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• The potential of model-independent methods for hydrological interpretations is examined. • Dominance of rain, snow, and their combination in catchment hydrology is inferred. • Lagged climate variables often fail to accurately represent current hydrologic states. Machine learning (ML) is increasingly perceived as a futuristic, superior data-driven approach to scientific discovery. It has already demonstrated remarkable performance in forecasting and prediction, yet its role in enhancing our understanding of hydrological processes remains underexplored. Traditional hydrological interpretations have relied heavily on model-dependent interpretation methods, focusing on the predictive accuracy of ML model predictions. Since hydrological models are built on a collection of assumptions and simplifications, model-dependent approaches might suffer from limited model realism, adequacy, accuracy, and equifinality issues. To address this gap, this study provides an ML approach that works in a model-independent context, working directly on hydroclimatic data collected through monitoring systems. We apply our model-independent interpretation approach to a carefully designed set of hydrologic data collected across the contiguous United States to address the following questions: (1) What are the primary controls of runoff-generation mechanisms, and how can such controls be attributed to catchment properties? (2) How and under what circumstances can the history of climate variables, such as precipitation, be a surrogate for present-time state variables, such as soil moisture and snowpack? We show that the ML approach aids in distinguishing catchments characterized by strong overland flow, interflow, or baseflow components and those primarily driven by rainfall, snowmelt, or a mix thereof. We further show that typical surrogate variables used in hydrology may come short in representing the dynamics of catchments that exhibit a complex interplay of rain and snow. [ABSTRACT FROM AUTHOR]

• Landscape memory is crucial to project lake water levels in the Boreal Plain. • Measurement & analysis of lake water levels in 26 Boreal Plain lakes over two decades. • Interdecadal, decadal, multi-year, intra-annual memory impacts lake water levels. • Variations in memory driven by differences in lake-landscape storage capacities. • Shorter term analyses should be placed in the context of the long-term cycles. Landscape hydrologic memory of meteorological cycles can have an important impact on catchment hydrological responses by propagating clustering of wet or dry conditions into extreme events. The non-linear and hysteretic hydrologic response functions driven by memory are often only studied at shorter temporal scales (event, season) despite larger interannual hydrologic responses evident in some systems. Within the Canadian Boreal, lakes and lake water levels provide an important indicator that can be used to assess the role of landscape memory on catchment hydrological function. Landscape memory has also been hypothesised to control the hydrological dynamics of shallow lake ecosystems that are also important biogeochemically and ecologically. Here we combine measurements of lake water levels in 26 lakes of varying type at varying temporal frequencies within the glaciated sub-humid Boreal Plain, to examine the impact and variability of interdecadal, decadal, multi-year, and intra-annual memory on lake water levels. We show multi-annual hysteresis of precipitation-lake water levels with varied characteristics in space and time. These spatial variations in landscape memory are driven by differences in storage capacities controlled by heterogeneity in glacial landforms, wetland-forest landcover and lake properties. Thus, the propensity for drought years or wet years to persist or accumulate into extreme landscape drying or wetting varies significantly between different lake-landscape characteristics. We show how landscape memory is crucial to project lake water levels by defining spatial variability of the impact of periods of meteorological drought and deluge vital for understanding system sensitivity, duration of recovery and in turn infer resilience on Boreal Plain hydrology. [ABSTRACT FROM AUTHOR]

[Display omitted] • We studied wildfire effects on streamflow, runoff and ET ratios in western Oregon. • Runoff increased the most in catchments with higher burned severity and area burned. • ET decreased 6.1–40.2% and was related to burn severity and area burned. • Slope, elevation, geology, aspect, and pre-fire vegetation influenced streamflow. • Our study provides insights useful to water and forest management. Wildfires can dramatically alter vegetation cover and soil properties across large scales, resulting in substantial shifts in runoff generation, streamflow, and water quality. In September 2020, extensive and high-severity wildfires burned more than 490,000 ha of forest land on the westside of the Cascade Mountain Range in the Pacific Northwest. Much of the area impacted by these fires is critical for the provision of water for downstream aquatic ecosystems, agriculture, hydropower, recreation, and municipal drinking water. We undertook a study to evaluate the effects of four of the large high severity wildfires from 2020 (Riverside, Beachie Creek, Lionshead, and Holiday Farm) on streamflow in nine burned catchments in western Oregon. We also included four unburned, reference catchments in our analysis to enable us to assess post-fire streamflow changes in the burned catchments. To quantify the effects of wildfire on the catchment water balance we used publicly available streamflow data and estimated precipitation, potential evapotranspiration (PET), and actual evapotranspiration (ET), using satellite-based meteorological data. We quantified catchment area burned and burn severity with the average differenced normalized burn ratio (dNBR). We compared hydrologic conditions for the pre-fire (2001–2020) and post-fire (2021–2022) periods by analyzing catchment runoff ratios, ET ratios (evaporative index: quotient of ET divided by precipitation, referred to as EI hereafter), and Budyko curves. We also used random forest models to explore factors influencing the variability in EI. During the post-fire period, we observed decreases in EI and increases in runoff ratio in the burned catchments. Post-fire declines in EI were positively related to burn severity (R 2 = 0.70 in 2021; 0.76 in 2022) and area burned (R 2 = 0.91 in 2021; 0.95 in 2022), and were primarily driven by decreases in ET. Declines in ET were highly variable, ranging from 10.7–40.2 % in the first year after the fires and 6.1–32.0 % in the second year after the fires, and were generally related to catchment burn severity and area burned. The greatest increases in runoff (16.1 % in 2021 and 19.8 % in 2022) occurred in the same catchment. These results were reinforced by the random forest analysis, which illustrated the importance of burn severity as a predictor of EI. Interestingly, the variability in changes in EI during the post-fire period was also associated with other geomorphic factors such as catchment slope, elevation, geology, aspect, and pre-fire vegetation type. Since the duration and seasonality of post-fire impacts on hydrology remain uncertain, our findings bring new insights and guide future studies into the post-fire responses on hydrology that are crucial for water and forest management. [ABSTRACT FROM AUTHOR]

• The main-channel region and groin region over North Passage (NP) displayed continuous erosion and constant deposition, respectively. • A huge mouth bar system was observed in NP and it shifted seaward accompanied by reduction of its extent. • The existence of estuarine turbidity maximum contributed the accretion of main-channel region of NP. • The estuarine construction and dredging work were responsible for the inverse changes of the main-channel region and groin region. Deltaic channels are key pathways connecting rivers and the ocean, which is of significance for in maintaining estuarine stability and material transport processes between land and sea. However, deltaic channels have experienced dramatic variations in the Anthropocene era, including seriously scouring induced by decline of riverine sediment delivered to the ocean, trenching caused by dredged works and episodic filling by storm events. Here, a series of hydrology, sediment as well as elevation data between 1979 and 2020 of North Passage (NP), one of most important navigated channels over the Changjiang Estuary Delta, were used to analyze its long-term morphodynamic processes. Results show that during 1979–2020, NP capacity presented stage changes: gentle increase stage between 1979 and 1997, fluctuation stage between 1997 and 2002, fast-descend stage between 2002 and 2007, slow growth stage between 2007 and 2020, and periodic signals of 13 and 25 months can be detected in NP volume. Meanwhile, the shoal zone and the deep channel showed long-term decrease and increase trend respectively, making NP turn into a narrow and deep deltaic channel. Moreover, NP exhibited two major morphodynamic patterns. The first mode indicates the long-term continuous erosion of the main-channel region and persistent deposition in the groin region. The second mode explains the transition of the erosion/deposition state of NP, where the huge mouth bar system in NP was shifted seaward accompanied by a decrease of its extent. The periodic changes of fluvial water and sediment may contribute the oscillation of erosion and accretion process of NP. The construction of engineering structures and long-term dredging works resulted in constant deposition in the groin region with continuous erosion in main-channel region. The existence of estuarine turbidity maximum was conductive to deposition in the main-channel region. [ABSTRACT FROM AUTHOR]

• Spatially and temporally variable groundwater fluxes are difficult to characterize. • New technology provides high-resolution subsurface temperature data. • New heat-as-a-tracer methods increase the information used for flux estimates. • These combined advances facilitate statistically robust groundwater flux estimates. • Strong discharge zones are no longer a limitation for heat tracing methods. Groundwater fluxes to many surface water systems are spatially heterogeneous with discharge focused into discrete, high-flux zones. Quantifying fluxes in these preferential discharge zones is critical to a range of surface water habitat and water quality processes, but characterization can be difficult due to short-scale spatial and temporal variability. Passive heat-as-a-tracer methods employing vertical temperature profiler (VTP) data can provide the necessary spatial and temporal resolution, but upward fluid flow strongly attenuates the thermal signals used for estimating fluxes. In preferential discharge zones it becomes difficult to measure the signals in the subsurface and the flux parameter can become insensitive in the analysis models, leading to large uncertainties. We use data from a high-flux site of contaminant-loaded groundwater discharge to the Quashnet River on Cape Cod, Massachusetts, USA, to demonstrate how recent advances in VTP instrumentation that allow for the acquisition of high-resolution (0.001 °C) temperature data at short (1 cm) offsets near the ground surface, combined with advances in flux estimation methods that exploit the information content of the high-resolution data, facilitate heat-as-a-tracer approaches for characterizing groundwater-surface water exchanges and make it possible to obtain accurate and statistically robust results in a preferential discharge zone with a specific discharge of ∼1 m/d. [ABSTRACT FROM AUTHOR]

• An enhanced silicate weathering contribution in the high-flow seasons is observed. • δ7Li and δ26Mg are seasonally varied because dominant weathering process changes. • Hydrology dominantly controls river δ7Li and δ26Mg seasonal variations. The response of chemical weathering to climate could be examined in a way by investigating seasonal changes in river geochemistry. Here, we determined the major elements, δ13C of DIC, 87Sr/86Sr, δ7Li, and δ26Mg of monthly time-series water samples at the Daojieba Hydrological Station in lower reach of the Nujiang River over a hydrological year. During high-flow seasons, the dissolved 87Sr/86Sr, δ7Li, and δ26Mg exhibit comparatively high values. 87Sr/86Sr ratio, together with δ13C of DIC, Si/Ca ratio, and pH value of river water, indicate an enhanced silicate weathering contribution to river dissolved load, and river δ7Li is impacted by secondary mineral formation-related fractionation during these periods. Similar to the 87Sr/86Sr ratio, δ26Mg of river water is indicative of a dominant source control. The positive relationship between Mg/Na ratio and δ26Mg value suggests that the prior weathering of Mg-rich silicate minerals could be important for elevated δ26Mg and Mg/Na molar ratio during high-flow seasons, while the Mg isotope fractionation signal can be seen when runoff increases dramatically. During low-flow seasons, river 87Sr/86Sr and δ26Mg values evolve to a carbonate weathering signal, whereas river δ7Li reflects a more congruent silicate weathering, and these changes point to the obvious influence of geothermal water in these periods. The covariations of δ7Li, δ26Mg, and discharge are observed. Changes in runoff can significantly impact the isotopic composition of Li and Mg by changing the hydrological flow path, in which dominant reactions of mineral dissolution and secondary mineral formation vary. This study highlights the hydrological control on Li and Mg isotopic compositions of the Nujiang River water, which could be considered the dominant control of monsoon climate on weathering processes in high-relief river basins at a short-term scale. [ABSTRACT FROM AUTHOR]

