The productivity of terrestrial vegetation is determined by a multitude of drivers between the land surface and atmosphere. Water availability is critical for vegetation productivity, but the vertical dimension of soil moisture has been largely overlooked. Here, we analyze dominant controls of global vegetation productivity represented by sun-induced fluorescence and spectral vegetation indices at the half-monthly time scale. We apply random forests to predict anomalies of vegetation productivity from a comprehensive set of hydro-meteorological variables including multi-layer soil moisture and quantify the variable importance. Dominant hydro-meteorological controls generally vary with latitudes: temperature in higher latitudes, solar radiation in lower latitudes, and soil moisture from sub-surface layers in between. We find that including vertically resolved soil moisture allows a better understanding of vegetation productivity and reveals a broader water-related control. This is found especially for semiarid regions, illustrating the global relevance of deep(er) rooting systems as an adaptation to water limitation.

Groundwater resources of the Southern High Plains/Ogallala Aquifer in Texas and New Mexico are being depleted due to groundwater mining for irrigation. Inevitably, resource depletion leads to calls for water conservation in agriculture. Conservation can take two forms: a reduction in irrigation depth, and a reduction in irrigated area, which in economics are termed the “intensive margin” and “extensive margin” of agricultural water use. In the Southern High Plains, we find different effects on water table elevation arising from these two approaches. This research presents a coupled model of landscape and groundwater hydrology. Model results indicate that a 50% reduction in irrigation water application would limit loss in irrigable area to about 1% of existing irrigable land per year. This is approximately half the rate of depletion from a ‘business as usual’ scenario. Relative benefits of each conservation approach varied: areas with a high density of irrigated land experienced greater benefits from a reduction in irrigation depth, whereas reducing irrigated acreage maintained water tables more in areas with a low density of irrigated land. This project demonstrates that strategies for irrigation water management can support conservation goals. However, model results also demonstrated that even a 50% reduction in irrigation water use –which would be politically and economically unfeasible in Texas and New Mexico – would still result in overall depletion of the regional aquifer.

On September 10, 2017, Hurricane Irma made landfall in the Florida Keys as a category 3 storm. Storm surge inundation heights in the lower Keys were in excess of 2m. In this study, we investigate the effect of the Hurricane Irma storm surge on the freshwater lens of Big Pine Key, FL using Electrical Resistivity Tomography (ERT) on three transect lines of 222 m, 250 m and 278 m length. Two transects, B1 and B3, were situated near the shoreline and crossed the lateral boundary of the previously mapped freshwater lens whereas, the third transect, B2, was inland in the interior of the lens. All transects experienced storm surge flooding from Irma. In this paper, we compare ERT imaging results of baseline data collected 6 years before Irma (November 2011) with data collected 3-4 months (November, 2017/January, 2018) and 8 months (May 2018) after the storm. The data were inverted using a difference inversion algorithm which uses the previous inversion results as a starting model. The resistivity models were then converted to salinity by applying an electrical formation factor. For the November 2017/January 2018 data, all profiles showed low resistivity/high salinity zones in the upper 2 m corresponding to saline water emplaced on top of the freshwater lens by the storm surge. The increase in salinity is most pronounced in the low elevation portions of the transects. On transects B1 and B2, the high salinity zones are mostly continuous. However, on the higher elevation sections of transect line B3, the high salinity zone is broken up and appears to be moving downward through the freshwater lens. The nearshore transects, B1 and B3 also show a greater amount of saltwater intrusion adjacent to the shoreline at depths below 5m. The May 2018 data were collected at the end of the climatological dry season but were collected immediately after 2 weeks of intense precipitation. These data show some limited recovery of the freshwater lens. This recovery is most pronounced in the lower elevation portions of the transects where standing water was observed during data collection. This suggests that both the impact of storm surge and the freshwater recovery due to precipitation are most pronounced in low elevation regions where both saline and fresh water can collect at the surface.

The Blue Nile Basin, Ethiopia, whose inter-annual variability in local precipitation has resulted in droughts and floods that lead to economic and food insecurity, is the area of interest for our NSF-PIRE project, which aims to develop novel forecast technologies to mitigate the stresses to local communities. As part of the PIRE project, a Citizen Science Initiative (PIRE CSI) was established in June 2017, a project that trains high school students in hydrologic data collection under the guidance of classroom teachers and graduate students and professors from Bahir Dar University in four watersheds of interest, located south of Lake Tana, Ethiopia. Four MSc graduate students were selected from Bahir Dar University and trained nine high school students who were nominated taking into account gender and the proximity of their schools to the watersheds. High school students are currently collecting soil moisture data using TDR, river stage measurements using optical levels and groundwater levels using shallow water level meters. The data collection is supported by an app (B-WING), developed specifically for the needs of the project. College-ready activities are being planned for the high school students, i.e. inviting them to Bahir Dar University to analyze some of the data, present their work at a workshop, and familiarize themselves with the university experience. Recently, the PIRE CSI was extended to involve local farmers as “citizen scientists”, collecting soil moisture data using low-cost, soil moisture sensors developed in-house at the University of Connecticut, that have been installed in 12 locations and two soil depths (20 cm and 40 cm). The collected data will be used for the initialization and validation of the hydrological models developed in the region. The PIRE CSI promotes the empowerment of local communities and establishes long-lasting partnerships between scientists and stakeholders. It is believed that the co-generation of knowledge may contribute to higher rates of forecast adaption by the local farmers and may trigger the student’s interest in STEM and encourage their uptake of scientific careers. Acknowledgment: This material is based upon work supported by the National Science Foundation under Grant No. 1545874.

Harmful algal blooms (HAB; Karenia brevis) occurrences have been reported from the coastal waters of Charlotte County in southwest Florida. We developed multivariate regression models that relate reported (January 2010 to October 2017) bloom occurrences to observations extracted from archival remote sensing data (Moderate Resolution Imaging Spectroradiometer [MODIS]) to accomplish the following: (1) identify factors controlling HAB propagation, (2) predict algal bloom distribution (same day, and 1, 2, and 3 days in advance), and (3) develop fully automated system for data distribution via a web-based GIS platform. These tasks were accomplished through three main steps: (1) automatic downloading and processing of daily MODIS products using SeaDAS software to extract relevant remote sensing variables (euphotic depth, wind direction, ocean chlorophyll three-band algorithm for MODIS [Chlorophyll a OC3M], wind speed, chlorophyll a Generalized Inherent Optical Property [GIOP], Fluorescence Line Height [Flh], diffused attenuation coefficient for downwelling irradiance at 490 nanometer [Kd_490], chlorophyll a Garver-Siegel- Maritorena [GSM], Turbidity index, Particulate backscattering coefficient at 547 nm [bbp_547_giop] and sea surface temperature [SST]), (2) development and calibration of multivariate regression models using relevant remote sensing and static variable (distance from river mouth, bathymetry) inputs for same day mapping and forecasting of HAB occurrences, and (3) automated posting of model outputs on a web-based GIS (http://mgs.geology.wmich.edu/bloom/). Findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature [SST], chlorophyll a [OC3M] and distance from the river mouth, and (2) the model predictions were successful at 90% for same day mapping and 65%, 72% and 71% for the one, two and three days in advance predictions, respectively.

Fracture dissolution in carbonate rocks is of great interest for the applications of CO2 geological storage and formation of conduits and caves in karst reservoirs. Taking into account the fracture roughness and interporosity fluid exchange between the fracture and the porous host rock, the classical cubic law for parallel-plate channels or Poiseuille's flow for tubes cannot describe the flow within the fracture's opening. The Reynolds number increases along the fracture as a result of the influx crossing the fracture walls. The wavy, irregular, nonparallel-plate shape of the boundaries affects the overall flow regime and the average flow model. The velocity field on the fracture boundaries possesses a slip and a normal component. The nonzero fluid velocity maintains the concentration gradient near the porous host rock and provides a fresh source of the solvent that facilitates dissolution. The aim of this work is to point out the role of fracture roughness and the influx of fluid from the porous host rock on fracture dissolution. The effective model of flow in a single fracture with permeable wavy walls is coupled to transport of dissolved calcite. The asymptotic solutions of the steady-state Navier-Stokes equations with slip boundary condition are used to determine the velocity field in the fracture opening. Two cases of axisymmetric and parallel-plate wavy fractures are considered. The inflow through the walls increases the Reynolds number along the fracture and results in local flow instabilities and formation of reverse flow. The local instabilities arise in relatively higher Reynolds numbers in parallel-plate wavy fractures than in cylindrical wavy fractures. The averaged pressure drop along the fracture is represented as quadratic and cubic corrections to the linear law. The corrections result from the effect of the inflow through the walls and the irregular geometry of channel. Asymptotic solutions to the reactive transport of the dissolved calcite in the acidified brine are derived for rate-limited reactions with a low Damkohler number and high Peclet number. The role of the fracture's walls corrugations, fractures aspect ratio, porous host rock permeability, and the interporosity fluid exchange between the fracture and host rock on the fracture dissolution is investigated.

