Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland

Streamflow simulation is often challenging in mountainous watersheds because of irregular topography and complex hydrological processes. Rates of change in precipitation and temperature with respect to elevation often limit the ability to reproduce stream runoff by hydrological models. Anthropogenic influence, such as water transfers in high altitude hydropower reservoirs increases the difficulty in modeling since the natural flow regime is altered by long term storage of water in the reservoirs. The Soil and Water Assessment Tool (SWAT) was used for simulating streamflow in the upper Rhone watershed located in the south western part of Switzerland. The catchment area covers 5220km2, where most of the land cover is dominated by forest and 14% is glacier. Streamflow calibration was done at daily time steps for the period of 2001-2005, and validated for 2006-2010. Two different approaches were used for simulating snow and glacier melt process, namely the temperature index approach with and without elevation bands. The hydropower network was implemented based on the intake points that form part of the inter-reservoir network. Subbasins were grouped into two major categories with glaciers and without glaciers for simulating snow and glacier melt processes. Model performance was evaluated both visually and statistically where a good relation between observed and simulated discharge was found. Our study suggests that a proper configuration of the network leads to better model performance despite the complexity that arises for water transaction. Implementing elevation bands generates better results than without elevation bands. Results show that considering all the complexity arising from natural variability and anthropogenic influences, SWAT performs well in simulating runoff in the upper Rhone watershed. Findings from this study can be applicable for high elevation snow and glacier dominated catchments with similar hydro-physiographic constraint

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50×50 and 25×25km2. The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds

Assessment of uncertainty in optimal watershed management to control nonpoint source pollution from agricultural watersheds

Best management practices (BMPs) provide a viable option, when implemented properly at a farm level, for reduction of nonpoint source (NPS) pollutant loads at a watershed scale. However, the watershed model used to simulate the BMPs is prone to several uncertainties. The important sources of uncertainty are the uncertainty in the estimation of the hydrologic model parameters, uncertainties in the land use and uncertainties in future climate. The main goal of implementing BMPs at a watershed is to place the management practices at a farm level that meets the two objectives of (a) minimization of pollutant loads and (b) minimization of total costs due to the implementation of BMPs. Some of the most common methods for the selection and placement of BMPs in a farm are (a) random selection based on a first come first serve basis, (b) targeting the farm areas that fall within the high contributing areas in the watershed, and (c) optimization method using multi-objective optimization. In this research we applied a multi-objective optimization tool for optimal BMP management and compared the results with several targeting and random placement techniques. Optimization solutions were far better when compared to any targeting and random placement techniques. Uncertainties in the land use change arise from the lack of knowledge on how much the land conversion leads to conversion of forests into croplands or urban areas. This source of uncertainty was studied in this research by developing synthetic land use change scenarios that have varied levels of conversion of land use from forest to croplands (corn or soybean) or forest to urban. It was observed that land use change impacts the water quality loads in the watershed. Climate change is another important source of variability in estimating water quality loads. Downscaled and bias corrected GCM data were used to obtain the variability in the future climate when compared to the historic climate data. This variability was used to modify the historic observed climate data and simulated with the Soil and Water Assessment Tool (SWAT) model to study how the variability in the historic averages would impact the water quality loads and therefore the optimal BMP solutions. Climate change is an important source of uncertainty, and water quality loads predicted by the SWAT model are highly sensitive to variability in climate change. The conclusions from the land use and climate sensitivity analysis was followed by a Monte-Carlo based uncertainty analysis to understand how the SWAT model parameters in combination with the land use and climate variables impact the water quality loads in the watershed. The uncertainty distribution obtained after the Monte-Carlo uncertainty analysis was used to estimate BMP pollution reduction effectiveness using a Latin-Hypercube technique. The variability in the BMP pollution reduction indices due to the uncertainty in the model parameters, land use, and climate change provide uncertainty bands around the BMP optimization solutions. These uncertainty bands around the Pareto-optimal fronts at the end of optimization provide useful information for the decision makers by providing a better understanding of the reductions that can be expected for any particular amount of money invested in the implementation of BMPs and vice versa

