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Prof. Dr. Prasad Daggupati
School of Engineering, University of Guelph, Guelph, ON, Canada

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0 Climate Change
0 Watershed Modeling
0 Water quantity and quality
0 Water scarcity and drought
0 Ephemeral gullies

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Climate Change
Watershed Modeling
Water quantity and quality
Water scarcity and drought
Ephemeral gullies

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Journal article
Published: 27 May 2021 in Water
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The Kabul River, while having its origin in Afghanistan, has a primary tributary, the Konar River, which originates in Pakistan and enters Afghanistan near Barikot-Arandu. The Kabul River then re-enters Pakistan near Laalpur, Afghanistan making it a true transboundary river. The catastrophic flood events due to major snowmelt events in the Hindu Kush mountains occur every other year, inundating many major urban centers. This study investigates the flood risk under 30 climate and dam management scenarios to assess opportunities for transboundary water management strategy in the Kabul River Basin (KRB). The Soil and Water Assessment Tool (SWAT) is a watershed-scale hydraulic modeling tool that was employed to forecast peak flows to characterize flood inundation areas using the river flood routing modelling tool Hydrologic Engineering Center—Analysis System -HEC-RAS for the Nowshera region. This study shows how integrated transboundary water management in the KRB can play a vital catalyst role with significant socio-economic benefits for both nations. The study proposes a KRB-specific agreement, where flood risk management is a significant driver that can bring both countries to work together under the Equitable Water Resource Utilization Doctrine to save lives in both Afghanistan and Pakistan. The findings show that flood mitigation relying on collaborative efforts for both upstream and downstream riparian states is highly desirable.

ACS Style

Yar Taraky; Yongbo Liu; Ed McBean; Prasad Daggupati; Bahram Gharabaghi. Flood Risk Management with Transboundary Conflict and Cooperation Dynamics in the Kabul River Basin. Water 2021, 13, 1513 .

AMA Style

Yar Taraky, Yongbo Liu, Ed McBean, Prasad Daggupati, Bahram Gharabaghi. Flood Risk Management with Transboundary Conflict and Cooperation Dynamics in the Kabul River Basin. Water. 2021; 13 (11):1513.

Chicago/Turabian Style

Yar Taraky; Yongbo Liu; Ed McBean; Prasad Daggupati; Bahram Gharabaghi. 2021. "Flood Risk Management with Transboundary Conflict and Cooperation Dynamics in the Kabul River Basin." Water 13, no. 11: 1513.

Journal article
Published: 23 May 2021 in Science of The Total Environment
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Greenhouse gas sampling from agricultural fields is laborious and time-consuming. Soil and topographical heterogeneity cause spatiotemporal variations, making nitrous oxide (N2O) estimation and management a challenge. Identification of representative monitoring locations, hotspots, and coldspots could facilitate the mitigation of agricultural N2O emissions. The objective of this study was to identify and characterize representative monitoring locations, hotspots, and coldspots of N2O emissions in agricultural fields (Baggs farm; BF and Research North farm; RN) in Cambridge, Ontario, Canada, under humid continental climate. Soil in both fields was classified as Orthic Melanic Brunisol, with some areas categorized as Gleyed Brunisolic Gray Brown Luvisol and Orthic Humic Gleysol. In total, 28 sampling points were selected following conditional Latin hypercube design using topographical parameters (digital elevation, slope, topographical wetness index, and Pennock landform classification). Gas samples were collected over a two-year crop rotation with corn (2019) and soybean (2020). Additional sampling was conducted at BF at spring thaw (2020). Time stability analysis using mean relative difference (MRD) and standard deviation of mean relative difference (SDRD) was performed to test the hypothesis that “simultaneous analysis of spatiotemporal variations in N2O emissions could help to identify and characterize representative monitoring locations, hotspots, coldspots and areas with few hot and cold moments. Most of the hotspots were located at shoulder positions, coldspots, and cold moments at backslope, and representative monitoring points were located at leveled positions or localized depressions. Time stability analysis coupled with multivariate groping analysis supported our hypothesis and helped successfully identify hotspots, coldspots, and representative locations based on landform classification with few exceptions. However, inclusion of additional topographical (curvature, contributing area, aspect) and morphological parameters (texture, thickness of soil horizon, depth to bedrock, and water table) are suggested for consideration in future research to manage variable-rate fertilizer application and mitigate N2O hotspots at landscape level.

ACS Style

Waqar Ashiq; Uttam Ghimire; Hiteshkumar Vasava; Kari Dunfield; Claudia Wagner-Riddle; Prasad Daggupati; Asim Biswas. Identifying hotspots and representative monitoring locations of field scale N2O emissions from agricultural soils: A time stability analysis. Science of The Total Environment 2021, 788, 147955 .

AMA Style

Waqar Ashiq, Uttam Ghimire, Hiteshkumar Vasava, Kari Dunfield, Claudia Wagner-Riddle, Prasad Daggupati, Asim Biswas. Identifying hotspots and representative monitoring locations of field scale N2O emissions from agricultural soils: A time stability analysis. Science of The Total Environment. 2021; 788 ():147955.

Chicago/Turabian Style

Waqar Ashiq; Uttam Ghimire; Hiteshkumar Vasava; Kari Dunfield; Claudia Wagner-Riddle; Prasad Daggupati; Asim Biswas. 2021. "Identifying hotspots and representative monitoring locations of field scale N2O emissions from agricultural soils: A time stability analysis." Science of The Total Environment 788, no. : 147955.

