The PRISM Data Library (DL) is designed to optimize the display, analysis, and retrieval of multiple domains datasets. Originally created for climate data, we aggregated data from agriculture and hydrology domains, as well as non-traditional domains for the DL such as ecology, finance, power outage and space weather data. These datasets range from simple geospatial point observations, to spatially gridded data products, to high-resolution satellite measurements, to GIS representation of administrative or domain-specific geographic entities. These datasets are represented in a consistent multi-dimensional (most often spatial and temporal) framework. As a result, dimension-wise comparisons are easily enabled through selection or transformation. Gridded data can be averaged over discrete geometrical entities (e.g. Counties, Bird Conservation Regions). The DL can be used in a browser, by connecting to servers at San Diego Supercomputing Center (SDSC) over the internet. Data selection, processing, and analysis are performed by the SDSC DL servers, and the resulting images or data files are sent back to the client’s desktop. This model optimizes the use of internet bandwidth.

Runoff events play an important role for nitrate export from catchments, but the variability of nutrient export patterns between events and catchments is high and the dominant drivers remain difficult to disentangle. Here, we rigorously asses if detailed knowledge on runoff event characteristics can help to explain this variability. To this end, we conducted a long-term (1955 - 2018) event classification using hydro-meteorological data, including soil moisture, snowmelt and the temporal organization of rainfall, in six neighboring mesoscale catchments with contrasting land use types. We related these event characteristics to nitrate export patterns from high-frequency nitrate concentration monitoring (2013 - 2017) using concentration-discharge relationships. Our results show that small rainfall-induced events with dry antecedent conditions exported lowest nitrate concentrations and loads but exhibited highly variable concentration-discharge relationships. We explain this by a low fraction of active flow paths, revealing the spatial heterogeneity of nitrate sources within the catchments and by an increased impact of biogeochemical retention processes. In contrast, large rainfall or snowmelt-induced events exported highest nitrate concentrations and loads and converged to similar chemostatic export patterns across all catchments, without exhibiting source limitation. We explain these homogenous export patterns by high catchment wetness that activated a high number of flow paths. Long-term hydro-meteorological data indicated an increase of events with dry antecedent conditions in summer and decreased snow-influenced events. These trends will likely continue and lead to an increased nitrate concentration variability during low-flow seasons and to changes in the timing of largest nitrate export peaks during high-flow seasons.

Water retention in soil exhibits diverse phenomena, including suction-saturation hysteresis, non-unique air entrapment at zero suction and negative suction under partial saturations. The constancy of suction after a long rest can be broken by relatively minor mechanical or hydraulic agitations such as low-amplitude wetting cycles -- this fact is here being related to metastable states that differ from the true equilibrium. The complete suction-saturation relationships are thus being recovered using non-equilibrium Landau's hydrodynamic theory and Onsager's reciprocity principles. Equilibrium suction does not pertain to hysteresis, yet can be approached through small amplitude agitations over long duration. Conditions for rate independence are being described, while rate-dependency are also accommodated and illustrated. Finally, it is shown that the new non-equilibrium theory retains the rigorously derived equilibrium result of the effective stress of partially saturated soils.

Predicting catchment stormflow responses after tropical deforestation remains difficult. We used five-minute rainfall and storm runoff data for 30 events to calibrate the Green–Ampt (GA) and the Spatially Variable Infiltration (SVI) model and predict runoff responses for a small, degraded grassland catchment on Leyte Island (the Philippines), where infiltration-excess overland flow is considered the dominant storm runoff generating process. SVI replicated individual stormflow hydrographs better than GA, particularly for events with a small runoff response or multiple peaks. Calibrated parameter values of the SVI model (i.e., spatially averaged maximum infiltration capacity, Im and initial abstraction, F0) varied markedly between events, but exhibited significant negative linear correlations with (mid-slope) soil water content at 10 cm (SWC10) – as did the ‘catchment effective’ hydraulic conductivity (Ke) of the GA model. SWC10-based values of F0 and Im in SVI resulted in satisfactory to good predictions (NSE > 0.50) for 18 out of 26 storms for which data on SWC10 were available, but failed to reproduce the hydrographs for six events (23%) with mostly small runoff responses. Median values of field-measured near-surface Ksat (~2–3 mm h-1, depending on method) were distinctly lower than the median Im (32 mm h-1) and, to a lesser extent, Ke (~8 mm h-1), confirming previously suspected under-estimation of field-measured Ksat. Using pre-storm topsoil moisture content and 5-min rainfall intensities as the driving variables to model infiltration with SVI gave more realistic results than the classic GA approach or the comparison of rainfall intensities with field-measured Ksat.

