Indrajeet Chaubey

Effectiveness of Low Impact Development Practices: Literature Review and Suggestions for Future Research

Low impact development (LID) is a land development strategy for managing stormwater at the source with decentralized micro-scale control measures. Since the emergence of LID practices, they have been successfully used to manage stormwater runoff, improve water quality, and protect the environment. However, discussions still surround the effectiveness of many of these practices, resulting in a reluctance to widely adopt them. This paper highlights evidence in the literature regarding the beneficial uses of LID practices. A discussion of how LID practices are represented in hydrologic/water quality models is also provided using illustrative examples of three computational models developed with algorithms and modules to support widespread adoption of LID practices. Finally, the paper suggests directions for future research opportunities.

Uncertainty in the model parameters due to spatial variability of rainfall

Most hydrologic/water quality (H/WQ) models that use rainfall as input assume spatial homogeneity of rainfall. Under this assumption this study assesses the variability induced in calibrated model parameters solely due to rainfall spatial variability. The AGNPS model and a network of 17 raingauges were used. Model parameters were estimated using rainfall observed at each gauge location, one at a time, as though that rainfall covered the entire catchment. A large uncertainty in the estimated parameters resulted from the spatial variability of rainfall. The uncertainty in the estimated parameters using the rainfall observed by a single gauge exceeded the rainfall measurement error. A large uncertainty in estimated model parameters can be expected if detailed variations in the input rainfall are not taken into account.

UNCERTAINTY IN TMDL MODELS

Although the U.S. Congress established the Total Maximum Daily Load (TMDL) program in the original Clean Water Act of 1972, Section 303(d), it did not receive attention until the 1990s. Currently, two methods are available for tracking pollution in the environment and assessing the effectiveness of the TMDL process on improving the quality of impaired water bodies: field monitoring and mathematical/computer modeling. Field monitoring may be the most appropriate method, but its use is limited due to high costs and extreme spatial and temporal ecosystem variability. Mathematical models provide an alternative to field monitoring that can potentially save time, reduce cost, and minimize the need for testing management alternatives. However, the uncertainty of the model results is a major concern. Uncertainty is defined as the estimated amount by which an observed or calculated value may depart from the true value, and it has important policy, regulatory, and management implications. The source and magnitude of uncertainty and its impact on TMDL assessment has not been studied in depth. This article describes the collective experience of scientists and engineers in the assessment of uncertainty associated with TMDL models. It reviews sources of uncertainty (e.g., input variability, model algorithms, model calibration data, and scale), methods of uncertainty evaluation (e.g., first-order approximation, mean value first-order reliability method, Monte Carlo, Latin hypercube sampling with constrained Monte Carlo, and generalized likelihood uncertainty estimation), and strategies for communicating uncertainty in TMDL models to users. Four case studies are presented to highlight uncertainty quantification in TMDL models. Results indicate that uncertainty in TMDL models is a real issue and should be taken into consideration not only during the TMDL assessment phase, but also in the design of BMPs during the TMDL implementation phase. First-order error (FOE) analysis and Monte Carlo simulation (MCS) or any modified versions of these two basic methods may be used to assess uncertainty. This collective study concludes that a more scientific method to account for uncertainty would be to develop uncertainty probability distribution functions and transfer such uncertainties to TMDL load allocation through the margin of safety component, which is selected arbitrarily at the present time. It is proposed that explicit quantification of uncertainty be made an integral part of the TMDL process. This will benefit private industry, the scientific community, regulatory agencies, and action agencies involved with TMDL development and implementation.

