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Inter‐catchment groundwater flow (IGF) plays an essential role in streamflow generation and water quality in forested headwaters. Multiple factors are thought to contribute to IGF, including climate, topographical, and geological factors. However, studies have not clarified the relationships between IGF and catchment properties in the headwater catchments due to the lack of observational data at scales smaller than 100 ha. This study examined possible factors influencing IGF using random forest analysis based on annual water balance data from 152 forested catchments ranging from 0.09 to 9400 ha in Japan. The results showed that catchment scale had the greatest influence on IGF, and IGF tended to decrease with increasing catchment area at scales of less than 10 ha. The average IGF stabilized around zero in catchments greater than 10 ha. The averaged IGF trend with catchment scale indicated more outward groundwater flow in catchments smaller than 10 ha, but no relationship between IGF and catchment size in catchments larger than 10 ha. The variability in IGF decreased with catchment size and was lowest at 10–100 ha. The decrease in variability in catchments less than 100 ha was mainly due to river confluence and the increased variability in catchments larger than 100 ha indicated potential observation errors increase in catchments of this size. Plain Language Summary Inter‐catchment groundwater flow (IGF) refers to groundwater flux across surface topographic boundaries. Recent studies have clarified that IGF significantly affects streamflow generation and water quality. However, direct observation of IGF is difficult, and the mechanisms underlying IGF are not fully understood. This study examined the factors influencing IGF, as well as its scale dependence and variability based on water balance data from 152 forested catchments ranging from 0.09 to 9400 ha in Japan. This showed that catchment area had the greatest influence on IGF. Groundwater tended to flow out of catchments smaller than 10 ha, and stabilized in those larger than 10 ha. Variability in the IGF decreased with catchment size at less than 100 ha. These results suggest that on the scale of hillslope and headwater catchments, IGF has a great influence on streamflow generation. Key Points We examined the factors influencing inter‐catchment groundwater flow (IGF) using water balance data for 152 forest catchments in Japan Catchment scale was the factor with the greatest influence on the IGF Groundwater tends to flow out of catchments smaller than 10 ha and IGF approaches zero when the catchment area exceeds 10 ha

Stream gauging stations provide critical streamflow measurements for hydrological applications; however, they may not accurately capture total catchment discharge due to unmonitored regional groundwater flow. Here, we evaluate the effectiveness of streamflow data from gauging stations worldwide to represent total catchment discharge through a modified hydrological model that includes baseflow signatures to constrain groundwater flow processes. We find that approximately 70% of gauging stations present biased estimations of total catchment discharge (bias >10%). This result implies that hydrology‐related processes may not be fully understood, and misleading conclusions may be drawn owing to the low streamflow measurement effectiveness. By influencing subsurface hydrological processes, catchment factors, including catchment area, topography, climate, and geological features, are linked to the effectiveness of streamflow measurements. Our findings highlight the importance of accurate streamflow measurement effectiveness for obtaining a reliable understanding of catchment hydrological processes to support sustainable water resource management. Plain Language Summary The outflow of water from catchments plays a critical role in supporting downstream ecosystems and human society. This catchment outflow includes surface and subsurface discharge. However, a portion of the subsurface discharge may not directly flow into river networks and thus remains unaccounted for in streamflow records at gauging stations. The extent to which these unmonitored subsurface flows exist across catchments and their contribution to the total catchment discharge remains unknown. To address this knowledge gap, we have developed a novel method that simulates the movement of both surface and subsurface flows, enabling us to evaluate the representativeness of streamflow records in capturing the total catchment discharge. Our findings indicate that approximately 70% of gauging stations worldwide inadequately capture the total catchment discharge (bias >10%). This observation highlights the importance of unmonitored subsurface discharge as a significant component of catchment water yield. Such insights enhance our understanding of catchment hydrological processes, supporting the development of sustainable water resource management strategies. Key Points A modified hydrological model was developed by including baseflow signatures to constrain groundwater flow processes Approximately 70% of gauging stations presented biased estimations of total catchment discharge The catchment hydrological response based on gauging station measurements is highly likely to be overestimated or underestimated

Although decades of effort have been devoted to building Physical‐Conceptual (PC) models for predicting the time‐series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML‐based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass‐Conserving‐Perceptron (MCP) as a way to bridge the gap between PC‐based and ML‐based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass‐conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off‐the‐shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall‐runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems. Plain Language Summary We develop a physically interpretable computational unit, referred to as the Mass‐Conserving‐Perceptron (MCP). Networks of such units can be used to model the conservative nature of the input‐state‐output dynamics of mass flows in geoscientific systems, while Machine Learning (ML) technology can be used to learn the functional nature of the physical processes governing such system behaviors. Testing using data from the Leaf River Basin demonstrates the considerable functional expressivity (capacity) and interpretability of even a single‐MCP‐node‐based model, while providing excellent predictive performance and the ability to conduct scientific hypothesis testing. The concept can easily be extended to facilitate ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems, thereby facilitating the development of synergistic physics‐AI modeling approaches. Key Points We develop a physically interpretable unit (Mass‐Conserving‐Perceptron) that can be used as a basic component of geoscientific models Off‐the‐shelf Machine Learning technology can be used to learn the functional nature of the physical processes governing system behaviors The concept can be extended to facilitate ML‐based representation of coupled mass‐energy‐information flows in geoscientific systems