The watershed determined by Aburrá Valley system, located in northwestern Colombia, has significant urban development and steep hills. These features, together with the typical intense storms of the region, make the watershed prone to the occurrence of flash floods during the rainy seasons, affecting vulnerable communities. We propose a hybrid observational-modeling strategy to generate 30-minute discharge forecasts in different locations of the watershed, using an operational distributed hydrological model, information from stream gauges, and weather radar-derived precipitation using a quantitative precipitation estimation (QPE) technique. The forecast methodology is triggered when any stream gauge of interest reports levels over a predefined threshold. As a first step, the model uses different rainfall scenarios for the following 30 minutes. Every 5 minutes, the model forecast is executed after updating the observed rainfall and the rainfall scenarios. The scenarios correspond to (i) a lagrangian extrapolation of the precipitation fields, (ii) to a cellular automata-based extrapolation and to (iii) the last observed rain field multiplied by a time-varying ad-hoc factor based on historical event analysis. To parametrize the hydrological model and to validate the prediction methodology, we use 173 storm events from 2013 to 2018. The methodology is evaluated using the Nash coefficient, the Klin-Gupta index, differences in time-to-peak discharge, peak-discharge differences, and total storm-event volume differences. Operationally, the forecasted streamflow corresponds to the scenario with the best historical performance, given the total amount of observed rainfall. The overall results suggest that the described approach is promising. However, there are still some cases in which the method leads to discharge underestimation. Considering the forecast uncertainty, the results show that it is possible to design flash floods alerts using this simple but robust methodology.

The frequency of major flooding events continues to increase, fueling the already growing concern in numerous fields about quantifying the inequitable distribution of flood hazards. Our previous work of overlaying high resolution flood exposure data with social vulnerability information has already begun to highlight how different communities experience varying levels of risk. However, this fails to capture the complex nature by which flooding affects interconnected infrastructure and service networks which further have an impact on an individual and community’s risk. Our goal is to quantitatively define an individual’s vulnerability to flooding, encompassing how both pluvial and fluvial inundation impacts an individual’s place of residence and disrupts their access to critical resources, including flood, gas, healthcare, and emergency services, while still considering an individual’s socioeconomic standing. With the goal of estimating household level disruption of access to critical resources in near real time, our approach relies on a multilayer network of social vulnerability, transportation infrastructure, essential resources, and emergency services. To estimate inundation in near real time, we utilize the Heigh Above Nearest Drainage (HAND) method and a topographic depression hierarchy algorithm to estimate fluvial and pluvial flooding. Using a minimum cost flow algorithm, we determine an individual’s relative cost to access resources before, during, and after a major flooding event. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This model will be useful in future descriptive and prescriptive analytical frameworks by identifying critical nodes across networks and providing actionable knowledge on at risk communities. Our model will inform agencies involved in flood management, urban planning, and emergency response on where they can best apply resources to increase the resiliency of communities and the infrastructure they rely on.

Selective water release from the deeper pools of reservoirs for energy generation alters the temperature of downstream rivers. Thermal destabilization of downstream rivers can be detrimental to riverine ecosystem by potentially disturbing the growth stages of various aquatic species. To predict this impact of planned hydropower dams worldwide, we developed, tested and implemented a framework called ‘FUture Temperatures Using River hISTory’ (FUTURIST). The framework used historical records of in-situ river temperatures from 107 dams in the U.S. to train an artificial neural network (ANN) model to predict temperature change between upstream and downstream rivers. The model was then independently validated over multiple existing hydropower dams in Southeast Asia. Application of the model over 216 planned dam sites afforded the prediction of their likely thermal impacts. Results predicted a consistent shift toward lower temperatures during summers and higher temperatures during winters. During Jun-Aug, 80% of the selected planned sites are likely to cool downstream rivers out of which 15% are expected to reduce temperatures by more than 6˚C. Reservoirs that experience strong thermal stratification tend to cool severely during warm seasons. Over the months of Dec-Feb, a relatively consistent pattern of moderate warming was observed with a likely temperature change varying between 1.0 to 4.5˚C. Such impacts, homogenized over time, raise concerns for the ecological biodiversity and native species. The presented outlook to future thermal pollution will help design sustainable hydropower expansion plans so that the upcoming dams do not face and cause the same problems identified with the existing ones.