Uncertainty estimation is an important part of practical hydrogeology. With most of the subsurface unobservable, attempts at system characterization will invariably be incomplete. Uncertainty estimation, then, must quantify the influence of unknown parameters, forcings, and structural deficiencies. In this endeavour, numerical modeling frameworks support an unparalleled degree of subsurface complexity and its associated uncertainty. When boundary uncertainty is concerned, however, the numerical framework can be restrictive. The interdependence of grid discretization and the enclosing boundaries make exploring uncertainties in their extent or nature difficult. The Analytic Element Method (AEM) may be an interesting complement, as it is computationally efficient, economic with its parameter count, and does not require enclosure through finite boundaries. These properties make AEM well-suited for comprehensive uncertainty estimation, particularly in data-scarce settings or exploratory studies. In this study, we explore the use of AEM for flow field uncertainty estimation, with a particular focus on boundary uncertainty. To induce versatile, uncertain regional flow more easily, we propose a new element based on conformal mapping. We then include this element in a simple Python-based AEM toolbox and benchmark it against MODFLOW. Coupling AEM with a Markov Chain Monte Carlo (MCMC) routine using adaptive proposals, we explore its use in a synthetic case study. We find that AEM permits efficient uncertainty estimation for groundwater flow fields, and its analytical nature readily permits continuing analyses which can support Lagrangian transport modelling or the placement of numerical model boundaries.

Through time rivers move, create erosion, deposit sediments and, consequently, change their shape, however, these movements are different for each river, since each one has specific characteristics and dynamics. While small movements occur every day, it takes us months, years, decades, or even centuries to notice those changes. As rivers are the fundamental sources of life in many cities, towns, and communities, and even determine geopolitical limits, it is more important than ever to understand the dynamics that involve them. Dancing Rivers is a visualization platform that characterizes river patterns and positions the importance of scientific studies of the Amazonian rivers. It includes and displays information about the research, analysis and methodology developed to characterize planimetric morphodynamics (spatial analysis with remote sensing) and altimetry features (description of river bedforms and sediment transport) of major Amazonian rivers: Marañon, Huallaga, Ucayali and the Amazon, with the purpose of disseminating and getting a better understanding of their physical dynamics, allowing stakeholders and researchers to make wise decisions involving the water resource and territorial planning.

Recently a new approach to change point analysis was presented in the statistical literature. This approach is based on the construction of confidence sets. One way to apply it is to take existing homogeneity tests as a basis. In this study Pettitt and CUSUM based homogeneity tests are used to derive distribution-free change point analysis methods. These are applied to a large number of synthetic data series, and the results are analyzed. The results are compared to results of the application of the classical Pettitt and CUSUM methods, and with a Bayesian approach. It is shown that the new methods perform as least as well as the classical methods and the Bayesian method. Unlike the classical methods, the Bayesian and confidence set based methods provide information on the uncertainty of the change point location. The methods are tested on normally distributed synthetic time series and on synthetic time series with a type II Generalized Extreme Value distribution.

This first paper of the two-part series focuses on demonstrating the predictability of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01 degree (∼ 1-km) and 0.25 degree (∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01 degree estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest predictability skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.

