducted to develop and demonstrate a method to evaluate water-quality impacts and the effectiveness of two widely used BMPs on a livestock pasture: off-stream watering site and stream fencing. The Soil and Water Assessment Tool (SWAT) model was built for the Pottawatomie Creek Watershed in eastern Kansas, independently calibrated at the watershed outlet for streamflow and at a pasture site for nutrients and sediment runoff, and also employed to simulate pollutant loads in a synthetic pasture. The pasture was divided into several subareas including stream, riparian zone, and two grazing zones. Five scenarios applied to both a synthetic pasture and a whole watershed were simulated to assess various combinations of widely used pasture BMPs: (1) baseline conditions with an open stream access, (2) an off-stream watering site installed in individual subareas in the pasture, and (3) stream or riparian zone fencing with an off-stream watering site. Results indicated that pollutant loads increase with increasing stocking rates whereas off-stream watering site and/or stream fencing reduce time cattle spend in the stream and nutrient loads. These two BMPs lowered organic P and N loads by more than 59% and nitrate loads by 19%, but TSS and sediment-attached P loads remained practically unchanged. An effectiveness index (EI) quantified impacts from the various combinations of off-stream watering sites and fencing in all scenarios. Stream bank contribution to pollutant loads was not accounted in the methodology due to limitations of the SWAT model, but can be incorporated in the approach if an amount of bank soil loss is known for various stocking rates. The proposed methodology provides an adaptable framework for pasture BMP assessment and was utilized to represent a consistent, defensible process to quantify the effectiveness of BMP proposals in a BMP auction in eastern Kansas.

As a continuation of efforts to provide a common background and platform for development of calibration and validation (C/V) guidelines for hydrologic and water quality (H/WQ) modeling, ASABE members worked to determine critical topics related to model C/V, perform a synthesis of a previously published special collection of articles and other relevant literature, and provide topic-specific recommendations based on the synthesis as well as personal modeling expertise. This article introduces a special collection of nine research articles covering key topics related to calibration and validation of H/WQ models. The topics include: terminology, hydrologic processes and model representation, spatial and temporal scales, model parameterization, C/V strategies, sensitivity, uncertainty, performance measures and criteria, and documentation and reporting. The main objective of this introductory article is to introduce and summarize key aspects of these topics, including recommendations. Individually, the articles provide model practitioners with detailed topic-specific recommendations related to model calibration, validation, and use. Collectively, the articles present recommendations to enhance H/WQ modeling.

The increasing use of hydrologic and water quality (H/WQ) models for technical, policy, and legal decision making calls for greater transparency in communicating the methods used and decisions made when using H/WQ models. The objectives of this article are to: (1) provide guidelines to properly document H/WQ model calibration, validation, and use, and (2) raise issues about how to improve model documentation and reproducibility, and encourage open discussion of these topics in the H/WQ modeling community. First, eight recommended elements of model documentation and reporting are identified and described, and good examples are provided for each. Next, steps to move the H/WQ modeling community toward a culture of full model reproducibility are discussed. The use by model practitioners of the consistent and comprehensive elements described herein for documentation and reporting of H/WQ model calibration, validation, and use will allow better interpretation of published modeling studies, improve the utility of modeling studies, and allow more systematic advancement of H/WQ models.

High corn prices cause farmers to plant more corn on fields that were planted to corn in the previous year, rather than alternating between corn and soybeans. Cultivating corn after corn requires greater nitrogen fertilizer and some of this nitrogen flows into waterways and causes environmental damage. We estimate the effect of crop prices on nitrogen losses for most fields in Iowa, Illinois, and Indiana using crop data from satellite imagery. Spatial variation in these high-resolution estimates highlights the fact that the environmental effects of agriculture depend not only on what is grown, but also on where and in what sequence it is grown. Our results suggest that the change in corn and soybean prices due to a billion gallons of ethanol production expands the size of the hypoxic zone in the Gulf of Mexico by roughly 30 square miles on average, although there is considerable uncertainty in this estimate.

Integrating the analysis of natural and social systems to achieve sustainability has been an international scientific goal for years (1, 2). However, full integration has proven challenging, especially in regard to the role of culture (3), which is often missing from the complex sustainability equation. To enact policies and practices that can achieve sustainability, researchers and policymakers must do a better job of accounting for culture, difficult though this task may be.

