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Simulation And Evaluation Of Stream Flow And Pesticide Prediction In Orestimba Creek Watershed Using The Anngnps Model
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Book Synopsis Simulation and Evaluation of Stream Flow and Pesticide Prediction in Orestimba Creek Watershed Using the AnnGNPS Model by : Chen Wang
Download or read book Simulation and Evaluation of Stream Flow and Pesticide Prediction in Orestimba Creek Watershed Using the AnnGNPS Model written by Chen Wang and published by . This book was released on 2014 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pesticides have been recognized as one major agricultural non-point source (NPS) pollution to the environment and surface water in United States. Numerous mathematical models have been developed over the last decades to simulate the fate and transport of NPS at watershed scale. Geographic Information System (GIS) combined with models extends the spatial and temporal scopes of the research by integrating a variety of climates, soils, land covers, and management practices. The Annualized Agricultural Nonpoint Source model (AnnAGNPS) has received considerable attention in the United States for estimating runoff, sediment yield, pesticide and nutrients transport from ungauged agricultural watershed. However, few studies have been conducted on pesticide loading prediction in surface water using AnnAGNPS. In this study, the AnnAGNPS model was calibrated and validated for prediction of stream flow and chlorpyrifos loading for an agricultural dominated watershed of Orestimba Creek, in Central Valley, California. Large amounts of chlorpyrifos are applied to almonds, walnuts and other stone-fruit orchards in this area every year, which caused significant concern regarding their contamination to the San Joaquin River. Variety of data obtained from multiple sources were utilized as model input, including climate, land use, topology, soil, crop management and schedule, non-crop data, and pesticide. The model's performance was quantitatively analyzed using mean, standard deviation, coefficient of determination (r 2), coefficient of efficiency (NSE), and root mean square error (RMSE). Model's prediction was considered to be unsatisfactory if NSE 0.36, satisfactory if 0.36
Book Synopsis Assessing Pesticide Loading and Concentration with Assistance of Integrated Hydrological Models in Streams of Small to Medium-sized Watersheds by : Wei Chen
Download or read book Assessing Pesticide Loading and Concentration with Assistance of Integrated Hydrological Models in Streams of Small to Medium-sized Watersheds written by Wei Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pesticides are increasingly used around the world alone with the expansion of intensive crop cultivation and food production. Pesticide residues from agriculture fields being carried to surface and ground water impose a potential threat to the aquatic ecosystem as well as to human health. However, monitoring potential threat of pesticide residuals in river systems is expensive and difficult. Previous studies indicated that traditionally used grab sampling methods could potentially underestimate the maximum concentrations of pesticide residues in streams by 10 to 1000 times. The objective of this study was to assess pesticide loading and concentration with assistance of integrated hydrological models in streams of small to medium-sized watersheds. Soil and Water Assessment Tool (SWAT) was selected for simulating hydrological processes together with pesticide loading and in stream pesticide concentration. Model predicted pesticide loading and pesticide concentration was compared with three years measured data from Black Brook Watershed and two Sub-basins within the same watershed. We found that the model predicted pesticide loading and in stream concentrations of three pesticides had the same seasonal trend with field surveys with some discrepancies. The discrepancies are likely caused by three main factors. 1. Model predicts the daily pesticide loading and daily average pesticide concentration and while actual pesticide concentrations change rapidly during stormflow period. 2. Current field sampling method could not capture the rapid change of pesticide concentration due to mechanical limitations. 3. Input data on exact pesticide application data were not available. In general, the pesticide modelling results indicate that the model is an effective tool in loading and concentration prediction in small agricultural watershed. We also found the model predicted pesticide loading during baseflow period were relatively high compare[d] with near zero pesticide concentration observed. This suggest[s] there is a need to improve in pesticide routing algorithm in SWAT model and current estimation during based flow period should be manually adjusted.