Lentic waters integrate atmosphere and catchment processes, and thus ultimately capture climate signals. However, studies of climate warming effects on lentic waters usually do not sufficiently account for a change in heat flux from the catchment through altered inflow temperature and discharge under climate change. This is particularly relevant for reservoirs, which are highly impacted by catchment hydrology and may be affected by upstream reservoirs or pre‐dams. This study explicitly quantified how the catchment and pre‐dams modify the thermal response of Rappbode Reservoir, Germany's largest drinking water reservoir system, to climate change. We established a catchment‐lake modeling chain in the main reservoir and its two pre‐dams utilizing the lake model GOTM, the catchment model mHM, and the stream temperature model Air2stream, forced by an ensemble of climate projections under RCP2.6 and 8.5 warming scenarios. Results exhibited a warming of 0.27/0.15°C decade−1 for the surface/bottom temperatures of the main reservoir, with approximately 8%/24% of this warming attributed to the catchment warming, respectively. The catchment warming amplified the deep water warming more than at the surface, contrary to the atmospheric warming effect, and advanced stratification by about 1 week, while having a minor impact on stratification intensity. On the other hand, pre‐dams reduced the inflow temperature into the main reservoir in spring, and consequently lowered the hypolimnetic temperature and postponed stratification onset. This shielded the main reservoir from climate warming, although overall the contribution of pre‐dams was minimal. Altogether, our study highlights the importance of catchment alterations and seasonality when projecting reservoir warming, and provides insights into catchment‐reservoir coupling under climate change. Plain Language Summary Climate change is altering the temperature and mixing characteristics of lakes and reservoirs, with potentially detrimental effects on water quality. Water temperature and mixing are affected by the atmosphere, but also by the amount and temperature of the inflowing stream water. So far, most climate change studies have not fully accounted for the effect of streams on lakes and reservoirs. Thus, we linked different computer models to estimate future warming of a large drinking water reservoir in Germany, and separate the contribution of the atmosphere and inflowing streams. We found that warming of the reservoir water was 24% stronger when considering streams, and even more specifically for the deep water. This means that studies only accounting for atmospheric warming are underestimating climate impacts on reservoirs, including the negative impacts on oxygen levels. Small upstream dams, known as pre‐dams may also influence water temperature, potentially dampening the effect of inflow warming, although with weak effects because of their small size. Our results suggest that neglecting inflowing streams underestimates both the climate warming impacts on reservoirs and also the sensitivity of deep water, further biasing projections for ecological variables. Key Points Catchment heat flux amplifies reservoir bottom/surface warming by up to 24%/8% and advances stratification onset by ca. 1 week Contrary to the atmospheric warming effect, inflow warming affects reservoir deep water more than surface water Inflow temperature and seasonality need more attention when studying reservoir responses to climate change

Topographically delineated catchments are the common spatial unit to connect human activities and climate change with their consequences for water availability as a prerequisite for sustainable water management. However, inter-catchment groundwater flow and limited connectivity within the catchment results in effective catchment areas different from those suggested by surface topography. Here, we introduce the notion of effective catchment area quantified through an effective catchment index (ECI), derived from observed streamflow, precipitation and actual evapotranspiration estimates, to understand the prevalence and significance of substantial differences between topographic and effective catchment areas in a global dataset. We evaluate our ECI analysis by comparing it to hydraulic head simulations of a global groundwater flow model and to the Budyko framework. We find that one in three studied catchments exhibit an effective catchment area either larger than double or smaller than half of their topographic area. These catchments will likely be affected by management activities such as groundwater pumping or land use change outside their topographic boundaries. Or alternatively, they affect water resources beyond their topographic boundaries. We find that the magnitude of the observed differences is strongly linked to aridity, mean slope, distance to coast, and topographic area. Our study provides a first-order identification of catchments where additional in-depth analysis of subsurface connectivity is needed to support sustainable water management.

Recent studies have revealed evidence of trends in the median or mean flood discharge in Europe over the last 5 decades, with clear and coherent regional patterns. The aim of this study is to assess whether trends in flood discharges also occurred for larger return periods, accounting for the effect of catchment scale. We analyse 2370 flood discharge records, selected from a newly available pan-European flood database, with record length of at least 40 years over the period 1960–2010 and with contributing catchment area ranging from 5 to 100 000 km2. To estimate regional flood trends, we use a non-stationary regional flood frequency approach consisting of a regional Gumbel distribution, whose median and growth factor can vary in time with different strengths for different catchment sizes. A Bayesian Markov chain Monte Carlo (MCMC) approach is used for parameter estimation. We quantify regional trends (and the related sample uncertainties), for floods of selected return periods and for selected catchment areas, across Europe and for three regions where coherent flood trends have been identified in previous studies. Results show that in northwestern Europe the trends in flood magnitude are generally positive. In small catchments (up to 100 km2), the 100-year flood increases more than the median flood, while the opposite is observed in medium and large catchments, where even some negative trends appear, especially in northwestern France. In southern Europe flood trends are generally negative. The 100-year flood decreases less than the median flood, and, in the small catchments, the median flood decreases less compared to the large catchments. In eastern Europe the regional trends are negative and do not depend on the return period, but catchment area plays a substantial role: the larger the catchment, the more negative the trend.