Regionalization approaches wherein utilities in close geographic proximity cooperate to manage drought risks and co-invest in new infrastructure are increasingly necessary strategies for leveraging economies of scale to meet growing demands and navigate deeply uncertain risks. Successful regional cooperative investment and management pathways, however, must equitably balance the interests of multiple partners while navigating power relationships between regional actors. In long-term infrastructure planning contexts, this challenge is heightened by the evolving system-state dynamics, which may be fundamentally reshaped by infrastructure investment. This work introduces Equitable, Robust, Adaptive, and Stable Deeply Uncertain Pathways (DU PathwaysERAS), an exploratory modeling framework for developing regional water supply management and infrastructure investment pathways. Our framework explores equity and power relationships within cooperative pathways using multiple rival framings of robustness, each representing a competing hypothesis about how performance objectives should be prioritized. To capture the time-evolving dynamics of infrastructure pathways, DU PathwaysERAS features new tools to measure the adaptive capacity of pathway policies and evaluate time-evolving vulnerability. We demonstrate our framework on a six-utility water supply partnership seeking to develop cooperative infrastructure investment pathways in the Research Triangle, North Carolina. Our results indicate that commonly employed framings of robustness can have large and unintended adverse consequences for regional equity. Results further illustrate that regional and individual vulnerabilities are highly interdependent, emphasizing the need to craft agreements that limit counterparty risks from the actions of cooperating partners. Beyond the Research Triangle, these results are broadly applicable to cooperative water supply infrastructure investment and management globally.

IDN Penelitian ini dilakukan dengan tujuan untuk memetakan pola aliran air tanah di sekitar Kali Sumpil di wilayah Kota Malang. Lokasi penelitian ini adalah di segmen Kali Sumpil sepanjang 5,6 km yang mengalir mulai dari Kecamatan Lowokwaru hingga ke pertemuan antara Kali Sumpil dan Kali Sari di Kecamatan Blimbing, Kota Malang. Pola aliran air tanah di lokasi penelitian dipetakan berdasarkan elevasi muka air tanah yang diukur dari 43 lokasi sumur gali milik warga yang tersebar di sepanjang aliran Kali Sumpil tersebut. Elevasi muka air tanah pada sumur gali warga di lokasi penelitian berkisar antara +493,88 m dpl di bagian hulu hingga +436,70 m dpl di bagian hilir. Elevasi muka air tanah tertinggi berada pada sumur gali SG-26 yang berada di sebelah kanan aliran bagian hulu Kali Sumpil, sedangkan elevasi muka air tanah terendah berada pada sumur gali SG-25 di sebelah kiri aliran bagian hilir Kali Sumpil. Secara umum, aliran air tanah di lokasi penelitian mengalir dari arah Barat Laut menuju ke arah Tenggara bersesuaian dengan arah aliran Kali Sumpil. Hubungan antara air tanah dan air permukaan adalah air tanah mengisi air permukaan Kali Sumpil. EN The objective of this study is to mapping the groundwater flow patterns around Sumpil River in Malang City. The location of this study is in one of the segments of Sumpil River along the 5.6 km which flows from Lowokwaru District to the confluence of Sumpil River and Sari River in Blimbing District, Malang City. The groundwater flow pattern in the area of the study was mapped based on the groundwater level measured from 43 resident’s dug wells scattered along Sumpil River. The groundwater level in the area of the study ranges from +493,88 m asl in the upstream area to +436,70 m asl in the downstream area. The highest groundwater level is in the SG-26 which is located to the right of the upstream flow of the Sumpil River, while the lowest groundwater level is in the SG-25 to the left of the downstream flow of the Sumpil River. In general, groundwater flow in the area of study flows from the Northwest to the Southeast in accordance with the direction of the Sumpil River flow. The interaction between groundwater and surface water is the groundwater flows to Sumpil River (gaining stream).