• Incorporating meta -learning into LSTM model reduces the overall PBIAS by 8.68%, improving the overall KGE by 7% compared to standard LSTM model. • Meta-LSTM model improves extreme runoff prediction, enhancing FLV by 2.73% and FHV by 11.04%, aiding in better flood management strategies. • The proposed meta -learning framework enhances performance across various deep learning models, demonstrating its versatility in runoff prediction tasks. In the field of hydrology, deep learning has become a prevalent tool for runoff simulation. However, the limitations stem from their primary focus on normality, which fails to accurately capture rare events. This study introduces a novel Long Short-Term Memory (LSTM) model, termed Meta-LSTM, which is based on the Model-Agnostic Meta-Learning (MAML) framework. The Meta-LSTM model is capable of dynamically fine-tuning its parameters to ensure effective adaptation to various runoff scenarios. From this Meta-LSTM model, the Meta-enhanced paradigm is subsequently proposed, ensuring effective adaptation to diverse models and datasets. We conducted experiments with various models on the CAMELS and CAMELS-AUS datasets. Compared to conventional deep learning models for runoff simulation, Meta-enhanced models exhibit substantial improvements. Specifically, the Meta-LSTM model reduces the PBIAS from −13.86 % to −5.18 %, increasing the Kling-Gupta Efficiency (KGE) by 7 % on the CAMELS dataset. For the Meta-GR4J-CNN model, the PBIAS decreases by 21 %, and the Nash-Sutcliffe Efficiency (NSE) increases by 2 % on the CAMELS-AUS dataset. This demonstrates the ability of the Meta-enhanced model to more accurately represent observed data without significant additional time costs. Our approach overcomes the problem of uniform training in simulations of complex scenarios and promises to significantly improve the accuracy of future hydrologic studies. [ABSTRACT FROM AUTHOR]

• Hydrological mixing ratios vary with source, season, and catchment characteristics. • Flux-weighted source isotopic apportionment may overlook reservoir intermixing. • Contiguous, hydrometeorlogically constrained, source isotopic apportionment is key. Hydrological mixing models rely on assumptions governing the rate and timing of mixing proportions, the degree to which isotope ratios conserve the mixing ratios and the estimation of the prior behaviour of meteoric reservoirs. However, these models only describe the integrated catchment response based on different sources having unique and constant isotopic compositions as opposed to location-specific information. Erroneous projection of the hydrological budget can have severe repercussions on climate and eco-hydrological studies, estimation of freshwater availability, and civil and engineering work. Consequently, we design a real-time, multi-parameter, hydrometeorological-constrained, Bayesian-mixing model that can relate meteoric reservoirs to streamflow, and temporal and spatial behaviour of flow paths. Developed for pan-Himalayan catchments (up to 5200 m), with four end-members and pre/post-monsoon seasonality, the isotopic analysis reveals distinct δ18O values: stream water (–9 ± 2.6 ‰), snowpack (–10.7 ± 8.1 ‰), lake water (–12.2 ± 2.5 ‰), and groundwater (–8.4 ± 1.6 ‰). Initial results based on the isotope-enabled, flux-weighted mixing didn't fully capture spatiotemporal isotopic heterogeneity, affecting hydrograph separation. A revised model, considering the transit time of meteoric reservoirs before their mixing with river discharge, was developed. The revised model illustrates that isotope-enabled mixing models must also consider the hydrometeorological properties of individual reservoirs, the high probability of intermixing between the various reservoirs, and the variable transit time of different meteoric waters in a catchment. With implications for isotope-enabled hydrology, this work describes a novel method applicable to high-mountain hydrology, separating them from other montane or lowland geographies. [ABSTRACT FROM AUTHOR]

• Permeable pavement (PP) hydrologic performance varies widely. • Long-term model needed to understand how design affects the PP water balance. • DRAINMOD, an agricultural drainage model, was tested herein for PP modeling. • Model calibrated to field-collected hydrologic data from four PP systems. • Model effective for long-term modeling of PP hydrology, not single storm events. The hydrologic performance of permeable pavement varies widely due to underlying soil type, drainage configuration, contributing drainage area, surface infiltration rate, and aggregate depth. A long-term hydrologic model is needed to better understand the influence of these design variables on surface runoff, drainage, exfiltration, and evaporation from permeable pavement systems. Most permeable pavement models have not been calibrated with field monitored data, are usually unable to accurately model internal water storage (IWS) zones, and do not account for evaporation from the aggregate profile. Because permeable pavement employs drainage and exfiltration as primary hydrologic mechanisms, it was hypothesized that DRAINMOD, a model shown to accurately simulate bioretention hydrology, could be calibrated to predict the hydrologic response from permeable pavements. Hydrologic data were collected from two permeable pavement applications in North Carolina (Boone and Durham) and two permeable pavement applications in Ohio (Perkins Township and Willoughby Hills) to calibrate and test the model. The specific permeable pavements applications varied widely in aggregate depth, drainage configuration, underlying soil type, and contributing drainage area. Nash-Sutcliffe Efficiencies for drainage ranged from 0.77 to 0.95 during calibration and validation of all sites. Nash-Sutcliffe Efficiencies for exfiltration/evaporation ranged from 0.55 to 0.97 for Boone and Perkins Township, but prediction of exfiltration/evaporation volumes on an event basis was poor for the remaining two sites due to low exfiltration rates (less than 0.20 mm/hr). Despite this, the cumulative volume of exfiltration/evaporation was predicted to within 1–9 % of what was measured during monitoring. Modeled and monitored surface runoff from the Willoughby Hills application (8 % of the water balance) was equivalent. The model predicted the percentage of drainage, surface runoff, and exfiltration/evaporation to within 2 % of what was monitored/estimated at each site, suggesting long-term modeling of permeable pavement hydrology with DRAINMOD is viable. [ABSTRACT FROM AUTHOR]

• Anthropogenic water regulation (AWR) has incorporated into the CIESM model. • The worldwide TWS simulation results improve with AWR incorporation. • The impact of AWR on regional hydroclimate change is significant. This study examines the impact of anthropogenic water regulation (AWR) on hydroclimatic systems by incorporating an AWR module into the Community Integrated Earth System Model (CIESM), validated against GRACE satellite data. This approach assesses the influence of human activities, including irrigation and groundwater extraction, on global and regional hydrology and climate. Key findings include: Implementation of the AWR module significantly improves terrestrial water storage (TWS) simulation accuracy in CIESM. The correlation coefficient for global-scale TWS improved from 0.33 in the control (CTRL) to 0.89 in the experiment (EXP). Notably, the model's performance enhanced in Northern India and the North China Plain, while the Central United States showed limited improvement. AWR markedly alters the global water cycle, evidenced by substantial increases in soil moisture (about 0.04 to 0.02 m3/m3) and evapotranspiration. These changes have led to increased latent heat flux (around 5 W/m2) and a decrease in temperature by 0.1 °C to 1 °C in heavily irrigated regions. The study highlights the role of AWR in modifying the energy balance, particularly in agricultural areas where irrigation exerts a significant cooling effect. Despite its potential, the study identifies considerable uncertainties in coupling AWR within the CIESM model, related to inherent model limitations, incomplete water intake data, variable groundwater levels, and simplifications in water transfer parameters. Future research should aim to refine these aspects, focusing on enhancing the physical mechanisms and performance of AWR modules in hydroclimatic simulations. In conclusion, this research underscores the substantial modifications in hydrological and climatic conditions due to human activities. The improved AWR scheme within CIESM provides valuable insights for understanding and predicting climate change impacts on water resources, demonstrating its utility in simulating complex hydroclimatic changes at both global and regional scales. [ABSTRACT FROM AUTHOR]

• We synthesise interdisciplinary knowledge about Australian non-perennial rivers. • Current research strengths include salinity, dryland ecology, droughts, and floods. • Lessons are relevant globally and four high-level research challenges are identified. Non-perennial rivers are valuable water resources that support millions of humans globally, as well as unique riparian ecosystems. In Australia, the Earth's driest inhabited continent, over 70% of rivers are non-perennial due to a combination of ancient landscape, dry climates, highly variable rainfall regimes, and human interventions that have altered riverine environments. Here, we review Australian non-perennial river research incorporating geomorphology, hydrology, biogeochemistry, ecology, and Indigenous knowledges. The dominant research themes in Australia were drought, floods, salinity, dryland ecology, and water management. Future research will likely follow these themes but must address emerging threats to river systems due to climate change and other anthropogenic impacts. Four high level opportunities for future research are identified, namely: (1) integrating Indigenous and western scientific knowledge; (2) quantifying climate change impacts on hydrological and biological function; (3) clarifying the meaning and measurement of "restoration" of non-perennial systems; and (4) understanding the role of groundwater. These challenges will require inter- and multi-disciplinary efforts supported by technological advances. The evolving body of knowledge about Australian rivers provides a foundation for comparison with other dryland areas globally where recognition of the importance of non-perennial rivers is expanding. [ABSTRACT FROM AUTHOR]

A bibliometric analysis was conducted to evaluate groundwater research from different perspectives in the period 1993-2012 based on the Science Citation Index-Expanded (SCIE) database. The bibliometric analysis summarizes output, categorical, geographical, and institutional patterns, as well as research hotspots in global groundwater studies. Groundwater research experienced notable growth in the past two decades. "Environmental sciences", "water resources" and "multidisciplinary geosciences" were the three major subject categories. The Journal of Hydrology published the largest number of groundwater-related publications in the surveyed period. Major author clusters and research regions are located in the United States, Western Europe, Eastern and Southern Asia, and Eastern Australia. The United States was a leading contributor to global groundwater research with the largest number of independent and collaborative papers, its dominance affirmed by housing 12 of the top 20 most active institutions reporting groundwater-related research. The US Geological Survey, the Chinese Academy of Sciences, and the USDA Agricultural Research Service were the three institutions with the largest number of groundwater-related publications. A keywords analysis revealed that groundwater quality and contamination, effective research technologies, and treatment technologies for water-quality improvement were the main research areas in the study period. Several keywords such as "arsenic", "climate change", "fluoride", "groundwater management", "hydrogeochemistry", "uncertainty", "numerical modeling", "seawater intrusion", "adsorption", "remote sensing", "land use", "USA"(as study site), and "water supply" received dramatically increased attention during the study period, possibly signaling future research trends.