Land use and land cover change (LUCC) can affect the hydrological response time of rivers. However, it is difficult to generate robust and quantitative evidence of this impact at the catchment scale. This lack of evidence also affects the development of rainfall‐runoff models to make ex‐ante predictions. Here, we analyze high‐frequency observational data from a network of pairwise catchments in the tropical Andes and find a statistically significant impact of intensive land use on the hydrological response time, which can be used for regionalization. First, we isolated individual rainfall response events from 5‐min precipitation and discharge time series of 16 catchments (8 pairs). We then fitted unit hydrographs on these events to estimate the catchment response times. These response times were subsequently regionalized by, first, applying a forward stepwise regression to select statistically significant catchment characteristics including land use and land cover, then, fitting a linear mixed‐effects model with the selected characteristics to account for within‐site variability between pairs. We find that catchments with intensive land use have a significantly quicker response than their natural counterparts. Differences were often sub‐hourly, highlighting the value of high‐frequency monitoring. Forward stepwise regression identified only catchment area and intensive land use percentage (LUP) as statistically significant predictors. Model coefficients show that, even when considering other catchment characteristics, increasing intensive LUP decreases response times. This study provides solid evidence and a robust methodology to quantify the impacts of LUCC on catchment hydrology.

While issues of pollution, floods and drought in our rivers are widely studied, there is a hidden crisis with respect to the widespread global extraction of sand. Large volumes of sand are needed in the construction industry to make concrete. So far, calls for greater monitoring of sand mining activity have largely gone unmet. This is due to the fact mining is extensive, often hidden (e.g. underwater) and thus very difficult to properly assess. To meet this challenge, we use remote sensing methods to detect and monitor sand mining activities at the catchment scale, across the Ganges-Brahmaputra-Meghna River system (catchment size 1.72 million km 2 ). Based on this analysis, here we show that mining activity is diverse and pervasive across the Ganges–Brahmaputra–Meghna Catchment system for our study period of 2016–2021, with rates of extraction increasing within some of the rivers. Results show the total estimate for sand extraction is ∼115 Mtyr −1 ± 20 Mtyr −1 , which is of a similar order of magnitude to the natural bedload flux of the catchment. While there are some limitations to deriving estimates based solely on imagery, this work highlights both the widespread spatial extent and large magnitude of sand mining for one of the world’s biggest catchments. Furthermore, given our estimated scale of sand extraction, it demonstrates the need to properly account for mining activities when considering delivery of sediment to deltas in terms of the management of these vulnerable systems in the face of rising sea-levels. Overall, this work stresses the urgent requirement for further similar studies of sand extraction in the world’s large rivers, which is vital to underpin sustainable management plans for the global sand commons.

Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (>10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE >0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge.

In low-lying coastal regions, flooding arises from oceanographic (storm surges plus tides and/or waves), fluvial (increased river discharge), and/or pluvial (direct surface run-off) sources. The adverse consequences of a flood can be disproportionately large when these different sources occur concurrently or in close succession, a phenomenon that is known as “compound flooding”. In this paper, we assess the potential for compound flooding arising from the joint occurrence of high storm surge and high river discharge around the coast of the UK. We hypothesise that there will be spatial variation in compound flood frequency, with some coastal regions experiencing a greater dependency between the two flooding sources than others. We map the dependence between high skew surges and high river discharge, considering 326 river stations linked to 33 tide gauge sites. We find that the joint occurrence of high skew surges and high river discharge occurs more frequently during the study period (15–50 years) at sites on the south-western and western coasts of the UK (between three and six joint events per decade) compared to sites along the eastern coast (between zero and one joint events per decade). Second, we investigate the meteorological conditions that drive compound and non-compound events across the UK. We show, for the first time, that spatial variability in the dependence and number of joint occurrences of high skew surges and high river discharge is driven by meteorological differences in storm characteristics. On the western coast of the UK, the storms that generate high skew surges and high river discharge are typically similar in characteristics and track across the UK on comparable pathways. In contrast, on the eastern coast, the storms that typically generate high skew surges are mostly distinct from the types of storms that tend to generate high river discharge. Third, we briefly examine how the phase and strength of dependence between high skew surge and high river discharge is influenced by the characteristics (i.e. flashiness, size, and elevation gradient) of the corresponding river catchments. We find that high skew surges tend to occur more frequently with high river discharge at catchments with a lower base flow index, smaller catchment area, and steeper elevation gradient. In catchments with a high base flow index, large catchment area, and shallow elevation gradient, the peak river flow tends to occur several days after the high skew surge. The previous lack of consideration of compound flooding means that flood risk has likely been underestimated around UK coasts, particularly along the south-western and western coasts. It is crucial that this be addressed in future assessments of flood risk and flood management approaches.