Review article
Published: 10 May 2021 in Soil and Tillage Research
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Global greenhouse gas emissions have reached an unprecedented level. Agriculture and land-use change, one of the most diverse sectors, emits 66 % of global anthropogenic nitrous oxide (N2O) into the atmosphere. A plethora of research has been compiled to estimate and quantify the N2O emissions from agricultural systems and understand the underlying processes (nitrification and denitrification). These processes are controlled by natural (e.g., environment, soil, and topography) and anthropogenic factors (e.g., agricultural management practices). Complexity in topographical attributes such as elevation, slope, and aspect contribute to spatial variability in soil properties and controls N2O emissions. Additionally, a set of agricultural best management practices (BMPs) such as conservation tillage (CT), cover cropping (CC), and soil organic amendments have been adopted to improve soil health. However, the potential impact of these BMPs on N2O emissions has not been considered in detail. Simultaneously, the inter-connectedness of these natural and anthropogenic factors makes soil N2O emissions extremely variable and complex. Recent studies reported the varied impacts of CT, CC, and organic amendments on N2O emissions. However, the landscape level and integrated impact of natural and anthropogenic factors and processes from temperate regions have not been reviewed, which is an obvious gap in the literature. In this review, we critically analyzed 226 recent studies (mainly from temperate regions) to investigate the status and progress of our understanding of N2O emissions from the agricultural crop production system. Most of the studies reported that topography increases spatial variations in N2O emissions by regulating soil physical, chemical, and biological properties. Landscape positions with higher soil moisture (foot slope, toe slope, localized depressions) had significantly higher contributions towards N2O emissions. Moreover, agricultural BMPs also increase soil N2O emissions alone and integrated with topographical variations. We identified potential mechanisms accelerating these emissions, mitigation options, and future research directions under each section based on critical literature analysis.

ACS Style

Waqar Ashiq; Hiteshkumar Vasava; Mumtaz Cheema; Kari Dunfield; Prasad Daggupati; Asim Biswas. Interactive role of topography and best management practices on N2O emissions from agricultural landscape. Soil and Tillage Research 2021, 212, 105063 .

AMA Style

Waqar Ashiq, Hiteshkumar Vasava, Mumtaz Cheema, Kari Dunfield, Prasad Daggupati, Asim Biswas. Interactive role of topography and best management practices on N2O emissions from agricultural landscape. Soil and Tillage Research. 2021; 212 ():105063.

Chicago/Turabian Style

Waqar Ashiq; Hiteshkumar Vasava; Mumtaz Cheema; Kari Dunfield; Prasad Daggupati; Asim Biswas. 2021. "Interactive role of topography and best management practices on N2O emissions from agricultural landscape." Soil and Tillage Research 212, no. : 105063.

Journal article
Published: 19 January 2021 in Agronomy
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Topography affects soil hydrological, pedological, and biochemical processes and may influence nitrous oxide (N2O) emissions into the atmosphere. While N2O emissions from agricultural fields are mainly measured at plot scale and on flat topography, intrafield topographical and crop growth variability alter soil processes and might impact N2O emissions. The objective of this study was to examine the impact of topographical variations on crop growth period dependent soil N2O emissions at the field scale. A field experiment was conducted at two agricultural farms (Baggs farm; BF and Research North; RN) with undulating topography. Dominant slope positions (upper, middle, lower and toeslope) were identified based on elevation difference. Soil and gas samples were collected from four replicated locations within each slope position over the whole corn growing season (May–October 2019) to measure soil physio-chemical properties and N2O emissions. The N2O emissions at BF ranged from −0.27 ± 0.42 to 255 ± 105 g ha−1 d−1. Higher cumulative emissions were observed from the upper slope (1040 ± 487 g ha−1) during early growing season and from the toeslope (371 ± 157 g ha−1) during the late growing season with limited variations during the mid growing season. Similarly, at RN farm, (emissions ranged from −0.50 ± 0.83 to 70 ± 15 g ha−1 d−1), the upper slope had higher cumulative emissions during early (576 ± 132 g ha−1) and mid (271 ± 51 g ha−1) growing season, whereas no impact of slope positions was observed during late growing season. Topography controlled soil and environmental properties differently at different crop growth periods; thus, intrafield variability must be considered in estimating N2O emissions and emission factor calculation from agricultural fields. However, due to large spatial variations in N2O emissions, further explorations into site-specific analysis of individual soil properties and their impact on N2O emissions using multiyear data might help to understand and identify hotspots of N2O emissions.

ACS Style

Waqar Ashiq; Hiteshkumar B. Vasava; Uttam Ghimire; Prasad Daggupati; Asim Biswas. Topography Controls N2O Emissions Differently during Early and Late Corn Growing Season. Agronomy 2021, 11, 187 .

AMA Style

Waqar Ashiq, Hiteshkumar B. Vasava, Uttam Ghimire, Prasad Daggupati, Asim Biswas. Topography Controls N2O Emissions Differently during Early and Late Corn Growing Season. Agronomy. 2021; 11 (1):187.

Chicago/Turabian Style

Waqar Ashiq; Hiteshkumar B. Vasava; Uttam Ghimire; Prasad Daggupati; Asim Biswas. 2021. "Topography Controls N2O Emissions Differently during Early and Late Corn Growing Season." Agronomy 11, no. 1: 187.

