Rajesh R. Shrestha

Multi-objective calibration and fuzzy preference selection of a distributed hydrological model

Multi-objective evaluation of distributed hydrological models enables an analysis of prediction behaviour of individual sub-systems within a catchment. The aim of this paper is to demonstrate an application of multi-response, multisite calibration strategy for a distributed hydrological model, so that model limitations can be identified and subsequently improved. The study was carried out for calibration of flows from two gauging stations in a 152km^2 catchment in Elbe Basin in Germany. The multi-objective optimisation tool NSGA-II was used for the calibration of distributed hydrological modelling code WaSiM-ETH. A fuzzy set theory based methodology was formulated for selection of preferred solution from numerous Pareto solutions in four-dimensional space. The methodology consistently led to selection of the solution which is able to reasonably represent the magnitude and dynamics of streamflow hydrograph. For a reasonable simulation of water balance in the downstream gauge, overprediction of water balance in the upstream gauge was necessary. The analysis of precipitation-discharge data and geological conditions in the river channel support the possibility of flow reduction in the upstream gauge and increase in the downstream gauge. Due to this limitation in observation data, additional optimisation runs were carried out by explicitly considering the effect. This led to a significant improvement in the performance of the model. Therefore, the study provides an effective implementation of the multi-objective calibration strategy for a distributed hydrological model, which can be used for the analysis of different catchments using a combination of different objective functions.

Terrestrial contribution to the heterogeneity in hydrological changes under global warming

This study investigates a physical basis for heterogeneity in hydrological changes, which suggests a greater detectability in wet than dry regions. Wet regions are those where atmospheric demand is less than precipitation (energy limited), and dry regions are those where atmospheric demand is greater than precipitation (water limited). Long-term streamflow trends in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at global scales show geographically heterogeneous detectability of hydrological changes. We apply the Budyko framework and state-of-the-art climate model data from CMIP5 to quantify the sensitivity and detectability of terrestrial hydrological changes. The Budyko framework quantifies the partitioning of precipitation into evapotranspiration and runoff components. We find that the terrestrial hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited rather than water limited. This additional source (the terrestrial part) contributes to 30–40% greater detectability in energy-limited regions. We also quantified the contribution of changes in the catchment efficiency parameter that oppose the effects of increasing evaporative demand in global warming scenarios. Incorporating changes to the catchment efficiency parameter in the Budyko framework reduces dry biases in global runoff change projections by 88% in the 21st century.

Analysis and propagation of uncertainties due to the stage-discharge relationship : a fuzzy set approach

Abstract River discharges are typically derived from a single-valued stage–discharge relationship. However, there is usually no one-to-one relationship between stage and discharge, and the use of a single-valued relationship may lead to uncertainties. This paper considers fuzzy set theory-based methods for analysis and propagation of uncertainties. The uncertainty analysis involves the application of fuzzy linear and nonlinear regression methods to define upper and lower bounds of the relationship, which expresses discharge values as fuzzy numbers. The resulting membership function of a peak discharge value is used for propagation of uncertainties in river channels and flood plains. This involves an application of the fuzzy alpha-level cut method together with a one-dimensional hydrodynamic model. The methods are demonstrated using data from the Lauffen gauging station on the River Neckar, Germany.

Evaluating Hydroclimatic Change Signals from Statistically and Dynamically Downscaled GCMs and Hydrologic Models

AbstractThis study analyzed potential hydroclimatic change in the Peace River basin in the province of British Columbia, Canada, based on two structurally different approaches: (i) statistically downscaled global climate models (GCMs) using the bias-corrected spatial disaggregation (BCSD) and (ii) dynamically downscaled GCM with the Canadian Regional Climate Model (CRCM). Additionally, simulated hydrologic changes from the GCM–BCSD-driven Variable Infiltration Capacity (VIC) model were compared to the CRCM integrated Canadian Land Surface Scheme (CLASS) output. The results show good agreements of the GCM–BCSD–VIC simulated precipitation, temperature, and runoff with observations, while the CRCM-simulated results differ substantially from observations. Nevertheless, differences (between the 2050s and 1970s) obtained from the two approaches are qualitatively similar for precipitation and temperature, although they are substantially different for snow water equivalent and runoff. The results obtained from th...

Physically Based and Data-Driven Models and Propagation of Input Uncertainties in River Flood Prediction

The understanding of the model capabilities and inherent uncertainties is vital in river flood prediction systems. This paper addresses the need by considering two conventional models: hydrodynamic (HD) model and Muskingum-Cunge (MC) hydrologic routing model, and two data-driven models: artificial neural network and adaptive network based fuzzy inference system. A major source of uncertainty in all of these models is in input discharge due to the stage-discharge relationship. The study considers the uncertainty by defining fuzzy uncertainty bounds of relationship, which is used for propagation of uncertainties in each of these models. This approach is applied to the Rhine-Neckar river confluence in Germany. The results of the study indicate that all four models are capable of producing good results. While the statistical performance of the MC routing model and two data-driven models are slightly better than the HD model, the HD model is more robust in handling uncertainties. The study therefore suggests t...

Fuzzy Nonlinear Regression Approach to Stage-Discharge Analyses: Case Study

River discharge is typically derived from a single valued stage-discharge relationship. However, the relationship is affected by different sources of uncertainty, especially, in the measurement of discharge and stage values. The measurement uncertainty propagates into stage-discharge relationship curve and affects the discharge values derived from the relation. A fuzzy set theory based methodology is investigated in this paper for the analysis of uncertainty in the stage-discharge relationship. Individual components of stage and discharge measurement are considered as a fuzzy numbers and the overall stage and discharge uncertainty is obtained through the aggregation of all uncertainties using fuzzy arithmetic. Building on the previous work—fuzzy discharge and stage measurements, we use fuzzy nonlinear regression—in this case study for the analysis of uncertainty in the stage-discharge relationship. The methodology is based on fuzzy extension principle and considers input and output variables as well as th...

Fuzzy set theory based methodology for the analysis of measurement uncertainties in river discharge and stage

The discharge and stage measurements in a river system are characterized by a number of sources of uncer- tainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardiza- tion (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage-discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology de- veloped in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points pro- vides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory pro- vides a suitable methodology for the uncertainty analysis of stage-discharge measurements.

A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada

AbstractRecent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature predict...

Projecting future nonstationary extreme streamflow for the Fraser River, Canada

We describe an efficient and flexible statistical modeling framework for projecting nonstationary streamflow extremes for the Fraser River basin in Canada, which is dominated by nival flow regime. The framework is based on an extreme value analysis technique that allows for nonstationarity in annual extreme streamflow by relating it to antecedent winter and spring precipitation and temperature. We used a representative suite of existing Variable Infiltration Capacity hydrologic model simulations driven by Coupled Model Intercomparison Project Phase 3 (CMIP3) climate simulations to train and evaluate a nonlinear and nonstationary extreme value model of annual extreme streamflow. The model was subsequently used to project changes under CMIP5-based climate change scenarios. Using this combination of process-based and statistical modeling, we project that the moderate (e.g., 2–20-year return period) extreme streamflow events will decrease in intensity. In contrast, projections of high intensity events (e.g., 100–200-year return period), which reflect complex interactions between temperature and precipitation changes, are inconclusive. The results provide a basis for developing a general understanding of the future streamflow extremes changes in nival basins and through careful consideration and adoption of appropriate covariates, the methodology could be employed for basins spanning a range of hydro-climatological regimes.