Future hydrological behavior in a changing world is typically predicted based on models that are calibrated on past observations, disregarding that hydrological systems and, therefore, model parameters may change as well. In reality, hydrological systems experience almost continuous change over a wide spectrum of temporal and spatial scales. In particular, there is growing evidence that vegetation adapts to changing climatic conditions by adjusting its root zone storage capacity, which is the key parameter of any terrestrial hydrological system. In addition, other species may become dominant, both under natural and anthropogenic influence. In this study, we test the sensitivity of hydrological model predictions to changes in vegetation parameters that reflect ecosystem adaptation to climate and potential land use changes. 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 the magnitude and seasonality of hydro-climatic variables. Additionally, long-term water balance characteristics of different dominant ecosystems are used to predict 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 2 K global warming are more pronounced when explicitly considering changes in the subsurface 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 2 K global warming in comparison to current-day conditions, using a process-based hydrological model with (a) a stationary system, i.e., no assumed 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. We found that the larger root zone storage capacities (+34 %) in response to a more pronounced climatic seasonality with warmer summers under 2 K global warming result in strong seasonal changes in the hydrological response. More specifically, streamflow and groundwater storage are up to −15 % and −10 % lower in autumn, respectively, due to an up to +14 % higher summer evaporation in the non-stationary scenarios compared to the stationary benchmark scenario. 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.@en

Contemplating the Meuse or any other river of the world, one may wonder about the journey of rain in becoming river. This fascinates hydrologists, as they develop theories to understand movement, storage and release of water through the landscape across climates. These theories are translated to hydrological models, which describe the complex reality in a simpler way. Models are then used to predict the hydrological cycle for the nearby or long-term future. This thesis aims to assist the Dutch Ministry of Infrastructure and Water Management in improving the reliability of hydrological modeling of the Meuse basin for operational and policy applications. Using in-situ and remote-sensing data, the value of representing additional processes in models is explored, as well as the creative use of additional data to improve hydrological predictions. First, water balance data is used to identify the potential presence of intercatchment groundwater flows (Chapter 3). These underground flow paths cross topographic catchment boundaries and mainly play a role in headwater catchments (< 500 km2) of the Meuse basin, which are underlain by productive aquifers. Representing this flux as a preferential threshold-initiated process improves low and high flow model performance and increases the consistency between modeled and remote-sensing estimates of actual evaporation. Besides the importance of quantifying the long-term hydrological partitioning of precipitation into streamflow, evaporation and potentially intercatchment groundwater flows, another key element of the hydrological response is the amount of water available in the root-zone of vegetation. The temporal dynamics of root-zone soil moisture control how much more water can be stored in the soil and how much water is available for transpiration. In Chapter 4, meaningful estimates of root-zone soil moisture are inferred from satellite observations of near-surface soil moisture, by establishing a link between the catchment-scale root-zone storage capacity and the Soil Water Index. Interestingly, hydrological models with different internal process representations of root-zone soil moisture, evaporation, snow and total storage at the catchment scale may lead to a similar aggregated streamflow response (Chapter 5). This discrepancy implies that models are not necessarily providing the right answers for the right reasons, as they cannot simultaneously be close to reality and different from each other. To circumvent the uncertainty of process representation, which is inherent to hydrological science, the use of multiple model structures is advocated for operational and policy applications. Nonetheless, testing the consistency between modeled hydrological behavior and independent remote-sensing data can foster model developments and lead to creating better models. Finally, we move beyond the use of historical in-situ and remote-sensing data to predict long-term hydrological behavior of the Meuse basin under projected global warming (Chapter 6). If environmental conditions change, it is likely to also assume ecosystem adaptation in response to climate change and a potential natural and/or anthropogenic shift in dominant species across the landscape. Non-stationarity in the representation of hydrological systems is introduced in a process-based model with three hydrological response units to account for the spatial variability of hydrological processes. More specifically, we adapt the root-zone storage capacity parameter using the information contained in the projected climate data. This is an important step forward in the great challenge of hydrological predictions under change. Despite data uncertainties and a lack of data at the required temporal and spatial resolutions, many possibilities are at hand with what is currently available to develop new theories, test and improve hydrological models. Requiring creativity, this is a beautiful challenge to further unravel the mysteries of the hydrological landscape.@en

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

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The spatiotemporal dynamics of water volumes stored in the unsaturated root zone are a key control on the response of terrestrial hydrological systems. Robust, catchment-scale root-zone soil moisture estimates are thus critical for reliable predictions of river flow, groundwater recharge, or evaporation. Satellites provide estimates of near-surface soil moisture that can be used to approximate the moisture content in the entire unsaturated root zone through the Soil Water Index (SWI). The characteristic time length (T, in days), as only parameter in the SWI approach, characterizes the temporal variability of soil moisture. The factors controlling T are typically assumed to be related to soil properties and climate; however, no clear link has so far been established. In this study, we hypothesize that optimal T values (T_opt) are linked to the interplay of precipitation and evaporation during dry periods, thus to catchment-scale vegetation accessible water storage capacities in the unsaturated root zone. We identify T_opt by matching modeled time series of root-zone soil moisture from a calibrated process-based hydrological model to SWI from several satellite-based near-surface soil moisture products in 16 contrasting catchments in the Meuse river basin. T_opt values are strongly and positively correlated with vegetation accessible water volumes that can be stored in the root zone, here estimated for each study catchment both as model calibration parameter and from a water-balance approach. Differences in T_opt across catchments are also explained by land cover (% agriculture), soil texture (% silt), and runoff signatures (flashiness index).