Journal article
Published: 09 December 2020 in Geoderma
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An accurate assessment of soil organic matter (SOM) and soil moisture content (SMC) is critical for applications in the fields of agriculture, environment, and engineering. However, characterization and measurement of these properties is costly, time-consuming, and labor-intensive. Research has demonstrated that soil spectral reflectance characteristics can be associated with various soil properties providing an indirect way of measurement. With advancements in technological and computational facilities, high resolution digital images and computer vision algorithms have shown potential to provide rapid and nondestructive characterization of soil properties. Additionally, acceptance of cell phones in everyday life made the digital photography easier and accessible. The objective of this study was to develop and compare various regression and machine learning algorithms to estimate SOM and SMC from cell phone images. A cell phone (LG G5 model) was used to capture images of 25 soil samples from two agricultural fields with highly variable SOM at 6 different soil moisture levels from over-dry to saturated. The images were preprocessed using contrast enhancement and segmentation techniques to deal with illumination inconsistencies and remove non-soil parts of the image including black cracks, leaf residues and specular reflection. A total of 22 color and texture features were extracted from images and predictive relationships were developed against laboratory measured soil properties. A set of 24 supervised regression and machine learning prediction models including six Linear Regression Models, three Decision/Regression Trees, six Support Vector Machines (SVM), four Gaussian Process Regression (GPR) Models, four Ensembles of Trees including random forest and cubist, and other models including Artificial Neural Network (ANN) were compared in this study to predict SOM and SMC. A z-score was used to identify a set of six optimum predictors (subset of 22). Exponential GPR and Cubist model performed the best for SMC prediction, with coefficients of determination (R2) values of 0.84 and 0.86, and RMSE of 10.18% and 10.43%, respectively, (internal validation with 10-fold cross-validation) when all (22) and a subset of 6 predictors were used. For SOM, ANN and Cubist produced satisfactory prediction accuracy with R2 values of 0.91 and 0.72 and RMSE values of 5.45% and 9.90%, respectively, when 22 and 6 predictors were used. The external validation results exhibited reasonable predictive ability with Exponential GPR and Matern 5/2 GPR producing R2 values of 0.92 and 0.95 and RMSE of 5.79% and 5.04%, respectively using 22 and 6 predictors for SMC. Medium Gaussian SVM and Squared Exponential GPR produced R2 values of 0.56 and 0.53 and RMSE of 8.59% and 8.27%, respectively using 22 and 6 predictors for SOM. This shows potential in fabricating an efficient proximal soil sensor using computer vision and machine learning which can be used to provide quick, accurate and nondestructive predictions of soil properties.

ACS Style

Perry Taneja; Hitesh Kumar Vasava; Prasad Daggupati; Asim Biswas. Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images. Geoderma 2020, 385, 114863 .

AMA Style

Perry Taneja, Hitesh Kumar Vasava, Prasad Daggupati, Asim Biswas. Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images. Geoderma. 2020; 385 ():114863.

Chicago/Turabian Style

Perry Taneja; Hitesh Kumar Vasava; Prasad Daggupati; Asim Biswas. 2020. "Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images." Geoderma 385, no. : 114863.

Short communication
Published: 06 November 2020 in Journal of Great Lakes Research
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The rapid rise in availability of large geospatial datasets for the development of hydrological models such as Soil and Water Assessment Tool (SWAT) has led to a dramatic increase in both the demand and availability of web services and tools that assist watershed modellers in incorporating data and knowledge into their modelling frameworks. Within the Canadian Great Lakes region, there is a huge potential for the application of SWAT in integrated water resources management. However, a potential barrier is the preparation of SWAT weather inputs that require time-intensive preprocessing of large data sets. Because such preprocessing is reproducible, the redundancy associated with it can be removed by introducing a web service that enables easy and open dissemination of climate data (including climate change and historical data) in SWAT-ready format. This short communication introduces such a web service called the Canadian Great Lakes Weather Data Service for SWAT (Can-GLWS). It hosts observed (historical) and projected (future) daily precipitation, daily maximum/minimum temperature, as well as weather generator database at regular grids (300 arc seconds or ~10 km) for use in SWAT simulations of the region. The novel Can-GLWS web service offers flexibility in selecting the region of interest by allowing them to be uploaded as a shapefile or to draw a rectangle or a polygon. We believe that such data as a service platform will help many practitioners to explore several issues pertaining to the sustainability of the freshwater resources of Canadian Great Lakes using the SWAT model.

ACS Style

Narayan K. Shrestha; Taimoor Akhtar; Uttam Ghimire; Ramesh P. Rudra; Pradeep K. Goel; Rituraj Shukla; Prasad Daggupati. Can-GLWS: Canadian Great Lakes Weather Service for the Soil and Water Assessment Tool (SWAT) modelling. Journal of Great Lakes Research 2020, 47, 242 -251.

AMA Style

Narayan K. Shrestha, Taimoor Akhtar, Uttam Ghimire, Ramesh P. Rudra, Pradeep K. Goel, Rituraj Shukla, Prasad Daggupati. Can-GLWS: Canadian Great Lakes Weather Service for the Soil and Water Assessment Tool (SWAT) modelling. Journal of Great Lakes Research. 2020; 47 (1):242-251.

Chicago/Turabian Style

Narayan K. Shrestha; Taimoor Akhtar; Uttam Ghimire; Ramesh P. Rudra; Pradeep K. Goel; Rituraj Shukla; Prasad Daggupati. 2020. "Can-GLWS: Canadian Great Lakes Weather Service for the Soil and Water Assessment Tool (SWAT) modelling." Journal of Great Lakes Research 47, no. 1: 242-251.

Review
Published: 12 October 2020 in Agriculture
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Non-point source (NPS) pollution is an important problem that has been threatening freshwater resources throughout the world. Best Management Practices (BMPs) can reduce NPS pollution delivery to receiving waters. For economic reasons, BMPs should be placed at critical source areas (CSAs), which are the areas contributing most of the NPS pollution. The CSAs are the areas in a watershed where source coincides with transport factors, such as runoff, erosion, subsurface flow, and channel processes. Methods ranging from simple index-based to detailed hydrologic and water quality (HWQ) models are being used to identify CSAs. However, application of these methods for Canadian watersheds remains challenging due to the diversified hydrological conditions, which are not fully incorporated into most existing methods. The aim of this work is to review potential methods and challenges in identifying CSAs under Canadian conditions. As such, this study: (a) reviews different methods for identifying CSAs; (b) discusses challenges and the current state of CSA identification; and (c) highlights future research directions to address limitations of currently available methods. It appears that applications of both simple index-based methods and detailed HWQ models to determine CSAs are limited in Canadian conditions. As no single method/model is perfect, it is recommended to develop a ‘Toolbox’ that can host a variety of methods to identify CSAs so as to allow flexibility to the end users on the choice of the methods.

ACS Style

Ramesh Rudra; Balew Mekonnen; Rituraj Shukla; Narayan Shrestha; Pradeep Goel; Prasad Daggupati; Asim Biswas. Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions. Agriculture 2020, 10, 468 .