The combination of X-ray imaging and CT image‒based computational fluid dynamics (CFD) simulation allows study of flow in fractured porous media. In this study, X-ray imaging was employed to unveil the morphological and aperture alterations of artificial fractures in wellbore cement cores that were exposed to CO2-saturated brine under geologic carbon sequestration (GCS) conditions. Direct pore-scale modelling of fluid flow through 3D fractures reconstructed from CT images was carried out to reveal velocity distribution in the fracture and for estimation of local and average permeability of the fracture. Varying-radius pipe representations of the fractures were established using the optimal characteristic radius formulation that was determined from the relation of flow cross-section shape and conductivity based on direct pore-scale modelling. Varying-radius pipeline modelling of fluid flow through simplified fractures was also implemented and the local and average permeability results based on varying-radius pipeline modelling were compared against those based on direct pore-scale modelling. The fracture after CO2 exposure in the reactive diffusion process was covered by substantial precipitated calcite, and the permeability of the fracture decreased from 4.15×10-8 m2 to 2.96×10-8 m2. In contrast, the fracture after CO2 exposure in the reactive flow process underwent significant dissolution, a large number of tensile micro-fractures were formed at the surface of the fracture, and the permeability of the fracture increased from 3.91×10-8 m2 to 4.23×10-8 m2. The relative error of the average fracture permeability obtained from direct pore-scale modelling (-7.33%‒4.05%) was comparable with that obtained from varying-radius pipeline modelling (-7.77%‒10.64%).

High-pressure fluid injections cause transient pore pressure changes over large distances, which may induce seismicity. The zone of influence for such an injection was studied at high spatial resolutions in six decameter-scaled fluid injection experiments in crystalline rock. Pore pressure time series revealed two distinct responses based on the lag time and magnitude of pressure change, namely, a near- and far-field response. The near-field response is due to pressure diffusion. In the far-field, the fast response time and decay of pressure changes are produced by effective stress changes in the anisotropic stress field. Our experiments prove for the first time that fracture fluid pressure perturbations around the injection point are not limited to the near-field and can extend beyond the pressurized zone.

Orographic impact on extreme sub-daily precipitation is critical for risk management but remains insufficiently understood due to complicated atmosphere-orography interactions and large uncertainties. We investigate the problem adopting a framework able to reduce uncertainties and isolate the systematic interaction of Mediterranean cyclones with a regular orographic barrier. The average decrease with elevation reported for hourly extremes is found enhanced at sub-hourly durations. Tail heaviness of 10-minute intensities is negligibly affected by orography, suggesting self-similarity of the distributions at the convective scale. Orography decreases the tail heaviness at longer durations, with a maximum impact around hourly scales. These observations are explained by an orographically-induced redistribution of precipitation towards stratiform-like processes, and by the succession of convective cores in multi-hour extremes. Our results imply a breaking of scale-invariance at sub-hourly durations, with important implications for natural hazards management in mountainous areas.

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants, and thereby can affect the global cycles of carbon and water. However, the extent to which these physiological effects of eCO2 influence the land-atmosphere exchange of carbon and water is uncertain. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We use a comprehensive process-based land surface model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The signal in gross primary production (GPP) emerges at relatively low eCO2 (Δ[CO2] ~ 20 ppm) where the leaf area index (LAI) is relatively high. Compared to GPP, the eCO2 effect causing reduced 28 transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (Δ[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass- dominated ecosystems. Our results provide a step towards when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.

Glacier surface elevation responds to a variety of localized processes occurring beneath the ice. Subglacial-lake volume change in particular is inherently time-dependent, producing time-varying perturbations in ice-surface elevation. Here, we introduce inverse methods for quantifying time-varying subglacial perturbations from altimetry data and, when available, horizontal surface velocity data. The forward model is based on a small-perturbation approximation of the Stokes equations that is solved efficiently with Fourier transform methods. The inverse methods are derived from variational least-squares optimization problems and the associated normal equations are solved with the conjugate gradient method. We conduct synthetic tests for reconstructing time-varying basal vertical velocity and drag perturbations that are motivated by subglacial-lake activity and slippery spots beneath Antarctic ice streams. We show that incorporation of horizontal surface velocity data as additional constraints can refine altimetry-based inversions or facilitate reconstruction of multiple fields, depending on whether the data are spatially discrete or continuous. We further validate the method by showing that it can reconstruct basal perturbations from synthetic elevation data that are produced by a nonlinear subglacial lake model. With the advent of high spatial and temporal resolution altimetry data from NASA's ICESat-2 mission, these inverse methods will facilitate further assessment of the relation between ice-sheet flow and subglacial processes.

