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The impact of an episodic river flood is intimately linked to its duration. Yet it is still unclear how often should a river be observed to accurately determine the occurrence and duration of extreme events. Here we assess flow statistics along with peak flow event detection and duration as a function of the discharge sampling period for large tributaries of the Mississippi basin using hourly gages over 2010–2022. Median event durations above high quantiles spatially vary from around 2 days upstream to 30 days downstream. Discharge mean, standard deviation, and quantiles can all be estimated within 2.5% error for sampling periods up to 8 days. A minimum temporal sampling 4× (2×) finer than peak flow event median duration is required to detect 95 ± 3% (85 ± 5%) of events and to estimate their duration within 90 ± 5% (75 ± 10%) median accuracy. Our findings have direct implications for future satellite missions concerned with capturing flood events. Plain Language Summary: High flow events and their duration in rivers are key aspects of flood studies. Yet, it is still unclear how often a river should be observed to detect floods and determine their durations. Analyzing hourly stream gage data in the Mississippi River basin from 2010 to 2022, we calculate that flood events vary widely in duration from upstream (2 days) to downstream (30 days). We show that observing large rivers every few days is sufficient to compute common long‐term river flow statistics within 2.5% error. However, capturing most floods and accurately assessing their duration (within 10% error) requires monitoring at least four times more frequently than the typical duration of these events. This means that if a major flood typically lasts 10 days, the river data should be collected at least every 2–3 days. Our findings have implications for the design of monitoring systems concerned with the accurate capture of floods, including those using Earth orbiting satellites. Key Points: Peak flow events in the Mississippi basin range from 2 days upstream to 30 days downstream, hence exhibiting acute spatial variabilityDecadal mean, standard deviation and quantiles of hourly discharge can all be computed within 2.5% error up to an 8‐day sampling periodSampling periods must be a factor of 4 finer than median peak flow event durations to estimate durations within 90 ± 5% median accuracy [ABSTRACT FROM AUTHOR]

Natural lakes and artificial reservoirs are important components of the Earth system and essential for freshwater, food, and energy. Relatively little is known about the variations of lake and reservoir surface area globally. For the first time, this study presents the global variation of lake and reservoir surface areas for all water bodies larger than 1 km2. Using radar remote sensing, we found that global aggregate area variations were only 2% of total surface area over a 3 year period. When considering the total surface area of shoreline regions that transition between land and water, these variations equaled 20% of total lake and reservoir surface area, largely driven by variations of smaller water bodies. Additionally, surface areas of reservoirs tends to be more variable than the surface area of lakes of similar size. The large surface area variations evidenced here, could have a previously underappreciated impact on the Earth System. Plain Language Summary: Natural lakes and artificial reservoirs are important parts of the Earth system and provide many benefits for humanity. Despite this importance, there are key things about lakes and reservoirs that are unknown globally, including how and when surface areas vary. This study uses satellite‐based radar observations to estimate surface area variations in a set of the world's largest lakes and reservoirs. We found that the total surface area variability was relatively small when aggregated globally, only accounting for 2% of mean global surface water area. However, the total shoreline area that transitioned between water and land as a result of lake and reservoir variability was much more substantial, around 20% of mean global surface area. The variability of smaller water bodies contributed more to these transitional areas than larger water bodies. We also found that artificial reservoirs tended to be more variable than similarly sized natural lakes. Ultimately, the large surface area variations evidenced here, particularly in small water bodies, could have a previously underappreciated impact on the Earth System. Key Points: Smaller water bodies show significantly more global sum total surface area variability than larger water bodiesReservoirs show more surface area variability than lakes of similar sizesGlobal intermittently inundated surface water body area is ∼20% of its average, and small water bodies made up the most significant portion [ABSTRACT FROM AUTHOR]

The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA's Surface Water and Ocean Topography mission. [ABSTRACT FROM AUTHOR]

The transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge. Plain Language Summary: Computer programs are routinely used to simulate the movement of water on Earth, including the flow of freshwater in rivers. A common approach for calculating river flow is to run a first computer program that estimates how much of the rain is partitioned into runoff, evaporation, or storage, then a second that transports the runoff through rivers. These computer programs are based on a simplified representation of nature and are imperfect. Until now, it has been unclear how the imperfections are transferred from one program to the next, and therefore challenging to understand the sources of errors. In this study, we present a methodology that can be used to backtrack errors in runoff, an otherwise unknown quantity, from observed errors in river flow. Our results reveal a surprising error source and can help diagnose limitations in simulations of the Earth's water cycle. Key Points: We present the first analytical equations for propagating runoff uncertainty into discharge uncertainty through a river networkOur methodology allows the inverse inference of runoff errors from observed discharge errors at continental scalesThe largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors [ABSTRACT FROM AUTHOR]

Abstract: Earth‐orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real‐time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet the temporal requirements for access to satellite‐based river data remain uncharacterized for time‐sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low‐latency/near‐real‐time satellite products, with an emphasis on the forthcoming SWOT satellite mission. We apply a kinematic wave model to a global hydrography data set and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively. Our findings suggest that a recently proposed ≤2‐day data latency for a low‐latency SWOT product is potentially useful for real‐time river applications. [ABSTRACT FROM AUTHOR]

Dams and reservoirs predictably alter the discharge of a river often confounding large-scale flow simulations. The Routing Application for Parallel computatIon of Discharge (RAPID) model has been widely used operationally. Until recently, RAPID could only represent reservoir outflow by directly specifying discharges from dams. Herein, we develop a non-data driven reservoir routing approach within RAPID. Results are comprehensively evaluated across the entire Mississippi River Basin (MRB). The reservoir routing resulted in considerable improvement in the Lower Mississippi and Arkansas-White-Red regions. Further, the accuracy of estimated reservoir releases also benefits when the inflow estimation improves. Simulation of the reservoir release with observed reservoir inflow resulted in highest Kling-Gupta efficiency (KGE) among all experiments. Inclusion of reservoir routing in RAPID will enhance the utility of this model in basins where dams exist and more importantly, improves streamflow predictions by providing better hydrological information for decision makers. • A reservoir approach was developed for large-scale river flow simulation. • Continental scale streamflow simulation improves with the reservoir model. • The impact of reservoirs on downstream flow depends on the proximity to the dam. [ABSTRACT FROM AUTHOR]

Highlights: [•] We determined relationships between drainage density (Dd ) and runoff (R). [•] We determined relationships between vegetation cover (V) and precipitation (P). [•] We show that Dd (R) follows a saturation–growth model. [•] We shows that V(P) exhibits a linear relationship. [•] We apply the determined relationships to data from climate prediction models. [Copyright &y& Elsevier]

Abstract: The permanent rivers and streams of the United States – i.e. the “blue lines” from the United States Geological Survey (USGS) maps – are available in a Geographic Information System dataset called NHDPlus in which network connectivity for approximately three million reaches is known. The USGS also measures stream flow at about twenty thousand gauges whose locations are known exactly on NHDPlus rivers. The Routing Application for Parallel computatIon of Discharge (RAPID) is a river network model that can be run on NHDPlus if given inflow to rivers from runoff. The second phase of the North American Land Data Assimilation System (NLDAS2) provides runoff estimates from four land surface models over the continent. One could therefore combine RAPID with NLDAS2 runoff to compute stream flow in all NHDPlus “blue lines” and compare computations with USGS observations over the United States. The work presented here investigates such a combination but focuses on the Texas Gulf Coast Hydrologic Region as a test case for regional-scale river flow modeling using off-the-shelf runoff products, thousands of mapped rivers and hundreds of stream flow gauges. Despite initial difficulties in the determination of water inflow from gridded NLDAS2 runoff datasets to NHDPlus rivers, the “blue line” approach is shown to be advantageous partly because hundreds of gauges are easily located on river networks and used throughout basins. Such use of a large number of gauges allows for new investigations related to the analysis of flow computations and to the optimization of model parameters thereby bridging the gap between continental-scale environmental modeling and local-scale planning and decision making. The quality of NLDAS2 runoff relative to locally-calibrated data is assessed through comparison between computed and observed river flow. With the exception of large outliers at a few gauges, the best NLDAS2 runoff products perform similarly to the regional dataset. Such outliers prevent the successful optimization of river model parameters but a careful selection of what stations to use for optimization is shown to overcome this difficulty. While river model parameters determined separately for each runoff dataset allow obtaining best results, a single set of parameters is sufficient to assess the relative quality of runoff datasets. The detailed NHDPlus description of river reaches also allows investigating the impact of increased spatial variability in model parameters which – following optimization of model computations – suggests that those parameters of reaches located on the main stems of large rivers tend to have the largest influence on the quality of flow simulations. [Copyright &y& Elsevier]

