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

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

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

Mapping water surface elevation and slope in the Mississippi River Delta using the AirSWOT Ka-band interferometric synthetic aperture radar

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km2 at incidence angles below 15°. At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments

Temporal variations in river water surface elevation and slope captured by AirSWOT

The Surface Water and Ocean Topography (SWOT) satellite mission aims to improve the frequency and accuracy of global observations of river water surface elevations (WSEs) and slopes. As part of the SWOT mission, an airborne analog, AirSWOT, provides spatially-distributed measurements of WSEs for river reaches tens to hundreds of kilometers in length. For the first time, we demonstrate the ability of AirSWOT to consistently measure temporal dynamics in river WSE and slope. We evaluate data from six AirSWOT flights conducted between June 7–22, 2015 along a ~90 km reach of the Tanana River, AK. To validate AirSWOT measurements, we compare AirSWOT WSEs and slopes against an in situ network of 12 pressure transducers (PTs). Assuming error-free in situ data, AirSWOT measurements of river WSEs have an overall root mean square difference (RMSD) of 11.8 cm when averaged over 1 km2 areas while measurements of river surface slope have an RMSD of 1.6 cm/km for reach lengths &gt;5 km. AirSWOT is also capable of recording accurate river WSE changes between flight dates, with an RMSD of 9.8 cm. Regrettably, observed in situ slope changes that transpired between the six flights are well below AirSWOT's accuracy, limiting the evaluation of AirSWOT's ability to capture temporal changes in slope. In addition to validating the direct AirSWOT measurements, we compare discharge values calculated via Manning's equation using AirSWOT WSEs and slopes to discharge values calculated using PT WSEs and slopes. We define or calibrate the remaining discharge parameters using a combination of in situ and remotely sensed observations, and we hold these remaining parameters constant between the two types of calculations to evaluate the impact of using AirSWOT versus the PT observations of WSE and slope. Results indicate that AirSWOT-derived discharge estimates are similar to the PT-derived discharge estimates, with an RMSD of 13.8%. Additionally, 42% of the AirSWOT-based discharge estimates fall within the PT discharge estimates' uncertainty bounds. We conclude that AirSWOT can measure multitemporal variations in river WSE and spatial variations in slope with both high accuracy and spatial sampling, providing a compelling alternative to in situ measurements of regional-scale, spatiotemporal fluvial dynamics

The impact of reach averaging Manning's equation for an in-situ dataset of water surface elevation, width, and slope

The Surface Water and Ocean Topography Mission (SWOT) will generate global, spatially continuous maps of water surface elevation and extent for large inland water bodies when it launches in 2021. We present an analysis of water surface elevation, width, and bathymetry timeseries data from a medium-sized (average annual discharge 14 m3/s) river to explore Manning's equation, an empirical open channel flow equation, in the context of SWOT discharge algorithms. While this equation is in theory inapplicable to natural channels due to the non-uniform and spatially heterogeneous nature of river systems, we explored approaches to adapt it to this context using reach-averaged variables. At twenty sites along a 6.5 km stretch of the Olentangy River in Ohio, USA, we collected automated and manual measurements of water surface elevation and river width, undertook a full bathymetric survey of the study area, and built a hydraulic model. The stretch of river was divided into five reaches, and hydraulic variables were reach-averaged. Using these variables, we used a modified form of Manning's equation to compute a reach-averaged roughness coefficient. Reach-averaged roughness coefficients varied nonlinearly with discharge and were 2–10 times larger at low flow than at high flow in the in-situ data, ranging from 0.06 to 0.61 in one of the study reaches. These results were compared with the output of an unsteady flow simulation using a calibrated 1-D hydraulic model which was run with constant roughness coefficients at each cross section. When reach-averaged data was used, model-derived roughness coefficient also varied by more than an order of magnitude, with a range of 0.02–0.82 for one reach. For both in-situ and model-derived datasets, using a two-parameter roughness coefficient which scaled with a power law on either discharge or stage reduced discharge estimation error, with error for one reach dropping from 81% to 8% relative root-mean square error (rRMSE) in the in-situ data and 58% to 8% nRMSE in the modeled data. These results imply that spatial averaging of hydraulic variables leads to large variations in reach averaged Manning's n, which we term the reach's “effective resistance”, and suggest that this variability can be accounted for with a simple parameterization in estimates of discharge that use spatially averaged data

Improving the transferability of suspended solid estimation in wetland and deltaic waters with an empirical hyperspectral approach

The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids

Evaluation of snow cover fraction for regional climate simulations in the Sierra Nevada

Mountain snow cover plays an important role in regional climate due to its high albedo, its effects on atmospheric convection, and its influence on lower-elevation runoff. Snowpack water storage is also a critical water resource and understanding how it varies is of great social value. Unfortunately, in situ measurements of snow cover are not widespread; therefore, models are often depended on to assess snowpack and snow cover variability. Here, we use a new satellite-derived snow product to evaluate the ability of the Weather Research and Forecasting (WRF) regional climate model with the Noah land surface model with multiparameterization options (Noah-MP) to simulate snow cover fraction (SCF) and snow water equivalent (SWE) on a 3 km domain over the central Sierra Nevada. WRF/Noah-MP SWE simulations improve upon previous versions of the Noah land surface model by removing the early bias in snow melt. As a result, WRF/Noah-MP now accurately simulates spatial variations in SWE. Additionally, WRF/Noah-MP correctly identifies the areas where snow is present and captures large-scale variability in SCF. Temporal RMSE of the domain-average SCF was 1863.9 km2 (24%). However, our study reveals that WRF/Noah-MP struggles to simulate SCF at the scale of individual grid cells. The equation used to calculate SCF fails to produce temporal variations in grid-scale SCF and depletion occurs too rapidly. Therefore SCF is a nearly binary metric inmountain environments. Sensitivity tests of the equation may improve simulation of SCF during accumulation or melt but does not remove the bias for the entire snow season. Though WRF/Noah-MP accurately simulates the presence or absence of snow, high-resolution, reliable SCF measurements may only be attainable if snow depletion equations are designed specifically for complex topographical areas

Bias Correction of Hydrologic Projections Strongly Impacts Inferred Climate Vulnerabilities in Institutionally Complex Water Systems

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes