Temporal and spatial variations in maximum river discharge from a new Russian data set

Floods cause more damage in Russia than any other natural disaster, and future climate model projections suggest that the frequency and magnitude of extreme hydrological events will increase in Russia with climate change. Here we analyze daily discharge records from a new data set of 139 Russian gauges in the Eurasian Arctic drainage basin with watershed areas from 16.1 to 50,000 km2 for signs of change in maximum river discharge. Several hypotheses about changes in maximum daily discharge and their linking with trends in precipitation over the cold season were tested. For the magnitude of maximum daily discharge we found relatively equal numbers of significant positive and negative trends across the Russian Arctic drainage basin, which draws into question the hypothesis of an increasing risk of extreme floods. We observed a significant shift to earlier spring discharge, which is consistent with documented changes in snowmelt and freeze‐thaw dates. Spatial analysis of changes in maximum discharge and cold season precipitation revealed consistency across most of the domain, the exception being the Lena basin. Trends in maximum discharge of the small‐ to medium‐sized rivers were generally consistent with aggregated signals found for the downstream gauges of the six largest Russian rivers. Although we observe regional changes in maximum discharge across the Russian Arctic drainage basin, no evidence of widespread trends in extreme discharge can be assumed from our analysis

Quantifying river form variations in the Mississippi Basin using remotely sensed imagery

Geographic variations in river form are often estimated using the framework of downstream hydraulic geometry (DHG), which links spatial changes in discharge to channel width, depth, and velocity through power-law models. These empirical relationships are developed from limited in situ data and do not capture the full variability in channel form. Here, we present a data set of 1.2 ×106 river widths in the Mississippi Basin measured from the Landsat-derived National Land Cover Dataset that characterizes width variability observationally. We construct DHG for the Mississippi drainage by linking digital elevation model (DEM)-estimated discharge values to each width measurement. Well-developed DHG exists over the entire Mississippi Basin, though individual sub-basins vary substantially from existing width–discharge scaling. Comparison of depth predictions from traditional depth–discharge relationships with a new model incorporating width into the DHG framework shows that including width improves depth estimates by, on average, 24%. Results suggest that channel geometry derived from remotely sensed imagery better characterizes variability in river form than do estimates based on DHG

Global extent of rivers and streams

The turbulent surfaces of rivers and streams are natural hotspots of biogeochemical exchange with the atmosphere. At the global scale, the total river-atmosphere flux of trace gasses such as carbon dioxide depends on the proportion of Earth’s surface that is covered by the fluvial network, yet the total surface area of rivers and streams is poorly constrained. We used a global database of planform river hydromorphology and a statistical approach to show that global river and stream surface area at mean annual discharge is 773,000 ± 79,000 square kilometers (0.58 ± 0.06%) of Earth’s nonglaciated land surface, an area 44 ± 15% larger than previous spatial estimates. We found that rivers and streams likely play a greater role in controlling land-atmosphere fluxes than is currently represented in global carbon budgets

Evaluation of present and future North American Regional Climate Change Assessment Program (NARCCAP) regional climate simulations over the southeast United States

In order to make well-informed decisions in response to future climate change, officials and the public require reliable climate projections at the scale of tens of kilometers, rather than the hundreds of kilometers that the current atmosphere-ocean general circulation models provide. Recent efforts such as the North American Regional Climate Change Assessment Program (NARCCAP) aim to address this need. This study has two principal aims: (1) evaluate the seasonal performance of the NARCCAP simulations over the southeast United States for both present (1971-2000) and future (2041-2070) periods and (2) assess the impact of a performance-based weighting scheme on bias and uncertainty. Application of the weighting scheme results in a substantial reduction in magnitude and percent area exhibiting significant bias in all seasons for both temperature and precipitation. The weighting scheme is then expanded to evaluate future change. Temperature changes are universally positive and outside the bounds of natural variability over the entire region and in all seasons. Application of the weighting scheme tightens confidence intervals by as much as 1.6°C. Future precipitation changes are modest, are of mixed sign, and vary by season and location. Though uncertainty is reduced by as much as 50%, the projected changes are generally not outside the bounds of natural background variability. Thus, under the NARCCAP simulations, stress on water resources is most likely to come from increased temperatures and not changes in mean seasonal precipitation. For energy use, the implication is that the ∼3°C temperature increase during the peak use summer season may place additional strain on power grids

A Calibration-Free Groundwater Module for Improving Predictions of Low Flows

Groundwater modules are critically important to the simulation of low flows in physically based land surface models (LSMs) and conceptual rainfall-runoff models (HBV). Here, we develop a Groundwater for Ungauged Basins (GrUB) module that uses only physically based properties for which data are widely available, thus allowing its application without the need for calibration. GrUB is designed to be computationally simple and readily adaptable to a wide variety of LSMs and rainfall-runoff models. We assess the performance of GrUB in 84 United States watersheds by incorporating it into HBV, a popular rainfall-runoff model. We compare predictions of low flows by the native (calibrated) HBV groundwater module with those by the (uncalibrated) GrUB module and find that GrUB generates error metrics that are equivalent or superior to those generated by the (calibrated) HBV groundwater module. To assess whether predictions by GrUB are robust to changes in the structure and parameterization of the overlying hydrologic model, we run tests for two artificial scenarios: Slow Recharge with rates of percolation below 0.1 mm/day, and Fast Recharge with rates of percolation of up to 1,000 mm/day. GrUB proves to be robust to these extreme changes, with mean absolute error (MAE) of predictions of low flows only increasing by an average of up to 17%, while average MAE increases by up to 158% when the same tests are performed on HBV without the GrUB module. We suggest GrUB as a potential tool for improving predictions of low flows in LSMs, as well as rainfall-runoff models when calibration data are sparse

An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale

Streamflow recession analysis is a widely used hydrologic tool that uses readily available discharge measurements to estimate otherwise unmeasurable watershed-scale properties, predict low flows, and parameterize many lumped hydrologic models. Traditional methods apply the simplifying assumptions of outflow from a Boussinesq aquifer, which predicts the slope of the recession curve relating streamflow to its derivative in log-log space to decrease from early-stage to late-stage recession. However, this prediction has not been validated in actual watersheds. Also, recent studies have shown that slopes of observed recession events are often much greater than traditional methods that predict with data point clouds. We analyze recession behavior of 1,027 streams from across the continental United States for periods of 10 to 118 years, identifying over 155,000 individual recession events. We find that the average slope of observed recession events is greater than that of the point cloud for all streams. Further, recession slopes of observed events decrease with time in only 10% of cases and instead increase with time in 74% of cases. We identify only nine watersheds where observed streamflow behavior often conforms to the predictions of traditional recession analysis, each of which is arid and flat with low permeability. Analysis of our extensive empirical results with a regionalization of catchment hydrologic characteristics indicates that heterogeneity of subsurface flow paths increases the nonlinearity and convexity of observed recession, likely as a function of watershed memory. The practical implications of our analysis are that streamflow is more stable during periods of extended drought than generally predicted

Spatial and Temporal Patterns in Baseflow Recession in the Continental United States

Baseflow is often treated according to a unique storage-discharge relationship. However, recent innovations in baseflow recession analysis have allowed novel findings regarding the variability of both the stability of baseflow and its nonlinearity (i.e., the concavity of the hydrograph), as well as the regional clustering of these characteristics. We investigate spatial and temporal patterns in the character of baseflow recession for over 1,000 watersheds in the continental United States. We discover seasonal patterns in both the stability and nonlinearity of baseflow which vary systematically across large regions. Further, we relate these baseflow characteristics to their potential physical drivers, including estimates of evapotranspiration, watershed storage, the distribution of watershed storage, and precipitation. While coincident watershed storage is the best predictor of baseflow stability in many regions (particularly the Appalachian Mountains), evapotranspiration from 2 to 3 months previous is the best predictor of baseflow stability in other regions (particularly the Pacific Northwest). We also discuss the novel finding that baseflow nonlinearity has increased significantly in most watersheds across the United States since 1980

A simple global river bankfull width and depth database

Hydraulic and hydrologic modeling has been moving to larger spatial scales with increased spatial resolution, and such models require a global database of river widths and depths to facilitate accurate river flow routing. Hydraulic geometry relationships have a long history in estimating river channel characteristics as a function of discharge. A simple near-global database of bankfull widths and depths (along with confidence intervals) was developed based on hydraulic geometry equations and the HydroSHEDS hydrography data set. The bankfull width estimates were evaluated with widths derived from Landsat imagery for reaches of nine major rivers, showing errors ranging from 8 to 62% (correlation of 0.88), although it was difficult to verify whether the satellite observations corresponded to bankfull conditions. Bankfull depth estimates were compared with in situ measurements at sites in the Ohio and Willamette rivers, producing a mean error of 24%. The uncertainties in the derivation approach and a number of caveats are identified, and ways to improve the database in the future are discussed. Despite these limitations, this is the first global database that can be used directly in hydraulic models or as a set of constraints in model calibration. Key Points Developed a global river database Used hydraulic geometry equations Leveraged existing hydrologic datasets

Watershed-Scale Effective Hydraulic Properties of the Continental United States

In land surface models (LSMs), the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the Earth's surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface on effective watershed-scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed-scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the conterminous United States for which sufficient streamflow data are available (n = 1,561). Then, we use machine learning methods to model these properties across the entire conterminous United States. Model validation results in high confidence for estimates of log(K) (r2 > 0.89; 1% 0.83; −70% < bias < −18%). Our estimates of effective K are, on average, two orders of magnitude higher than comparable soil-texture-based estimates of average K, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effective S compare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5–3,355 mm). Because estimates of S are much lower than the global maximums generally used in LSMs (e.g., 5,000 mm in Noah-MP), they may serve both to limit model spin-up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed-scale effective hydraulic variables that are necessary for the implementation of LSMs for the entire conterminous United States

Integrating Community Science Research and Space-Time Mapping to Determine Depth to Groundwater in a Remote Rural Region

Continuous depth to groundwater (DTG) data collection is challenging in remote regions. Community participation offers a way to both increase data collection and involves the local community in scientific projects. Local knowledge, which is often descriptive, can be difficult to include in quantitative analysis; however, it can increase scientists' ability to formulate hypotheses or identify relevant environmental processes. We show how Community Science Research can add useful descriptive information for a study based in rural Colombia. To estimate the spatiotemporal distribution of DTG, the community collected water level measurements during a wet (La Niña) year and an average year. We built one spatial and two spatiotemporal models (with and without probabilistic data) using Bayesian Maximum Entropy. Due to the inclusion of local knowledge, the spatiotemporal model with probabilistic data reduced its mean square error by a factor of 15 compared to the spatial model. Using this model, we found that 13% of the study area has a high probability of very shallow DTG (<0.1 m) during an average year, whereas during La Niña, this area increases to 56%. The difference in shallow DTG between the average and wet year implies that after reaching a precipitation threshold, the study region may lose its flow regulation capacity, contributing to flooding during extreme precipitation events. Our approach presents a method to incorporate local knowledge in data-driven models by combining qualitative and quantitative information