Subhasis Giri

Evaluation of targeting methods for implementation of best management practices in the Saginaw River Watershed.

Increasing concerns regarding water quality in the Great Lakes region are mainly due to changes in urban and agricultural landscapes. Both point and non-point sources contribute pollution to Great Lakes surface waters. Best management practices (BMPs) are a common tool used to reduce both point and non-point source pollution and improve water quality. Meanwhile, identification of critical source areas of pollution and placement of BMPs plays an important role in pollution reduction. The goal of this study is to evaluate the performance of different targeting methods in 1) identifying priority areas (high, medium, and low) based on various factors such as pollutant concentration, load, and yield, 2) comparing pollutant (sediment, total nitrogen-TN, and total phosphorus-TP) reduction in priority areas defined by all targeting methods, 3) determine the BMP relative sensitivity index among all targeting methods. Ten BMPs were implemented in the Saginaw River Watershed using the Soil and Water Assessment Tool (SWAT) model following identification of priority areas. Each targeting method selected distinct high priority areas based on the methodology of implementation. The concentration based targeting method was most effective at reduction of TN and TP, likely because it selected the greatest area of high priority for BMP implementation. The subbasin load targeting method was most effective at reducing sediment because it tended to select large, highly agricultural subbasins for BMP implementation. When implementing BMPs, native grass and terraces were generally the most effective, while conservation tillage and residue management had limited effectiveness. The BMP relative sensitivity index revealed that most combinations of targeting methods and priority areas resulted in a proportional decrease in pollutant load from the subbasin level and watershed outlet. However, the concentration and yield methods were more effective at subbasin reduction, while the stream load method was more effective at reducing pollutants at the watershed outlet. The results of this study indicate that emphasis should be placed on selection of the proper targeting method and BMP to meet the needs and goals of a BMP implementation project because different targeting methods produce varying results.

Understanding the relationship of land uses and water quality in Twenty First Century: A review

Rising food, housing and energy demand of increasing population creates an immense pressure on water resources, especially on water quality. The water quality around the globe is degrading primarily due to intense agricultural activities associated with rapid urbanization. This study attributes to cause of water quality problem, indices to measure water quality, methods to identify proper explanatory variables to water quality and its processing to capture the special effect, and finally modeling of water quality using identified explanatory variables to provide insights. This would help policymakers and watershed managers to take necessary steps to protect water quality for the future as well as current generation. Finally, some knowledge gaps are also discussed which need to be addressed in the future studies.

Application of analytical hierarchy process for effective selection of agricultural best management practices.

In this study an analytical hierarchy process (AHP) was used for ranking best management practices (BMPs) in the Saginaw River Watershed based on environmental, economic and social factors. Three spatial targeting methods were used for placement of BMPs on critical source areas (CSAs). The environment factors include sediment, total nitrogen, and total phosphorus reductions at the subbasin level and the watershed outlet. Economic factors were based on total BMP cost, including installation, maintenance, and opportunity costs. Social factors were divided into three favorability rankings (most favorable, moderately favorable, and least favorable) based on area allocated to each BMP. Equal weights (1/3) were considered for the three main factors while calculating the BMP rank by AHP. In this study three scenarios were compared. A comprehensive approach in which environmental, economic, and social aspects are simultaneously considered (Scenario 1) versus more traditional approaches in which both environmental and economic aspects were considered (Scenario 2) or only environmental aspects (sediment, TN, and TP) were considered (Scenario 3). In Scenario 1, only stripcropping (moderately favorable) was selected on all CSAs at the subbasin level, whereas stripcropping (49-69% of CSAs) and residue management (most favorable, 31-51% of CSAs) were selected by AHP based on the watershed outlet and three spatial targeting methods. In Scenario 2, native grass was eliminated by moderately preferable BMPs (stripcropping) both at the subbasin and watershed outlet levels due the lower BMP implementations cost compared to native grass. Finally, in Scenario 3, at subbasin level, the least socially preferable BMP (native grass) was selected in 100% of CSAs due to greater pollution reduction capacity compared to other BMPs. At watershed level, nearly 50% the CSAs selected stripcropping, and the remaining 50% of CSAs selected native grass and residue management equally.

An Integrated Approach for Targeting Critical Source Areas to Control Nonpoint Source Pollution in Watersheds

This study presents an integrated approach for targeting critical source areas (CSAs) to control nonpoint source pollution in watersheds. CSAs are the intersections between hydrologically sensitive areas (HSAs) and high pollution producing areas of watersheds. HSAs are the areas with high hydrological sensitivity and potential for generating runoff. They were based on a soil topographic index in consistence of a saturation excess runoff process. High pollution producing areas are the areas that have a high potential for generating pollutants. Such areas were based on simulated pollution loads to streams by the Soil and Water Assessment Tool. The integrated approach is applied to the Neshanic River watershed, a suburban watershed with mixed land uses in New Jersey in the U.S. Results show that several land uses result in water pollution: agricultural land causes sediment, nitrogen and phosphorus pollution; wetlands cause sediment and phosphorus pollution; and urban lands cause nitrogen and phosphorus pollution. The primary CSAs are agricultural lands for all three pollutants, urban lands for nitrogen and phosphorus, and wetlands for sediment and phosphorus. Some pollution producing areas were not classified into CSAs because they are not located in HSAs and the pollutants generated in those areas are less likely to be transported by runoff into streams. The integrated approach identifies CSAs at a very fine scale, which is useful for targeting the implementation of best management practices for water quality improvement, and can be applied broadly in different watersheds to improve the economic efficiency of controlling nonpoint source pollution.