Book Synopsis Description and Application of the Stream Simulation and Assessment Model Version IV (SSAM IV) by : William J. Grenney
Download or read book Description and Application of the Stream Simulation and Assessment Model Version IV (SSAM IV) written by William J. Grenney and published by . This book was released on 1981 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Semi-empirical Prediction of Pesticide Loading in the Sacramento and San Joaquin Rivers During Winter Storm Season by :
Download or read book Semi-empirical Prediction of Pesticide Loading in the Sacramento and San Joaquin Rivers During Winter Storm Season written by and published by . This book was released on 2003 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Precipitation-runoff and Streamflow-routing Models for the Willamette River Basin, Oregon by : Antonius Laenen
Download or read book Precipitation-runoff and Streamflow-routing Models for the Willamette River Basin, Oregon written by Antonius Laenen and published by . This book was released on 1997 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development and application of watershed regressions for pesticides (WARP) for estimating atrazine concentration distributions in streams by :
Download or read book Development and application of watershed regressions for pesticides (WARP) for estimating atrazine concentration distributions in streams written by and published by DIANE Publishing. This book was released on with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development, Calibration and Application of Runoff Forecasting Models for the Allegheny River Basin by : William J. Charley
Download or read book Development, Calibration and Application of Runoff Forecasting Models for the Allegheny River Basin written by William J. Charley and published by . This book was released on 1988 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Improved Framework for Watershed Discretization and Model Calibration by : Amin Haghnegahdar
Download or read book An Improved Framework for Watershed Discretization and Model Calibration written by Amin Haghnegahdar and published by . This book was released on 2015 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale (~103-106 km2) physically-based distributed hydrological models have been used increasingly, due to advances in computational capabilities and data availability, in a variety of water and environmental resources management, such as assessing human impacts on regional water budget. These models inevitably contain a large number of parameters used in simulation of various physical processes. Many of these parameters are not measurable or nearly impossible to measure. These parameters are typically estimated using model calibration, defined as adjusting the parameters so that model simulations can reproduce the observed data as close as possible. Due to the large number of model parameters, it is essential to use a formal automated calibration approach in distributed hydrological modelling. The St. Lawrence River Basin in North America contains the largest body of surface fresh water, the Great Lakes, and is of paramount importance for United States and Canada. The Lakes' water levels have huge impact on the society, ecosystem, and economy of North America. A proper hydrological modelling and basin-wide water budget for the Great Lakes Basin is essential for addressing some of the challenges associated with this valuable water resource, such as a persistent extreme low water levels in the lakes. Environment Canada applied its Modélisation Environnementale-Surface et Hydrologie (MESH) modelling system to the Great Lakes watershed in 2007. MESH is a coupled semi-distributed land surface-hydrological model intended for various water management purposes including improved operational streamflow forecasts. In that application, model parameters were only slightly adjusted during a brief manual calibration process. Therefore, MESH streamflow simulations were not satisfactory and there was a considerable need to improve its performance for proper evaluation of the MESH modelling system. Collaborative studies between the United States and Canada also highlighted the need for inclusion of the prediction uncertainty in modelling results, for more effective management of the Great Lakes system. One of the primary goals of this study is to build an enhanced well-calibrated MESH model over the Great Lakes Basin, particularly in the context of streamflow predictions in ungauged basins. This major contribution is achieved in two steps. First, the MESH performance in predicting streamflows is benchmarked through a rather extensive formal calibration, for the first time, in the Great Lakes Basin. It is observed that a global calibration strategy using multiple sub-basins substantially improved MESH streamflow predictions, confirming the essential role of a formal model calibration. At the next step, benchmark results are enhanced by further refining the calibration approach and adding uncertainty assessment to the MESH streamflow predictions. This enhancement was mainly achieved by modifying the calibration parameters and increasing the number of sub-basins used in calibration. A rigorous multi-criteria comparison between the two experiments confirmed that the MESH model performance is indeed improved using the revised calibration approach. The prediction uncertainty bands for the MESH streamflow predictions were also estimated in the new experiment. The most influential parameters in MESH were also identified to be soil and channel roughness parameters based on a local sensitivity test. One of the main challenges in hydrological distributed modelling is how to represent the existing spatial heterogeneity in nature. This task is normally performed via watershed discretization, defined as the process of subdividing the basin into manageable “hydrologically similar” computational units. The model performance, and how well it can be calibrated using a limited budget, largely depends on how a basin is discretized. Discretization decisions in hydrologic modelling studies are, however, often insufficiently assessed prior to model simulation and parameter. Few studies explicitly present an organized and objective methodology for assessing discretization schemes, particularly with respect to the streamflow predictions in ungauged basins. Another major goal of this research is to quantitatively assess watershed discretization schemes for distributed hydrological models, with various level of spatial data aggregation, in terms of their skill to predict flows in ungauged basins. The methodology was demonstrated using the MESH model as applied to the Nottawasaga river basin in Ontario, Canada. The schemes differed from a simple lumped scheme to more complex ones by adding spatial land cover and then spatial soil information. Results reveal that calibration budget is an important factor in model performance. In other words, when constrained by calibration budget, using a more complex scheme did not necessarily lead to improved performance in validation. The proposed methodology was also implemented using a shorter sub-period for calibration, aiming at substantial computational saving. This strategy is shown to be promising in producing consistent results and has the potential to increase computational efficiency of this comparison framework. The outcome of this very computationally intensive research, i.e., the well-calibrated MESH model for the Great Lakes and all the final parameter sets found, are now available to be used by other research groups trying to study various aspects of the Great Lakes System. In fact, the benchmark results are already used in the Great Lakes Runoff Intercomparison Project (GRIP). The proposed comparison framework can also be applied to any distributed hydrological model to evaluate alternative discretization schemes, and identify one that is preferred for a certain case.
Book Synopsis Simulation of Net Infiltration and Potential Recharge Using a Distributed-Parameter Watershed Model of the Death Valley Region, Nevada and California by :
Download or read book Simulation of Net Infiltration and Potential Recharge Using a Distributed-Parameter Watershed Model of the Death Valley Region, Nevada and California written by and published by DIANE Publishing. This book was released on with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparison of Topmodel Streamflow Simulations Using Nexrad-Based and Measured Rainfall Data, McTier Creek Watershed, South Carolina by : U.S. Department of the Interior
Download or read book Comparison of Topmodel Streamflow Simulations Using Nexrad-Based and Measured Rainfall Data, McTier Creek Watershed, South Carolina written by U.S. Department of the Interior and published by CreateSpace. This book was released on 2014-07-23 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rainfall is an important forcing function in most water-shed models. As part of a previous investigation to assess interactions among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations in the Edisto River Basin, the topography-based hydrological model (TOPMODEL) was applied in the McTier Creek watershed in Aiken County, South Carolina. Measured rainfall data from six National Weather Service (NWS) Cooperative (COOP) stations surrounding the McTier Creek watershed were used to calibrate the McTier Creek TOPMODEL.
Book Synopsis Improving Post Processing of Ensemble Streamflow Forecast for Short-to-long Ranges by : Babak Alizadeh
Download or read book Improving Post Processing of Ensemble Streamflow Forecast for Short-to-long Ranges written by Babak Alizadeh and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: A novel multi-scale post-processor for ensemble streamflow prediction, MS-EnsPost, and a multiscale probability matching (MS-PM) technique for bias correction in streamflow simulation are developed and evaluated. The MS-PM successively applies probability matching (PM) across multiple time scales of aggregation to reduce scale-dependent biases in streamflow simulation.For evaluation of MS-PM, 34 basins in four National Weather Service (NWS) River Forecast Centers (RFC) in the US were used. The results indicate that MS-PM improves over PM for streamflow prediction at a daily time step, and that averaging the empirical cumulative distribution functions to reduce sampling uncertainty marginally improves performance. The performance of MS-PM, however, quickly reaches a limit with the addition of larger temporal scales of aggregation due to the increasingly large sampling uncertainties. MS-EnsPost represents a departure from the PM-based approaches to avoid large sampling uncertainties associated with distribution modeling, and to utilize fully the predictive skill in model-simulated and observed streamflow that may be present over a range of temporal scales.MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow,multiscale regression over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For evaluation of MS-EnsPost, 139 basins in eight RFCs were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over the existing streamflow ensemble post processor in the NWS Hydrologic Ensemble Forecast Service, EnsPost, are attributed. The ensemble mean prediction results show that MS-EnsPost reduces the root mean square error of Day-1 to -7 predictions of mean daily flow from EnsPost by 5 to 68 percent, and for most basins, the improvement is due to both bias correction and multiscale regression. The ensemble prediction results show that MS-EnsPost reduces the mean Continuous Ranked Probability Score of Day-1 to -7 predictions of mean daily flow from EnsPost by 2 to 62 percent, and that the improvement is due mostly to improved resolution than reliability. Examination of the mean Continuous Ranked Probability Skill Scores (CRPSS) indicates that, for most basins, the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the mean CRPSS results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snow and, for non-snow-driven basins, mean annual precipitation.The positive impact of MS-EnsPost is particularly significant for a number of basins impacted by flow regulations. Examination of the multiscale regression weights indicates that the multiscale regression procedure is able to capture and reflect the scale-dependent impact of flow regulations on predictive skills of observed and model-predicted flow. One of the motivations for MS-EnsPost is to reduce data requirement so that nonstationarity may be considered.Comparative evaluation of MS-EnsPost with EnsPost indicates that, under reduced data availability, MS-EnsPost generally outperforms EnsPost for those basins exhibiting significant changes in flow regime.