Rainfall runoff and leaching are the main driving forces that nitrogen, an important non-point source (NPS) pollutant, enters streams, lakes and groundwater. Hydrological processes thus play a pivotal role in NPS pollutant transport. However, existing environmental models often use oversimplified hydrological components and do not properly account for overland flow process. To better track the pollutant transport at a watershed scale, a new model is presented by integrating nitrogen-related processes into a comprehensive hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). This new model, called DHSVM-N, features a nitrate transport process at a fine resolution, incorporates landscape connectivity, and enables proper investigations of the interactions between hydrological and biogeochemical processes. Results from the new model are compared with those based on Soil & Water Assessment Tool (SWAT). The new model is shown capable of capturing the “hot spots” and spatial distribution patterns of denitrification, reflecting the important role in which heterogeneity of the watershed characteristics plays. In addition, a set of control experiments are designed using DHSVM-N and its variant to study the respective role of hydrology and nitrate transport process in modeling the denitrification process. Our results highlight the importance of adequately representing hydrological processes in modeling denitrification. Results also manifest the importance of having a good transport model with accurate flow pathways that considers realistic landscape connectivity and topology in identifying the denitrification hot spots and in properly estimating the amount of nitrate removed by denitrification.

Water analyses typically result in numerous characteristic values. The bunch of parameters hamper temporal or spatial comparisons of different samples. In order to facilitate the evaluation and interpretation of hydrochemical data, hydrogeologists use various special diagrams. One of them is the Schoeller diagram, in which concentrations of different species are plotted on logarithmic scales. Entries of an analysis are connected and form a characteristic signature. In the Schoeller diagram, parallel or subparallel signatures indicate relatedness of waters. Here we present the idea of standardized Schoeller diagrams: different logarithmic axes are shifted with respect to each other, in order for the signature of a selectable sample to form a straight line. This standardization greatly facilitates the comparison of water samples to the chosen standard and increases the informative value of a Schoeller diagram. As to our knowledge, there is no software that could generate standardized Schoeller diagrams, we have developed a Matlab tool for this purpose. The tool is available for free and allows fast generation of standardized Schoeller diagrams with many possibilities to implement user-specific wishes. We hope that this tool will contribute to a wider use of Schoeller diagrams.

Stable water isotopologues tend to fractionate from ordinary water during evaporation processes resulting in an enrichment of the isotopic species in the soil. The fractionation process can be split into equilibrium fractionation and kinetic fractionation. Due to the complex coupled processes involved in simulating soil-water evaporation accurately, defining the kinetic fractionation correctly remains an open research area. In this work, we present a multi-phase multi-component transport model that resolves flow through both the near surface atmosphere and the soil, and models transport and fractionation of the stable water isotopologues using the numerical simulation environment DuMuX. Using this high resolution coupled model, we simulate transport and fractionation processes of stable water isotopologues in soils and the atmosphere without further parameterization of the kinetic fractionation process as is commonly done. In a series of examples, the transport and distribution of stable-water isotopologues are evaluated numerically with varied conditions and assumptions. First, an unsaturated porous medium connected to constant laminar flow conditions is introduced. The expected vertical isotope profiles in the soil as described in literature are reproduced. Further, by examining the spatial and temporal distribution of the isotopic composition, is determined the enrichment of the isotopologues in soil is linked with the different stages of the evaporation process. Building on these results, the robustness of the isotopic fractionation in our model is analysed by isolating single fractionation parameters. The effect of wind velocity and turbulent atmospheric conditions is investigated, leading to different kinetic fractionation scenarios and varied isotopic compositions in the soil.