A B S T R A C T Soil conservation practices have been implemented to control soil degradation from sheet and rill erosion, but excessive sediment runoff remains among the most prevalent water quality problems in the world. Ephemeral gully (EG) erosion has been recognized as a major source of sediment in agricultural watersheds; thus, predicting location and length of EGs is important to assess sediment contribution from EG erosion. Geomorphological models are based on topographic information and ignore other important factors such as precipitation, soil, topography, and land use/land management practices, whereas physically based models are complex, require detailed input information, and are difficult to apply to larger areas. In this study, an approach was developed to incorporate a process-based Overland FlowTurbulent (OFT) EG model that contained factors accounting for drainage area, surface roughness, slope, soil critical shear stress, and surface runoff in the ArcGIS environment. Two hydrologic models, Soil Water Assessment Tool (SWAT) and ArcCN-Runoff (ACR), were adopted to simulate precipitation excess in Goose Creek watershed in central Kansas, USA. These two realizations of the OFT model were compared with the Slope-Area (SA) topographic index model for accuracy of EG location identification and length calculation. The critical threshold index in the SA model was calibrated in a single field in the watershed prior to EG identification whereas the OFT models were uncalibrated. Results demonstrated overall similar performance between calibrated SA model and uncalibrated OFT-SWAT model, and both outperformed the uncalibrated OFT-ACR model. In simulation of EG location, the OFT-SWAT model resulted in 12% fewer false negatives but 8% more false positives than the SA model, compared with 19% fewer false positive and 6% more false negatives than the OFT-ACR model. Greater errors in runoff estimation by ACR translated directly into errors in EG simulation. All models over-predicted EG lengths compared with observed data, though OFT-SWAT and SA models did so with better fit exceedance probability curves, about zero Nash-Sutcliff model efficiency and ≤40% bias compared to -3 model efficiency and >100% bias for OFT-ACR. Success of the uncalibrated OFT-SWAT model in producing satisfactory predictions of EG location and EG length shows promise for process-based EG simulation. The OFT-SWAT model used data and parameters also commonly used for SWAT model development, which should simplify its adoption to other watersheds and regions. Further testing is needed to determine the robustness of the OFT-SWAT model to dissimilar field and hydrologic conditions. It is expected that inclusion of more site-specific physical properties in OFT-SWAT would improve model performance in predicting location and length of EGs, which is essential for accurate estimation of EG sediment erosion rates.

This article introduces a collection of papers from the third biennial ASABE 21st Century Watershed Technology Conference and Workshop: Improving Water Quality and the Environment, held in Bari, Italy, on 26-31 May 2012. This Special Collection consists of seven articles (plus one published previously) selected from the 59 meeting papers. The technical presentations at the conference focused on solving spatial and temporal water quality and quantity problems and addressed topics such as watershed management in developing countries, water quality standards, agricultural best management practice (BMP) effectiveness, emerging problems, impact of extreme weather conditions, biological monitoring, increasing stakeholder involvement, political and economic implications, watershed implementation planning, forest and rangeland water quality issues, and water resources education. At first glance, these topics seem quite diverse. However, a growing trend in cutting-edge hydrological science research, and a thread that emerged from these papers, is that of “data mining.” In this article, we make an editorial statement that briefly introduces the basic principles of data mining and uses this concept as an organizing theme to feature eight articles of this Special Collection.

Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: TSA = 30 for S1 and TSA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Watershed models have been widely used to estimate soil erosion and evaluate the effectiveness of conservation practices at different temporal and spatial scales; however, little progress has been made in applying these theoretical model results to the practical challenge of allocating conservation practice funding to meet specific soil loss objectives. Black Kettle Creek subwatershed (7,809 ha [19,295 ac]) of Little Arkansas River Watershed (360,000 ha [889,579 ac]) in south central Kansas was the focus of an innovative project to target conservation practice funding and pay directly for modeled sediment reduction. Detailed data (10 m [33 ft] digital elevation model topography, Soil Survey Geographic database soils, and a manually developed land use/land cover layer) were input into the Soil and Water Assessment Tool model, and the calibrated model was used to quantify soil erosion for each field. Effectiveness of locally relevant best management practices (BMPs) was simulated for each field. The simulated field-scale effectiveness for implemented BMPs ranged from 9% to 83% for single BMPs and 67% to 100% for selected combinations of BMPs. An in-field signup sheet was developed with field-specific sediment loss–based payments calculated for each BMP option. BMP implementation was 16.7% of cropland area prior (preinstalled BMPs) to the project, and 30.6% of cropland area (postinstalled BMPs) was added due to project-funded implementation. Postinstalled BMP implementation (47.3% of cropland) resulted in 35.8% sediment yield reduction compared to the no-BMPs scenario and 21.9% reduction compared to preinstalled BMP conditions, which was better than initially projected for this project. Inclusion of nontargeted fields and less-than-optimal BMPs had no influence on achieving soil loss objectives because payments were based on implemented soil loss rather than implemented area. Targeting of conservation practices based on payments scaled directly by project outcome (in this case, dollars per ton of sediment reduction) using a modeling approach allowed flexibility for both adopters (farmers) and funders (project staff) while assuring the project objective (i.e., sediment reduction) was met.

Soil erosion by the environmental agents of water and wind is a continuing global menace that threatens the agricultural base that sustains our civilization. For over 70 years, ASABE members have been at the forefront of research to understand erosion processes, measure erosion and related processes, and model very complex sediment detachment, transport, and deposition. The ASABE Erosion Control Group (SW-22) and Erosion Control Research (SW-223) committees periodically sponsor international symposia to provide an avenue for exchange of ideas and information by engineers, scientists, and students from around the world. The two most recent symposia were the Soil Erosion Research for the 21st Century Symposium held in January 2001 in Honolulu, Hawaii, and the International Symposium on Erosion and Landscape Evolution (ISELE) held in September 2011 in Anchorage, Alaska. This article describes these two events, provides a description of major outcomes, and introduces a collection of papers that were presented as part of the 2011 ISELE in Alaska. The ISELE sessions focused on seven themes: water erosion process research; aeolian erosion and fugitive dust emission; highly disturbed, urban, and arid lands; erosion measurement and assessment; prevention and control of upland and in-stream erosion; soil erosion modeling; and impacts of global change on erosion and landscape evolution. More than 120 people from 16 countries attended the ISELE and gave 112 oral and poster presentations. From those presentations, 24 papers were accepted for publication in Transactions of the ASABE (22 papers, this issue) and Applied Engineering in Agriculture (two papers, next issue). The results from these symposia and in these papers show active and vibrant research to address soil erosion problems, particularly in the face of global environmental changes.

To provide a common background and platform for consensual development of calibration and validation guidelines, model developers and/or expert users of the commonly used hydrologic and water quality models globally were invited to write technical articles recommending calibration and validation procedures specific to their model. This article introduces a special collection of 22 research articles that present and discuss calibration and validation concepts in detail for 25 hydrologic and water quality models. The main objective of this introductory article is to introduce and summarize key aspects of the hydrologic and water quality models presented in this collection. The models range from field to watershed scales for simulating hydrology, sediment, nutrients, bacteria, and pesticides at temporal scales varying from hourly to annually. Individually, the articles provide model practitioners with detailed, model-specific guidance on model calibration, validation, and use. Collectively, the articles in this collection present a consistent framework of information that will facilitate development of a proposed set of ASABE model calibration and validation guidelines.

Soil erosion from agricultural fields is a fundamental water quality and quantity concern throughout the U.S. Watershed models can help target general areas where soil conservation measures are needed, but they have been less effective at making field-level recommendations. The objectives of this study were to demonstrate a method of field-scale targeting using ArcSWAT and to assess the impact of topography, soil, land use, and land management source data on field-scale targeting results. The study was implemented in Black Kettle Creek watershed (7,818 ha) in south-central Kansas. An ArcGIS toolbar was developed to post-process SWAT hydrologic response unit (HRU) output to generate sediment yields for individual fields. The relative impact of each input data source on field-level targeting was assessed by comparing ranked lists of fields on the basis of modeled sediment-yield density (Mg ha-1) from each data-source scenario. Baseline data of field-reconnaissance land use and management were compared to NASS and NLCD data, 10 m DEM topography were compared to 30 m, and SSURGO soil data were compared to STATSGO. Misclassification of cropland as pasture by NASS and aggregation of all cropland types to a single category by NLCD led to as much as 75% and 82% disagreement, respectively, in fields identified as having the greatest sediment-yield densities. Neither NASS nor NLCD data include land management data (such as terraces, contour farming, or no-till), but such inclusion changed targeted fields by as much as 71%. Impacts of 10 m versus 30 m DEM topographic data and STATSGO versus SSURGO soil data altered the fields targeted as having the highest sediment-yield densities to a lesser extent (about 10% to 25%). SWAT results post-processed to field boundaries were demonstrated to be useful for field-scale targeting. However, use of incorrect source data directly translated into incorrect field-level sediment-yield ranking, and thus incorrect field targeting. Sensitivity was greatest for land use data source, followed closely by inclusion of land management practices, with less sensitivity to topographic and soil data sources.

