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• 1. [Article] Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times

  The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow ...

  Citation × Citation Citation Abstract Copy Title: Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times Author: Demirel, Mehmet, Booij, Martijn, Hoekstra, Arjen Year: 2012 The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow forecasting with lead times of 14 and 90 days. First, the Rhine basin is subdivided into seven sub-basins. By considering the dominant processes in the sub-basins, five low flow indicators were selected: precipitation, potential evapotranspiration, groundwater storage, snow storage and lake storage. Correlation analysis was then carried out to determine the relationship between observed low flows and preselected indicators with varying lags (days) and temporal resolutions (from 1 day to 7 months). The results show that the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 14 days are potential evapotranspiration with a large lag and temporal resolution, and lake levels with a small lag and temporal resolution. In the other sub-basins groundwater levels with a small lag and temporal resolution are important in addition to potential evapotranspiration with a large lag and temporal resolution. The picture is slightly different for forecasts with a lead time of 90 days. The snow storage in the Alpine sub-basins and the precipitation in the other sub-basins also become relevant for low flows. Consequently, the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 90 days are potential evapotranspiration with a large lag and temporal resolution, lake levels with a small lag and temporal resolution and snow storage with a small lag and large temporal resolution. The resultant correlation maps provide appropriate lags and temporal resolutions for indicators to forecast low flows in the River Rhine with different lead times. Title: Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times Author: Demirel, Mehmet, Booij, Martijn, Hoekstra, Arjen Year: 2012 The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow forecasting with lead times of 14 and 90 days. First, the Rhine basin is subdivided into seven sub-basins. By considering the dominant processes in the sub-basins, five low flow indicators were selected: precipitation, potential evapotranspiration, groundwater storage, snow storage and lake storage. Correlation analysis was then carried out to determine the relationship between observed low flows and preselected indicators with varying lags (days) and temporal resolutions (from 1 day to 7 months). The results show that the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 14 days are potential evapotranspiration with a large lag and temporal resolution, and lake levels with a small lag and temporal resolution. In the other sub-basins groundwater levels with a small lag and temporal resolution are important in addition to potential evapotranspiration with a large lag and temporal resolution. The picture is slightly different for forecasts with a lead time of 90 days. The snow storage in the Alpine sub-basins and the precipitation in the other sub-basins also become relevant for low flows. Consequently, the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 90 days are potential evapotranspiration with a large lag and temporal resolution, lake levels with a small lag and temporal resolution and snow storage with a small lag and large temporal resolution. The resultant correlation maps provide appropriate lags and temporal resolutions for indicators to forecast low flows in the River Rhine with different lead times. Close

• 2. [Article] Seasonality of Low Flows and Dominant Processes in the Rhine River

  Low flow forecasting is crucial for sustainable cooling water supply and planning of river navigation in the Rhine River. The first step in reliable low flow forecasting is to understand the characteristics ...

  Citation × Citation Citation Abstract Copy Title: Seasonality of Low Flows and Dominant Processes in the Rhine River Author: Tongal, Hakan, Demirel, Mehmet, Booij, Martijn Year: 2013 Low flow forecasting is crucial for sustainable cooling water supply and planning of river navigation in the Rhine River. The first step in reliable low flow forecasting is to understand the characteristics of low flow. In this study, several methods are applied to understand the low flow characteristics of Rhine River basin. In 108 catchments of the Rhine River, winter and summer low flow regions are determined with the seasonality ratio (SR) index. To understand whether different numbers of processes are acting in generating different low flow regimes in seven major sub-basins (namely, East Alpine, West Alpine, Middle Rhine, Neckar, Main, Mosel and Lower Rhine) aggregated from the 108 catchments, the dominant variable concept is adopted from chaos theory. The number of dominant processes within the seven major sub-basins is determined with the correlation dimension analysis. Results of the correlation dimension analysis show that the minimum and maximum required number of variables to represent the low flow dynamics of the seven major sub-basins, except the Middle Rhine and Mosel, is 4 and 9, respectively. For the Mosel and Middle Rhine, the required minimum number of variables is 2 and 6, and the maximum number of variables is 5 and 13, respectively. These results show that the low flow processes of the major sub-basins of the Rhine could be considered as non-stochastic or chaotic processes. To confirm this conclusion, the rescaled range analysis is applied to verify persistency (i.e. non-randomness) in the processes. The estimated rescaled range statistics (i.e. Hurst exponents) are all above 0.5, indicating that persistent long-term memory characteristics exist in the runoff processes. Finally, the mean values of SR indices are compared with the nonlinear analyses results to find significant relationships. The results show that the minimum and maximum numbers of required variables (i.e. processes) to model the dynamic characteristics for five out of the seven major sub-basins are the same, but the observed low flow regimes are different (winter low flow regime and summer low flow regime). These results support the conclusion that a few interrelated nonlinear variables could yield completely different behaviour (i.e. dominant low flow regime). Title: Seasonality of Low Flows and Dominant Processes in the Rhine River Author: Tongal, Hakan, Demirel, Mehmet, Booij, Martijn Year: 2013 Low flow forecasting is crucial for sustainable cooling water supply and planning of river navigation in the Rhine River. The first step in reliable low flow forecasting is to understand the characteristics of low flow. In this study, several methods are applied to understand the low flow characteristics of Rhine River basin. In 108 catchments of the Rhine River, winter and summer low flow regions are determined with the seasonality ratio (SR) index. To understand whether different numbers of processes are acting in generating different low flow regimes in seven major sub-basins (namely, East Alpine, West Alpine, Middle Rhine, Neckar, Main, Mosel and Lower Rhine) aggregated from the 108 catchments, the dominant variable concept is adopted from chaos theory. The number of dominant processes within the seven major sub-basins is determined with the correlation dimension analysis. Results of the correlation dimension analysis show that the minimum and maximum required number of variables to represent the low flow dynamics of the seven major sub-basins, except the Middle Rhine and Mosel, is 4 and 9, respectively. For the Mosel and Middle Rhine, the required minimum number of variables is 2 and 6, and the maximum number of variables is 5 and 13, respectively. These results show that the low flow processes of the major sub-basins of the Rhine could be considered as non-stochastic or chaotic processes. To confirm this conclusion, the rescaled range analysis is applied to verify persistency (i.e. non-randomness) in the processes. The estimated rescaled range statistics (i.e. Hurst exponents) are all above 0.5, indicating that persistent long-term memory characteristics exist in the runoff processes. Finally, the mean values of SR indices are compared with the nonlinear analyses results to find significant relationships. The results show that the minimum and maximum numbers of required variables (i.e. processes) to model the dynamic characteristics for five out of the seven major sub-basins are the same, but the observed low flow regimes are different (winter low flow regime and summer low flow regime). These results support the conclusion that a few interrelated nonlinear variables could yield completely different behaviour (i.e. dominant low flow regime). Close

• 3. [Article] Low Flow Forecasting with a Lead Time of 14 days for Navigation and Energy Supply in the Rhine River

  Low flow forecasting, days or even months in advance, is particularly important to the efficient operation of power plants and freight shipment. This study presents a low flow forecasting model with a ...

  Citation × Citation Citation Abstract Copy Title: Low Flow Forecasting with a Lead Time of 14 days for Navigation and Energy Supply in the Rhine River Author: Demirel, Mehmet C., Booij, Martijn Year: 2011 Low flow forecasting, days or even months in advance, is particularly important to the efficient operation of power plants and freight shipment. This study presents a low flow forecasting model with a lead time of 14 days for the Rhine River. The forecasts inherit uncertainty sources mainly because of model parameterization. Therefore, a systematic uncertainty analysis is applied to indicate the major uncertainty sources in the results. Firstly, the Rhine basin is divided into 7 major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges are routed to Lobith after German-Dutch border with another data-driven model. Five pre-selected low flow indicators (basin averaged precipitation, basin averaged potential evapotranspiration; basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins) are used as inputs to the models. The basin discretization and the selection of indicators are based on a literature study and seasonality analysis of the discharge time series from 108 sub-basins. The correlations between indicator and low flows with varying temporal resolution and varying lags between indicator and low flows were used to identify appropriate temporal scales of the model inputs. We assume that a suitable model structure for the Rhine basin has been determined; that is, the sub-system boundaries have been specified, the important state variables and input and output fluxes to be included have been identified and selected for each sub-basin. The results in this study show that the data-driven models used for each sub-basin are capable of representing the essential characteristics of the system based on lagged and temporally averaged low flow indicators and are forecasting low flows adequately. In addition to the forecast results, the uncertainties due to specific model parameters in the calibrated data-driven model corresponding to key processes are also given. Title: Low Flow Forecasting with a Lead Time of 14 days for Navigation and Energy Supply in the Rhine River Author: Demirel, Mehmet C., Booij, Martijn Year: 2011 Low flow forecasting, days or even months in advance, is particularly important to the efficient operation of power plants and freight shipment. This study presents a low flow forecasting model with a lead time of 14 days for the Rhine River. The forecasts inherit uncertainty sources mainly because of model parameterization. Therefore, a systematic uncertainty analysis is applied to indicate the major uncertainty sources in the results. Firstly, the Rhine basin is divided into 7 major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges are routed to Lobith after German-Dutch border with another data-driven model. Five pre-selected low flow indicators (basin averaged precipitation, basin averaged potential evapotranspiration; basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins) are used as inputs to the models. The basin discretization and the selection of indicators are based on a literature study and seasonality analysis of the discharge time series from 108 sub-basins. The correlations between indicator and low flows with varying temporal resolution and varying lags between indicator and low flows were used to identify appropriate temporal scales of the model inputs. We assume that a suitable model structure for the Rhine basin has been determined; that is, the sub-system boundaries have been specified, the important state variables and input and output fluxes to be included have been identified and selected for each sub-basin. The results in this study show that the data-driven models used for each sub-basin are capable of representing the essential characteristics of the system based on lagged and temporally averaged low flow indicators and are forecasting low flows adequately. In addition to the forecast results, the uncertainties due to specific model parameters in the calibrated data-driven model corresponding to key processes are also given. Close