Hydrological alterations, which can be represented by the extent of the changes in the flow patterns resulting from anthropogenic factors, can reduce aquatic biodiversity by disrupting the life cycles of organisms. However, past studies have faced difficulties in quantifying the impacts of dams and climate change, which are major drivers of hydrological alterations. Here, we aimed to evaluate and compare the hydrological alterations caused by dams and climate change throughout the Omaru River catchment, Japan, using a distributed hydrological model. First, to assess the impacts of dam and climate change independently, we performed runoff analyses using either dam discharge or future climatic data (two future periods × three representative concentration pathways; RCPs). Subsequently, we derived indicators of hydrologic alterations (IHA) to quantify changes in flow alterations by comparing them to IHA under natural conditions (i.e., without dam or climate change data). The runoff analyses showed high reproducibility throughout the study period (Nash-Sutcliffe efficiency = 0.921–0.964). We found that dams altered IHAs more than climate change. However, on a catchment-scale standpoint, climate change induced wider ranges of flow alterations, such as low flow metrics along the tributaries and uppermost main stem, suggesting a watershed-level shrinkage in important corridors of aquatic organisms by reducing upstream length and water level. We also observed that the altered flow by water withdrawals were ameliorated by the confluence of tributaries and downstream hydropower outflows. Our approach, which used a distributed hydrological model, developed a better understanding of flow alterations by dams and climate change.

The sustainable management of groundwater demands a faithful characterization of the subsurface. This, in turn, requires information which is generally not readily available. To bridge the gap between data need and availability, numerical models are often used to synthesize plausible scenarios not only from direct information but also additional, indirect data. Unfortunately, the resulting system characterizations will rarely be unique. This poses a challenge for practical parameter inference: Computational limitations often force modelers to resort to methods based on questionable assumptions of Gaussianity, which do not reproduce important facets of ambiguity such as Pareto fronts or multi-modality. In search of a remedy, an alternative could be found in Stein Variational Gradient Descent, a recent development in the field of statistics. This ensemble-based method iteratively transforms a set of arbitrary particles into samples of a potentially non-Gaussian posterior, provided the latter is sufficiently smooth. A prerequisite for this method is knowledge of the Jacobian, which is usually exceptionally expensive to evaluate. To address this issue, we propose an ensemble-based, localized approximation of the Jacobian. We demonstrate the performance of the resulting algorithm in two cases: a simple, bimodal synthetic scenario, and a complex numerical model based on a real-world, pre-alpine catchment. Promising results in both cases - even when the ensemble size is smaller than the number of parameters - suggest that Stein Variational Gradient Descent can be a valuable addition to hydrogeological parameter inference.

In their study, Dong and Ochsner (2018) used an extensive dataset of 18 cosmic-ray neutron rover surveys to assess the influence of precipitation and soil texture on mesoscale soil moisture patterns. Based on their analysis, they concluded that soil texture, represented by sand content, often exerts a stronger influence on mesoscale soil moisture variability than precipitation, represented by the antecedent precipitation index. However, we consider that Dong and Ochsner (2018) made a mistake in their calculation of volumetric soil moisture, such that their analysis on the influence of soil texture on soil moisture is not valid. This result does however not bring into question the paper’s conclusion on the influence of soil texture on mesoscale soil moisture patterns.

Hydrologic and water quality models are often used to understand and simulate non-point source nutrient inputs to receiving waterbodies afflicted by eutrophication. The most widely used hydrologic-water quality model for estimating non-point source nutrient loads from agricultural uplands is the Soil and Water Assessment Tool (SWAT). SWAT uses the QUAL-2E 1-dimensional steady-state model to simulate in-stream processes that govern the transport of nutrients through channels and rivers. However, the instream-solute transport routine within SWAT is limited in predicting phosphorus cycling and algal dynamics. In this study, we improve the in-stream module of SWAT+, a restructured version of SWAT. We apply the modified SWAT+ to the Western Lake Erie Basin to examine how improved representation of the in-stream module influences nutrient dynamics from the edge-of-field through streams and to the watershed outlet. Our source code modifications focus on improving the representation of phosphorus exchange between the stream bed and the water column. This phosphorus exchange is governed by the equilibrium phosphorus concentration (EPC), which determines whether the stream bed is a phosphorus source or sink, and a phosphorus transformation coefficient which determines the rate of P exchange. These improvements to the in-stream routine within SWAT+ will aid decision-makers in understanding the time lags and management levers needed to achieve water quality targets for large basins.