AMA Style

Ramesh Rudra, Balew Mekonnen, Rituraj Shukla, Narayan Shrestha, Pradeep Goel, Prasad Daggupati, Asim Biswas. Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions. Agriculture. 2020; 10 (10):468.

Chicago/Turabian Style

Ramesh Rudra; Balew Mekonnen; Rituraj Shukla; Narayan Shrestha; Pradeep Goel; Prasad Daggupati; Asim Biswas. 2020. "Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions." Agriculture 10, no. 10: 468.

Review
Published: 18 September 2020 in CATENA
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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.

ACS Style

Kyle R. Douglas-Mankin; Swapan K. Roy; Aleksey Y. Sheshukov; Asim Biswas; Bahram Gharabaghi; Andrew Binns; Ramesh Rudra; Narayan Kumar Shrestha; Prasad Daggupati. A comprehensive review of ephemeral gully erosion models. CATENA 2020, 195, 104901 .

AMA Style

Kyle R. Douglas-Mankin, Swapan K. Roy, Aleksey Y. Sheshukov, Asim Biswas, Bahram Gharabaghi, Andrew Binns, Ramesh Rudra, Narayan Kumar Shrestha, Prasad Daggupati. A comprehensive review of ephemeral gully erosion models. CATENA. 2020; 195 ():104901.

Chicago/Turabian Style

Kyle R. Douglas-Mankin; Swapan K. Roy; Aleksey Y. Sheshukov; Asim Biswas; Bahram Gharabaghi; Andrew Binns; Ramesh Rudra; Narayan Kumar Shrestha; Prasad Daggupati. 2020. "A comprehensive review of ephemeral gully erosion models." CATENA 195, no. : 104901.

Research article
Published: 29 July 2020 in Hydrological Sciences Journal
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In humid regions, surface runoff is often generated by saturation-excess runoff mechanisms from relatively small variable source areas (VSAs). However, the majority of the current hydrologic models are based on infiltration-excess mechanisms. In this study, the AGricultural Non-Point Source Pollution (AGNPS) model was used to integrate the VSA concept using topographic wetness index (TWI). Both the original and AGNPS-VSA models were evaluated for a small agricultural field in Ontario, Canada. The results indicate that the AGNPS-VSA model performed better than original model. The AGNPS-VSA model predicted that only the saturated portion of the field with higher TWI values produced runoff, whereas the original AGNPS model showed uniform hydrologic response from the entire field. The results of this study are important for accurately mapping the locations of VSAs. This new model could be a powerful tool in identifying critical source areas for applying targeted best management practices to minimize pollutant loads to receiving waters.

ACS Style

Kishor Panjabi; Ramesh Rudra; Pradeep Goel; Prasad Daggupati; Narayan Kumar Shrestha; Rituraj Shukla; Binbin Zhang; Nabil Allataifeh. Mapping runoff generating areas using AGNPS-VSA model. Hydrological Sciences Journal 2020, 65, 2224 -2232.

AMA Style

Kishor Panjabi, Ramesh Rudra, Pradeep Goel, Prasad Daggupati, Narayan Kumar Shrestha, Rituraj Shukla, Binbin Zhang, Nabil Allataifeh. Mapping runoff generating areas using AGNPS-VSA model. Hydrological Sciences Journal. 2020; 65 (13):2224-2232.

Chicago/Turabian Style

Kishor Panjabi; Ramesh Rudra; Pradeep Goel; Prasad Daggupati; Narayan Kumar Shrestha; Rituraj Shukla; Binbin Zhang; Nabil Allataifeh. 2020. "Mapping runoff generating areas using AGNPS-VSA model." Hydrological Sciences Journal 65, no. 13: 2224-2232.

Journal article
Published: 25 July 2020 in Science of The Total Environment
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How anticipated climate change might affect long-term outcomes of present-day agricultural conservation practices remains a key uncertainty that could benefit water quality and biodiversity conservation planning. To explore this issue, we forecasted how the stream fish communities in the Western Lake Erie Basin (WLEB) would respond to increasing amounts of agricultural conservation practice (ACP) implementation under two IPCC future greenhouse gas emission scenarios (RCP4.5: moderate reductions; RCP8.5: business-as-usual conditions) during 2020–2065. We used output from 19 General Circulation Models to drive linked agricultural land use (APEX), watershed hydrology (SWAT), and stream fish distribution (boosted regression tree) models, subsequently analyzing how projected changes in habitat would influence fish community composition and functional trait diversity. Our models predicted both positive and negative effects of climate change and ACP implementation on WLEB stream fishes. For most species, climate and ACPs influenced species in the same direction, with climate effects outweighing those of ACP implementation. Functional trait analysis helped clarify the varied responses among species, indicating that more extreme climate change would reduce available habitat for large-bodied, cool-water species with equilibrium life-histories, many of which also are of importance to recreational fishing (e.g., northern pike, smallmouth bass). By contrast, available habitat for warm-water, benthic species with more periodic or opportunistic life-histories (e.g., northern hogsucker, greater redhorse, greenside darter) was predicted to increase. Further, ACP implementation was projected to hasten these shifts, suggesting that efforts to improve water quality could come with costs to other ecosystem services (e.g., recreational fishing opportunities). Collectively, our findings demonstrate the need to consider biological outcomes when developing strategies to mitigate water quality impairment and highlight the value of physical-biological modeling approaches to agricultural and biological conservation planning in a changing climate.

ACS Style

Michael E. Fraker; S. Conor Keitzer; James S. Sinclair; Noel R. Aloysius; David A. Dippold; Haw Yen; Jeffrey G. Arnold; Prasad Daggupati; Mari-Vaughn V. Johnson; Jay F. Martin; Dale M. Robertson; Scott P. Sowa; Michael J. White; Stuart A. Ludsin. Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate. Science of The Total Environment 2020, 747, 141112 .

AMA Style

Michael E. Fraker, S. Conor Keitzer, James S. Sinclair, Noel R. Aloysius, David A. Dippold, Haw Yen, Jeffrey G. Arnold, Prasad Daggupati, Mari-Vaughn V. Johnson, Jay F. Martin, Dale M. Robertson, Scott P. Sowa, Michael J. White, Stuart A. Ludsin. Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate. Science of The Total Environment. 2020; 747 ():141112.

Chicago/Turabian Style

Michael E. Fraker; S. Conor Keitzer; James S. Sinclair; Noel R. Aloysius; David A. Dippold; Haw Yen; Jeffrey G. Arnold; Prasad Daggupati; Mari-Vaughn V. Johnson; Jay F. Martin; Dale M. Robertson; Scott P. Sowa; Michael J. White; Stuart A. Ludsin. 2020. "Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate." Science of The Total Environment 747, no. : 141112.

Orginal article
Published: 13 May 2020 in Freshwater Biology
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Many aquatic ecosystems are experiencing multiple anthropogenic stressors that threaten their ability to support ecologically and economically important fish species. Two of the most ubiquitous stressors are climate change and non‐point source nutrient pollution. Agricultural conservation practices (ACPs, i.e. farming practices that reduce runoff, prevent erosion, and curb excessive nutrient loading) offer a potential means to mitigate the negative effects of non‐point source pollution on fish populations. However, our understanding of how ACP implementation amidst a changing climate will affect fish production in large ecosystems that receive substantial upstream sediment and nutrient inputs remains incomplete. Towards this end, we explored how anticipated climate change and the implementation of realistic ACPs might alter the recruitment dynamics of three fish populations (native walleye Sander vitreus and yellow perch Perca flavescens and invasive white perch Morone americana) in the highly productive, dynamic west basin of Lake Erie. We projected future (2020–2065) recruitment under different combinations of anticipated climate change (n = 2 levels) and ACP implementation (n = 4 levels) in the western Lake Erie catchment using predictive biological models driven by forecasted winter severity, spring warming rate, and Maumee River total phosphorus loads that were generated from linked climate, catchment‐hydrology, and agricultural‐practice‐simulation models. In general, our models projected reduced walleye and yellow perch recruitment whereas invasive white perch recruitment was projected to remain stable or increase relative to the recent past. Our modelling also suggests the potential for trade‐offs, as ACP implementation was projected to reduce yellow perch recruitment with anticipated climate change. Overall, our study presents a useful modelling framework to forecast fish recruitment in Lake Erie and elsewhere, as well as offering projections and new avenues of research that could help resource management agencies and policy‐makers develop adaptive and resilient management strategies in the face of anticipated climate and land‐management change.

ACS Style

David A. Dippold; Noel R. Aloysius; Steven Conor Keitzer; Haw Yen; Jeffrey G. Arnold; Prasad Daggupati; Michael E. Fraker; Jay F. Martin; Dale M. Robertson; Scott P. Sowa; Mari‐Vaughn V. Johnson; Mike J. White; Stuart A. Ludsin. Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie. Freshwater Biology 2020, 65, 1487 -1508.

AMA Style

David A. Dippold, Noel R. Aloysius, Steven Conor Keitzer, Haw Yen, Jeffrey G. Arnold, Prasad Daggupati, Michael E. Fraker, Jay F. Martin, Dale M. Robertson, Scott P. Sowa, Mari‐Vaughn V. Johnson, Mike J. White, Stuart A. Ludsin. Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie. Freshwater Biology. 2020; 65 (9):1487-1508.

Chicago/Turabian Style

David A. Dippold; Noel R. Aloysius; Steven Conor Keitzer; Haw Yen; Jeffrey G. Arnold; Prasad Daggupati; Michael E. Fraker; Jay F. Martin; Dale M. Robertson; Scott P. Sowa; Mari‐Vaughn V. Johnson; Mike J. White; Stuart A. Ludsin. 2020. "Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie." Freshwater Biology 65, no. 9: 1487-1508.

Review
Published: 29 April 2020 in Atmosphere
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Crops can uptake only a fraction of nitrogen from nitrogenous fertilizer, while losing the remainder through volatilization, leaching, immobilization and emissions from soils. The emissions of nitrogen in the form of nitrous oxide (N2O) have a strong potency for global warming and depletion of stratospheric ozone. N2O gets released due to nitrification and denitrification processes, which are aided by different environmental, management and soil variables. In recent years, researchers have focused on understanding and simulating the N2O emission processes from agricultural farms and/or watersheds by using process-based models like Daily CENTURY (DAYCENT), Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT). While the former two have been predominantly used in understanding the science of N2O emission and its execution within the model structure, as visible from a multitude of research articles summarizing their strengths and limitations, the later one is relatively unexplored. The SWAT is a promising candidate for modeling N2O emission, as it includes variables and processes that are widely reported in the literature as controlling N2O fluxes from soil, including nitrification and denitrification. SWAT also includes three-dimensional lateral movement of water within the soil, like in real-world conditions, unlike the two-dimensional biogeochemical models mentioned above. This article aims to summarize the N2O emission processes, variables affecting N2O emission and recent advances in N2O emission modeling techniques in SWAT, while discussing their applications, strengths, limitations and further recommendations.

ACS Style

Uttam Ghimire; Narayan Kumar Shrestha; Asim Biswas; Claudia Wagner-Riddle; Wanhong Yang; Shiv Prasher; Ramesh Rudra; Prasad Daggupati. A Review of Ongoing Advancements in Soil and Water Assessment Tool (SWAT) for Nitrous Oxide (N2o) Modeling. Atmosphere 2020, 11, 450 .

AMA Style

Uttam Ghimire, Narayan Kumar Shrestha, Asim Biswas, Claudia Wagner-Riddle, Wanhong Yang, Shiv Prasher, Ramesh Rudra, Prasad Daggupati. A Review of Ongoing Advancements in Soil and Water Assessment Tool (SWAT) for Nitrous Oxide (N2o) Modeling. Atmosphere. 2020; 11 (5):450.

Chicago/Turabian Style

Uttam Ghimire; Narayan Kumar Shrestha; Asim Biswas; Claudia Wagner-Riddle; Wanhong Yang; Shiv Prasher; Ramesh Rudra; Prasad Daggupati. 2020. "A Review of Ongoing Advancements in Soil and Water Assessment Tool (SWAT) for Nitrous Oxide (N2o) Modeling." Atmosphere 11, no. 5: 450.

Journal article
Published: 26 February 2020 in Journal of Hydrology
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The accurate prediction of bedload transport in gravel-bed rivers remains a significant challenge in river science. However the potential for data mining algorithms to provide models of bedload transport have yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid data mining models. Using bedload transport data collected in laboratory flume experiments, the performance of four types of recently developed standalone data mining techniques - the M5P, random tree (RT), random forest (RF) and the reduced error pruning tree (REPT) - are assessed, along with four types of hybrid algorithms trained with a Bagging (BA) data mining algorithm (BA-M5P, BA-RF, BA-RT and BA-REPT). The main findings are four-fold. First, the BA-M5P model had the highest prediction power (R2 = 0.943; RMSE = 0.061 kg m−1 s−1; MAE = 0.040 kg m−1 s−1; NSE = 0.945; PBIAS = −1.60) followed by M5P, BA-RT, RT, BA-RF, RF, BA-REPT, and REPT. All models displayed ‘very good’ performance except the BA-REPT and REPT model, which were ‘satisfactory’. Second, the M5P, BA-RT, and RT models underestimated, and the BA-M5P, BA-RF, RF, BA-REPT and REPT models overestimated, bedload transport rates. Third, flow velocity had the most significant impact on bedload transport rate (PCC = 0.760) followed by shear stress (PCC = 0.709), discharge (PCC = 0.668), bed shear velocity (PCC = 0.663), bed slope (PCC = 0.490), flow depth (PCC = 0.303), median sediment diameter (PCC = 0.247), and relative roughness (PCC = 0.003). Fourth, the maximum depth of tree was the most sensitive operator in decision tree-based algorithms, and batch size, number of execution slots and number of decimal places did not have any impact on model’ prediction power. Overall the results revealed that hybrid data mining techniques provide more accurate predictions of bedload transport rate than standalone data mining models. In particular, M5P models, trained with a Bagging data mining algorithm, have great potential to produce robust predictions of bedload transport in gravel-bed rivers.

ACS Style

Khabat Khosravi; James R. Cooper; Prasad Daggupati; Binh Thai Pham; Dieu Tien Bui. Bedload transport rate prediction: Application of novel hybrid data mining techniques. Journal of Hydrology 2020, 585, 124774 .

AMA Style

Khabat Khosravi, James R. Cooper, Prasad Daggupati, Binh Thai Pham, Dieu Tien Bui. Bedload transport rate prediction: Application of novel hybrid data mining techniques. Journal of Hydrology. 2020; 585 ():124774.

Chicago/Turabian Style

Khabat Khosravi; James R. Cooper; Prasad Daggupati; Binh Thai Pham; Dieu Tien Bui. 2020. "Bedload transport rate prediction: Application of novel hybrid data mining techniques." Journal of Hydrology 585, no. : 124774.

Journal article
Published: 02 January 2020 in Journal of Great Lakes Research
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Hot-spots and hot-moments of phosphorus loads in an agricultural watershed depend not only on the watershed characteristics but also on the type and intensity of storms. Not all storms will generate phosphorus that can be considered problematic. A threshold storm is thus proposed and defined as the maximum storm intensity in which the phosphorus generated in a watershed is below seasonal phosphorus tolerance limit. To evaluate the threshold storm approach, separate Agricultural Non-point Source (AGNPS) models for three diverse small agricultural watersheds in southern Ontario, Canada were calibrated for runoff volume, sediment yield, and total phosphorus and run for representative storms with increasing return periods (2-year through 100-year). Results showed that in an upland watershed (Holtby), a 4.8-year early spring storm tend to generate phosphorus load above the threshold limit for the season. The same for low-land watersheds (Wigle and Jeannette) were, respectively, 14.9-year and 12.4-year. In all three watersheds, summer storms up to 100-year will fail to reach the seasonal tolerance limit for phosphorus. The critical source areas, identified based on the threshold storms, were distributed uniformly across the watersheds. As a phosphorus problem is essentially a source problem, such a simple yet robust approach to identify critical source areas of phosphorus can be useful in designing cost-effective best management practices.

ACS Style

B. Zhang; N.K. Shrestha; R. Rudra; R. Shukla; P. Daggupati; P.K. Goel; W.T. Dickinson; N. Allataifeh. Threshold storm approach for locating phosphorus problem areas: An application in three agricultural watersheds in the Canadian Lake Erie basin. Journal of Great Lakes Research 2020, 46, 132 -143.

AMA Style

B. Zhang, N.K. Shrestha, R. Rudra, R. Shukla, P. Daggupati, P.K. Goel, W.T. Dickinson, N. Allataifeh. Threshold storm approach for locating phosphorus problem areas: An application in three agricultural watersheds in the Canadian Lake Erie basin. Journal of Great Lakes Research. 2020; 46 (1):132-143.

Chicago/Turabian Style

B. Zhang; N.K. Shrestha; R. Rudra; R. Shukla; P. Daggupati; P.K. Goel; W.T. Dickinson; N. Allataifeh. 2020. "Threshold storm approach for locating phosphorus problem areas: An application in three agricultural watersheds in the Canadian Lake Erie basin." Journal of Great Lakes Research 46, no. 1: 132-143.