Book Synopsis Assessment of Uncertainty in Flow Model Parameters, Channel Hydraulic Properties, and Rainfall Data of a Lumped Watershed Model by : Jairo Nelvedir Díaz-Ramírez
Download or read book Assessment of Uncertainty in Flow Model Parameters, Channel Hydraulic Properties, and Rainfall Data of a Lumped Watershed Model written by Jairo Nelvedir Díaz-Ramírez and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Among other sources of uncertainties in hydrologic modeling, spatial rainfall variability, channel hydraulic variability, and model parameter uncertainty were evaluated. The Monte Carlo and Harr methods were used to assess 90% certainty bounds on simulated flows. The lumped watershed model, Hydrologic Simulation Program FORTRAN - HSPF, was used to simulate streamflow at the outlet of the Luxapallila Creek watershed in Mississippi and Alabama. Analysis of parameter uncertainty propagation on streamflow simulations from 12 HSPF parameters was accomplished using 5,000 Monte Carlo random samples and 24 Harr selected points for each selected parameter. Spatial rainfall variability propagation on simulated flows was studied using six random grid point sets of Next Generation Weather Radar (NEXRAD) rainfall data (i.e., 109, 86, 58, 29, 6, and 2 grid points) from the baseline scenario (115 NEXRAD grid points). Uncertainty in channel hydraulic properties was assessed comparing the baseline scenario (USGS FTABLE) versus the EPA RF1 FTABLE scenario. The difference between the baseline scenario and the remaining scenarios in this study was evaluated using two criteria: the percentage of observed flows within the HSPF 90% certainty bounds (Reliability) and the width of the HSPF 90% certainty bounds (Sharpness). Daily observed streamflow data were clustered into three groups to assess the model performance by each class: below normal, normal, and above normal flows. The parameter uncertainty propagation results revealed that the higher the model Sharpness the lower the model Reliability. The model Sharpness and Reliability results using 2 NEXRAD grid points were markedly different from those results using the remaining NEXRAD data sets. The hydraulic property variability of the main channel affected storm event paths at the watershed outlet, especially the time to peak flow and recessing limbs of storm events. The comparison showed that Harr's method could be an appropriate initial indicator of parameter uncertainty propagation on streamflow simulations, in particular for hydrology models with several parameters. Parameter uncertainty was still more important than those sources of uncertainty accomplished in this study because all of the median relative errors of model Reliability and Sharpness were lower than +/- 100%.
Book Synopsis Towards Improved Modeling for Hydrologic Predictions in Poorly Gauged Basins by : Koray Kamil Yilmaz
Download or read book Towards Improved Modeling for Hydrologic Predictions in Poorly Gauged Basins written by Koray Kamil Yilmaz and published by . This book was released on 2007 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds. Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration. Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system."Signature measures"are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.
Book Synopsis Using Satellite-Based Hydro-Climate Variables and Machine Learning for Streamflow Modeling at Various Scales in the Upper Mississippi River Basin by : Dongjae Kwon
Download or read book Using Satellite-Based Hydro-Climate Variables and Machine Learning for Streamflow Modeling at Various Scales in the Upper Mississippi River Basin written by Dongjae Kwon and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Streamflow data are essential to study the hydrologic cycle and to attain appropriate water resource management policies. However, the availability of gauge data is limited due to various reasons such as economic, political, instrumental malfunctioning, and poor spatial distribution. Although streamflow can be simulated by process-based and machine learning approaches, applicability is limited due to intensive modeling effort, or its black-box nature, respectively. Here, we introduce a machine learning (Boosted Regression Tree (BRT)) approach based on remote sensing data to simulate monthly streamflow for three of varying sizes watersheds in the Upper Mississippi River Basin (UMRB). By integrating spatial land surface and climate variables that describe the subwatersheds in a basin as an input dataset and streamflow as an output learning dataset in a machine learning model (MLM), relationships between watershed characteristics and streamflow are established. The testing results of NSE with UMRB, IRW, and RRW of 0.8042, 0.7593, and 0.6856, respectively showed the remote sensing-based MLM can be effectively applied to streamflow prediction and has advantages for large basins compared with the performances of process-based approaches. Further, Predictor Importance (PI) analysis revealed the most important remote sensing variables and the most representative subwatersheds.
Book Synopsis Evaluation of a Model for Simulating Continuous Streamflow from Small Watersheds by : James Elmer Montgomery
Download or read book Evaluation of a Model for Simulating Continuous Streamflow from Small Watersheds written by James Elmer Montgomery and published by . This book was released on 1975 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Hydrologic Simulations of the Maquoketa River Watershed Using SWAT by : Manoj Jha
Download or read book Hydrologic Simulations of the Maquoketa River Watershed Using SWAT written by Manoj Jha and published by . This book was released on 2009 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling the Effects of Fire on Streamflow in a Chaparral Watershed by : Christine Eleana McMichael
Download or read book Modeling the Effects of Fire on Streamflow in a Chaparral Watershed written by Christine Eleana McMichael and published by . This book was released on 2004 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:
