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Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

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]

For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users. [ABSTRACT FROM AUTHOR]

Reservoirs are ubiquitous water infrastructure, providing functional capability to manage, and often mitigate, hydrological variability across space and time. The presence and operation of a reservoir control the downstream flow regime, such that in many locations understanding reservoir operations is crucial to understanding the hydrological functioning of a catchment. Despite many advances in modelling reservoir operations, inclusion of reservoirs in large-scale hydrological modelling remains challenging, particularly when the number of reservoirs is large and data access is limited. Here we design a set of simple reservoir operating rules (with only two calibrated parameters) focused on simulating small water supply reservoirs across large scales with various types of open-access data (i.e. catchment attributes and flows at downstream gauges). We integrate our rules into a national-scale hydrological model of Great Britain and compare hydrological simulations with and without the new reservoir component. Our simple reservoir operating rules significantly increase model performance in reservoir-impacted catchments, particularly when the rules are calibrated individually at each downstream gauge. We also test the feasibility of using transfer functions (which transform reservoir and catchment attributes into operating rule parameters) to identify a nationally consistent calibration. This works well in ∼ 50 % of the catchments, while nuances in individual reservoir operations limit performance in others. We suggest that our approach should provide a lower benchmark for simulations in catchments containing water supply reservoirs and that more complex methods should only be considered where they outperform our simple approach. [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]

Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers. [ABSTRACT FROM AUTHOR]

Baseflow is the delayed component of streamflow from subsurface storage and is critical for sustaining ecological flows and ensuring water resource security. Understanding controls on and changes in baseflow, including the seasonality of baseflow, is therefore an important task. Baseflow seasonality has typically been investigated using pre-defined hydrological seasons. Instead, here, we investigate baseflow seasonality using data-led approaches that identify and cluster average annual baseflow hydrographs that exhibit early-, mid-, or late-seasonality. We apply a novel functional data analysis (FDA) approach and examine temporal changes in the timing of seasonal peaks in annual standardised baseflow hydrographs for 671 catchments across Great Britain (GB). We use data from the CAMELS-GB dataset for the period 1976 to 2015 split into two twenty-year time blocks (1976-1995 and 1996-2015). Functional clustering enables groups of catchments with similar distributions between time blocks to be identified. Changes in baseflow seasonality with time are investigated by identifying and characterising catchments that move between functional clusters and time blocks, while analysis of the timing of baseflow peaks provides additional temporal resolution to the early-, mid-, and late-season discretisation generated by the functional clustering. The analysis shows that baseflow seasonality has a spatio-temporally coherent structure across GB and catchment characteristics are a first order control on the form of seasonal baseflow clusters. Changes in climate are inferred to be the first order control on changes in baseflow seasonality between the two time blocks. A change to earlier seasonal baseflow in snowmelt influenced catchments in upland northern GB is associated with systematic warming across the two time blocks, and a move to earlier (later) baseflow seasonality across lowland southern, central and eastern (western, north-western and northern) catchments in GB is associated with earlier (later) seasonality in effective rainfall (defined as precipitation minus potential evapotranspiration). These changes in baseflow seasonality in non-snow-melt influenced catchments are consistent with the proposition that, in temperate environments, climate warming leading to vegetation phenology-mediated changes in evapotranspiration may be modifying the timing of hydrological cycles. [ABSTRACT FROM AUTHOR]

The comparison of models in geosciences involves refining a single model or comparing various model structures. However, such model comparison studies are potentially invalid without considering the uncertainty estimates of observations in evaluating relative model performance. The temporal sampling of the observation and simulation time series is an additional source of uncertainty as a few observation and simulation pairs, in the form of outliers, might have a disproportionate effect on the model skill score. In this study we highlight the importance of including observation uncertainty and temporal sampling uncertainty when comparing or evaluating hydrological models. In hydrology, large-sample hydrology datasets contain a collection of catchments with hydro-meteorological time series, catchment boundaries and catchment attributes that provide an excellent test-bed for model evaluation and comparison studies. this study, two model experiments that cover different purposes for model evaluation are set up using 396 catchments from the CAMELS-GB dataset. The first experiment, intra-model, mimics a model refinement case by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model with and without additional calibration. The second experiment, inter-model, is a model comparison based on the streamflow estimates of the distributed PCR-GLOBWB and wflow_sbm hydrological models. The temporal sampling uncertainty, the result of outliers in observation and simulation pairs, is found to be substantial throughout the case study area. High temporal sampling uncertainty indicates that the model skill scores used to evaluate model performance are heavily influenced by only a few data points in the time series. This is the case for half of the simulations 210) of the first intra-model experiment and 53 catchment simulations of the second inter-model experiment as indicated by larger sampling uncertainty than the difference in the KGE-NP model skill score. These cases highlight the importance of reporting and determining the cause of temporal sampling uncertainty before drawing conclusions on large-sample hydrology based model performance. The streamflow observation uncertainty analysis shows similar results. One third of the catchments simulations (123) of the intra-model experiment contains smaller streamflow simulation differences between models than streamflow observation uncertainties, compared to only 4 catchment simulations of the inter-model experiment due to larger differences between streamflow simulations. These catchments simulations should be excluded before drawing conclusions based on large-samples of catchments. The results of this study demonstrate that it is crucial for benchmark efforts based on large-samples of catchments to include streamflow observation uncertainty and temporal sampling uncertainty to obtain more robust results. [ABSTRACT FROM AUTHOR]