Integrating statistical and hydrological models to identify implementation sites for agricultural conservation practices

Watershed models are scarcely used by watershed managers due to their complexity. This study facilitates information transfer by introducing simpler techniques related to easily obtained watershed characteristics, including distance to the watershed outlet and stream order. The Soil and Water Assessment Tool (SWAT) was calibrated for the Saginaw River Watershed, Michigan. Five agricultural best management practices (BMPs) were implemented in SWAT one at a time in each subbasin. Five statistical models were used to determine the pollution reduction at the watershed outlet using distance and BMP type, with results suggesting that a mixed effects model (model 5) was optimal. This model included subbasin as a random effect, while distance to watershed outlet and BMP type were fixed effects. Native grass and strip cropping were the most effective BMPs for reducing sediment and nutrient transport. Subbasins containing stream orders 1-3 were ideal for BMP implementation throughout the watershed. Novel techniques identify the best location for conservation practice installation.Trellis plots help to determine the optimal distance to maximize pollution reduction.Surface plots were created to visualize watershed response to pollution reduction.This study helps decision makers and stakeholders in local and watershed-scale planning.

Evaluation of Swat Performance on a Mountainous Watershed in Tropical Africa

Soil and water assessment Tool is used to model the hydrology of a mountainous catchment in tropical Africa. Land cover and soil characteristics for the catchment were used to determine initial model parameters that were later adjusted during a calibration process. The model was calibrated and validated against measured stream flow. Although the model performed satisfactorily for simulating monthly river flows based on SWAT model calibration guidelines, it fell short of capturing daily peak flows. Average error on prediction of daily peak flows was -19.8%, while the median error was 10.8%.Overall, the average simulated daily peak flow was 2.6cms less than the corresponding observed daily peak flow, indicating a model tendency to under predict the magnitude of peak events. The inability of the model to capture peak flows was found to be the main limiting factor for its performance.

Assessing Soil Moisture Patterns Using a Soil Topographic Index in a Humid Region

Knowledge of soil moisture is essential for soil conservation and efficient water resources management especially related to control nonpoint-source pollution. Soil topographic indices (STI) are often used to understand the soil moisture patterns in landscapes and make effective landscape management decisions. This study assessed the relationships between soil moisture measurements and STI values in two study sites in North-central New Jersey, USA. The soil moisture measurements were taken in these study sites using a time domain reflectometry probe during thirteen sampling events between April 2013 and July 2015. The STI values at the sampling points were derived from a 3-m LiDAR digital elevation model and SSURGO soil database. The Spearman’s correlation analysis based on these data in all sampling points identified a significant positive correlation between soil moisture and STI. Strong positive relationships between soil moisture and STI were also identified when using binned data to eliminate the impacts of unevenness in data distribution and the impacts of micro-variations in topography, vegetation, soil compaction, and instrumental errors. The linear mixed modeling results revealed significant and positive impacts of STI and precipitation, and significant but negative impacts of temperature on soil moisture. The degrees of these effects vary across two study sites, which reflect the complex and dynamic interactions among soils, topography and climate in landscapes that affect soil moisture. Given the stochastic nature of climate factors such as precipitation and temperature, the static STI would be a reliable factor to predict soil moisture patterns in the landscape. The findings support various STI-based conservation planning efforts in New Jersey and beyond such as targeting hydrologically sensitive areas for natural resources protection and preservation and best management practice implementation.

Evaluation of neuro-fuzzy and Bayesian techniques in estimating suspended sediment loads

Sediment is considered the largest surface water pollutant by volume, which is crucial for surface water planning and management. Different management scenario evaluations require multiple in-stream suspended sediment forecasts and estimations. Physically-based models are considered to be good modeling techniques for suspended sediment estimation; nevertheless, they require a large number of parameters and intensive calculations. This study aims to enhance suspended sediment predicting techniques using efficient fusion modeling that can be used for evaluations by watershed managers and stakeholders. Adaptive neuro-fuzzy inference system (ANFIS) and Bayesian regression models were tested to find the best alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT) model to predict suspended sediment loads in the Saginaw River watershed. For both methods, four different method-types were tested, namely General, Temporal, Spatial and Spatiotemporal. Results of the study showed that both methods can be used as good alternatives to the SWAT model at the global level for watershed estimations. The best suspended sediment replicating models, the Bayesian Spatiotemporal and ANFIS Spatial, produced results with Nash–Sutcliffe model efficiency values of 0.95 and 0.94, respectively. For the subbasin level, Bayesian and ANFIS techniques showed satisfactory results for 84 and 77 subbasins, respectively, out of 155 subbasins in the watershed. Box-Cox transformation of suspended sediment load values, made the use of the Bayesian model feasible and improved the prediction of the ANFIS models. However, suspended sediment data exhibited a bimodal distribution after transformation, making the modeling process challenging and complex.