Journal article
Published: 13 November 2019 in Geoderma
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Soil organic matter (SOM) is considered as the backbone of soil health and soil quality. Thus, its’ estimation is critical to support the development of management decision including precision agriculture. To overcome challenges of laborious, rather expensive and time-consuming laboratory measurements, recent advances in image acquisition systems provided a new dimension of image-based SOM prediction. However, challenges remain in using soil images taken directly in the field due to variable soil surface conditions including vegetation cover, illumination, and soil moisture. Soil moisture can significantly influence soil color and thus confounds the relationship between SOM and soil color. This study quantifies the effects of soil moisture on the relationship between SOM and color parameters derived from cell phone images and establishes suitable SOM prediction models under varying conditions of soil moisture contents (SMCs). To simulate the continuous variation of soil moisture in the field, air-dried ground soil samples were saturated and allowed to dry naturally. Images were captured with a cellular phone over time representing various SMCs (set of images). Final set of images were captured on oven-dried samples. Images were preprocessed using illumination normalization to avoid illumination inconsistencies and segmentation technique to remove non-soil parts of the images including black cracks, leaf residues and specular reflection before modelling. Five color space models including RGB, HIS, CIELa*b*, CIELc*h* and CIELu*v* were used to quantify soil color parameters. Univariate linear regression models were developed between SOM and color parameters and an optimal set of color parameters that are capable of resisting variation in SMC was determined. It was observed that SMC exerted a considerable influence on SOM prediction accuracy when its value reached > 10%. The threshold of 10% SMC was considered as the critical SMC. Consequently, stepwise multiple linear regression (SMLR) models were developed for soil samples with SMC below and above the critical SMC. For the soil samples at below the critical SMC, the color parameter R based model produced satisfactory prediction accuracy for SOM with R2cv, RMSEcv, and RPDcv values of 0.936, 4.44% and 3.926, respectively. For the soil samples at above the critical SMC, the SOM predictive model including SMC as a predictor variable showed better accuracy (R2cv = 0.819, RMSEcv = 7.747%, RPDcv = 2.328) than that without including SMC (R2cv = 0.741, RMSEcv = 9.382%, RPDcv = 1.922). This study showed potential of cellular phone to be used as a proximal soil sensor fast, accurate and non-destructive estimation of SOM both in the laboratory and field conditions.

ACS Style

Yuanyuan Fu; Perry Taneja; Shaomin Lin; Wenjun Ji; Viacheslav Adamchuk; Prasad Daggupati; Asim Biswas. Predicting soil organic matter from cellular phone images under varying soil moisture. Geoderma 2019, 361, 114020 .

AMA Style

Yuanyuan Fu, Perry Taneja, Shaomin Lin, Wenjun Ji, Viacheslav Adamchuk, Prasad Daggupati, Asim Biswas. Predicting soil organic matter from cellular phone images under varying soil moisture. Geoderma. 2019; 361 ():114020.

Chicago/Turabian Style

Yuanyuan Fu; Perry Taneja; Shaomin Lin; Wenjun Ji; Viacheslav Adamchuk; Prasad Daggupati; Asim Biswas. 2019. "Predicting soil organic matter from cellular phone images under varying soil moisture." Geoderma 361, no. : 114020.

Journal article
Published: 14 August 2019 in Water
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For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.

ACS Style

Haw Yen; Seonggyu Park; Jeffrey G. Arnold; Raghavan Srinivasan; Celray James Chawanda; Ruoyu Wang; Qingyu Feng; Jingwen Wu; Chiyuan Miao; Katrin Bieger; Prasad Daggupati; Ann Van Griensven; Latif Kalin; Sangchul Lee; Aleksey Y. Sheshukov; Michael J. White; Yongping Yuan; In-Young Yeo; Minghua Zhang; Xuesong Zhang. IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+. Water 2019, 11, 1681 .

AMA Style

Haw Yen, Seonggyu Park, Jeffrey G. Arnold, Raghavan Srinivasan, Celray James Chawanda, Ruoyu Wang, Qingyu Feng, Jingwen Wu, Chiyuan Miao, Katrin Bieger, Prasad Daggupati, Ann Van Griensven, Latif Kalin, Sangchul Lee, Aleksey Y. Sheshukov, Michael J. White, Yongping Yuan, In-Young Yeo, Minghua Zhang, Xuesong Zhang. IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+. Water. 2019; 11 (8):1681.

Chicago/Turabian Style

Haw Yen; Seonggyu Park; Jeffrey G. Arnold; Raghavan Srinivasan; Celray James Chawanda; Ruoyu Wang; Qingyu Feng; Jingwen Wu; Chiyuan Miao; Katrin Bieger; Prasad Daggupati; Ann Van Griensven; Latif Kalin; Sangchul Lee; Aleksey Y. Sheshukov; Michael J. White; Yongping Yuan; In-Young Yeo; Minghua Zhang; Xuesong Zhang. 2019. "IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+." Water 11, no. 8: 1681.

Journal article
Published: 04 July 2019 in Remote Sensing
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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

ACS Style

Duie Tien Bui; Khabat Khosravi; Himan Shahabi; Prasad Daggupati; Jan F. Adamowski; Assefa M. Melesse; Binh Thai Pham; Hamid Reza Pourghasemi; Mehrnoosh Mahmoudi; Sepideh Bahrami; Biswajeet Pradhan; Ataollah Shirzadi; Kamran Chapi; Saro Lee. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing 2019, 11, 1589 .

AMA Style

Duie Tien Bui, Khabat Khosravi, Himan Shahabi, Prasad Daggupati, Jan F. Adamowski, Assefa M. Melesse, Binh Thai Pham, Hamid Reza Pourghasemi, Mehrnoosh Mahmoudi, Sepideh Bahrami, Biswajeet Pradhan, Ataollah Shirzadi, Kamran Chapi, Saro Lee. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing. 2019; 11 (13):1589.

Chicago/Turabian Style

Duie Tien Bui; Khabat Khosravi; Himan Shahabi; Prasad Daggupati; Jan F. Adamowski; Assefa M. Melesse; Binh Thai Pham; Hamid Reza Pourghasemi; Mehrnoosh Mahmoudi; Sepideh Bahrami; Biswajeet Pradhan; Ataollah Shirzadi; Kamran Chapi; Saro Lee. 2019. "Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model." Remote Sensing 11, no. 13: 1589.