• Used STICLs for the first time to study hydrologic connectivity in perennial channels. • Examined hydrologic connectivity in longitudinal, lateral, and vertical dimensions. • Examined influence of hydrology and geomorphology on variations in connectivity. • STICLs can capture temporal changes in flood inundation in perennial rivers. The concept of hydrologic connectivity has commonly been used as an indicator of river habitat health and considered in environmental flow policies and river management schemes. Field-based data loggers, used to infer flow intermittency, have been used to study the connectivity in ephemeral channels in a variety of physiographic environments; however, no previous studies have utilized and analyzed them in portions of perennial channels and floodplains that experience inundation intermittently. To address this gap, we developed an array of Stream Temperature, Intermittency, and Conductivity Loggers (STICL) and deployed them in a coastal lowland river to analyze temporal changes to hydrologic connectivity induced by surface water in the longitudinal, lateral, and vertical dimensions of the channel and the floodplain. We examine the efficacy of STICLs to monitor surface-water connectivity within the floodplain that experience intermittent inundation. We also examine influence of hydrologic regime and geomorphology on the variations in connectivity. Our results show that the electrical conductivity (EC) records from our STICLs can capture flood inundation dynamics and how these dynamics affect surface-water connectivity. Our estimates of flood inundation duration and the dynamics of surface water connectivity can be improved by better accounting for how EC can vary with temperature and by handling the uncertainty in our STICLs readings in a more explicit manner. The STICLs provide a promising tool to study river hydromorphological processes in detail. Thus, we recommend the development of formal approaches and frameworks for deploying and analyzing these types of data loggers within lowland landscapes. [ABSTRACT FROM AUTHOR]

• Pulse reserve paradigm observed across biomes over India. • Pulse reserve is distinctly visible from both X and L-band satellite data. • Spatio-temporally and seasonally varying Pulse reserve characteristics. • Pulse reserve characteristics sensitive to pulse soil moisture and EVI. • These characteristics less sensitive to clay fraction opposed to global traits. The pulse-reserve paradigm associated with the plant's increased water uptake following surface soil moisture pulses is not well understood in India. India has a very high spatio-temporal climate and ecological variability. Such ecological and climatological diversity makes the country-scale analysis of the pulse-reserve paradigm challenging. Here, we present the first analysis of this phenomenon using 9 years of space based passive microwave estimates of surface soil moisture and vegetation water content from both X-band and L-band. We observed from both datasets that the pulse-reserve paradigm is widespread in India across seasons and biomes. The seasonal climate in India drives the precipitation characteristics, thus uphold the pulse-reserve paradigm. The estimated Soil Moisture Threshold (SMT), at which the vegetation water content drops, has substantial spatio-temporal variations, unlike other regions worldwide. The comparisons between X-band and L-band products show significant differences in the magnitudes of soil moisture and vegetation optical depths due to inherent properties. However, most of the findings related to the pulse-reserve paradigm, specifically the sensitivity of processes on different factors, remain similar. We found a strong dependence of SMT on seasonal climate, vegetation greenness, and pulse soil moisture. The association of SMT with soil characteristics, such as clay fraction, is weak in India, contrary to global traits. Our analysis provides new insights into the plant water uptake mechanisms in India to improve the parameterization schemes for better representation of the soil–plant continuum structural and functional behavior. [ABSTRACT FROM AUTHOR]

• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [ABSTRACT FROM AUTHOR]

• Critical rainfall for recharging flat sandy lands is 15 mm in hyper-arid desert. • Lateral gravity water flow and surface flow on sandy slopes were identified. • Water flow on sandy slope improves the recharge rate of deep soil from rainfall. • Local rainfall has the capacity to sustain the development of Badain Jaran Lake. • Extreme rainfall does not play a decisive role in the sustaining of desert lake. To elucidate the slope hydrology and their influence on the regional water balance and desert lake development, water migration and soil water conditions were studied in the megadunes of Badain Jaran Desert. Simulated rainfall of 15–60 mm with infiltration depths of ∼ 0.3–0.5 m, the lateral water migration ratios of ∼ 52.9 %-86.2 % and the differential distribution of tracers with depth in the flat sand lands indicate that 15 mm rainfall is the critical rainfall for effective recharge of deep soil water and that there is a shift from piston flow to preferential flow of water during infiltration. Soil micro-bedding and 4.2 % soil gravity water from locally wet surface soils and 6 mm of natural rainfall resulted in lateral migration of the tracer ∼ 1.2 m and 0.4 m in the slope surface and soil respectively, indicate surface flow and lateral gravity water flow resulting from differences in hydraulic conductivity and water-holding capacity of the soil micro-bedding. Soil water storage at 0–4 m depth showed that the bottom of the slope was ∼ 30 mm higher than the middle of the slope after the rainy season and ∼ 9 mm higher before the rainy season; in general the post-rainy season was ∼ 30–40 mm higher than the pre-rainy season. Surface flow and lateral gravity water flow in unsaturated soils increase the intensity of lateral water movement on the slope and improve the effective conversion rate of rainfall to deep soil water, especially below 15 mm. Annual recharge of deep soil water, assessed using soil water change and critical rainfall, will not be less than 8.5 mm and may even reach an upper limit of ∼ 30–40 mm. The megadune slopes respond strongly to local precipitation, which is sufficient to maintain the lake, but extreme precipitation does not play a decisive role. The methods and results provided in this work can advance our understanding of the desert hydrological cycle and water resources. [ABSTRACT FROM AUTHOR]

• The influence of human activities on river bed scour is discussed. • The influences of human activities on bifurcation relationship of branch and morphology of bar are revealed. • The relationship between the Yangtze River and major rivers in the world is compared and analyzed. Reservoirs and river management projects have operational impacts on the runoff, sediments, and morphologies of tidal reaches. Therefore, it is of great significance to analyze the influence of human activities on the hydrology and geomorphology of the tidal reaches of the Yangtze River (YR). We measured topographic and water–sediment flux over the past 40 and 60 years, respectively, to determine changes in the characteristics of riverbed lowering or deposition, shoal areas, and diversion ratio of branch channel (DRBC), along with the dynamics and driving mechanisms. The results indicated that cumulative scouring of the tidal reaches occurred from 1981 to 2021 (time-averaged annual riverbed lowering depth = 4.35 cm), which was mainly controlled by reductions in sediment influxes, followed by sand mining and waterway dredging. Riverbed lowering or deposition in the tidal reaches were characterized by deep channel lowering and shoal deposition from 1981 to 2001. From 2001 to 2021, lowering of both deep channel and shoal occurred, with the riverbed lowering volume of the low-flow channel accounting for 79.3 % of the total riverbed lowering volume. The shoal areas exhibited an increasing trend from 1981 to 2001, but reduced by 2.35 % in the 2001–2021 period mainly due to the operation of Three Gorges Reservoir (TGR). Moreover, the implementation of waterway improvement projects slightly increased the shoal areas since 2016. Particularly, the DRBC accounted for 15–50 % of the increase in bifurcation ratio during the 1962–1981 periods. Reductions in sediment influxes and unbalanced riverbed lowering caused by adjustments of flow processes increased the DRBC from 1981 until prior to the implementation of the river and waterway improvement projects. After the implementation, further increases in the DRBC were effectively inhibited or the ratio was decreased. In the near future, the amounts of sediment influxes in the YR Basin will remain low or even further decrease, leading to sustained or intensified scouring of the tidal reaches, and the impacts of shrinking shoal and scouring of branch channels should be focused upon. Overall, the findings of this study provide an in-depth understanding of the impacts of human activities on the riverbed morphologies of tidal reaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nitrification remains a key process throughout the year in both tributaries. • Ammonium uptake velocity in the NS is double WS's (8 m/yr) during low-flow periods. • Rainstorms trigger particle-associated and sedimentary denitrification. • Inhibitory effect of NH 4 + contributes to NO 3 − export to the adjacent Taiwan Strait. • Main N inputs are from sewage (NH 4 +), soil (NO 3 −) & shift from sewage to soil (PN). The sources and transformations of various nitrogen (N) species in rivers under the influence of anthropogenic inputs are the main concern of eutrophication research, especially in intermediate- and small-sized rivers, and are essential to understand for practical management. A key challenge lies in determining how N species can be altered by diverse biogeochemical processes along flow paths under different hydrological controls (e.g. river discharge). To better understand the response of in-stream N dynamics, we examined N transformation processes and identified the sources of various N species in the Jiulong River (JLR), a eutrophic river in China composed of two main tributaries: the North Stream (NS) and the West Stream (WS), using multiple isotopes (δ15N NO3 , δ18O NO3 , δ15N NO2 , δ15N NH4 , δ15N PN , δD H2 O and δ18O H2O). Our results reveal that hydrological and biogeochemical processes exert a joint control on river N dynamics. Key processes, such as nitrification and ammonium (NH 4 +) uptake, predominated in both tributaries during non-rainfall periods, while during rain events, nitrification remained significant. However, denitrification occurred differently in the NS (mainly on particles) and the WS (primarily in sediment), and was influenced by factors such as sediment characteristics and water flow patterns. We also observed differences in the uptake velocity of denitrification and NH 4 + between the two tributaries, highlighting the variable importance of these two processes. The main sources of N species varied based on hydrological conditions, with different contributions in the NS and WS. During non-rainfall periods, point sources significantly controlled N species compositions in both tributaries, with manure & sewage contributing 46 % and 42 % of the NH 4 +, 30 % and 50 % of the nitrate (NO 3 −), and sewage accounting for 62 % and 56 % of the particulate N (PN) in the NS and WS, respectively. During rainstorms, non-point sources became the predominant contributors of NO 3 − in both tributaries (soil organic N: NS = 48 %, WS = 38 %; chemical fertilizers: NS = 29 %, WS = 26 %) and PN (soil: NS = 52 %, WS = 57 %). The insights revealed using multiple isotopes serve as a foundational basis for advanced modeling and scenario development when managing subtropical rivers under complex human interferences and climate change. [ABSTRACT FROM AUTHOR]

• ML & POCS were used to reconstruct GRACE-like terrestrial water storage anomalies. • Reconstruction model results were assessed in 3 Canadian river basins. • Glacier mass was the most influential variable in the MLAs within Canada. Terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) have become indispensable for hydrological monitoring since the first mission launched in 2002. Evaluation of basin dynamics is enabled through TWSA integration with independent hydrological and climate datasets such as precipitation, evapotranspiration, and surface runoff. With over 20 years of observations, long-term statistical analysis reveals water storage trends, provided the 11-month gap between the two missions is filled. This study compares four methods for reconstructing GRACE-like TWSA over Canada, namely: extreme gradient boosting, artificial neural networks, automated machine learning, and projection onto convex sets. The GRACE mascon product released by the Jet Propulsion Laboratory is used over the study region. Nine sets of hydrological and climate parameters are used as predictors for the machine learning models, namely GRACE average seasonal signals, TWSA from the GLDAS Catchment Land Surface Model, precipitation, air temperature, glacier surface mass balance, ocean tides, the North Atlantic Oscillation, the Multivariate ENSO Index, and sea surface temperature. For each of the four methods, 20 % of the GRACE TWSA data (the 'testing data') is omitted from the model training, and the model is used to fill the artificial gap. Root mean square error (RMSE) is computed using the difference between testing data and model predictions. Normalized RMSE expresses the RMSE as a proportion of the range of the GRACE TWSA data. The automated machine learning algorithm performs best in terms of mean normalized root mean square error, with 6 % (2.4 cm equivalent water height RMSE), followed by projection onto convex sets, extreme gradient boosting, and artificial neural networks. Inclusion of glacier surface mass balance models derived from the GMAO Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis and the Randolph Glacier Inventory improved the automated machine learning average root mean square error by 70 %. Results indicate that filling the gap between missions allows for more comprehensive analysis of terrestrial water storage changes, including basin-by-basin analysis, which is ongoing. [ABSTRACT FROM AUTHOR]

• Surface-groundwater interactions in hard rock, water-limited environment in Spain. • GSFLOW model (7.6 ha) at 5x5m grid applying hydrological terrain-units (hydrotopes) • 20-year, daily model calibration using heads, 4 soil moisture profiles and MODIS. ET • Hydrotope-dependent net recharge largely influenced by rainfall interception loss. • Tree hydrotopes exhibited larger water losses than grassland hydrotopes. Hard rock systems are typically characterized by undulated landscapes, short flow path, dense drainage network, and low aquifer storage. If located in water-limited environments (WLE), then typically have variable rainfall characteristics, frequent floods and droughts, and savannah type of vegetation capable to uptake groundwater. In such environments, surface–groundwater interactions are of primary importance. In this study, surface–groundwater interactions are analyzed focusing on spatiotemporal net recharge dependence on hydrotopes, where a hydrotope is conceptualized as spatial unit (or object), which at a given scale of assessment can be assumed as internally homogeneous (e.g. tree canopy or rock outcrop), but hydro(geo)logically different than the adjacent area. The study was carried out in the small (7.6 ha) Trabadillo study area (TSA) in Spain, representative for hard rocks of WLE, using 3D-GSFLOW integrated hydrological model (IHM) at very fine grid (5x5m), extracted from coarser grid (100x100m) of Sardon 3D-GSFLOW model (Hassan et al., 2014 , Journal of Hydrology 517, 390–410). The IHM was calibrated using: (i) 20-year head observation; (ii) 8-year soil moisture observations at 4 profiles, each at different hydrotope and each at 4 different depths; and (iii) MODIS satellite earth observation of evapotranspiration. Despite applying spatially uniform rainfall as forcing, surface–groundwater interactions were highly spatially variable, primarily, because of hydrotope-differences in rainfall interception loss and secondarily, because of differences in transpiration, in subsurface evaporation and in hydrological properties of subsurface. The high temporal variability of rainfall, ranging yearly from 310 to 925 mm, resulted in high temporal variability of all water fluxes, including groundwater fluxes; e.g. gross recharge (R g) varied annually from 2 to 445 mm, net recharge (R n), from −50 mm to + 89 mm, while the 20-year mean R n was close to zero, because R g was counterbalanced by groundwater exfiltration and less by groundwater evapotranspiration. The high temporal variability of groundwater fluxes and many dry years with largely negative R n , in combination with low aquifer storage, emphasized TSA dependence on rainfall and its vulnerability to droughts. The hydrotope modeling concept applied at the very fine scale, emphasized also large R n dependence on land cover type, especially on density and species composition of trees, invisible at coarser scale modeling. As such, the proposed hydrotope modelling approach can be applied as a tool to test whether in a given environment planting (or cutting) trees will result in increase or decrease of water resources. [ABSTRACT FROM AUTHOR]

Precipitation is the key input for hydrometeorological modeling and applications. In many regions of the world, including populated areas, ground-based measurement of precipitation (whether from radar or rain gauge) is either sparse in time and space or nonexistent. Therefore, high-resolution satellite-based precipitation products are recognized as critical data sources, especially for rapidly-evolving hydrometeorological events such as flash floods which primarily occur during summer/warm seasons. As " proof of concept" , a recently proposed algorithm called Rain Estimation using Forward Adjusted-advection of Microwave Estimates (REFAME) and its variation REFAMEgeo are evaluated over the contiguous United States during summers of 2009 and 2011. Both methods are originally designed for near real-time high resolution precipitation estimation from remotely sensed data. High-resolution Q2 (ground radar) precipitation data, in conjunction with two operational near real-time satellite-based precipitation products (PERSIANN, PERSIANN-CCS) are used as evaluation reference and for comparison. The study is performed at half-hour temporal resolution and at a range of spatial resolutions (0.08-, 0.25-, 0.5-, and 1-degree latitude/longitude). The statistical analyses suggest that REFAMEgeo performs favorably among the studied products in terms of capturing both spatial coverage and intensity of precipitation at near real-time with the temporal resolution offered by geostationary satellites. With respect to volume precipitation, REFAMEgeo together with REFAME demonstrates slight overestimation of intense precipitation and underestimation of light precipitation events. Compared to REFAME, It is observed that REFAMEgeo maintains stable performance, even when the amount of accessible microwave (MW) overpasses is limited. Based on the encouraging outcome of this study which was intended as " proof of concept" , further testing for other seasons and data-rich regions is the next logical step. Upon confirmation of the relative reliability of the algorithm, it is reasonable to recommend the use of its precipitation estimates for data-sparse regions of the world. © 2012 Elsevier B.V.

GRACE (Gravity Recovery And Climate Experiment) is one of the most important satellite instrument for terrestrial hydrology and water cycle analysis. It is the unique source of information for the Total Water Storage Change (TWSC) that includes the mostly unknown groundwater storage change. Its use for many socio-economic applications is however drastically reduced by its spatial (≃ 200 km) and temporal (≃ monthly) resolutions. A statistical/physical dynamical downscaling is proposed here to obtain High Resolution (HR) 1 km and daily TWSC estimates, using auxiliary information from other satellite and modeling data (i.e. precipitation, evaporation and river network direction from topography). This scheme utilizes the Water Budget (WB) closure, an hydrological model and a set of statistical tools. The quality of the results is demonstrated over the Po river basin, a very challenging basin for GRACE observations due to its relatively small size and complex contrasting surface types. It is shown that terrestrial runoff and river discharge are well disentangled in the obtained HR TWSC fields. The new TWSC data can also show the impact of weather events such as strong precipitations. This study demonstrates how the optimal combination of multiple Earth data can help us better describe the terrestrial water cycle at higher space/time resolutions. [Display omitted] • Estimates of TWSC at 1km and daily resolution over the Po River basin. • The downscaling scheme combines GRACE data and indirect satellite observations. • The proposed scheme greatly leverages the two independent sources of information. [ABSTRACT FROM AUTHOR]

• A comprehensive overview of Deep Learning state-of-the-art architectures is discussed. • In-depth exploration of physics-guided Deep Learning and explainable AI concepts are provided. • Challenges and opportunities for DL applications in hydrology and water resources are highlighted. • Examples of DL methods for a variety of water resources applications are provided. Over the past few years, Deep Learning (DL) methods have garnered substantial recognition within the field of hydrology and water resources applications. Beginning with a discussion on fundamental concepts of DL, we discussed the state-of-the-art DL architectures such as Long-Short-Term-Memory (LSTM), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Encoder-Decoder models that have gained much attention over the recent years. The recent advancements in the DL model, such as the Attention model and Transformer Neural Network, that are designed to handle sequential time series data, are also discussed. An overview of integrating physics-based hydrological models with state-of-the-art DL models, known as Physics-Guided Deep Learning (PGDL), and its potential for improving the accuracy and interpretability of hydrological predictions are discussed. We emphasized that PGDL has the potential to enhance the physical consistency and robustness of the hydrologic predictions. We further delve into Explainable Artificial Intelligence (XAI), examining various techniques for constructing interpretable models. The objective is to empower users to comprehend and confidently trust machine learning algorithms' results (model outputs). Furthermore, we delved into the diverse applications of Deep Learning (DL) in hydrology and water resources sectors, encompassing areas such as drought and flood forecasting, remote sensing applications, water quality assessments, subsurface flow inversion problems, groundwater level prediction, and hydro-climate variable downscaling. [ABSTRACT FROM AUTHOR]

[Display omitted] • Precipitation, pumping, and tidal modules were designed to quantify GWL variation. • Transfer-function models and wavelet analysis were integrated to model 3 stresses. • Climate forcings can explain most of the GWL fluctuation in the unconfined aquifer. • Groundwater withdrawal is the dominant driver of GWLs in the confined aquifer. • The influence of tides on GWLs is minor compared to the other two stresses. In coastal aquifers, dynamic stresses such as climate forcings, groundwater withdrawals, and ocean tidal fluctuations cause nonlinear responses to groundwater levels. Such responses to the stresses impact groundwater resources and related flooding and infrastructure risks at multiple scales. We used time-series models such as transfer-function models and wavelet analysis to quantify the relative contribution of these stresses to groundwater-level fluctuation in wells from the unconfined and confined aquifers in an Atlantic coastal aquifer. Climate forcings, such as precipitation and temperature, explained most of the groundwater-level variation for wells in the unconfined aquifer, whereas groundwater withdrawals were the dominant driver of groundwater levels for wells in the confined aquifer. The impact of groundwater withdrawals also was detected in several wells in the unconfined aquifer. Although the influence of ocean tides on groundwater levels commonly is observed in coastal aquifers, we found that daily groundwater withdrawals can obscure the semi-diurnal coherence signal of the two series. The magnitude of groundwater-level fluctuation that could be explained solely by tides was minor compared to that explained by climate or withdrawal stresses. Transfer-function modeling showed seasonal withdrawals from wells in confined aquifers had a significant, yet heterogeneous influence on groundwater levels in coastal aquifers, which highlights climate and withdrawals as key compounding stresses in coastal hydrology. This study demonstrates the value of time-series approaches to advance characterization of groundwater systems in areas with limited hydrogeologic parameter information. [ABSTRACT FROM AUTHOR]

