Climate change poses prodigious challenges to water resource management. In addressing these challenges, traditional grey measures lack adaptability and long-term sustainability. In contrast, Nature Based Solutions (NBSs) offer flexible, sustainable and eco-friendly alternatives. The design and effectiveness of NBS-based adaptation strategies could be potentially assessed through integrated hydrological models, which simulate both surface and subsurface complex hydrological processes. However, the accuracy of models to efficiently simulate the hydrological processes significantly depends on the quality and availability of input data. Earth Observation (EO) datasets offer a wide range of hydrological data with comprehensive spatial and temporal coverage, yet their quality remains uncertain and requires evaluation across different scales and regions.....

This article presents a methodology for designing and assessing drought-related Nature-Based Solutions (NBS) adaptation strategies on a catchment scale using an integrated hydrological model that simultaneously provides surface water and groundwater results. The Aa of Weerijs catchment, shared between Belgium and the Netherlands, was used for demonstrating the methodology. The model was developed with the MIKE SHE modelling system, using a combination of globally available and local data. Different types of NBS (ditch blocking, infiltration ponds, wetland restoration and heathland restoration) were combined spatially to develop two adaptation strategies with different spatial extents. Their design was based on drought-related Key Performance Indicators (KPIs) linked with water management actions by key stakeholders (bans on water extraction), both on the surface and groundwater. The KPI values were obtained by model simulations under current and future climate conditions, and with the implementation of the two adaptation strategies. The results show that the strategy with a larger spatial extent gives better KPI values, almost eliminating days with no groundwater availability in the downstream part of the catchment, reaching the goal of increased infiltration and groundwater recharge. Additionally, our results show that there is significant accumulation of positive effects from upstream to downstream.

The hydrological processes within the catchment are generally influenced by both climate change (CC) and land use/land cover (LULC) change. However, most of the studies are focused on their individual impact on the catchment’s hydrology, while their combined effects have received little attention. This study employs the physically based distributed hydrological model, MIKE SHE, to study the separate and combined effect of climate and LULC change on the hydrology of a mesoscale catchment in the near future (2050s). An Artificial Neural Network–Cellular Automata (ANN-CA) based prediction model was trained to simulate the future LULC map. The future meteorological data under four CC scenarios was obtained from the Royal Netherlands Meteorological Institute (KNMI). The model results showed that the combined effects of CC with LULC changes did not significantly differ from the individual impact of CC on the catchment scale. However, on the local scale, the changes in LULC can significantly influence the variations in groundwater table, soil moisture, and actual evapotranspiration ranging from approximately–6–15%,–9–27%, and–30–10% respectively, depending on the specific change in LULC class and season. In summary, this research provides valuable insights into the complex interactions between LULC changes, CC, and hydrology.

This study addresses the challenging task of analysing multifunctional landscapes through an innovative integrated modelling approach. Acknowledging the limitations of disciplinary models in assessing diverse landscape functions, we present a conceptual framework for their integration. Demonstrating the feasibility and effectiveness of this approach in a Netherlands case study, we assess alternative land use changes for drought and carbon sequestration. Results underscore the framework's efficacy in elucidating the intricate relationship between carbon and water across multiple model runs and iterations. Notably, the alternative land use scenario reveals an average increase in soil moisture during dry periods and an increase in soil organic carbon content across four model runs. This softly coupled approach offers valuable insights into environmental modelling, facilitating navigation of complex integration challenges for researchers and practitioners. Furthermore, it enhances modelling transparency by elucidating variable representation and processes, providing a foundation for informed decisions in sustainable landscape management.

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10 km²), meso-scale (10 km²–1000 km²), and macro-scale (>1000 km²). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions.