No abstract

No abstract

Glacierized high‐mountainous catchments are often the water towers for downstream region, and modeling these remote areas are often the only available tool for the assessment of water resources availability. Nevertheless, data scarcity affects different aspects of hydrological modeling in such mountainous glacierized basins. On the example of poorly gauged glacierized catchment in Central Asia, we examined the effects of input discretization, model complexity, and calibration strategy on model performance. The study was conducted with the GSM‐Socont model driven with climatic input from the corrected High Asia Reanalysis data set of two different discretizations. We analyze the effects of the use of long‐term glacier volume loss, snow cover images, and interior runoff as an additional calibration data. In glacierized catchments with winter accumulation type, where the transformation of precipitation into runoff is mainly controlled by snow and glacier melt processes, the spatial discretization of precipitation tends to have less impact on simulated runoff than a correct prediction of the integral precipitation volume. Increasing model complexity by using spatially distributed input or semidistributed parameters values does not increase model performance in the Gunt catchment, as the more complex model tends to be more sensitive to errors in the input data set. In our case, better model performance and quantification of the flow components can be achieved by additional calibration data, rather than by using a more distributed model parameters. However, a semidistributed model better predicts the spatial patterns of snow accumulation and provides more plausible runoff predictions at the interior sites.

No abstract

No abstract

Abstract. Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation.This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data.The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (SouthEastern Germany, Czech Republic) has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a) the meteorological forecast uncertainty and b) the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the sysCorrespondence to: J. Dietrich (joerg.dietrich@rub.de) tems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good performance within a lead time of 2 to 3 days. Some of the deterministic very short-range forecast initializations were able to predict the dynamics of the event, but others underpredicted rainfall. Thus a lagged average ensemble approach is suggested. The findings from the case study support the often proposed added value of ensemble forecasts and their probabilistic evaluation for flood management decisions.

This study investigates rainfall characteristics in the Upper Zambezi River Basin and implications for rain-fed agriculture. Seventeen indices describing the character of each rainy season were calculated using a bias-corrected version of TRMM-B42 v6 rainfall estimate for 1998-2010. These were correlated with maize yields obtained by applying a SVATmodel. Finally, a self-organizing map (SOM) was trained to examine multivariate relationships. The results reveal a significant spatio-temporal variability of rainfall indices and yields, with a gradient from north to south. Yields greater than 1 t/ha are found to be only achievable with rainy seasons longer than 160 days. For shorter durations, the interplay of total rainfall, dry spell frequency and maximum dry/wet spell durations determines agricultural success. Using total rainfall alone or wet day frequency as estimators for yields is insufficient. Alternating wet and dry spells affect yields most negatively. The results have significance in the context of agricultural planning under changing climatic conditions and agricultural planning, as well as for the development of forecasting mechanisms.

Mombasa faces severe water scarcity problems. The existing supply is unable to satisfy the demand. This article demonstrates the combination of satellite image analysis and modelling as tools for the development of an urban rainwater harvesting policy. For developing a sustainable remedy policy, rooftop rainwater harvesting (RRWH) strategies were implemented into the water supply and demand model WEAP (Water Evaluation and Planning System). Roof areas were detected using supervised image classification. Future population growth, improved living standards, and climate change predictions until 2035 were combined with four management strategies. Image classification techniques were able to detect roof areas with acceptable accuracy. The simulated annual yield of RRWH ranged from 2.3 to 23 million cubic meters (MCM) depending on the extent of the roof area. Apart from potential RRWH, additional sources of water are required for full demand coverage.