Forests are an integral component of the landscape, and maintaining their functional integrity is fundamental for the sustainability of ecosystems and societies alike. Tools, innovations, and practices, analogous to those developed to improve agricultural production and quantify environmental impacts, are needed to ensure the sustainability of these forested landscapes as well as the ecosystem goods and services they produce. This article introduces ten technical articles on critical ecohydrologic processes, protection and restoration, and the effects of management practices on the hydrology and water quality of forests and forested wetlands, using both monitoring and modeling approaches. Prepared by experts in forest science, forest and agricultural hydrology, and water management, the studies reported in this special collection are concentrated in the Atlantic Coastal plain and focus on forests with shallow water tables. Experimental studies describe the effects of riparian vegetation harvest, human disturbance, and future climatic change on groundwater, the significance of emergent vegetation after harvest, and long-term hydrologic water balance of a managed pine forest. Modeling studies use the SWAT model to predict streamflow dynamics of a less disturbed, coastal forested watershed, and DRAINMOD to determine the impacts of minor silvicultural drainage on wetland hydrology and to improve wetland restoration. Finally, a study describes potential uncertainties associated with infrequent water sampling of nutrient loads from drained forested watersheds. This introductory article summarizes these studies of shallow water table forests and relates them to the broader field of forest hydrology, including its challenges and opportunities, while identifying pressing issues of land use and climate change. The results from these studies should help guide management and restoration of forest wetland ecosystems and direct future forest hydrologic research, including research in large prior converted agricultural landscapes.

Climate change impacts watershed hydrology and contributes to alteration of hydrologic regimes in streams. However, global climate models (GCMs) operate at spatial and temporal scales that are too large to capture important watershed-scale hydrologic shifts. A method of disaggregating monthly ensemble GCM data into temperature and precipitation data series for daily, watershed-specific hydrologic simulations with SWAT was developed and assessed in the Soldier Creek watershed in northeast Kansas. A stochastic weather generator (WINDS) was employed to produce a baseline scenario (no changes from late 20th century conditions) and two scenarios based on ensemble means of 15 GCMs representing future conditions (A2 storyline) referred to as the 2050 and 2100 scenarios. Future hydrologic regimes exhibited non-linear annual and monthly responses in hydrologic budget components, such as surface runoff, baseflow, and soil moisture, to temperature and precipitation changes. For the 2050 scenario, the combination of higher temperatures along with decreased annual precipitation and increased spring precipitation resulted in higher surface runoff, baseflow, and streamflow in May and June with a longer drought season later in summer. The significant decrease in streamflow, runoff, and baseflow for the 2100 scenario reflected an increase in monthly and annual temperature rather than a direct result of precipitation decline. The 2100 scenario also produced a reduction in low-flow duration, an increase in the number of drought occurrences, and a decrease in flood frequency and duration. In retrospect, use of the stochastic weather generator to temporally downscale monthly GCM results while incorporating site-specific climate variability (such as occurs with convective storms often missed in coarse-resolution GCM data) produced more meaningful analysis of hydrological impacts, which is critical to predicting and understanding the impacts of climate change. Although this method allowed simulation of future-climate shifts based on GCM-simulated monthly shifts, it could not simulate potential shifts in climate patterns within a month, such as changes in transitional probabilities that govern the intensity and distribution of storms with months. In future work, translation of regional climate model responses into WINDS stochastic parameter adjustments will allow more accurate and efficient simulation. The severity of the increased drought and decreased flood responses simulated in this study would not be anticipated by review of precipitation trends alone nor by analysis of annual hydrologic responses alone. Similarly, many critical hydrologic responses reflected interactions between climate variables (e.g., precipitation and temperature) at sub-annual temporal scales, which highlights the need to consider climatic interactions in future studies of climate change impacts.

This study compared the utility and results of four watershed models that include water quality elements: Spreadsheet Tool for Estimating Pollutant Load (STEPL), a GIS-based model to calculate pollutant loads for watersheds (PLOAD), Long-Term Hydrologic Impact Assessment (L-THIA), and ArcGIS Soil and Water Assessment Tool (ArcSWAT). These models are different in their levels of complexity, data requirements, time step, and simulation techniques. Therefore, different scenarios were conducted to test some of the standard applications of each model based on data quality and model capabilities. Climatological, land use, and soil datasets as well as flow rates, sediment, nitrate, and total P concentrations were collected for the Pomona Lake watershed in Kansas over 30 years. Stakeholders' information was integrated into the models to the extent possible. Using results obtained from STEPL, L-THIA, PLOAD, and uncalibrated ArcSWAT for estimating long-term (30 years) average pollution loads in a watershed can result in incorrect load estimation. In addition, the STEPL, L-THIA, and PLOAD models may not be the most reliable in identifying pollutant source areas in a watershed. Therefore, in the context of effective resource allocation, use of simple models (such as STEPL, L-THIA, and PLOAD) is not suggested for critical source area targeting for watershed planning if the nature of the problem is complex. However, the simple models can still be used to evaluate relative contribution of different land uses to overall pollution loads.

