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Spatial patterns in hydrology: Towards comprehensive evaluation and calibration of distributed hydrological models
Convener: Simon Stisen  | Co-Conveners: Julian Koch , Luis Samaniego , Heye Bogena , Luca Brocca 
 / Mon, 13 Apr, 10:30–12:00  / Room R11
 / Attendance Mon, 13 Apr, 17:30–19:00  / Red Posters
Currently, hydrological model evaluations remain focused on comparing simulations to a single spatially aggregated catchment scale observation in the form of river discharge, with the conviction that it provides some inherent insight into the internal hydrological behavior of the river basin. This notion is outdated and limits the use of models for science based water management and decision support systems. Even complex distributed models simulating multiple states and fluxes, rarely increase our process understanding when only their lumped catchment scale response is evaluated.
Therefore, a paradigm shift is required, moving away from the aggregated evaluation of hydrological models towards a spatially distributed approach. Recent advances in fully distributed and grid based model codes, computational power and spatial data availability have prepared the ground for bringing the science forward. However, hydrological model evaluation and calibration lack methodologies for incorporating spatial pattern information.
This session focusses on advances in distributed hydrological modelling with special attention to the progress made within spatial model evaluation and calibration. Specifically, the ability of models to reproduce observed spatial patterns will be targeted. The current scientific challenges regarding spatial model evaluation and calibration arise from several factors including: A lack of robust spatial performance metrics, a need for high quality observational data sets, over-parameterization of models and equifinality.

We encourage contributions in several areas:
• Development and testing of performance metrics specifically suited for evaluating spatial model performance
• Exploration and analysis of spatial observational data sets derived from remote sensing or distributed sensor networks, and suited for hydrological model evaluation
• Model parameter regionalization and regularization approaches
• Calibration frameworks focusing on spatial model performance
• Case studies including spatial model evaluation e.g. examining or assessing rainfall, soil moisture, evapotranspiration, droughts or floods