The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin. [ABSTRACT FROM AUTHOR]

Submarine groundwater discharge (SGD) delivers water and dissolved chemicals from continents to oceans, and its spatial distribution affects coastal water quality. Unlike rivers, SGD is broadly distributed and relatively difficult to measure, especially at continental scales. We present spatially resolved estimates of fresh (land-derived) SGD for the contiguous United States based on historical climate records and high-resolution hydrographic data. Climate controls regional patterns in fresh SGD, while coastal drainage geometry imparts strong local variability. Because the recharge zones that contribute fresh SGD are densely populated, the quality and quantity of fresh SGD are both vulnerable to anthropogenic disturbance. Our analysis unveils hot spots for contaminant discharge to marine waters and saltwater intrusion into coastal aquifers. [ABSTRACT FROM AUTHOR]

The National Elevation, Hydrography and Land Cover datasets of the United States have been synthesized into a geospatial dataset called NHDPlus which is referenced to a spheroidal Earth, provides geospatial data layers for topography on 30 m rasters, and has vector coverages for catchments and river reaches. In this article, we examine the integration of NHDPlus with the Noah-distributed model. In order to retain compatibility with atmospheric models, Noah-distributed utilizes surface domain fields referenced to a spherical rather than spheroidal Earth in its computation of vertical land surface/atmosphere water and energy budgets (at coarse resolution) as well as horizontal cell-to-cell water routing across the land surface and through the shallow subsurface (at fine resolution). Two data-centric issues affecting the linkage between Noah-distributed and NHDPlus are examined: (1) the shape of the Earth; and (2) the linking of gridded landscape with a vector representation of the stream and river network. At mid-latitudes the errors due to projections between spherical and spheroidal representations of the Earth are significant. A catchment-based “pour point” technique is developed to link the raster and vector data to provide lateral inflow from the landscape to a one-dimensional river model. We conclude that, when Noah-distributed is run uncoupled to an atmospheric model, it is advantageous to implement Noah-distributed at the native spatial scale of the digital elevation data and the spheroidal Earth of the NHDPlus dataset rather than transforming the NHDPlus dataset to fit the coarser resolution and spherical Earth shape of the Noah-distributed model. [ABSTRACT FROM AUTHOR]

The flow of fresh groundwater to the ocean through the coast (fresh submarine groundwater discharge or fresh SGD) plays an important role in global biogeochemical cycles and coastal water quality. In addition to delivering dissolved elements from land to sea, fresh SGD forms a natural barrier against salinization of coastal aquifers. Here we estimate groundwater discharge rates through the near‐global coast (60°N to 60°S) at high resolution using a water budget approach. We find that tropical coasts export more than 56% of all fresh SGD, while midlatitude arid regions export only 10%. Fresh SGD rates from tectonically active margins (coastlines along tectonic plate boundaries) are also significantly greater than passive margins, where most field studies have been focused. Active margins combine rapid uplift and weathering with high rates of fresh SGD and may therefore host exceptionally large groundwater‐borne solute fluxes to the coast. Plain Language Summary: Fresh groundwater flows from land to sea through coastal rocks and sediment. While the amount of groundwater flow is small compared with rivers, it plays an important role in carrying dissolved chemicals like nutrients to sea, and it helps protect aquifers against salinization. We estimate groundwater flow through the near‐global coast. Tropical regions have more groundwater flow, while dry midlatitudes have less. Dry midlatitude regions are therefore more vulnerable to salinization, which is problematic because these regions are also more likely to depend on groundwater to meet their water needs. Additionally, mountainous coastlines along tectonic plate boundaries have relatively large rates of groundwater discharge and may be associated with higher dissolved chemical fluxes to the coast. Key Points: Almost half of fresh submarine groundwater discharge (SGD) enters the ocean at wet equatorial regionsHigh‐relief, tectonically active margins have higher ratios of fresh SGD to river dischargeTotal annual volume of fresh SGD is ~489 km3/year, or ~1% of river discharge [ABSTRACT FROM AUTHOR]

Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom 'groundwater' layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management. [ABSTRACT FROM AUTHOR]

As a key component of the National Flood Interoperability Experiment ( NFIE), this article presents the continental scale river flow modeling of the Mississippi River Basin ( MRB), using high-resolution river data from NHD Plus. The Routing Application for Parallel computatIon of Discharge ( RAPID) was applied to the MRB with more than 1.2 million river reaches for a 10-year study (2005-2014). Runoff data from the Variable Infiltration Capacity ( VIC) model was used as input to RAPID. This article investigates the effect of topography on RAPID performance, the differences between the VIC- RAPID streamflow simulations in the HUC-2 regions of the MRB, and the impact of major dams on the streamflow simulations. The model performance improved when initial parameter values, especially the Muskingum K parameter, were estimated by taking topography into account. The statistical summary indicates the RAPID model performs better in the Ohio and Tennessee Regions and the Upper and Lower Mississippi River Regions in comparison to the western part of the MRB, due to the better performance of the VIC model. The model accuracy also increases when lakes and reservoirs are considered in the modeling framework. In general, results show the VIC- RAPID streamflow simulation is satisfactory at the continental scale of the MRB. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth's water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors. [ABSTRACT FROM AUTHOR]

Since 2002, National Aeronautics and Space Administration's Gravity Recovery and Climate Experiment (GRACE) allows scientists of various disciplines to analyze and map changes in total water storage globally. Although the raw data are available to the public, the process of viewing, manipulating, and analyzing GRACE data can be difficult for those without strong technological backgrounds in programming or geospatial software. This is particularly true for water managers in developing countries, where GRACE data could be a valuable asset for sustainable water resource management. To address this problem, we developed an open‐source utility for subsetting GRACE datasets to selected regions of interest and packaged that utility in a web application that allows water managers to quickly and easily access and visualize GRACE data. For a selected region, we use data from the three Global Land Data Assimilation System land surface models: Noah, variable infiltration capacity, and Catchment Land Surface Model, and decompose the GRACE data for total water storage into surface water, soil moisture, and groundwater components along with associated uncertainty estimates. The resulting groundwater data are in the form of groundwater storage changes over time, and a better understanding of groundwater storage trends helps water managers to more effectively respond to drought and agricultural demand in a selected region and thereby more sustainably manage groundwater resources. We demonstrate these uses in a case study from the Hindu‐Kush Himalayan region. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of river discharge (RSQ) algorithms provide a useful source of observations to supplement river gauge records. RSQ algorithms have existed for over a decade yet their widespread use has been impeded by a lack of operational usability and quantitative characterization of uncertainty. Here we present RODEO, an algorithm for estimating river discharge using Landsat observations in near-real time. RODEO is validated with 456 gauges (median Kling-Gupta efficiency = 0.3) and uses a novel quantile rating curve technique that pairs Landsat river widths with discharge estimates from a global hydrologic model. RODEO also characterizes the uncertainty of RSQ estimates (estimated root-mean-square error = +7%), enabling RSQ retrievals to be used for data assimilation into hydrologic models. With the goal of expanding the RSQ user base, the RODEO algorithm is implemented as a freely available, off-the-shelf cloud-based Google Earth Engine application that provides RSQ estimates across North America from 1984-present. • We present RODEO, a user-friendly application for estimating river discharge from Landsat 5, 7, and 8. • River widths can be quickly extracted from Landsat images and used to estimate discharge. • RODEO characterizes discharge uncertainty allowing for remote sensing measurements to be used for data assimilation. [ABSTRACT FROM AUTHOR]

Better understanding and quantification of river floods for very local and "flashy" events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr. [ABSTRACT FROM AUTHOR]

