Appropriate Models In Decision Support Systems For River Basin Management

In recent years, new ideas and techniques appear very quickly, like sustainability, adaptive management, Geographic Information System, Remote Sensing and participations of new stakeholders, which contribute a lot to the development of decision support systems in river basin management. However, the role of models still needs to be emphasized, especially for model-based decision support systems. This paper aims to find appropriate models for decision support systems. An appropriate system is defined as ‘the system can produce final outputs which enable the decision makers to distinguish different river engineering measures according to the current problem’. An appropriateness framework is proposed mainly based on uncertainty and sensitivity analysis. A flood risk model is used, as a part of the Dutch River Meuse DSS to investigate whether the appropriate framework works. The results showed that the proposed approach is applicable and helpful to find appropriate models

Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days

Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Deterministic-statistical model coupling in a DSS for river-basin management

This paper presents a method for appropriate coupling of deterministic and statistical models. In the decision-support system for the Elbe river, a conceptual rainfall-runoff model is used to obtain the discharge statistics and corresponding average number of flood days, which is a key input variable for a rule-based model for floodplain vegetation. The required quality of the discharge time series cannot be determined by a sensitivity analysis because a deterministic model is linked to a statistical model. To solve the problem, artificial discharge time series are generated that mimic the hypothetical output of rainfall-runoff models of different accuracy. The results indicate that a feasible calibration of the rainfall-runoff model is sufficient to obtain consistency with the vegetation model in view of its sensitivity to changes in the number of flood days in the floodplains

Appropriate modelling of climate change impacts on river flooding

Global climate change is likely to increase temperatures, change precipitation patterns\ud and probably raise the frequency of extreme events. Impacts of climate change on river\ud flooding may be considerable and may cause enormous economical, social and\ud environmental damage and even loss of lives. This necessitates the application of robust\ud and accurate flood estimation procedures to provide a strong basis for investments in\ud flood protection measures with climate change.\ud A broad palette of models is available to fulfil this requirement. More complex models\ud generally have larger data requirements and computational costs, but may result in\ud smaller model output uncertainties and associated costs. It would seem that an optimum\ud complexity associated with minimum total costs or uncertainty exists. This raises the\ud question what such an appropriate model should look like given the specific modelling\ud objective and research area. Or which physical processes and data should be\ud incorporated and which mathematical process formulations should be used at which\ud spatial and temporal scale, to obtain an appropriate model level?\ud Therefore, the main objectives of this study are the determination of the appropriate\ud model complexity dependent on modelling objective and research area and the\ud assessment of the climate change impact on river flooding with an appropriate model.\ud The Meuse basin in Belgium and France serves as an application area in this thesis. The\ud first objective is dealt with in chapter 2, 3, 4 and 5 and constitutes the main part of this\ud thesis. The second objective is mainly treated in chapter 4 and 6

Uncertainty in climate change impacts on low flows

It is crucial for low flow management that information about the impacts of climate change on low flows and the uncertainties therein becomes available. This has been achieved by using information from different Regional Climate Models for different emission scenarios to assess the uncertainty in climate change for the River Meuse in Northwestern Europe. A hydrological model has been used to simulate low flows for current and changed climate conditions. The uncertainty in the hydrological model is represented by the uncertainty in its parameters. Climate change results in an increase of the average annual discharge deficit (a low flow measure) of about 2.6 108 m3 or 35%. This impact is considerable, resulting in an increase of water shortages in the Meuse basin during low flow periods. The uncertainty in this impact is about 10% as a result of uncertainties in climate change and HBV parameters, and does not disguise the climate change signal.\u

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 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