Predicting the impact of changing climate and anthropogenic influences on stream discharge dynamics and baseflow conditions requires insight into the main factors that regulate storage and transfer of water from hillslope aquifers to surface streams. Classically, it is assumed that above a certain scale, hydrological laws involved at small-scale can be simplified, allowing the representation of the landscape and its subsurface in models as a homogeneous hillslope with effective slope, length and hydraulic properties. From a comprehensive analysis of hydrological, geological and geomorphological databases available in the Swiss Alps we provide evidence that such simplification might lead to inaccurate estimates of streamflow dynamics at baseflow. We reveal that recession behavior strongly deviates from that predicted by idealized homogeneous theories. A correlation analysis allows us to identify which key features of the landscape might control this deviation, with particular attention to slope, drainage density, depth to bedrock, and lithology as the main drivers. We summarize the current knowledge of physical mechanisms that could lead to complex hydrological behavior in Alpine contexts, and we finally discuss implications in defining modeling strategies for the Critical Zone community.

Despite human-induced changes in floodplains over the past century, comprehensive data of long-term land use change within floodplains of large river basins are limited. Data of long-term and large-scale floodplain land use are required to effectively quantify floodplain functions and development trajectories. They also provide a holistic perspective on the future of floodplain management and restoration – and concomitantly flood-risk mitigation. Here, we present the first available dataset that provides spatially explicit estimates of land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at a 250-m resolution. We derived this MRB floodplain land use change dataset from two input data sources: (i) the high-resolution global floodplain extent dataset GFPLAIN250m, and (ii) the annual FOREcasting SCEnarios of Land-use Change (FORE-SCE) dataset for the continental United States. Our results suggest that MRB floodplains have transitioned irreversibly from natural ecosystems to predominantly agricultural land use (e.g., more than 10,000 km2 of wetlands have been lost due to agricultural expansion). Developed land use within the floodplain has also steadily increased. The dataset is publicly available through HydroShare: https://gishub.org/mrb-data as well as an interactive online map interface: https://gishub.org/mrb-floodplain. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios.

Low soil temperature stress is a critical factor affecting the root water uptake (RWU) rate of plants. In current land surface models, the RWU amount is determined by the soil water extracted from different soil layers, which calculates by the relative soil water availability and the root fraction of each layer in the rooting zone. The effect of low soil temperature stress is not considered, which may produce biases in the simulation of transpiration. In this study, with the utilization of the in-situ observation data from three FLUXNET sites, we introduced three functions to represent the low soil temperature stress in the Common Land Model (CoLM) and evaluated their effects on the energy fluxes simulation. Then the three low soil temperature stress functions were also evaluated in the global offline simulations by using the FLUXNET-MTE (multi-tree ensemble) data. Results show that the default CoLM overestimates the latent heat flux but underestimates the sensible heat flux in the local spring and early summer at three study sites. By incorporating the low soil temperature stress function into CoLM, the bias in energy flux simulation is significantly reduced. The global offline simulations indicate that considering the effect of low soil temperature stress can improve the model performance on the simulating of the latent heat flux in those high latitude areas. Therefore, we recommend incorporating the effect of low soil temperature stress into land surface models, which is beneficial to increasing the reliability of the models’ results, especially over the cold regions.

The increasing availability of surface water inundation data has encouraged modelers and managers to include small yet abundant surface water storage systems (e.g., wetlands and other landscape depressions) in process-based models. Yet, these model applications have been largely limited to small- to meso- watershed scales, with drainage areas ranging from a few hectares to several thousand square kilometers. The conventional practice of overlooking these surface water storage systems in basin-scale (e.g., >10,000 m2) hydrologic modeling may be missing the total picture of flood and drought hazards. To fill this gap, we developed a 30-m resolution topography-based wetland and depression storage (maximum surface area and storage volume) database for the Upper Mississippi, Ohio, and Missouri River Basins ⎼ encompassing the 2.35 million km2 upstream domain of the Mississippi River system. Further, we integrated this depression dataset into a process-based model to simulate sub-catchment and river reach-scale hydrologic fluxes (surface runoff, soil wetness, evapotranspiration) and flows (streamflow). Compared with a “no depression” conventional model constructed for the Missouri and Upper Mississippi River Basins, our exploratory analyses demonstrate that a depression-integrated model (i) significantly alters the spatial patterns and magnitudes of water yields, (ii) improves streamflow simulation accuracy, and (iii) provides realistic spatial distributions of landscape wetness conditions. These emerging findings provide us with new insights into the effects of small surface water storage and stimulates a reassessment of current practices for basin-scale hydrologic modeling and water management.