The highly intermittent and sparse nature of the Surface Water and Ocean Topography (SWOT) river observations in both space and time poses a major challenge to users for most applications. The locally restricted river information also hinders the realization of the mission's full value for hydrologic sciences. We tackle the challenges with a two-pronged approach:First, a river discharge interpolation scheme is developed based on the Inverse Streamflow Routing (ISR) model, which assembles the discrete discharge estimates in a river basin into a spatially complete and temporally continuous data record. Through discharge-to-runoff inversion, the ISR interpolation propagates observed discharge information exhaustively across the reachable space/time within the river basin in both upstream and downstream directions.Second, to further extend the propagation of SWOT information beyond basin boundaries and to improve gap-filling in space/time, the ISR model is updated to include spatiotemporal correlations in runoff errors. Such error correlations in runoff field, as an exponentially decayed isotropic function in space/time (beyond basins), allows us to propagate storm signals picked up by SWOT from one basin to its neighbors.Synthetic experiments are conducted over the Ohio River basin, as well as 16 global river basins. Significant and robust improvements are found in these experiments with large SWOT observation gaps much better filled and higher KGE score in interpolated discharge records. Results suggest that we can boost the utility of SWOT river products and its value for hydrologic sciences by expanding a very local/intermittent observation platform into a far-reaching source of hydrologic information through both river hydrodynamics in channel networks and spatiotemporal coherence of weather systems in the atmosphere. [ABSTRACT FROM AUTHOR]

Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA's ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there. [ABSTRACT FROM AUTHOR]

Continental-scale flood discharge modeling requires a high level of efficiency and flexibility. To this end, this study documents the implementation and application of a vector-based river routing model in the community WRF-Hydro modeling framework. Using Hurricane Ike as a case study, the hybrid vector–grid modeling framework's sensitivity to the land grid resolution and the coupling interface is assessed. Results show the model is more sensitive to the coupling interface than the grid resolution, and a 1-km land grid with an area-weighted coupling interface exhibits the optimal simulation results. A geographic information system (GIS) based approach is adopted to improve the regional representativeness of the flow travel time estimation. The model's computational efficiency and complexity are compared to a grid-based routing scheme, demonstrating its advantages for large-scale “offline” hydrological applications with GIS-supported features. Trade-offs between the modeling efficiency and complexity are then discussed to inform future large-scale flood prediction applications. [ABSTRACT FROM AUTHOR]

Abstract: The National Weather Service (NWS) forecasts floods at approximately 3,600 locations across the United States (U.S.). However, the river network, as defined by the 1:100,000 scale National Hydrography Dataset‐Plus (NHDPlus) dataset, consists of 2.7 million river segments. Through the National Flood Interoperability Experiment, a continental scale streamflow simulation and forecast system was implemented and continuously operated through the summer of 2015. This system leveraged the WRF‐Hydro framework, initialized on a 3‐km grid, the Routing Application for the Parallel Computation of Discharge river routing model, operating on the NHDPlus, and real‐time atmospheric forcing to continuously forecast streamflow. Although this system produced forecasts, this paper presents a study of the three‐month nowcast to demonstrate the capacity to seamlessly predict reach scale streamflow at the continental scale. In addition, this paper evaluates the impact of reservoirs, through a case study in Texas. Validation of the uncalibrated model using observed hourly streamflow at 5,701 U.S. Geological Survey gages shows 26% demonstrate PBias ≤ |25%|, 11% demonstrate Nash‐Sutcliffe Efficiency (NSE) ≥ 0.25, and 6% demonstrate both PBias ≤ |25%| and NSE ≥ 0.25. When evaluating the impact of reservoirs, the analysis shows when reservoirs are included, NSE ≥ 0.25 for 56% of the gages downstream while NSE ≥ 0.25 for 11% when they are not. The results presented here provide a benchmark for the evolving hydrology program within the NWS and supports their efforts to develop a reach scale flood forecasting system for the country. [ABSTRACT FROM AUTHOR]

The article reports on a workshop hosted by the SWOT Applications Working Group on April 5-6, 2017, at the U.S. Geological Survey (USGS) headquarters in Reston, Virginia, to discuss how the applications community can use SWOT data to address problems of societal importance. The availability of the Surface Water and Ocean Topography (SWOT) data in near-real time was given particular emphasis.

