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Groundwater is a crucial part of the hydrologic cycle and the largest accessible freshwater source for humans and ecosystems. However, most hydrological models lack explicit representation of surface-groundwater interactions, leading to poor prediction performance in groundwater-dominated catchments. This study presents DECIPHeR-GW v1, a new surface-groundwater hydrological model that couples a Hydrological Response Units (HRU)-based hydrological model and a two-dimensional gridded groundwater model. By using a two-way coupling method, the groundwater model component receives recharge from HRUs, simulates surface-groundwater interactions, and returns groundwater levels and groundwater discharges to HRUs, where river routing is then performed. These interactions are happening at each time step in our new surface-groundwater model. Depending on the storage capacity of the surface water model component and the position of the modelled groundwater level, three scenarios are developed to derive recharge and capture surface-groundwater interactions dynamically. Our new coupled model was calibrated and evaluated against daily flow timeseries from 669 catchments and groundwater level data from 1804 wells across England and Wales. The model provides streamflow simulation with a median KGE of 0.83 across various catchment characteristics, with high performance particularly for the drier chalk catchments in southeast England, where the average KGE increased from 0.49 in the benchmark DECIPHeR model to 0.7. Furthermore, our model reproduces temporal patterns of the groundwater level timeseries, with more than half of the wells achieving a Spearman correlation coefficient of 0.6 or higher when comparing simulations to observations. Overall, this new DECIPHeR-GW model demonstrates remarkable accuracy and computational efficiency in reproducing streamflow and groundwater levels, making it a valuable tool for addressing water resources and management issues over large domains. [ABSTRACT FROM AUTHOR]

Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments. Plain Language Summary: We developed a machine learning‐based approach to investigate why the effects of changes in climate and land cover (LC) on floods vary spatially. To inform the model, we used climate and LC data for 1,268 catchments in Great Britain over four decades. We found that increasing rainfall and urban development tend to lead to larger floods, especially in rivers fed largely by groundwater. In contrast, rising air temperature and afforestation, in the absence of any changes in rainfall, lead to smaller floods, particularly in areas where rivers are less fed by groundwater. We believe that our findings can be used to develop more targeted flood management strategies. Key Points: We employ partial dependence analysis and sensitivity testing to assess where changes in climate or land cover might affect flooding the mostRising precipitation and urbanization tend to lead to larger floods in catchments with high baseflow contribution and low runoff ratioRising air temperature with unchanged precipitation lowers flood magnitudes, especially in dry catchments with low baseflow index [ABSTRACT FROM AUTHOR]

Continuous streamflow is critical for sustaining ecological systems and ensuring water resource security. Understanding controls on and changes in flows, including the seasonality of baseflow, is therefore an important task. Baseflow seasons have typically been investigated separately, potentially missing hydroecologically important timing changes. Instead, we apply a functional data analysis clustering approach to seasonal patterns of baseflow hydrographs for 671 catchments across Great Britain (GB). The baseflow clusters are characterized as early-, mid-, and late-season peaks. The spatial distribution of the baseflow seasonality clusters is closely connected to the baseflow index and a partition tree shows the influence of catchment topological, hydrogeological and soil factors. Changes in timing of baseflow seasonality are compared to climate seasonality. In GB there appears to be a small but systematic influence of a warming climate on baseflow seasonality via effective rainfall with a tendency for earlier seasonal baseflow peaks, with greater timing changes in snow-influenced catchments. [ABSTRACT FROM AUTHOR]