The Soil and Water Assessment Tool (SWAT) version 2005 includes a microbial sub-model to simulate fecal bacteria transport at the watershed scale. The objectives of this study were to demonstrate methods to characterize fecal coliform bacteria (FCB) source loads and to assess the model sensitivity to five user-defined model parameters (BACTKDQ: bacteria soil partition coefficient in surface runoff, TBACT: temperature adjustment factor, WDLPQ: less-persistent bacteria die-off in solution phase, WDLPS: less-persistent bacteria die-off in sorbed phase, and BACTKKDB: bacteria partition coefficient in manure) and one input parameter (BACTLPDB: FCB concentration in manure). Fecal bacterial source loads were described and applied spatially for confined livestock, seasonal grazing livestock, failing human septic systems, and indigenous large mammal, small mammal, and avian wildlife. The relative sensitivity index (S) was tested using the independent parameter perturbation (IPP) method. Validation results for an uncalibrated SWAT model using nine runoff events from Rock Creek watershed (77 km2) were considered adequate to proceed with sensitivity analyses. Flow simulation resulted in good coefficient of determination (R2) of 0.67 and Nash-Sutcliffe Efficiency Index (E) of 0.55, and FCB source load characterization methods were sufficiently precise to result in fair correlation (R2 = 0.29) and reasonable measured vs. predicted response slope (0.69). Within the ranges recommended for use in SWAT, BACTKDQ had moderate sensitivity (S < 2.67) within -99.5% from 175 (baseline value), BACTLPDB had low sensitivity (S < 0.25) within -90% from 3.29 × 107 cfu 100 mL-1, BACTKKDB had low sensitivity (S < 0.12) within -89% from 0.9, TBACT had low sensitivity (S < 0.36) ±20% from 1.07, WDLPQ had low sensitivity (S < 0.25) ±50% from 0.23, and WDLPS had no sensitivity (S < 0.06) ±50% from 0.023 when compared with all surface runoff events. This study recommends that SWAT could adopt default values of 0.23 for WDLPQ and 0.023 for WDLPS without adversely affecting results. Moderate sensitivity for BACTKDQ indicates that users should select these with caution considering locally relevant data. The sensitivity of BACTKDQ was found high when compared with nine measured surface runoff events.

This article introduces the five papers in the “International Watershed Technology” collection. These papers were selected from 60 technical presentations at the fifth biennial ASABE 21st Century Watershed Technology Conference and Workshop: Improving the Quality of Water Resources at Local, Basin, and Regional Scales, held in Quito, Ecuador, on 3-9 December 2016. The conference focused on solving spatial and temporal water quality and quantity problems and addressed topics such as watershed management in developing countries, aquatic ecology and ecohydrology, ecosystem services, climate change mitigation strategies, flood forecasting, remote sensing, and water resource policy and management. While diverse, the presentation topics reflected the continuing evolution of the “data mining” and “big data” themes of past conferences related to geospatial data applications, with increasing emphasis on practical solutions. The papers selected for this collection represent applications of spatial data analyses toward practical ends with a theme of “tools and techniques for sustainability.” The papers address a range of topics, including the matching of crops with water availability, and assessing the environmental impacts of agricultural production. The papers identify some of the latest tools and techniques for improving sustainability in watershed resource management that are relevant to both developing and developed countries. Keywords: Data mining, Modeling, Research, Sustainability, Water quality, Water supply.

Accurate, consistent water surface elevations (stages) among gages within a large waterbody are essential for accurate hydrological analyses. This study developed and tested a process of statistical stage bias-correction and quantified corrections needed to maintain consistency among gages. Ponded conditions with consistent water-surface elevations were identified within an Everglades wetland area using four screening criteria: three-day antecedent rainfall ≤ 0.25 mm/day, seven-day antecedent rainfall ≤ 5.1 mm/week, mean stage change from prior day ≤ 0.15 m, and difference between median marsh and canal stages ≤ 0.015 m. Historical stage values were adjusted based on long-term mean inter-gage bias. Adjustments up to 0.0472 m were needed to offset mean inter-gage bias among 12 canal and five marsh gages. Bias-corrected stages improved consistency over an independent one-year period. These methods apply to deriving statistical bias-correction values in other seasonally ponded water bodies and identifying gages requiring datum maintenance.