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Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. The moisture content of the root-zone regulates the partitioning between drainage and water fluxes and reliable estimates are, therefore, critical for predictions of runoff. Remotely-sensed soil moisture products are available globally, however, they are representative of the upper-most few centimeters of the soil. The Soil Water Index (SWI) features a single parameter, representing the characteristic time length T of temporal soil moisture variability, and enables to infer root-zone soil moisture from near-surface estimates. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established. In this study, we hypothesize that the optimal value for T can be linked to the seasonal signals of the interplay between precipitation (water supply) and evaporation (atmospheric water demand), and, thus, to catchment-scale vegetation-accessible water storage capacities in the unsaturated root-zone. We first identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of unsaturated root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T positively and strongly correlate to catchment-scale estimates of unsaturated root-zone capacities, estimated both as model calibration parameter and from a simple water-balance approach. This correlation provides evidence that the T value, used to infer root-zone soil moisture, reflects the vegetation-accessible water storage capacities in the unsaturated root-zone, that can be estimated from water-balance data.@en

Intercatchment groundwater flows (IGFs), defined as groundwater flows across topographic divides, can occur as regional groundwater flows that bypass headwater streams and only drain into the channel further downstream or directly to the sea. However, groundwater flows can also be diverted to adjacent river basins due to geological features (e.g., faults, dipping beds and highly permeable conduits). Even though intercatchment groundwater flows can be a significant part of the water balance, they are often not considered in hydrological studies. Yet, assuming this process to be negligible may introduce misrepresentation of the natural system in hydrological models, for example in regions with complex geological features. The presence of limestone formations in France and Belgium potentially further exacerbates the importance of intercatchment groundwater flows, and thus brings into question the validity of neglecting intercatchment groundwater flows in the Meuse basin. To isolate and quantify the potential relevance of net intercatchment groundwater flows in this study, we propose a three-step approach that relies on the comparison and analysis of (1) observed water balance data within the Budyko framework, (2) results from a suite of different conceptual hydrological models and (3) remote-sensing-based estimates of actual evaporation. The data of 58 catchments in the Meuse basin provide evidence of the likely presence of significant net intercatchment groundwater flows occurring mainly in small headwater catchments underlain by fractured aquifers. The data suggest that the relative importance of net intercatchment groundwater flows is reduced at the scale of the Meuse basin, as regional groundwater flows are mostly expected to be self-contained in large basins. The analysis further suggests that net intercatchment groundwater flow processes vary over the year and that at the scale of the headwaters, net intercatchment groundwater flows can make up a relatively large proportion of the water balance (on average 10 % of mean annual precipitation) and should be accounted for to prevent overestimating actual evaporation rates.

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Currently, the hydrological model used in the operational forecasting system of the river Meuse is lumped and does not account for the heterogeneity of the landscape, topography and vegetation. Previous studies have shown the importance of model structure distribution in different hydrological response units (HRUs) to improve model simulations. These HRUs take into account the different dominant runoff generation processes that occur in different parts of the landscape. The conceptualization of a runoff response with a very rapid time scale is essential to model the rapid runoff generated by very high intensity rainfall events. The parameterization of this rapid runoff response in the different sub-catchments of the Meuse is very sensitive due to the non-linearity of this threshold process and to the spatio-temporal variability of high-intensity rain events. In this study, we formulate several hypotheses on what controls the very quick runoff response in the Meuse basin and we try to use additional sources of data to test the a-priori assumptions that we made in the conceptualization of the HRUs in our hydrological model and to facilitate model parameterization. We hypothesize that by using appropriate runoff signatures, we may be able to assess the importance of the threshold response in the different catchments. The selection of specific storm events is useful to split the runoff in different time scales to improve the a-priori estimation of the very rapid runoff parameterization. Linking these differences to topographic and physiographic properties of the catchment like soil texture and land use may help us to explain the difference in observed spatial patterns. Especially the assessment of the fraction of roads and paved areas that cross the different hydrological response units may help to explain the observed spatial patterns. Additionally, we believe that deriving permanent and temporary wet areas using the Modified Normalized Difference Water Index (MNDWI) may guide us in strengthening or adapting the assumptions we made concerning the HRU classes. @en

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison 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 a variety of 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. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events. We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. 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 studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models.@en