Direct groundwater discharge delivers nutrients from land and lakebed sediments to the Great Lakes, which impacts lake water quality. Broad spatial distributions of discharging groundwater are often difficult to measure directly. We present high resolution estimates of direct groundwater discharge across 43% of the Great Lakes coastline based on a water budget approach that uses hydroclimatic models and high-resolution hydrographic data available within the United States. We also integrate land use data to identify coastal areas vulnerable to high groundwater-borne nutrient loads. Estimated rates of direct groundwater discharge along the Great Lakes coast are highly variable, but generally are greatest for Lake Erie and Lake Michigan. Almost one-third of Lake Erie’s United States coastline is vulnerable to groundwater sources of nutrients. To assess uncertainties and limitations in our vulnerability analysis, a vulnerable site along Lake Erie was selected for detailed field measurements of direct groundwater discharge rates and nutrient fluxes. Measured discharge rates were significantly lower than water budget-based estimates (354 ± 25 m 3 y −1 m −1 compared to 588 ± 181 m 3 y −1 m −1 ). Dissolved phosphorous concentrations in the lakebed were elevated compared to onshore groundwater, while nitrate concentrations were lower, indicative of a highly reactive sediment-water interface. Some of the measured phosphorus may be locally sourced from desorption of legacy P or mineralization of organic matter in the lakebed, which our vulnerability framework does not include. Much of the land-derived nitrogen may be transformed along groundwater flow paths prior to discharge. While model-based estimates of direct groundwater discharge and vulnerability to nutrient loading are important for managing Great Lakes water quality, direct field observations remain essential for quantifying fluxes. [ABSTRACT FROM AUTHOR]

Warning systems with the ability to predict floods several days in advance have the potential to benefit tens of millions of people. Accordingly, large-scale streamflow prediction systems such as the Advanced Hydrologic Prediction Service or the Global Flood Awareness System are limited to coarse resolutions. This article presents a method for routing global runoff ensemble forecasts and global historical runoff generated by the European Centre for Medium-Range Weather Forecasts model using the Routing Application for Parallel computatIon of Discharge to produce high spatial resolution 15-day stream forecasts, approximate recurrence intervals, and warning points at locations where streamflow is predicted to exceed the recurrence interval thresholds. The processing method involves distributing the computations using computer clusters to facilitate processing of large watersheds with high-density stream networks. In addition, the Streamflow Prediction Tool web application was developed for visualizing analyzed results at both the regional level and at the reach level of high-density stream networks. The application formed part of the base hydrologic forecasting service available to the National Flood Interoperability Experiment and can potentially transform the nation's forecast ability by incorporating ensemble predictions at the nearly 2.7 million reaches of the National Hydrography Plus Version 2 Dataset into the national forecasting system. [ABSTRACT FROM AUTHOR]

The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM. [ABSTRACT FROM AUTHOR]

This article presents a framework for integrating a regional geographic information system ( GIS)-based nitrogen dataset (Texas Anthropogenic Nitrogen Dataset, TX- ANB) and a GIS-based river routing model (Routing Application for Parallel computation of Discharge) to simulate steady-state riverine total nitrogen ( TN) transport in river networks containing thousands of reaches. A two-year case study was conducted in the San Antonio and Guadalupe basins during dry and wet years (2008 and 2009, respectively). This article investigates TN export in urbanized (San Antonio) vs. rural (Guadalupe) drainage basins and considers the effect of reservoirs on TN transport. Simulated TN export values are within 10 percent of measured export values for selected stations in 2008 and 2009. Results show that in both years the San Antonio basin contributed a larger quantity than the Guadalupe basin of delivered TN to the coastal ocean. The San Antonio basin is affected by urban activities including point sources, associated with the city of San Antonio, in addition to greater agricultural activities. The Guadalupe basin lacks major metropolitan areas and is dominated by rangeland, rather than fertilized agricultural fields. Both basins delivered more TN to coastal waters in 2009 than in 2008. Furthermore, TN removal in the San Antonio and Guadalupe basins is inversely related to stream orders: the higher the order the more TN delivery (or the less TN removal). [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model's ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km2) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50-60 cm in the improved Muskingum method and 40-50 cm in the kinematic-diffusive wave method. The results also highlight SWOT's potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km2, the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters. [ABSTRACT FROM AUTHOR]