[Display omitted] • Catchment-scale water quality models and nitrate isotope techniques are revised. • Challenges and limitations in modeling of nitrate transport are discussed. • Ways and ideas to combine water quality models and nitrate isotopes are presented. • Incorporation of AI models in water quality studies is suggested. Models that simulate water flow and quality, particularly related to nitrate ions, are commonly used on a catchment-scale. However, tracking nitrate ions is a challenging task due to the intricate processes that affect them, such as phase exchanges, transformations, and interactions with various environmental media. In general, models capable of carrying out all tasks required to simulate water flow and quality at the same time, are rare. Additionally, most available models focus only on specific compartments of the watershed, such as surface water, topsoil, unsaturated zone, or groundwater. Taken together, these two challenges can lead to oversimplified representations of a system's hydrology, as catchment internal processes become neglected due to missing information (lack of informative measurements, or models not focusing on all watershed compartments). Attempting to combine these models or to couple different watershed compartments results in complex calculations, increased run times, and a large number of parameters to estimate. Artificial Intelligence (AI) models have been massively used in environmental studies but, so far, the majority of them have been tested theoretically and not under real conditions. To overcome these challenges, stable isotope data are often employed to calibrate and validate internal catchment processes of these models. While water stable isotopes (δ18O and δ2H of H 2 O) have been extensively used in many water flow models, the use of nitrate isotopes (δ15N and δ18O of NO 3 –) in water quality models remains poorly explored. Nitrate isotopes can help trace the origin of NO 3 – contamination and disentangle the complex reactions and dynamics that nitrate undergoes during transport. Hence, we propose that incorporating nitrate isotopes into catchment-scale water flow and quality models can substantially enhance the accuracy of these models. This review provides an overview of the current use of catchment hydrological models in predicting flow and fate of solutes. We discuss their limitations and highlight the potential of combining these models with nitrate isotopes. Ultimately, this approach may reduce prediction uncertainties and provide more effective guidance for water management decisions. [ABSTRACT FROM AUTHOR]

• Metric clustering was effective in identifying river sections with similar hydrology. • Results revealed four hydrological zones from highly intermittent to highly persistent. • Hydrological zones aligned well with a pre-existing groundwater discharge map. • The most persistent and connected zones were located in the mid-section of the lower Fitzroy river. As the human population grows and the demand for freshwater intensifies, river systems previously overlooked for water production are increasingly being earmarked for development. Many of these rivers are intermittent or in remote locations, and most are insufficiently instrumented (i.e., few to no gauging stations) and poorly understood, even though sustainable water resource development and the effective management of these systems relies on a thorough understanding of their hydrology. Remote sensing of surface water has been posed as a viable method for describing the morphology, resilience and fragmentation of river hydrology, much like metrics generated from discharge data. The low cost and increasing frequency and quality of spectral images means that this approach has a great potential to characterise hydrology at fine scales across large areas. However, the approach remains largely untested with previous research only examining small river sections. This study aimed to characterise a hydrologically diverse 400 km reach of an intermittent lowland river using newly developed, spatially derived, ecohydrological metrics. We used multidimensional clustering to identify river sections with similar hydrology, examined their spatial arrangement along the river and compared their characteristics with discharge data from four gauging stations. Metric clustering revealed four hydrological types (zones) that spanned a continuum from highly intermittent to highly persistent. Spatially, zones were at the scale of pool-run/riffle geomorphic units that alternated along the study area. Density maps of hydrological zones revealed that the mid-section of the study reach had higher persistence and longitudinal connectivity, a finding that aligned with a pre-existing groundwater discharge map generated from field sampling of environmental tracers. Limited gauging station coverage constrained comparisons, but available stations in Persistent or Refuge zones exhibited similar hydrograph responses. The ability to map hydrology continuously along the length of a river is a significant advance compared to a gauged approach, which can only classify hydrology at a single point (i.e., the gauge). Continuous classification increases our ability to describe spatial patterns in hydrology, which could markedly affect how a river is managed. [ABSTRACT FROM AUTHOR]

Recent years have seen rapid progress in the adoption of fully convolutional neural networks (FCN) to classify optical satellite imagery, made possible by a combination of new FCN architectures, next-generation GPUs, and publicly available satellite imagery from, e.g., the Landsat and Sentinel missions. These satellites offer repeat global coverage at intervals of only a few days at a spatial resolution of ≥ 10 m, which is sufficient for some but not all applications of interest. A smaller body of literature considers similar tools to classify commercial satellite imagery that offer 1 – 2 orders of magnitude higher spatial resolutions but with limited spatial and temporal coverage. In this work, we develop a super-resolution FCN to achieve the best of both worlds: land cover classification at commercial-level spatial resolutions but with the spatiotemporal coverage of public satellite imagery. To do so, we label 1 – 2 m resolution commercial imagery and use this as training data for super-resolution FCN. As a specific application, we focus on the segmentation of rivers, with the goal of tracking changes in reach-averaged river widths, depths, and discharges over time. We present detailed performance analyses and demonstrate that, surprisingly, we achieve ≳ 90% classification accuracy at meter-scale resolutions from free Sentinel-2 imagery. We extensively validate our model through in situ USGS gage data and ground-truth GPS tracing of river shorelines. By making our super-resolution FCN codes and training weights publicly available, we hope that these tools will be of use to the broader hydrology community and beyond, as the models can be re-trained for other segmentation tasks. • Super-resolution fully convolutional neural networks for water classification. • SR-FCN produce 2-m resolution water masks from 10-m Sentinel-2 imagery. • River masks match those from commercial imagery with ∼ 90 % accuracy. • Model predictions are validated by USGS gage data and ground-truth GNSS tracking. • 2-m resolution classification of land-cover with global and 5-day repeat coverage. [ABSTRACT FROM AUTHOR]

• We discuss the discomforts of hydrologists in their dialogue with society. • We propose 4 ways to better align scientific stance, research practices & discourse. • This helps to refocus research practices and engage in and with society. • Socio-hydrology allows a collective, interdisciplinary reflexive process. • This is potentially a way to integrate socio-hydrology in society. Although there is a deep historical relationship between hydrology and society, the relationship has considerably evolved in the last three decades. Hydrologists, in particular those involved in designing of decision-support tools, are experiencing a widening gap between an academic discipline which has progressively moved away from field-based applied natural science to computational hydrology, and the multiplication of stakeholders involved in the water-related issues addressed by research. The challenge for hydrology is now to negotiate this shift and to rethink its engagement in society. This paper provides a description of a planned process designed to improve hydrology-society interactions and to foster reflexivity in socio-hydrology. Based on an interdisciplinary reflexive process undertaken in Tunisia from 2016 to 2020, we identified three types of discomforts in the dialogue with society, inviting scientists to lucidly engage with these discomforts. We formulated four key reflexive propositions to achieve a better alignment of scientific stance, research practices and discourse. The first proposition concerns the need to explain more clearly the value systems scientists engage in and with society. The second concerns the need to position hydrology in society and not outside it, by reconsidering the functions that research fulfils in society. The third is an invitation to redefine the perimeter of the research interlocutors and the way to reach them. The fourth is to revisit scientific practices to build on the strengths of the dialogue between field-based natural science and computational hydrology. The paper concludes that adopting a reflexive posture towards these four dimensions of the dialogue between hydrology and society is an effective way to overcome discomforts and to refocus research stance, practices and discourse. It is a way to renew hydrology's place in society and to contribute to the current thinking in socio-hydrology initiated by hydrologists. [ABSTRACT FROM AUTHOR]

• Continuous simulation for regionalization. • Large sample dataset with high differences in terms of catchment properties. • Several alternative objective functions for calibration purposes and regionalization strategy were tested. Flood frequency analysis lies at the core of hydrology and water engineering as one of the most required estimates for water planning and design of hydraulic structures. For ungauged basins, where no information is available, various flood regionalisation techniques have varying degrees of complexity and resulting performance, depending on the study's goal, the region analysed, and the information available. This study evaluates the use of hydrological models for flood regionalisation in Chile, using 1) A large sample dataset of 101 catchments; 2) the continuous simulation approach with the GR4J model; 3) the leave-one-out strategy for performance testing; and, 4) two regionalisation methods: Nearest Neighbour (NN) and Physical Similarity (PS), together with several alternative objective functions for calibration purposes and regionalisation strategy (in all cases adopting a single criterion, single variable and determinist approach for the parameter's selection). Our results showed that performance (both in calibration–validation and regionalisation) is highly variable (in terms of reproducing the runoff hydrograph and flood statistics), depending on the catchment's aridity (e.g., around 66–82% of catchments with NSE above 0 in humid regions but it severely drops to 12–44% of catchments with NSE above 0 when evaluating arid catchments). We also found that flood-specific calibration strategies produce better results for floods but poorer performance in runoff hydrograph reproduction. Finally, we highlight that our regionalisation results were in close agreement with those from one of the currently recommended methods by Chilean engineering for flood regionalisation. This is particularly promising, considering that the continuous simulation approach gives access to the complete time series and not only flood statistics. We end this manuscript by discussing several sources of uncertainty, hoping that these can be accounted for in future studies. [ABSTRACT FROM AUTHOR]

The Generalized Likelihood Uncertainty Estimation (GLUE) methodology is used for model calibration, testing and predictive uncertainty estimation in the application of the MIKE SHE hydrologic model for estimating monthly streamflow in a semi-arid shrubland (chaparral) catchment in central California. Monte Carlo simulation is used to randomly generate one thousand parameter sets for a 20-year calibration period encompassing variable climatic and wildfire conditions, from which behavioural (acceptable) MIKE SHE parameter sets are identified and 5% and 95% uncertainty bounds for monthly streamflow are calculated. This group of behavioural parameter sets is subsequently used to predict streamflow and to construct uncertainty bounds for a 12-year test period with climatic and fire characteristics different from those of the calibration period. More than two-thirds of the observations in each period fell within the corresponding uncertainty bounds, suggesting a similar level of model performance in the calibration and test periods. Prediction errors (i.e. observations falling outside the uncertainty bounds) were generally associated with large rainfall and wildfire events and are indicative of deficiencies in model structure, uncertainty in input data, and/or errors in observed streamflow. The effect of uncertainty in remote sensing-based LAI model inputs on the uncertainty associated with MIKE SHE streamflow predictions receives special attention in this work due to the fire-prone nature of the study area and the increasing use of remotely sensed LAI estimates in distributed hydrological modelling applications. Results from MIKE SHE simulations for seven LAI input scenarios (including the baseline LAI sequence used in the model calibration and testing phase of this study) indicate that differences in predictive uncertainty between scenarios are usually less than ±10%. This is evidence that the baseline LAI trajectory is generally appropriate for distributed hydrological modelling of chaparral catchments. © 2005 Elsevier B.V. All rights reserved.

The roles of cooperation and non-cooperation in the sustainable exploitation of a jointly used groundwater resource have been quantified mathematically using an analytical game-theoretic formulation. Cooperative equilibrium arises when ground-water users respect water-level constraints and consider mutual impacts, which allows them to derive economic benefits from ground-water indefinitely, that is, to achieve sustainability. This work shows that cooperative equilibrium can be obtained from the solution of a quadratic programming problem. For cooperative equilibrium to hold, however, enforcement must be effective. Otherwise, according to the commonized costs-privatized profits paradox, there is a natural tendency towards non-cooperation and non-sustainable aquifer mining, of which overdraft is a typical symptom. Non-cooperative behavior arises when at least one ground-water user neglects the externalities of his adopted ground-water pumping strategy. In this instance, water-level constraints may be violated in a relatively short time and the economic benefits from ground-water extraction fall below those obtained with cooperative aquifer use. One example illustrates the game theoretic approach of this work. © 2004 Elsevier B.V. All rights reserved.

A two-component model of fiver runoff simulation in alpine regions of Central Asia has been implemented. The first component was devoted to the estimation of daily water input. The second component converts water input to river runoff hydrograph. The method of water input simulation was based on mean daily air temperature and daily precipitation at a reference meteorological station, and on the distribution of air temperature, precipitation, and watershed area as a function of elevation. The runoff simulation method took into account the water retention capacity of the snow, the amount of refrozen melt water, and the ice melt under glacial moraines. The method of hydrograph derivation was based on single- and two-reservoir models. Calibration and validation of the river runoff simulation model were done for the Oigaing and Ala Archa alpine drainage river basins. The runoff hydrograph generation model was calibrated on the basis of daily data for dry, wet, find normal years. The two-reservoir hydrograph model produced minor improvements in prediction as compared with the single-reservoir runoff hydrograph method. Simulation errors increased with increasing annual fiver runoff and depend on snowmelt, glacier runoff, the amount of precipitation, and air temperature. The simulated river runoff was less than observed values during autumn-winter. In the Ala Archa river basin, where glacier melt is a significant component of river runoff, and in years with large glacier melt, the simulated river runoff underestimated observed values. (C) 2000 Elsevier Science B.V.

Climate-change scenarios were created from scaling factors derived from several general circulation models to assess the likely impacts of aquifer pumping on the water resources of the Edwards Balcones Fault Zone (BFZ) aquifer, Texas, one of the largest aquifer systems in the United States. Historical climatic time series in periods of extreme water shortage (1947-1959), near-average recharge (1978-1989), and above-average recharge (1975-1990) were scaled to 2 x CO2 conditions to create aquifer recharge scenarios in a warmer climate. Several pumping Scenarios were combined with 2 x CO2 climate scenarios to assess the sensitivity of water resources impacts to human-induced stresses on the Edwards BFZ aquifer. The 2 X CO2 climate-change scenarios were linked to surface hydrology and used to drive aquifer dynamics with alternative numerical simulation models calibrated to the Edwards BFZ aquifer. Aquifer simulations indicate that, given the predicted growth and water demand in the Edwards BFZ aquifer region, the aquifer's ground water resources appear threatened under 2 x CO2 climate scenarios. Our simulations indicate that 2 X CO2 climatic conditions could exacerbate negative impacts and water shortages in the Edwards BFZ aquifer even if pumping does not increase above its present average level. The historical evidence and the results of this article indicate that without proper consideration to variations in aquifer recharge and sound pumping strategies, the water resources of the Edwards BFZ aquifer could be severely impacted under a warmer climate. (C) 2000 Elsevier Science B.V.

• Preferential flow in unsaturated saprock (SPF) was revealed in forested hillslopes. • GPR, controlled infiltration, and subsurface moisture monitoring were combined. • SPF is controlled by bedding structure and gravitational force along slope gradient. • SPF laterally directs water from planar and convex hillslopes to concave hillslopes. • The 3D fill-and-spill behavior of SPF is crucial to understand hillslope hydrology. The role of preferential flow through unsaturated saprock (fractured bedrock with weathering restricted to fracture margins) in hillslope hydrology remains inadequately described. To address this issue, ground-penetrating radar (GPR), controlled infiltration, and high-frequency subsurface moisture monitoring were integrated to characterize saprock preferential flow (SPF) in the Susquehanna Shale Hills Critical Zone Observatory in Pennsylvania, U.S.A. In a planar hillslope with shallow fractured shale bedrock (starting at 0.1–0.3 m below ground), two pulses of water (79.5 L in total) were released followed by time-lapse GPR surveys. Differentiating GPR images collected before and after infiltration revealed lateral SPF in the direction of bedding plane fractures near the infiltration trench but with limited development of SPF down gradient along the hillslope. This was confirmed by soil and saprock moisture monitoring at a soil pit (0.2 m downslope of the GPR grid) where only one out of fifteen probes responded to the controlled infiltration. Lateral SPF frequently occurred at the GPR grid during a 24-day period with ten rainfall events, especially under the wet initial conditions. Additional infiltration experiments in a convex hillslope and a nearby bare slope with exposed saprock demonstrated the impact of fracture patterns on the routing of SPF. Three types of SPF in hillslope hydrology were identified, including (1) vertical percolation, (2) exfiltration from saprock to soil, and (3) stormwater transported downslope from planar and convex hillslopes to concave hillslopes. A 3D fill-and-spill model is proposed for the study site and similar areas that recognizes the importance of subsurface flow networks, with shallow saprock and concave hillslopes as essential controls of hillslope subsurface flow. [ABSTRACT FROM AUTHOR]

• JMS can be used to analyze relationship among more than two variates. • JMS method effectively proved the nonlinear relationship between soil temperature and soil moisture. • JMS method could diagrammatically illustrate the frozen soil process with soil data. Joint multifractal spectrum analysis (JMS) is an effective approach for investigation of relationship among hydrological variables and is regarded as one potential way to reveal nonlinear relationship in hydrology. In this study, the joint multifractal spectrum analysis was for the first time used to analyze soil moisture-soil temperature-precipitation (SM-ST-P) relationship. Pearson's linear correlation coefficient considering lag effects and scale effects was also applied for the aims of comparison. Soil data were collected at four sites in Tibet and three depths for each site (10 cm, 30 cm and 60 cm) at 2-hourly frequency from November 2016 to November 2017. Correlation analysis showed that the coefficients varied at every measurement point but failed to provide general and comprehensive analysis of SM-ST-P relationship. On the contrary, multifractal spectra of SM-ST-P confirmed that the relationship was strong for all measurement points. Furthermore, JMS analysis provided holistic results including verification of multifractal of SM-ST-P relationship, relationship evaluation for each part of variables separately (high value, middle value, low value), intuitive diagrammatic comparison of SM-ST and SM-P relationships, etc. Seasonal JMS analysis was adopted to investigate the freezing-thawing process in the study area as well. The multifractal spectra of frozen soil site and no frozen soil site are totally different for dry cold period but similar for rainy warm period. The difference in multifractal of SM-ST-P relationship with and without frozen soil presented the latent application of JMS to quantitatively detect the process of frozen soil. This study implied the capability of JMS analysis in hydrology, especially for multivariable relationship like SM-ST-P relationship. [ABSTRACT FROM AUTHOR]

• Integrated modeling reduces dependence on calibration using Manning's n. • Integrated model simulates flooding for different antecedent conditions accurately. • Performance across two watersheds shows the broad applicability of integrated models. Modeling and mapping of floods using one (1D) and two-dimensional (2D) models has resulted in significant progress in our understanding of river hydraulics and hydrology. The existing modeling approach can be improved further through a better understanding of the interactions between river hydrodynamics and subsurface processes. Saturated conditions in subsurface and floodplains can lead to longer and deeper inundation from low intensity but continuous flood events. To understand the combined effect of surface-subsurface hydrology and hydrodynamics on streamflow generation and subsequent inundation during floods, this study uses an integrated surface water-groundwater (SW-GW) modeling approach for simulating flood conditions. Simulation of a high flooding event for the Upper Wabash River basin in Indiana, USA, shows that the river-floodplain hydrodynamics is simulated more accurately without the need for extensive calibration by using a two-dimensional integrated SW-GW model. Additionally, an integrated model provides a more realistic simulation of flood hydrodynamics for different antecedent soil conditions. Specifically, results show that the streamflow, flood stages and inundation area obtained for the dry scenario are significantly lower when compared to the saturated scenario. These findings are further validated by applying the integrated modeling approach for an entirely different watershed (Croton River) in the state of New York. Overall, the findings from this study suggest that the current practice of simulating floods which assumes an impervious surface may not be providing realistic estimates of flood inundation, and that an integrated approach incorporating all the hydrologic and hydraulic processes in the river system must be adopted. [ABSTRACT FROM AUTHOR]

• Runoff was 40% higher and leaching 95% lower in compacted than uncompacted plots. • Soil compaction increased rainsplash and sheetwash, but not rill erosion. • Mulch and slash did not affect soil hydrology, but strongly reduced rainsplash. • Mulch cover, but not slash cover, reduced sheetwash and rill erosion. • Compaction and soil cover contact were drivers of hydrologic and erosive variables. Soil compaction during post-fire logging can increase runoff and erosion. Increasing surface cover is an effective way to reduce erosion, but this has not been tested on soils impacted by both fire and compaction. We measured the effects of compaction (bulk density of 0.9 or 1.1 g cm−3) and surface cover (0% or 60%) using bark mulch or logging slash (compacted only) on runoff, leaching and erosion. Four 0.5 m2 plots per treatment were filled with burned soil and inclined to 18°. Two 30-min simulated rainfall events with 72 mm h−1 intensity and one concentrated flow event were applied to each plot. The runoff from the dry and wet runs totaled 32 mm in the uncompacted bare plots and the value was not significantly different for the compacted bare plots, any of the mulch plots, or the compacted slash plots. Leaching was higher for the uncompacted bare plots (7 mm) than for the compacted bare plots (1 mm), and mulch or slash did not affect leaching. The uncompacted bare plots produced 38 g of rainsplash and 497 g m−2 of sheetwash, and these were about half the values for the compacted bare plots. Mulching strongly reduced the rainsplash and sheetwash for both the uncompacted (10 g and 184 g m−2, respectively) and compacted conditions (19 g and 431 g m−2, respectively), although the difference in sheetwash was only significant for the uncompacted plots. Slash also reduced rainsplash (19 g) relative to the compacted bare plots (74 g) but did not affect sheetwash. Concentrated flow produced 858 g m−2 of rilling from the uncompacted bare plots, which was greater than the compacted plots (237 g m−2). Mulching reduced rilling by 95% in the uncompacted plots and 78% in the compacted plots, but slash did not reduce rilling. The reductions in rainsplash erosion caused by the mulch and slash were attributed to the increase in surface cover, which reduced the energy imparted to the soil by the rainfall. The mulch, with its high degree of contact with the soil, also reduced particle detachment and transport by overland flow. In contrast, the slash did not have complete contact with the soil surface and did not affect sheetwash or rilling. Compaction and the type of material used to increase surface cover are important factors to consider when assessing potential impacts of logging on runoff and sediment delivery from burned soils. [ABSTRACT FROM AUTHOR]