Effectiveness of Vegetative Filter Strips in Retaining Surface-applied Swine Manure Constituents

Simulated rainfall was used to evaluate the effectiveness of vegetative filter strips (VFS) of varying lengths (0, 3, 6, 9, 15, and 21 m) in reducing sediment and nutrient losses from plots treated with liquid swine manure at 200 kg N/ha. Mass transport of ammonia nitrogen (NH3-N), total Kjeldahl nitrogen (TKN), ortho-phosphorus (PO4-P), total phosphorus (TP), and total suspended solids (TSS) was reduced significantly (p < 0.05) by fescue (Festuca arundinacea Schreb.) VFS. The 3 and 21 m VFS removed 65 and 87% of incoming TKN, 71 and 99% of incoming NH3-N, 65 and 94% of incoming PO4-P, and 67 and 92% of the incoming TP, respectively. Effectiveness of VFS, however, did not increase significantly beyond 3 m for TSS and chemical oxygen demand and averaged 61 and 50%, respectively. Mass transport of TKN, NH3-N, PO4-P, and TP was minimized at the 9 m VFS length. The VFS did not significantly reduce nitrate nitrogen and fecal coliform from the incoming runoff. First-order kinetics described the removal of manure constituents.

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.

Effectiveness of best management practices in improving water quality in a pasture-dominated watershed

The nonpoint source pollution problem can be controlled by implementing various best management practices (BMPs) in the watershed. However, before such practices are adopted, their effectiveness at various spatial and temporal scales must be evaluated. The objective of this research was to evaluate a suite of BMPs in a pasture-dominated watershed in their effectiveness at controlling nutrient losses. A total of 171 different BMP combinations incorporating grazing and pasture management, riparian and buffer zones, and poultry litter applications were evaluated for their effectiveness using the Soil and Water Assessment Tool (SWAT) model. The SWAT model was parameterized using detailed farm and watershed-scale data. The stochasticity in weather was captured by generating 250 various possible weather realizations for a 25-year period, using measured historical climate data for the watershed. Model results indicated that losses of both total nitrogen, mineral phosphorus, and total phosphorus increased with an increase in litter application rates. For the same application rates, greatest losses were predicted for fall application timings compared to spring and summer applications. Overgrazing resulted in greater nutrient losses compared to baseline conditions for all application rates, timings, and litter characteristics, indicating that overgrazing of pasture areas must be avoided if any improvement in the water quality is to be expected. Variability in weather conditions significantly affected BMP performance; under certain weather conditions, an increase in pollutant losses can be greater than reductions due to BMPs implemented in the watershed. Buffer strips and grazing management were two most important BMPs affecting the losses of total nitrogen and total phosphorus from the pasture areas.

Effectiveness of Vegetative Filter Strips in Controlling Losses of Surface-applied Poultry Litter Constituents

Vegetative filter strips (VFS) have been shown to have high potential for reducing nonpoint source pollution from cultivated agricultural source areas, but information from uncultivated source areas amended with poultry litter is limited. Simulated rainfall was used in analyzing effects of VFS length (0, 3.1, 6.1, 9.2, 15.2, and 21.4 m) on quality of runoff from fescue (Festuca arundinacea Schreb.) plots (1.5 ¥ 24.4 m) amended with poultry litter (5 Mg/ha). The VFS reduced mass transport of ammonia-nitrogen (NH3-N), total Kjeldahl nitrogen (TKN), ortho-phosphorus (PO4-P), total phosphorus (TP), chemical oxygen demand (COD), and total suspended solids (TSS). Mass transport of TKN, NH3-N, TP, and PO4-P were reduced by averages of 39, 47, 40, and 39%, respectively, by 3.1 m VFS and by 81, 98, 91, and 90%, respectively, by 21.4 m VFS. Effectiveness of VFS in terms of mass transport reduction was unchanged, however, beyond 3.1 m length for TSS and COD and averaged 35 and 51%, respectively. The VFS were ineffective in removing nitrate-nitrogen from the incoming runoff. Removal of litter constituents was described very well (r2 = 0.70 to 0.94) by a first-order relationship between constituent removal and VFS length.

Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations ☆

Abstract Previous publications have outlined recommended practices for hydrologic and water quality (H/WQ) modeling, but limited guidance has been published on how to consider the projects purpose or models intended use, especially for the final stage of modeling applications – namely evaluation, interpretation, and communication of model results. Such guidance is needed to more effectively evaluate and interpret model performance and more accurately communicate that performance to decision-makers and other modeling stakeholders. Thus, we formulated a methodology for evaluation, interpretation, and communication of H/WQ model results. The recommended methodology focuses on interpretation and communication of results, not on model development or initial calibration and validation, and as such it applies to the modeling process following initial calibration. The methodology recommends the following steps: 1) evaluate initial model performance; 2) evaluate outliers and extremes in observed values and bias in predicted values; 3) estimate uncertainty in observed data and predicted values; 4) re-evaluate model performance considering accuracy, precision, and hypothesis testing; 5) interpret model results considering intended use; and 6) communicate model performance. A flowchart and tables were developed to guide model interpretation, refinement, and proper application considering intended model uses (i.e., Exploratory, Planning, and Regulatory/Legal). The methodology was designed to enhance application of H/WQ models through conscientious evaluation, interpretation, and communication of model performance to decision-makers and other stakeholders; it is not meant to be a definitive standard or a required protocol, but together with recent recommendations and published best practices serve as guidelines for enhanced model application emphasizing the importance of the models intended use.

Water quality impacts of corn production to meet biofuel demands.

The overall goal of this project was to quantify the long-term water quality impacts of land management changes associated with increased demands for corn as a transportation biofuel feedstock in the United States. A modeling approach that considers a nonpoint source model, Groundwater Loading Effects of Agricultural Management Systems and National Agricultural Pesticide Risk Analysis, was used to simulate annual losses in runoff, percolation, erosion, nitrate-nitrogen, total phosphorus, atrazine (1-chloro-3-ethylamino-5-isopropylamino-2,4,6-triazine), and pyraclostrobin (Methyl {2-[1-(4-chlorophenyl)-1H-pyrazol-3-yloxymethyl] phenyl} methoxycarbamate) to the edge-of-field and bottom-of-root zones associated with multiple cropping scenarios. Model results for representative soils, throughout Indiana, were analyzed to determine 10% (worst case) and 50% (average case) probability of exceedence in the aforementioned water quality indicators. Modeling results indicated significant differences (p<0.05) in water quality indicators between continuous corn and corn-soybean rotations. The results showed that agricultural management decisions would have greater impacts on nutrient, runoff, erosion, and pesticides losses from agricultural fields compared to water quality indicators associated with the projected changes in crop rotation systems. The model results point to the need for additional research to fully understand the water impacts of land management decisions associated with corn grain as a feedstock for biofuel production.

Environmental and Economic Trade-Offs in a Watershed When Using Corn Stover for Bioenergy

There is an abundant supply of corn stover in the United States that remains after grain is harvested which could be used to produce cellulosic biofuels mandated by the current Renewable Fuel Standard (RFS). This research integrates the Soil Water Assessment Tool (SWAT) watershed model and the DayCent biogeochemical model to investigate water quality and soil greenhouse gas flux that results when corn stover is collected at two different rates from corn-soybean and continuous corn crop rotations with and without tillage. Multiobjective watershed-scale optimizations are performed for individual pollutant-cost minimization criteria based on the economic cost of each cropping practice and (individually) the effect on nitrate, total phosphorus, sediment, or global warming potential. We compare these results with a purely economic optimization that maximizes stover production at the lowest cost without taking environmental impacts into account. We illustrate trade-offs between cost and different environmental performance criteria, assuming that nutrients contained in any stover collected must be replaced. The key finding is that stover collection using the practices modeled results in increased contributions to atmospheric greenhouse gases while reducing nitrate and total phosphorus loading to the watershed relative to the status quo without stover collection. Stover collection increases sediment loading to waterways relative to when no stover is removed for each crop rotation-tillage practice combination considered; no-till in combination with stover collection reduced sediment loading below baseline conditions without stover collection. Our results suggest that additional information is needed about (i) the level of nutrient replacement required to maintain grain yields and (ii) cost-effective management practices capable of reducing soil erosion when crop residues are removed in order to avoid contributions to climate change and water quality impairments as a result of using corn stover to satisfy the RFS.