Inverse Modeling of Beaver Reservoir\u27s Water Spectral Reflectance

Estimation of inherent optical properties (IOP) needed for water quality evaluation by remote sensing models is very complex, primarily due to the large number of model simulations needed to find optimal parameter values. This study presents an approach for optimally parameterizing the IOP values of a physical hyperspectral optical - Monte Carlo (PHO-MC) model. An artificial neural network (ANN) based pseudo simulator combined with the Nondominated Sorted Genetic Algorithm II (NSGA II) was used to efficiently perform a large number of model simulations and to search the optimal parameter values for IOP determination. Concentrations of suspended matter (sm), chlorophyll-a (chl), and total dissolved organic matter (DOM) along with the reflectance data at 16 different wavelengths were measured at 48 sampling stations in the Beaver Reservoir, Arkansas, between 2003 and 2005 and were used to evaluate the IOP values. Measured concentrations and reflectance data from 24 sampling stations were used to optimize IOP parameter values for sm, chl, and DOM. The data collected from the remaining 24 sampling stations were used for the validation of PHO-MC model-predicted reflectance by using optimized IOP values. PHO-MC predicted reflectance values were significantly correlated (r = 0.90, p \u3c 0.01) with the corresponding measured reflectance values, indicating that the pseudo simulator combined with the NSGA II accurately estimated the IOP values. An estimated 10 10 years of calculation time was reduced to less than 3 min by using the pseudo simulator and NSGA II to supplement the PHO-MC model for estimating the IOP values

Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland

Stream flow simulation is often challenging in mountainous watersheds because of irregular topography and complex hydrological processes. Rates of change in precipitation and temperature with respect to elevation often limit the ability to reproduce stream runoff by hydrological models. Anthropogenic influence, such as water transfers in high altitude hydropower reservoirs increases the difficulty in modeling since the natural flow regime is altered by long term storage of water in the reservoirs. The Soil and Water Assessment Tool (SWAT) was used for simulating stream flow in the upper Rhone watershed located in the south western part of Switzerland. The catchment area covers 5220 km2, where most of the land cover is dominated by forest and 14 % is glacier. Stream flow calibration was done at daily time steps for the period of 2001–2005, and validated for 2006–2010. Two different approaches were used for simulating snow and glacier melt process, namely the temperature index approach with and without elevation bands. The hydropower network was implemented based on the intake points that form part of the inter-reservoir network. Sub-basins were grouped into two major categories with glaciers and without glaciers for simulating snow and glacier melt processes. Model performance was evaluated both visually and statistically where a good relation between observed and simulated discharge was found. Our study suggests that a proper configuration of the network leads to better model performance despite the complexity that arises for water transaction. Implementing elevation bands generates better results than without elevation bands. Results show that considering all the complexity arising from natural variability and anthropogenic influences, SWAT performs well in simulating runoff in the upper Rhone watershed. Findings from this study can be applicable for high elevation snow and glacier dominated catchments with similar hydro-physiographic constraints

Breaking walls towards fully open source hydrological modeling

Hydrological models are powerful mathematical tools to address environmental problems and are often used for watershed management and planning. Hydrological models are data driven and the lack of data availability often limits model development. In this paper, we address several challenges in building and run- ning a hydrological model for streamflow simulations based solely on freely available data and open source software. The Soil and Water Assessment Tool (SWAT) hydrological modeling software has been used in the Map Window Geographic Information System (GIS). All spatial and non-spatial data used in this study were obtained from various free of charge online sources. Model calibration and validation represent major chal- lenges following the initial model construction since they involve several trial and error processes to reach acceptable model performances. These critical steps were programmed here as automated scripts in the R open source statistical package. The challenges of model building are described step by step through video tutorials. Using a case study in the Mendoza watershed in Argentina, we show that simulated streamflow exhibits sound agreement with the observed streamflow considering daily time steps (NSE = 0.69, R2 = 0.72 and Percent bias = +9%). The workf low demonstrated in this study can be applied for other watersheds, espe- cially in data-sparse regions that may lack key regional or local data sets

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km2. The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds

Comparing the Selection and Placement of Best Management Practices in Improving Water Quality Using a Multiobjective Optimization and Targeting Method

Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method