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River basins are by definition temporally-varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different to those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibits significantly growing urbanization during the last 30 years. Alternative deterministic modelling options include a lumped water balance model with one time-varying parameter and a semi-distributed scheme based on the concept of hydrological response units. Model inputs and errors are respectively represented through linear and nonlinear stochastic models. The resulting nonlinear stochastic framework maximizes the exploitation of the existing information by taking advantage of the calibration protocol used in this issue. [ABSTRACT FROM AUTHOR]

The modelling of human-modified basins that are inadequately measured constitutes a challenge for hydrological science. Often, models for such systems are detailed and hydraulics-based for only one part of the system while for other parts oversimplified models or rough assumptions are used. This is typically a bottom-up approach, which seeks to exploit knowledge of hydrological processes at the micro-scale at some components of the system. Also, it is a monomeric approach in two ways: first, essential interactions among system components may be poorly represented or even omitted; second, differences in the level of detail of process representation can lead to uncontrolled errors. Additionally, the calibration procedure merely accounts for the reproduction of the observed responses using typical fitting criteria. The paper aims to raise some critical issues, regarding the entire modelling approach for such hydrosystems. For this, two alternative modelling strategies are examined that reflect two modelling approaches or philosophies: a dominant bottom-up approach, which is also monomeric and, very often, based on output information, and a top-down and holistic approach based on generalized information. Critical options are examined, which codify the differences between the two strategies: the representation of surface, groundwater and water management processes, the schematization and parameterization concepts and the parameter estimation methodology. The first strategy is based on stand-alone models for surface and groundwater processes and for water management, which are employed sequentially. For each model, a different (detailed or coarse) parameterization is used, which is dictated by the hydrosystem schematization. The second strategy involves model integration for all processes, parsimonious parameterization and hybrid manual-automatic parameter optimization based on multiple objectives. A test case is examined in a hydrosystem in Greece with high complexities, such as extended surface-groundwater interactions, ill-defined boundaries, sinks to the sea and anthropogenic intervention with unmeasured abstractions both from surface water and aquifers. Criteria for comparison are the physical consistency of parameters, the reproduction of runoff hydrographs at multiple sites within the studied basin, the likelihood of uncontrolled model outputs, the required amount of computational effort and the performance within a stochastic simulation setting. Our work allows for investigating the deterioration of model performance in cases where no balanced attention is paid to all components of human-modified hydrosystems and the related information. Also, sources of errors are identified and their combined effect are evaluated. [ABSTRACT FROM AUTHOR]