Satellites provide a temporally discontinuous record of hydrological conditions along Earth's rivers (e.g., river width, height, water quality). The degree to which archived satellite data effectively capture the overall population of river flow frequency is unknown. Here, we use the entire archives of Landsat 5, 7, and 8 to determine when a cloud-free image is available over the United States Geological Survey (USGS) river gauges located on Landsat-observable rivers. We compare the flow frequency distribution derived from the daily gauge record to the flow frequency distribution derived from ideally sampling gauged discharge based on the timing of cloud-free Landsat overpasses. Examining the patterns of flow frequency across multiple gauges, we find that there is not a statistically significant difference between the flow frequency distribution associated with observations contained within the Landsat archive and the flow frequency distribution derived from the daily gauge data (α = 0.05), except for hydrological extremes like maximum and minimum flow. At individual gauges, we find that Landsat observations span a wide range of hydrological conditions (97% of total flow variability observed in 90% of the study gauges) but the degree to which the Landsat sample can represent flow frequency distribution varies from location to location and depends on sample size. The results of this study indicate that the Landsat archive is, on average, representative of the temporal frequencies of hydrological conditions present along Earth's large rivers with broad utility for hydrological, ecologic and biogeochemical evaluations of river systems. [ABSTRACT FROM AUTHOR]

The highly intermittent and sparse nature of the Surface Water and Ocean Topography (SWOT) river observations in both space and time poses a major challenge to users for most applications. While interpolation/assimilation efforts exist to assemble discrete SWOT river observations into a spatially complete and temporally continuous record, the efficacy of such efforts is severely compromised by large observation gaps. In this study, we introduced spatiotemporal error correlations in runoff fields into the Inverse Streamflow Routing (ISR) model to improve the gap-filling capability for creating continuous daily discharge. This enhanced ISR model was first investigated in the Ohio River basin, which showed very significant improvements with better reproduced spatial and temporal dynamics of the runoff fields and the discharge time series. Results using 25 validation gauges showed a more concentrated Kling-Gupta Efficiency distribution, where the median was improved from 0.494 to 0.750. The enhanced ISR model was further tested over 16 representative global river basins to verify the effectiveness and robustness of the proposed method. The best fitted e -fold correlation length and time leading to the greatest improvements varied largely among the 16 basins. While all basins benefitted from the spatiotemporal correlations in the SWOT gap-filling, basins suffering from poorer initial guess and fewer SWOT observations, such as the Dnieper, Kolyma and Murray basins, showed larger performance gains than others. Although the experiments were idealized and did not reflect all potential errors that may be present in a real-world application, we demonstrate that the utility of SWOT river products and their value in hydrologic sciences may be boosted by expanding a local/intermittent observation platform into a far-reaching source of hydrologic information through both river hydrodynamics in channel networks and spatiotemporal coherence of runoff errors. • Spatiotemporal correlations in runoff errors introduced for discharge assimilation. • SWOT daily discharge estimation dramatically improved in the Ohio basin. • Significant and robust improvements found over 16 diversified global basins. [ABSTRACT FROM AUTHOR]

Global or national scale flood early warning systems (FEWS) can benefit developing countries and ungauged regions that lack observational data, computational infrastructure, and/or the human capacity for streamflow modelling. Existing land surface models (LSM) typically generate forecasts using coarse resolution grid cells which, at least for streamflow, have little value when used for flood warning at local scales. We present the design and development of a new automated computational system, using existing, well-established open source software tools that quickly downscales (or maps) the runoff generated from such coarse grid-based LSMs onto high-resolution vector-based stream networks then routes the results using a vector-based river routing model. We conducted experiments using the ERA-Interim/Land reanalysis data – a 35-year retrospective gridded runoff data product from the European Center for Medium-range Weather Forecasts (ECMWF) – to assess our fast downscaling system. The accuracy of our approach is comparable to the Global Flood Awareness System (GloFAS) – a well-established gridded routing model using the same forcings – but our method provides streamflow predictions on significantly higher resolution stream networks. We found that the river network resolution has negligible effect on the simulated streamflow with our model routing. In other words, we can forecast streamflow for very small stream segments and potentially improve local flood awareness and response much more successfully than previously possible using readily available climate forcings from LSMs. • An automated system to quickly downscale or map global gridded runoff into high-resolution vector-based river networks. • This approach has comparable accuracy with GloFAS but provides higher resolution predictions with lower computational cost. • River network resolution has a negligible effect on the simulated streamflow with this approach. [ABSTRACT FROM AUTHOR]