Journal article
Published: 06 April 2019 in CATENA
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A WASCoB constituting a modest detention pond (berm), surface inlets and tile drains; designed to capture the flow and release it gradually into the drainage system is an efficient watershed BMP. Henceforth; a toolbox, CoBAGNPS for the AGNPS model, is developed to simulate WASCoBs through the AGNPS model. The toolbox utilizes the inputs from AGNPS, through the launching of an application for execution of the WASCoB module. Finally, the output files are generated after routing flow through WASCoBs. The toolbox was applied with a case study in the Gully creek watershed and one of its sub-basins (DFTILE sub-basin) located in Ontario, Canada. The toolbox reproduced the required outputs successfully. Henceforth, significantly enhancing the capability of the AGNPS model to simulate flow through a WASCoB and a network of WASCoBs. Furthermore; the efficiency of the drainage system is also analyzed under different scenarios of pipe risers and tile drains. Also, few scenario analyses were considered in which different diameter drainage pipes were considered to route flow for extreme events for WASCoB3 in the DFTILE sub-basin. A 375-mm diameter drainage pipe is efficient in routing flow for a 10-year 24-h design storm without it overtopping the berm. Finally, another component of the toolbox was tested, where the flow from the DFTILE sub-basin was directly routed to the outlet of the Gully creek watershed (GULGUL 5) through a drainage pipe of 200 mm with 1% slope, assuming a lag time. The hydrograph at the watershed outlet increased marginally due to the small size of the DFTILE sub-basin.

ACS Style

Anand K. Gupta; Ramesh P. Rudra; Bahram Gharabaghi; Prasad Daggupati; Pradeep K. Goel; Rituraj Shukla. CoBAGNPS: A toolbox for simulating water and sediment control basin, WASCoB through AGNPS model. CATENA 2019, 179, 49 -65.

AMA Style

Anand K. Gupta, Ramesh P. Rudra, Bahram Gharabaghi, Prasad Daggupati, Pradeep K. Goel, Rituraj Shukla. CoBAGNPS: A toolbox for simulating water and sediment control basin, WASCoB through AGNPS model. CATENA. 2019; 179 ():49-65.

Chicago/Turabian Style

Anand K. Gupta; Ramesh P. Rudra; Bahram Gharabaghi; Prasad Daggupati; Pradeep K. Goel; Rituraj Shukla. 2019. "CoBAGNPS: A toolbox for simulating water and sediment control basin, WASCoB through AGNPS model." CATENA 179, no. : 49-65.

Journal article
Published: 29 March 2019 in Sustainability
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Water security is the capability of a community to have adequate access to good quality and a sufficient quantity of water as well as safeguard resources for the future generations. Understanding the spatial and temporal variabilities of water security can play a pivotal role in sustainable management of fresh water resources. In this study, a long-term water security analysis of the Grand River watershed (GRW), Ontario, Canada, was carried out using the soil and water assessment tool (SWAT). Analyses on blue and green water availability and water security were carried out by dividing the GRW into eight drainage zones. As such, both anthropogenic as well as environmental demand were considered. In particular, while calculating blue water scarcity, three different methods were used in determining the environmental flow requirement, namely, the presumptive standards method, the modified low stream-flow method, and the variable monthly flow method. Model results showed that the SWAT model could simulate streamflow dynamics of the GRW with ‘good’ to ‘very good’ accuracy with an average Nash–Sutcliffe Efficiency of 0.75, R2 value of 0.78, and percentage of bias (PBIAS) of 8.23%. Sen’s slope calculated using data from over 60 years confirmed that the blue water flow, green water flow, and storage had increasing trends. The presumptive standards method and the modified low stream-flow method, respectively, were found to be the most and least restrictive method in calculating environmental flow requirements. While both green (0.4–1.1) and blue (0.25–2.0) water scarcity values showed marked temporal and spatial variabilities, blue water scarcity was found to be the highest in urban areas on account of higher water usage and less blue water availability. Similarly, green water scarcity was found to be highest in zones with higher temperatures and intensive agricultural practices. We believe that knowledge of the green and blue water security situation would be helpful in sustainable water resources management of the GRW and help to identify hotspots that need immediate attention.

ACS Style

Baljeet Kaur; Narayan Kumar Shrestha; Prasad Daggupati; Ramesh Pal Rudra; Pradeep Kumar Goel; Rituraj Shukla; Nabil Allataifeh. Water Security Assessment of the Grand River Watershed in Southwestern Ontario, Canada. Sustainability 2019, 11, 1883 .

AMA Style

Baljeet Kaur, Narayan Kumar Shrestha, Prasad Daggupati, Ramesh Pal Rudra, Pradeep Kumar Goel, Rituraj Shukla, Nabil Allataifeh. Water Security Assessment of the Grand River Watershed in Southwestern Ontario, Canada. Sustainability. 2019; 11 (7):1883.

Chicago/Turabian Style

Baljeet Kaur; Narayan Kumar Shrestha; Prasad Daggupati; Ramesh Pal Rudra; Pradeep Kumar Goel; Rituraj Shukla; Nabil Allataifeh. 2019. "Water Security Assessment of the Grand River Watershed in Southwestern Ontario, Canada." Sustainability 11, no. 7: 1883.

Journal article
Published: 21 September 2018 in Water
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The Eagle Creek watershed, a small subbasin (125 km2) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.

ACS Style

Katherine R. Merriman; Prasad Daggupati; Raghavan Srinivasan; Chad Toussant; Amy M. Russell; Brett Hayhurst. Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio. Water 2018, 10, 1299 .

AMA Style

Katherine R. Merriman, Prasad Daggupati, Raghavan Srinivasan, Chad Toussant, Amy M. Russell, Brett Hayhurst. Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio. Water. 2018; 10 (10):1299.

Chicago/Turabian Style

Katherine R. Merriman; Prasad Daggupati; Raghavan Srinivasan; Chad Toussant; Amy M. Russell; Brett Hayhurst. 2018. "Assessing the Impact of Site-Specific BMPs Using a Spatially Explicit, Field-Scale SWAT Model with Edge-of-Field and Tile Hydrology and Water-Quality Data in the Eagle Creek Watershed, Ohio." Water 10, no. 10: 1299.