• 4. [Article] Uncertainty Analysis of a Low Flow Model for the Rhine River

  It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights ...

  Citation × Citation Citation Abstract Copy Title: Uncertainty Analysis of a Low Flow Model for the Rhine River Author: Demirel, Mehmet C., Booij, Martijn Year: 2011 It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights in neural networks. This study aims at applying a structured uncertainty analysis to a data-driven model for low flow forecasting with a lead time of 14 days in the Rhine River. In the modeling phase, the Rhine basin is divided into seven major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges were routed to Lobith with another data-driven model. Basin averaged precipitation, basin averaged potential evapotranspiration, basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins are selected as low flow indicators and used as inputs to the models. The basin discretization and the selection of low flow indicators were not arbitrary since the dominant processes were considered by applying seasonality analysis to discharge time series from 108 sub-basins. Moreover, the correlations between indicators and low flows with varying temporal resolution and varying lags were used to identify appropriate temporal scales of the model inputs. The structure of the model can inherit uncertainty too due to many factors, including the lack of a robust hydrological theory at the spatial scale of the seven sub-basins. However, the parameter uncertainty is assumed to be the largest uncertainty source compared to other uncertainty sources. The effects of the input uncertainty were not assessed since averaging over sub-basins significantly reduces the measurement uncertainties. The model parameter sets were estimated using inverse modeling. The uncertainty of each weight is expressed as a probability distribution. Sensitivity analysis was applied for reducing the dimension and size of parameter space before uncertainty analysis. Finally, Monte Carlo Simulation was used to estimate the posterior distributions of the model outputs. The results in this study provide the effects of uncertainties in low flow model parameters on the model outputs. It has also been shown that the explicit assessment of uncertainties in the data-driven model parameters can lead to significant improvements in the information supply for low flow forecasting. Title: Uncertainty Analysis of a Low Flow Model for the Rhine River Author: Demirel, Mehmet C., Booij, Martijn Year: 2011 It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights in neural networks. This study aims at applying a structured uncertainty analysis to a data-driven model for low flow forecasting with a lead time of 14 days in the Rhine River. In the modeling phase, the Rhine basin is divided into seven major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges were routed to Lobith with another data-driven model. Basin averaged precipitation, basin averaged potential evapotranspiration, basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins are selected as low flow indicators and used as inputs to the models. The basin discretization and the selection of low flow indicators were not arbitrary since the dominant processes were considered by applying seasonality analysis to discharge time series from 108 sub-basins. Moreover, the correlations between indicators and low flows with varying temporal resolution and varying lags were used to identify appropriate temporal scales of the model inputs. The structure of the model can inherit uncertainty too due to many factors, including the lack of a robust hydrological theory at the spatial scale of the seven sub-basins. However, the parameter uncertainty is assumed to be the largest uncertainty source compared to other uncertainty sources. The effects of the input uncertainty were not assessed since averaging over sub-basins significantly reduces the measurement uncertainties. The model parameter sets were estimated using inverse modeling. The uncertainty of each weight is expressed as a probability distribution. Sensitivity analysis was applied for reducing the dimension and size of parameter space before uncertainty analysis. Finally, Monte Carlo Simulation was used to estimate the posterior distributions of the model outputs. The results in this study provide the effects of uncertainties in low flow model parameters on the model outputs. It has also been shown that the explicit assessment of uncertainties in the data-driven model parameters can lead to significant improvements in the information supply for low flow forecasting. Close