Abstract
Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.
Highlights
Reliable estimations of spatially distributed actual evapotranspiration (AET) are useful for various sustainable water resources management practices such as irrigation planning, agricultural drought monitoring and water demand forecasting in large cultivated areas (Wei et al, 2017)
Two of these metrics are derived from the hydrograph, i.e. Kling–Gupta efficiency (KGE, Gupta et al, 2009) and KGE of only below-average streamflow (KGElow), whereas the spatial efficiency metric focuses on the spatial pattern of actual evapotranspiration
While KGE and KGElow evaluate high and low streamflow dynamics and biases, the bias-insensitive spatial efficiency (SPAEF) focuses on only spatial patterns of AET
Summary
Reliable estimations of spatially distributed actual evapotranspiration (AET) are useful for various sustainable water resources management practices such as irrigation planning, agricultural drought monitoring and water demand forecasting in large cultivated areas (Wei et al, 2017). Since AET cannot be measured directly by satellite, surface energy balance models are developed to estimate AET based on data from a range of spectral and thermal bands (Guzinski et al, 2013; Norman et al, 1995; Su, 2002). This paper utilises monthly patterns of AET first to understand and organize ET-related model spatial parameterisations and to pursue a calibration This is because adding only temporal aspects of the spatial observations to the objective function is not sufficient for achieving significant improvements in simulated spatial patterns if model parameterisation is not flexible enough to physically adjust to the observed pattern. The study by Loosvelt et al (2013) is one of few other examples that incorporate PTFs for soil texture and moisture components of a hydrologic model
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