Abstract The repetitive narrative that, “Tsunami waves and receded coastal water initiated by an earthquake are closely related,” is analyzed through the sequential events that followed the earthquake; a mechanism based on the interaction of receded water with magma is suggested to explain the amplification of Tsunami wave deep beneath the ocean floor (Fig.1), where earthquake occurred under the seabed’s or in coastline; the mechanism explained how the water is amplified into steam in the magma chamber, as its volume increased 1,700 times, the transformed water encompass the tremendous force that uplifted the ocean water endowed it with such destructive force; the mechanism explained characteristics related to Tsunami wave, including relation with earthquake and volcano, receding costal water, the foams, inundation, runup, the nature of the great force of Tsunami, ideas to calculate the magnitude of Tsunami force and energy, volume of receded water, volume of tsunami wave, the repetition of its wave; these and related issues are stated; the idea is derived based on the continual flow of lava from earth’s interior and the existence of magma reservoir bellow earth’s surface in places like Yellowstone in USA and hotspot beneath Hawaii, such magma chamber when existed under seabed, if opened by crack during earthquake, can easily lead to an interaction between receded water and the magma, resulted in the suggested mechanism, all is based on logical analyses and deep thinking to attained the Tsunami Mechanism in response to 2004 and 2011 human tragedies; thus understanding this mechanism will help laying measures to counter the phenomenon, which will reflect positively in saving lives and mitigate its destructive force and reduce consequences of its impact on local societies and above all to understand its true mechanism.

Accurate forecasts of total water level (i.e., a combination of tides, surge, wave and freshwater components) is imperative for stakeholders and federal agencies to adopt strategies for potential flooding hazards in a timely-manner. In that regard, the National Water Center in partnership with several federal agencies have been providing forecast services to the United States since 2017. However, the complex interaction of dynamical forcing conditions among other factors (e.g., anthropogenic activities, land cover change, etc.) reduce the National Water Model’s (NWM) ability to provide accurate Total Water Level (TWL) prediction in Coastal Transition Zones (CTZs). In this study, we use an existing inland to coastal model coupling framework (i.e., NWM, HWRF, Delft3D-FM and ADCIRC) to analyze the influence of dynamical forcing conditions (e.g., local wind, surge and river discharge) on TWL prediction in Delaware Bay, USA. In addition, we quantify the contribution of each component in TWL for Hurricanes Isabel and Sandy based on a systematic set of scenarios generated in Delft3D-FM. It is revealed that in both hurricanes, storm surge-induced water level is the main contributor to TWL followed by astronomical tides. River discharge induced-water level is rather small compared to the other components. Analyses of spatial variation of TWL as well as temporal variation of error in prediction suggest that wind forcing plays a key role in TWL prediction followed by river discharge. Moreover, our results suggest that the wind module of Delft3D-FM greatly improves the model performance at TWL peak when compared to the other forcing.

Multiphysics urban flood models are commonly used for urban infrastructure development planning and evaluating risk due to climate change and sea level rise. However, these integrated flood models rely on several parameters that are hard to measure directly, and the resulting uncertainty in model prediction needs to be quantified, often without observable data. As a part of the Urban Flooding Open Knowledge Network (UFOKN) project, in this study we quantify parametric uncertainty in urban flood models. UFOKN incorporates flood model predictions in combination with machine learning, data and computer science, epidemiology, socioeconomics, and transportation and electrical engineering to minimize economic and human losses from future urban flooding in the United States. As a case study, we choose the Interconnected Channel and Pond Routing (ICPR) numerical model to simulate flooding in the city of Minneapolis in response to the design storms (e.g., 100-year rainfall). Through a sensitivity study, we reduce the number of uncertain model parameters to the Manning’s roughness coefficient and vertical hydraulic conductivity of soil, and construct the distributions of these parameters using open databases. We employ the multilevel Monte Carlo (MLMC) method that combines a small number of high-resolution ICPR simulations with a larger number of low-resolution simulations to reduce the computational cost of computing the key statistics of the quantities of interest describing the urban flooding. Our results show that the uncertainty in the flood predictions (as described by the coefficient of variation of the flood water depth) is distributed highly non-uniformly in the urban area with the coefficient of variation exceeding 0.5 limited to a relatively few computational elements in the ICPR model. Our results demonstrate that urban flood models such as ICPR can provide reliable flood predictions and can be used for a targeted data acquisition to further reduce the parametric uncertainty.

