Abstract. Flood forecasting is of increasing importance as it comes to an increasing variability in global and local climates. But rainfall-runoff models are far from being perfect. In order to achieve a better prediction for emerging flood events, the model outputs have to be continuously updated. This contribution introduces a rather simple, yet effective updating procedure for the conceptual semi-distributed rainfallrunoff model PREVAH, whose runoff generation module relies on similar algorithms as the HBV-Model. The current conditions of the system, i.e. the contents of the upper soil reservoirs, are updated by the proposed method. The testing of the updating procedure on data from two mountainous catchments in Switzerland reveals a significant increase in prediction accuracy with regards to peak flow.

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In this contribution, we introduce a stochastic framework for decision support for optimal planning and operation of water supply in irrigation. This consists of (1) a weather generator for simulating regional impacts of climate change on the basis of IPCC scenarios, (2) a tailormade evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply, (3) a mechanistic model for simulating water transport and crop growth in a sound manner, and (4) a kernel density estimator for estimating stochastic productivity, profit, and demand functions by a nonparametric method. As a result of several simulation/optimization runs within the framework, we present stochastic crop-water production functions (SCWPF) for different crops which can be used as a basic tool for assessing the impact of climate variability on the risk for the potential yield for specific crops and specific agricultural areas. A case study for an agricultural area in the Al Batinah region of the Sultanate of Oman is used to illustrate these methodologies. In addition, microeconomic impacts of climate change and the vulnerability of the agro-ecological system are discussed.

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Summary Inverse methods are often used for estimating soil hydraulic parameters from experiments on flow of water through soil. We propose here an alternative method using neural networks. We teach a problem‐adapted network of radial basis functions (RBF) the relationship between soil parameters and transient flow patterns using a numerical flow model. The trained RBF network accurately identifies soil parameters from flow patterns not contained in the training scenarios. A comparison with the inverse method (Annealing‐Simplex) reveals a similarly good prediction by both approaches for randomly perturbed data and data from the real world. Nonetheless, the inverse method showed dependency on initial parameter estimates not required by the RBF network. Training demands moderately more computation and manpower than the inverse technique, but the absolutely stable and simple network application requires negligible resources. Thus, for individual applications, the network approach is slightly surpassed by the Annealing‐Simplex method. However, the RBF network has to be trained only once and, subsequently, it can be applied easily and without effort upon any number of laboratory experiments with standardized experimental setups.