[1] The requirements for hydrological models have increased considerably during the previous decades to cope with the resolution of extensive remotely sensed data sets and a number of demanding applications. Existing models exhibit deficiencies such as overparameterization, the lack of an effective technique to integrate the spatial heterogeneity of physiographic characteristics, and the nontransferability of parameters across scales and locations. A multiscale parameter regionalization (MPR) technique is proposed as a way to address these issues simultaneously. Using this technique, parameters at a coarser scale, in which the dominant hydrological processes are represented, are linked with their corresponding ones at a finer resolution in which input data sets are available. The linkage is done with upscaling operators such as the harmonic mean, among others. Parameters at the finer scale are regionalized through nonlinear transfer functions which link basin predictors with global parameters to be determined through calibration. MPR was compared with a standard regionalization (SR) method in which basin predictors instead of model parameters are first aggregated. Both methods were tested in a basin located in Germany using a distributed hydrologic model. Results indicate that MPR is superior to SR in many respects, especially if global parameters are transferred from coarser to finer scales. Furthermore, MPR, as opposed to SR, preserves the spatial variability of state variables and conserves the mass balance with respect to a control scale. Cross-validation tests indicate that the transferability of the global parameters to ungauged locations is possible.Citation: Samaniego, L., R. Kumar, and S. Attinger (2010), Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523,

Key Points A calibrated model does not guarantee cross‐scale and location transferability Calibration parameters of MPR exhibit quasi‐scale invariance, but HRU does not MPR also outperfromed HRU for parameter transferability across locations

Global warming may exacerbate soil moisture droughts. However, evaluations of future droughts are not conclusive because of the uncertainty in estimates of future warming. Here, we estimate the impacts of differential climate change at 1-3 K on the largest soil moisture droughts across Europe to understand the implications of the goal of the 2015 Paris climate change agreement to constrain global warming to below 1.5 degrees. The results show that under an increase of 3 K compared to 1.5 K, drought area will increase by 40% (± 24%) and will potentially affect 42% more people. Similarly, an event like the 2003 drought will become two times more frequent. Adapting to a temperature increase of 3 K implies adjusting to an increase

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Multicompartment and multiscale long‐term observation and research are important prerequisites to tackling the scientific challenges resulting from climate and global change. Long‐term monitoring programs are cost intensive and require high analytical standards, however, and the gain of knowledge often requires longer observation times. Nevertheless, several environmental research networks have been established in recent years, focusing on the impact of climate and land use change on terrestrial ecosystems. From 2008 onward, a network of Terrestrial Environmental Observatories (TERENO) has been established in Germany as an interdisciplinary research program that aims to observe and explore the long‐term ecological, social, and economic impacts of global change at the regional level. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, and remote sensing will be used to record and analyze states and fluxes for different environmental compartments from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere.

Early 21st-century droughts in Europe have been broadly regarded as exceptionally severe, substantially affecting a wide range of socio-economic sectors. These extreme events were linked mainly to increases in temperature and record-breaking heatwaves that have been influencing Europe since 2000, in combination with a lack of precipitation during the summer months. Drought propagated through all respective compartments of the hydrological cycle, involving low runoff and prolonged soil moisture deficits. What if these recent droughts are not as extreme as previously thought? Using reconstructed droughts over the last 250 years, we show that although the 2003 and 2015 droughts may be regarded as the most extreme droughts driven by precipitation deficits during the vegetation period, their spatial extent and severity at a long-term European scale are less uncommon. This conclusion is evident in our concurrent investigation of three major drought types – meteorological (precipitation), agricultural (soil moisture) and hydrological (grid-scale runoff) droughts. Additionally, unprecedented drying trends for soil moisture and corresponding increases in the frequency of agricultural droughts are also observed, reflecting the recurring periods of high temperatures. Since intense and extended meteorological droughts may reemerge in the future, our study highlights concerns regarding the impacts of such extreme events when combined with persistent decrease in European soil moisture.

Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.

Estimating spatially distributed parameters remains one of the biggest challenges for large‐domain hydrologic modeling. Many large‐domain modeling efforts rely on spatially inconsistent parameter fields, e.g., patchwork patterns resulting from individual basin calibrations, parameter fields generated through default transfer functions that relate geophysical attributes to model parameters, or spatially constant, default parameter values. This paper provides an initial assessment of a multiscale parameter regionalization (MPR) method over large geographical domains to derive seamless parameters in a spatially consistent manner. MPR applies transfer functions at the native scale of the geophysical data, and then scales these model parameters to the desired model resolution. We developed a stand‐alone framework called MPR‐flex for multimodel use and applied MPR‐flex to the variable infiltration capacity model to produce hydrologic simulations over the contiguous United States (CONUS). We first independently calibrate 531 basins across CONUS to obtain a performance benchmark for each basin. To derive the CONUS parameter fields, we perform a joint MPR calibration using all but the poorest behaved basins to obtain a single set of transfer function parameters that are applied to the entire CONUS. Results show that CONUS‐wide calibration has similar performance compared to previous simulations using a patchwork quilt of partially calibrated parameter sets, but without the spatial discontinuities in parameters that characterize some previous CONUS‐domain model simulations. Several avenues to improve CONUS‐wide calibration remain, including selection of calibration basins, objective function formulation, as well as MPR‐flex improvements including transfer function formulations and scaling operator optimization.