• We list parameters assumed to control flooding in poljes. • We test them with simple models. • We combine the findings to model a real field case. • Two modelling approaches are used. • The method can be applied to other poljes. Poljes are large flat closed depressions that can be found in most karst areas around the world. They are prone to regular flooding, which occurs after abundant rainfall or snowmelt events. The floods can form several meters deep intermittent lakes that can persist for months. Human settlements around poljes have been traditionally positioned above the level of the most severe floods. However, many neighbouring villages expanded during the last decades to areas that might be endangered by large flood events. In addition, a recent study has shown that several poljes encountered floods of more severe amplitude in mid-Holocene due to a prevailing humid climate. As current knowledge of polje's hydrology appears to be still insufficient, further research is needed. This work aims to identify the factors controlling polje flooding and evaluates their impact on the flood amplitude and duration. To do so, a list of parameters potentially controlling flooding is established. The relevance of each parameter is tested by using two idealized numerical models. The first one considers exclusively the polje from the springs to the ponors, whereas the second integrates the aquifer adjacent to the polje outflow side. First results show the usefulness of simple numerical models to identify the effect of each parameter on the flood amplitude and duration. The findings are combined to simulate a flood of medium intensity and long duration that occurred on a typical polje. A comparison of the results with real data demonstrates that the aquifer draining the polje needs to be considered to understand properly the flooding dynamics. The approach presented within this work has the potential to be used in other poljes encountering similar hydrological situations, but also lays foundation for flooding modelling in other karst areas. [ABSTRACT FROM AUTHOR]

• A glacio-hydrological model for a highly glacierized Himalayan catchment. • Snow accumulation is the major driver for glacier mass balances and runoffs. • The modelled runoff from snow melt is almost three times higher than ice melt. Glacier-wide mass balances and runoffs are reconstructed over 1969–2016 for Chhota Shigri Glacier catchment (India) applying a glacio-hydrological model. The model is forced using in-situ daily air-temperature and precipitation records from the meteorological stations at Bhuntar Observatory (1092 m a.s.l.), glacier base camp (3850 m a.s.l.) and glacier side moraine (4863 m a.s.l.). The modelled mean annual mass balance is −0.30 ± 0.36 m w.e.a−1 (meter water equivalent per year), while the mean catchment-wide runoff is 1.56 ± 0.23 m w.e.a−1 over 1969–2016. Three periods are distinguished in the reconstructed mass balance and runoff series. Periods I (1969–1985) and III (2001–2016) show glacier mass wastage at rates of −0.36 and −0.50 m w.e.a−1, respectively, corresponding to catchment-wide runoffs of 1.51 and 1.65 m w.e.a−1, respectively. Conversely, period II (1986–2000) exhibits steady-state conditions with average mass balances of −0.01 m w.e.a−1, and corresponding runoff of 1.52 m w.e.a−1. The reduced ice melt (0.20 m w.e.a−1) over period II, in agreement with steady-state conditions, is compensated by the increased snow melt (1.03 m w.e.a−1), providing almost similar catchment-wide runoffs for period I and II. The increased runoff after 2000 is mainly governed by increased ice melt (0.32 m w.e.a−1) over period III. Snow accumulation in winter and summer seasons together control the glacier-wide mass balances as well as catchment-wide runoffs. Snow melt contributes the maximum to the total mean annual runoff with 63% share while glacier melt and rain contribute 17% and 20% respectively over the whole period. [ABSTRACT FROM AUTHOR]

• Remote sensing application in hydrology study under changing climate was reviewed. • The development characteristic was explained visually by CiteSpace and ArcGIS. • Networks maps were constructed to illustrate research hotspots and emerging trends. • Consistency or transferability among satellite data will continue to be focused on. Remote sensing, as a crucial method to obtain information on water environmental processes, has become a major source of data, particularly of water environment and water resources, which are sensitive to global climate change. The bibliometric analysis provided here shows the research characteristics and developments of remote sensing-based observations of water environmental processes under a changing climate from 2000 to 2018. Visualized knowledge mapping is introduced to investigate the development status, scientific collaboration, involved disciplines, research hotspots and emerging trends of this field. The breadth and depth of remote sensing application in water environmental process studies have improved significantly as the number of related publications rose at an average annual growth rate of 15.97% in the 21st century. The United States and China were the leading contributors with the largest number of publications and all of the top 15 most active institutions. In addition, this field is a highly interdisciplinary field that covers a wide range of interests, from water resources to environmental science, geology, engineering, ecology, and agriculture. The application of remote sensing technology has significantly promoted the estimation of evapotranspiration and soil moisture, thereby offering a more complete perspective to the understanding of the water cycle. Additionally, climate change and its complex interactions with water environmental processes, including the occurrence of drought events, are of great significance and require special attention. [ABSTRACT FROM AUTHOR]

• A hydrologic model was developed for a snow-dominated vernal pool. • The hydrologic model was coupled to a model of the vernal pool plant community. • Climate change scenarios were simulated with the coupled models. • Climate change scenarios had shorter inundation lengths than historical period. • Model results suggest vernal pool species declines with climate change. Vernal pools are seasonal wetlands that have a high diversity of endemic and native plant species, yet they are threatened by agricultural conversion and urban development and face threats posed by climate change resulting from altered precipitation and temperature regimes. We developed an approach to investigate the potential impacts of climate change on hydrology and vegetation communities of vernal pools by creating a mass-balance hydrologic model that is coupled to a statistical model of plant community distribution. The hydrologic and vegetative models were calibrated using field measurements from a vernal pool in northeastern California that experiences snow-dominated hydrology and is larger than vernal pools in more studied areas like Central California, but representative of other northern California vernal pools. Using downscaled data from global climate models, the coupled model suggests that warmer conditions will lead to the pool being inundated for a shorter time, but with little change in maximum depth. Reduced hydroperiods suggest possible declines in vernal pool specialist species with future climate change. The coupled model is an integrated approach for understanding the impact of altered environmental conditions on unique hydrology and plant community composition of vernal pool ecosystems, but the model approach could be improved with longer term data and by applying it at more sites to broaden the applicability of the approach and to enable better process representation. [ABSTRACT FROM AUTHOR]

• Use radar map semi-quantitatively as a pattern indicator for the surface rainfall. • Non-linear combination of radar quantile map and rain gauge observations. • Calculate the probability mass function of the displacement vector. • The displacement information is integrated in a stochastic manner. • Reduce the mismatch between radar and gauge data by moving the radar map properly. Rain gauges are the foundation in hydrology to collect rainfall data, however gauge observations alone are limited at representing the complete rainfall distribution. On the other hand, weather radar can provide a nearly complete spatial distribution of rainfall qualitatively at high temporal and spatial resolution, yet concerns about the biases in the precipitation estimates hamper the direct use of radar data in hydrological applications. Thus the focus of this study is to derive a scheme to merge radar precipitation estimates and the contemporaneous point-wise rain gauge observations. It is common practice to construct radar-gauge pairs by assuming the vertical and instant falling of the hydrometeors from aloft onto the ground. On many occasions, the assumption is invalid, as it ignores a significant fact that the hydrometeors are very likely to be laterally advected in the air by the wind while descending, especially from a large height and/or with the existence of snow. The laterally displacement of rainfall will result in a great discrepancy of radar and gauge data. Concerning these issues, a method to quantify the displacement of rainfall is proposed and the results of the quantification are integrated into much improved surface precipitation estimates. [ABSTRACT FROM AUTHOR]

• Long-term data of tritium in precipitation in Northeast Asia are reported. • Tritium concentrations were maximum in spring due to tropopause folding. • Tritium concentrations were minimum in summer due to the effect of marine air. • The fractionation of tritium was observed in precipitation. • Conversion factors of tritium concentration for different regions of East Asia are provided. Several studies have documented the characteristics of seasonal, decadal, and spatial variations of tritium in different regions including North America, Australia, and Europe. However, long-term data such as these studies were not yet reported for Northeast Asia. In this study, we document the extensive spatial-temporal monitoring data of tritium levels in precipitation measured at 16 stations in Korea over the last 20 years, including the long-term trend since 1961 by using measured and estimated data. The long-term variation of tritium concentrations in the Korean precipitation follows the global trend, reaching the maximum in 1963 owing to nuclear bomb testing, followed by a consistent level since the late 1990s. Tritium concentrations in precipitation from 1998 to 2017 were maximum in spring (April) due to tropopause folding and minimum in summer (July) due to the dominant inflows of marine air. The tritium concentrations in precipitation were lower toward the coastal stations, due to the influence of marine moisture for all seasons. Our results characterize the differences in tritium levels in groundwater recharging areas for different periods in this region. These results will serve as important tritium reference data for hydrology studies in Northeast Asia, which is an important region for understanding global hydrological cycles. [ABSTRACT FROM AUTHOR]

