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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 satellitebased 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.

Emphasizing the physical intricacies of integrated hydrology and feedbacks in simulating connected, variably saturated groundwater‐surface water systems, the Integrated Hydrologic Model Intercomparison Project initiated a second phase (IH‐MIP2), increasing the complexity of the benchmarks of the first phase. The models that took part in the intercomparison were ATS, Cast3M, CATHY, GEOtop, HydroGeoSphere, MIKE‐SHE, and ParFlow. IH‐MIP2 benchmarks included a tilted v‐catchment with 3‐D subsurface; a superslab case expanding the slab case of the first phase with an additional horizontal subsurface heterogeneity; and the Borden field rainfall‐runoff experiment. The analyses encompassed time series of saturated, unsaturated, and ponded storages, as well as discharge. Vertical cross sections and profiles were also inspected in the superslab and Borden benchmarks. An analysis of agreement was performed including systematic and unsystematic deviations between the different models. Results show generally good agreement between the different models, which lends confidence in the fundamental physical and numerical implementation of the governing equations in the different models. Differences can be attributed to the varying level of detail in the mathematical and numerical representation or in the parameterization of physical processes, in particular with regard to ponded storage and friction slope in the calculation of overland flow. These differences may become important for specific applications such as detailed inundation modeling or when strong inhomogeneities are present in the simulation domain.

Integrated land surface-groundwater models are valuable tools in simulating the terrestrial hydrologic cycle as a continuous system and exploring the extent of land surface-subsurface interactions from catchment to regional scales. However, the fidelity of model simulations is impacted not only by the vegetation and subsurface parameterizations, but also by the antecedent condition of model state variables, such as the initial soil moisture, depth to groundwater, and ground temperature. In land surface modeling, a given model is often run repeatedly over a single year of forcing data until it reaches an equilibrium state: the point at which there is minimal artificial drift in the model state or prognostic variables (most often the soil moisture). For more complex coupled and integrated systems, where there is an increased computational cost of simulation and the number of variables sensitive to initialization is greater than in traditional uncoupled land surface modeling schemes, the challenge is to minimize the impact of initialization while using the smallest spin-up time possible. In this study, multicriteria analysis was performed to assess the spinup behavior of the ParFlow.CLM integrated groundwater-surface water-land surface model over a 208 km 2 subcatchment of the Ringkobing Fjord catchment in Denmark. Various measures of spin-up performance were computed for model state variables such as the soil moisture and groundwater storage, as well as for diagnostic variables such as the latent and sensible heat fluxes. The impacts of initial conditions on surface water-groundwater interactions were then explored. Our analysis illustrates that the determination of an equilibrium state depends strongly on the variable and performance measure used. Choosing an improper initialization of the model can generate simulations that lead to a misinterpretation of land surface-subsurface feedback processes and result in large biases in simulated discharge. Estimated spin-up time from a series of spin-up functions revealed that 20 (or 21) years of simulation were sufficient for the catchment to equilibrate according to at least one criterion at the 0.1% (0.01%) threshold level. Amongst a range of convergence metrics examined, percentage changes in monthly values of groundwater and unsaturated zone storages produced a slow system convergence to equilibrium, whereas criteria based on ground temperature allowed a more rapid spin-up. Slow convergence of unsaturated and saturated zone storages is a result of the dynamic adjustment of the water table in response to a physically arbitrary or inconsistent initialization of a spatially uniform water table. Achieving equilibrium in subsurface storage ensured equilibrium across a spectrum of other variables, hence providing a good measure of system-wide equilibrium. Overall, results highlight the importance of correctly identifying the key variable affecting model equilibrium and also the need to use a multicriteria approach to achieve a rapid and stable model spin-up.

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Abstract:The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite-based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub-catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC-FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC-FEWS, produced good results with values of R 2 NS between 0Ð84 and 0Ð87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products.

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