Understanding how streamflow and its components, baseflow and quickflow, vary spatially according to climate and landscape characteristics is fundamental for dealing with different water-related issues. Analytical formulations have been proposed to investigate their long-term behavior and additional influencing factors, suggesting that they are mainly controlled by the aridity index ( Φ). Nevertheless, these studies assume the catchment as a closed water balance system, neglecting inter-catchment groundwater flow (IGF). This simplification makes the analysis of the long-term streamflow components and their main control mechanisms challenging, given that many catchments cannot be considered as closed hydraulic entities. Here, we assessed the controls of the mean-annual streamflow components and their behavior under an open water balance assumption, using observed data of 731 Brazilian catchments with diverse hydroclimatic conditions. Our results indicate that indeed streamflow components are primarily controlled by at the mean annual timescale. The consideration of an open water-balance significantly improved the performance of the functional forms to describe streamflow components while also elucidating the assessment of other influencing factors on the streamflow behavior. Land cover, groundwater, climate seasonality and topographic attributes appeared as the main control mechanisms beyond aridity. Overall, our study provides new insights of the main control mechanism of the streamflow behavior at the mean-annual scale, while shedding light on the importance of the open water-balance assumption for model development and water resources management.

Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historic land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001-2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~ 222.7 Mm by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping better inform land and resource management decisions.

Meltwater infiltration and refreezing in snow and firn are important processes for Greenland Ice Sheet mass balance, acting to reduce meltwater runoff and mass loss. To advance understanding of meltwater retention processes in firn, we deployed vertical arrays of time-domain reflectometry sensors and thermistors to continuously monitor meltwater infiltration, refreezing, and wetting-front propagation in the upper 4 m of snow and firn over the 2016 melt season at DYE-2, Greenland. The dataset provides a detailed record of the co-development of the firn wetting and thawing fronts through the melt season. These data are used to constrain a model of firn thermodynamics and hydrology that is then used in simulations of the long-term firn evolution at DYE-2, forced by ERA5 climate reanalyses over the period 1950-2020. Summer 2016 meltwater infiltration reached a depth of 1.8 m below the surface, which is close to the modelled long-term mean at this site. Modelled meltwater infiltration increased at DYE-2 from 1990-2020, driving increases in firn density, ice content, and temperature; 10-m firn temperatures increased by 1°C per decade over this period. Modelled meltwater infiltration reached 6 to 7 m depth during extreme melt seasons in Greenland such as 2012 and 2019, causing 3-4°C increases in 10-m firn temperatures which persist for several years. A similar event occurred in 1968 in the model reconstructions. These deep infiltration events strongly impact the firn at DYE-2, and may be more influential than the background warming trend in governing meltwater retention capacity in the Greenland percolation zone.

Understanding transit times (TT) and residence times (RT) distributions of water in catchments has recently received a great deal of attention in hydrologic research since it can inform about important processes relevant to the quality of water delivered by streams and landscape resilience to anthropogenic inputs. The theory of transit time distributions (TTD) is a practical framework for understanding TT of water in natural landscapes but, due to its lumped nature, it can only hint at the possible internal processes taking place in the subsurface. While allowing for the direct observation of water movement, Electrical Resistivity Imaging (ERI) can be leveraged to better understand the internal variability of water ages within the subsurface, thus enabling the investigation of the physical processes controlling the time-variability of TTD. We estimated time variable TTD through the storage selection (SAS) framework following a traditional lumped-systems approach, based on sampling of output tracer concentrations, as well as through an ERI SAS approach based on spatially distributed images of water ages. We compared the ERI-based SAS results with the output-based estimates to discuss the viability of ERI at laboratory experiments for understanding TTD. The ERI-derived images of the internal evolution of water ages were able to elucidate the internal mechanisms driving the time-variability of ages of water being discharged by the system, which was characterized by a delayed discharge of younger water starting at the highest storage level and continuing throughout the water table recession.

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100-year events) using synoptic-scale information from climate models, which is generally represented more accurately than the local-scales, and without requiring climate models to explicitly resolve extremes. The novel approach offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.