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This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions in the Meuse basin. Through a comprehensive analysis of 286 catchments across Europe and the US that are hydro-climatically comparable to the Meuse basin, we construct inter-decadal distributions of past deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA). These distributions of ΔIE were then used to estimate inter-decadal changes in Sr,max and to quantify the associated consequences for streamflow predictions in the Meuse basin. Our findings reveal that, while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE=0.01 and an interquartile range (IQR) of -0.01 to 0.03. Consequently, these minor deviations lead to limited inter-decadal changes in Sr,max , mostly ranging between -10 and +21 mm (-5 % to +10 %). When these changes (ΔSr,max) are accounted for in hydrological models, the impact on streamflow predictions in the Meuse basin is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first-order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology. [ABSTRACT FROM AUTHOR]

The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration. [ABSTRACT FROM AUTHOR]

The wflow_sbm hydrologic model, recently released by Deltares, as part of the Wflow.jl (v0.6.1) modelling framework is being used to better understand and potentially address multiple operational and water resources planning challenges from catchment scale, national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrologic modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalitities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrologic models. Most wflow_sbm parameters are based on physical characteristics or processes and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally results in a satisfactory (0.4 = Kling-Gupta Efficiency (KGE) < 0.7) to good (KGE = 0.7) performance a-priori (without further tuning). Wflow_sbm includes relevant hydrologic processes as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resources modelling, the addition of a deep groundwater concept and distributed computing with a focus on multi-node parallelism. [ABSTRACT FROM AUTHOR]

To predict future hydrological behavior in a changing world, often use is made of models calibrated on past observations, disregarding that hydrological systems, hence model parameters, will change as well. Yet, ecosystems likely adjust their root-zone storage capacity, which is the key parameter of any hydrological system, in response to climate change. In addition, other species might become dominant, both under natural and anthropogenic influence. In this study, we propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root-zone storage capacity at the catchment scale in response to changes in magnitude and seasonality of hydro-climatic variables. Additionally, the Budyko characteristics of different dominant ecosystems in sub-catchments are used to simulate the hydrological behavior of potential future land-use change, in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2K global warming are more pronounced when explicitly considering changes in the sub-surface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2K global warming in comparison to current-day conditions using a process-based hydrological model with (a) a stationary system, i.e. no changes in the root-zone storage capacity of vegetation and historical land use, (b) an adapted root-zone storage capacity in response to a changing climate but with historical land use, and (c,d) an adapted root-zone storage capacity considering two hypothetical changes in land use from coniferous plantations/agriculture towards broadleaved forest and vice-versa. We found that the larger root-zone storage capacities (+34%) in response to a more pronounced seasonality with drier summers under 2K global warming strongly alter seasonal patterns of the hydrological response, with an overall increase in mean annual evaporation (+4%), a decrease in recharge (-6%) and a decrease in streamflow (-7%), compared to predictions with a stationary system. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change. [ABSTRACT FROM AUTHOR]

Streamflow is often the only variable used to constrain hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate a total of twelve hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar streamflow performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar streamflow performance represent internal states and fluxes differently. Substantial dissimilarities between models are found for annual and seasonal evaporation and interception rates, the number of days per year with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. Relatively small root-zone storage capacities for several models lead to drying-out of the root-zone storage and significant reduction of evaporative fluxes each summer, which is not suggested by remotely-sensed estimates of evaporation and root-zone soil moisture. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, we could evaluate the plausibility of model representations only to some extent, as many of these internal variables remain unknown, highlighting the need for experimental research. We also encourage modelers to rely on multi-model and multi-parameter studies to reveal to decision-makers the uncertainties inherent to the heterogeneity of catchments and the lack of evaluation data. [ABSTRACT FROM AUTHOR]

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although comparative studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. Nash-Sutcliffe Efficiency), clear differences could be observed for specific events. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the Ourthe catchment. This intercomparison enhanced the understanding of the hydrological functioning of the catchment and, above all, helped to evaluate each model against a set of alternative models. [ABSTRACT FROM AUTHOR]

The root zone moisture content of a catchment is a main predictor of the runoff response. With increasing spatial and temporal resolution of satellite-based estimates of soil moisture, independent data sources become available to determine the initial state of a catchment for hydrological predictions. Discharge predictions using soil moisture as unique input can be achieved through cumulative distribution matching (CDF) of discharge and soil moisture observations (van Dijk et al., 2016). Hydrological models also keep track of the root zone state through continuous accounting of incoming and outgoing fluxes; however, modeled root zone dynamics are likely affected by subjective model structure choices. The Antecedent Precipitation Index (API) is a commonly used soil moisture proxy that relies on precipitation and temperature, which is typically the data used to force hydrological models. In this study, we determine the value of satellite-based soil moisture products (including NASA SPL3SMP-E (O'Neill et al. 2018) and VanderSat) during specific hydrological events in several catchments of the Meuse basin, when compared to a benchmark soil moisture proxy using only meteorological data (API). To this end, we compare the predicted discharges using the CDF relations from different soil moisture products for several events. We use additional sources of data, including groundwater levels to determine the hydrological processes that likely occurred to evaluate the soil moisture responses. This data-driven approach is complementary to commonly used approaches of hydrological modeling or data-assimilation and aims to pinpoint the value of different soil moisture products in relation to occurring hydrological processes.References:Van Dijk, A. I. J. M., G. R. Brakenridge, A. J. Kettner, H. E. Beck, T. De Groeve, and J. Schellekens (2016), River gauging at global scale using optical and passive microwave remote sensing, Water Resour. Res., 52, 6404–6418, doi:10.1002/2015WR018545.O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish. 2018. SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 5. SPL3SMP-E. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/ZX7YX2Y2LHEB. [2018-10-19]. [ABSTRACT FROM AUTHOR]