• Topographical changes have impacts on the onset of runoff. • The effects of topographical change can be hidden by low resolution topography. • Runoff signature variations can be explained by changes in runoff connectivity. Runoff generation from rainfall events is a complex, spatial and temporally dependent process strongly governed, among other factors, by catchment surface topography. Although it is widely known that many catchments experience morphological evolution, it is often ignored in analysis for different reasons ranging from simplification to lack of data. However, young catchments and early landscapes (such as those which are affected by natural or anthropogenic disturbances) do exhibit topography changes which in turn affect catchment hydrodynamics, hydrology and in particular runoff. In this work, we study the runoff generation and hydrodynamics of the Hühnerwasser artificial catchment (Brandenburg, Germany) during a period of erosion-based topographical changes (2006–2010). Nine Digital Elevation Models from such period were used as topography over which physically-based simulations were performed. The results suggest that topographic evolution in this catchment mostly affects the onset of runoff, whereas peak discharges and receding hydrograph limbs are less affected. These differences in hydrological signatures can be explained through the changes in the spatial distribution of runoff hydrodynamics and their impact on surface runoff connectivity. Relatively small topographical differences produce changing ponding conditions and modify flowpaths which becomes evident only through inspection of the spatial distribution of hydrodynamic variables. Moreover, the study shows that in order for simulations to be able to capture such responses, appropriate computational mesh and topographical data resolution are critical, since connectivity itself can be greatly affected by low resolution data or representation. [ABSTRACT FROM AUTHOR]

• Synoptic scale observation of water isotopes over high elevations of Western Ghats. • Study reports elevation effect on stable isotope ratio of water in the Western Ghats. • Reports role of humid tropical mountains in controlling seasonal monsoon moisture. • Traces moisture source and mixing processes along highlands of the Western Ghats. • Provides insight into processes controlling regional hydrology in the Western Ghats. • Unravels factors affecting local microclimate to seasonal climate in South India. Forest ecosystem plays a major role in controlling moisture dynamics over the continents, particularly in the humid tropics. The montane forest ecosystem in South India supports a characteristic warm tropical climate which affects the weather pattern and the monsoon system. This study focuses on better understanding of the influence of dual monsoonal rainfall on the surface water and groundwater in south-west India, and the role of the Western Ghats mountain belt in governing the water isotope characteristics (i.e., isotopic elevation, rainfall amount and continental effects) in the humid tropics of South India. This is achieved through a spatial study of stable isotope ratios of surface and subsurface water (oxygen, δ18O and hydrogen, δ2H), collected from different tropical river basins located between Kozhikode (Kerala, 10° 30′ N) and Udupi (Karnataka, 13° 30′ N), in the wettest places and highest peaks of the Western Ghats between 2011 and 2014. The results on stable isotope ratios of ground water, river water and springs show that the water from the tropical mountain belt of the Western Ghats exhibit a low elevation effect with an isotopic lapse rate of 0.09‰/100 m for δ18O up to 2050 m asl. Beyond 2050 m asl, a considerable effect of elevation with an isotopic lapse rate of 2.5‰/100 m for δ18O is observed. The water samples from the Nilgiri ranges (1950–2300 m asl) exhibited higher isotopic lapse rate of 1.5‰/100 m for δ18O unlike that of the mountains (Ezhimala, Agumbe and Chembra) close to the eastern Arabian Sea. The difference in isotopic lapse rate is mainly dependent on (i) the dominating seasonality of oceanic source moisture over the subcontinent leading to higher depletion of heavy isotopes for more inland groundwater during the winter monsoon on the eastern slopes of the Western Ghats (ii) the degree of terrestrial moisture feedback mechanisms along windward slopes of the Ghats belt (i.e., Arabian Sea coast of India) leading to relative enrichment of heavy isotopes in groundwater fed by the highly recycled vapour sources rather than depletion due to amount, elevation or continental effect, and iii) deep cooling of orographically uplifted air moisture at high elevations of inland tropical mountains. The observed isotopic elevation effect on the groundwater and surface water is unique and constrained by specific time scale and mountain ranges within the Western Ghats belt. This study provides new understanding on factors controlling hydrological budget along higher parts of the Western Ghats mountain belt in South India experiencing humid tropical climate. [ABSTRACT FROM AUTHOR]

• The efficiency of SM products to improve unmodelled effects in LSM are examined. • Assimilation of ASCAT and CCI products effectively captures the dynamics of SM. • ASCAT based SM products have high skill to improve LSM simulations. • Blended SM products are more effective to capture the daily SM variability. Realistic representation of surface states using the land surface model (LSM) is extremely challenging owing to human-induced changes and uncertainty in forcing data. In this study, we focus on two crucial objectives pertaining to hydrology namely (a) to understand the ability of different soil moisture (SM) products to improve simulation of unmodeled irrigation processes through data assimilation process, and (b) to learn the feasibility of these SM products to correct the spatial surface soil moisture artifacts caused due to error in precipitation forcing. The utility of SM products evaluated in the present study are retrieved from different satellite sensors and algorithms such as the active satellite-based the Advanced Scatterometer (ASCAT), merged SM from European Space Agency Climate Change Initiative (ESA CCI V4.2) and the latest passive microwave-based SMOS INRA-CESBIO (SMOS-IC) SM product. The results presented for three years (2010, 2011 and 2012) suggest that assimilation of ASCAT and CCI based products effectively captures the SM changes due to irrigation. Similarly, it corrects the spatial artifacts caused due to precipitation errors. However, the single sensor based products have a limitation in spatial samples per day which is critical to capture dynamic SM products over a larger area. Hence, blended products are more effective on a larger area to capture the dynamics in a more effective manner at daily temporal resolutions. [ABSTRACT FROM AUTHOR]

• Discussing of karst processes such as breakthrough time, exchange flow, preferential flow. • Modelling of evolution of karst rock in three dimensions. • State-of-the-art examples of 3D karst aquifer evolution. Limestone, dolomite, gypsum, anhydrite, and salt are rocks, which can be dissolved, either by water alone, or in the case of limestone and dolomite more efficiently by water enriched with carbon dioxide. With time, the secondary porosity in these rocks increases substantially, and flow through these soluble rocks becomes very effective and fast. The time period for a substantial increase in secondary porosity ranges from years (salt) to 10,000–100,000 years (limestone, dolomite). The temporal evolution of such a soluble rock can be described by numerical models, coupling flow, transport, and dissolution. On the fracture-scale, numerous models describe fingering and wormhole formation in soluble rocks. On the aquifer-scale, numerical models predict preferential flow patterns and a strong exchange-flow component. We present results from three-dimensional numerical models describing the evolution of a karst aquifer in its early stage. Our numerical approach, which describes the large aquifer-scale, is capable to simulate the strongly heterogeneous development of secondary permeability in the aquifer. Based on irregularities in the model (e.g. geometry, chemistry, lithology, hydrology), preferential pathways evolve under a catchment-control situation, limiting the inflow into the system through the amount of water available from a catchment. We compare qualitatively our large aquifer-scale results to small fracture-scale results and show that the fingering described by the latter models is driven by similar mechanisms as the preferential growth in the former models. [ABSTRACT FROM AUTHOR]

• A hybrid model is developed to estimate fracture karstification in quartz sandstone. • Chemical potential allows the removal of silica from the rock pores. • Diffusion-dissolution increases porosity to a point where disaggregation occurs. • The Grain Release Threshold marks an increase in the fracture enlargement rate. • Model outcomes are in agreement with field hydrogeochemisty data. A hybrid numerical, finite differences and semi-analytical/empirical model has been developed to evaluate the spatial and time scale of chemical weathering along a fracture in quartz sandstones. The model is based on the diffusion transport occurring from the bedrock mass toward the fracture walls driven by the SiO 2 concentration gradient between water in the bedrock pores (high silica content) and water flowing in the fracture (low silica content). Because of molecular diffusion, intergranular water becomes undersaturated with respect to silica. This promotes dissolution of quartz at grain boundaries and results in a porosity profile decreasing from the fracture walls toward the rock interior. Bedrock individual grains are released and gradually removed by the water flowing into the fracture when a critical value of porosity is reached (Grain Release Threshold, GRT): this process drastically increases the fracture enlargement rate. The model outcomes establish a minimum time of 90 ka to reach the GRT. This time is independent of length, aperture and dip of the fracture, but is controlled by parameters such as the initial fracture aperture, water temperature, quartz grain size, and initial bedrock porosity. It is attested that the water flowing in the fracture remains undersaturated with respect to silica over very long timescales (in the order of 105 years) and over very long flow paths. In turn, this suggests that the extremely slow reaction between quartz and water is the key-factor for the formation of subterranean karst-like features in quartz sandstone, otherwise silica saturation would be reached after short distances and deep weathering in this lithology would be prevented. Finally, the model outcomes were compared to field data from the Gran Sabana caves (Venezuela) and other quartz sandstone weathering landforms elsewhere, showing that dissolution/diffusion in the rock matrix is a reliable mechanism for the formation of these peculiar karst-like features. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological Reanalysis HR is proposed using hydrologic model and data assimilation of in-situ observations. • Unbiased large dataset of climate reanalysis is used as forcing for the hydrologic model. • Local data assimilation technique is used to improve hydrologic spatio-temporal fields. • HR is used in the Amazon to validate and study their hydrology along the 20th century. Abstract Adequate and accurate hydrological data is necessary to manage water resources, which are critical in developing countries where such information is limited. In recent years, global reanalysis datasets have been developed to provide this information in climatic fields and, more recently, in hydrologic fields. Nevertheless, these latest efforts have been limited to temporal coverage (∼30 years) and mostly simplified hydraulic scheme routing in rivers, which can be inadequate for regional and long term scale objectives. In this article, a dataset (called hydrological reanalysis across the 20th century [HRXX] in the Amazon Basin) was developed as a case study spanning back to the year 1900 through the use of: 1) a large-scale hydrologic-hydrodynamic model (MGB) forced by a long-term climatic reanalysis of rainfall (ERA-20CM) with bias removed; and 2) a data assimilation (DA) technique coupled with a localization method (LEnKF) to use several ground observations of daily discharge within a radius of influence. Several tests were assessed to find the best bias removal method, the optimal radius of influence for the localization method and the final HRXX dataset. A total of 114 hydrological ground observations of daily information were used for assimilation and validation purposes, and several statistics indexes were employed to assess their performance. Results indicate that both bias correction and the DA with localization method greatly improved the simulations. Overestimations of the peaks in the open-loop (OL) (free run) simulation, mainly in the southern and northern regions of the Amazon Basin, were removed, and recession timing in the east-central region, as seen at the Óbidos gauge station, were corrected. An average performance of ∼0.6 and ∼0.7 of the Nash–Sutcliffe and Kling–Gupta indexes was reached, even when only a few of the longest ground observations were used, which can be representative of the oldest periods (since ∼1930). To assess extreme events, the Pearson correlation coefficient was used for maximum and minimum annual water level anomaly values, reaching 0.6 and 0.7, respectively, at the Manaus gauge station, which is remarkable considering that the analysis covers approximately 110 years. Considering the results of this case study and the global coverage of rainfall datasets, this methodology can be transferred to other regions in order to better estimate and create a hydrological reanalysis that adequately represents the hydrologic and hydraulic spatio-temporal fields. [ABSTRACT FROM AUTHOR]