Introduction HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

Researchers dealing with flood hazard and risk assessment typically refer to available in-situ gauging stations for the calibration and validation of hydrological and hydrodynamic (HD) models. However, lack of dense gauging data such as stage-discharge relationship (rating curves) results in higher uncertainty in hydrological studies. The multi-mission satellite altimetry observations are capable since long time to monitor continental water bodies at regular interval. In this study, combined use of hydrodynamic model and satellite altimetry data has been exploited for establishing virtual gauging network in flood-prone sparsely gauged river basin. The virtual gauging stations were established at locations where ground tracks of various altimeters (Jason-2/3, SARAL/AltiKa, and Sentinel-3A/3B) cross the river channel. Rating curves were generated at these virtual stations using calibrated and validated HD model. High agreement between the simulated and altimetry-based water levels at virtual stations showed the potential of satellite altimetry data for the multi-site validation of HD model and constructed rating curves. The availability of water level time series and rating curves at multiple virtual locations in addition to existing in-situ physical gauging stations provides an opportunity to expand gauging network in sparsely gauged basin. Thus, the proposed framework may open new perspectives for enhancing river flow dynamic studies considering upcoming satellite missions like SWOT which will ensure more observations in future.

Stream water temperature (T) is a variable of critical importance and decision-making relevance to aquatic ecosystems, energy production, and human’s interaction with the river system. Here, we propose a basin-centric stream water temperature model based on the long short-term memory (LSTM) model trained over hundreds of basins over continental United States, providing a first continental-scale benchmark on this problem. This model was fed by atmospheric forcing data, static catchment attributes and optionally observed or simulated discharge data. The model achieved a high performance, delivering a high median root-mean-squared-error (RMSE) for the groups with extensive, intermediate and scarce temperature measurements, respectively. The median Nash Sutcliffe model efficiency coefficients were above 0.97 for all groups and above 0.91 after air temperature was subtracted, showing the model to capture most of the temporal dynamics. Reservoirs have a substantial impact on the pattern of water temperature and negative influence the model performance. The median RMSE was 0.69 and 0.99 for sites without major dams and with major dams, respectively, in groups with data availability larger than 90%. Additional experiments showed that observed or simulated streamflow data is useful as an input for basins without major dams but may increase prediction bias otherwise. Our results suggest a strong mapping exists between basin-averaged forcings variables and attributes and water temperature, but local measurements can strongly improve the model. This work provides the first benchmark and significant insights for future effort. However, challenges remain for basins with large dams which can be targeted in the future when more information of withdrawal timing and water ponding time were accessible.

Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying recharge water all-at-once, rather than in increments, increases denitrification within the vadose zone, but transports higher concentrations of NO3- deeper into the profile. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. We conclude that ideal AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table.

Antecedent Soil moisture (ASM) is one of the most important factors affecting the rainfall-runoff modelling process. Its existence and influence in computing runoff using Soil Conservation Service Curve Number (SCS-CN) method have been a point of discussion for many decades. In this study, a novel procedure has been proposed to estimate the ASM by modifying the SCS-CN method and verify its applicability by comparing the ASM with the observed soil moisture. Natural rainfall, runoff and soil moisture data from eight small experimental farms with similar land use and varying slopes viz. 1%, 3% , 5% and 8%, located at Roorkee, India have been utilized. The ASM is computed by optimizing two parameters i.e., absolute maximum potential retention (Sabs) and initial abstraction coefficient (λ), and the optimization is carried out by minimizing Root mean square error (RMSE). Results show that there exists a good agreement between observed and computed ASM for slopes 1%, 3% and 5% (R2 > 0.5). A fair correlation for slope of 8% (R2 of 0.31) is observed, which may be due to the inadequacy of data. This study will be helpful in calculating ASM for ungauged catchments and will further broaden the applications of SCS-CN method.