Development of the Global Width Database for Large Rivers

River width is a fundamental parameter of river hydrodynamic simulations, but no global-scale river width database based on observed water bodies has yet been developed. Here we present a new algorithm that automatically calculates river width from satellite-based water masks and flow direction maps. The Global Width Database for Large Rivers (GWD-LR) is developed by applying the algorithm to the SRTM Water Body Database and the HydroSHEDS flow direction map. Both bank-to-bank river width and effective river width excluding islands are calculated for river channels between 60S and 60N. The effective river width of GWD-LR is compared with existing river width databases for the Congo and Mississippi Rivers. The effective river width of the GWD-LR is slightly narrower compared to the existing databases, but the relative difference is within ±20% for most river channels. As the river width of the GWD-LR is calculated along the river channels of the HydroSHEDS flow direction map, it is relatively straightforward to apply the GWD-LR to global- and continental-scale river modeling

Rising minimum daily flows in northern Eurasian rivers: A growing influence of groundwater in the high‐latitude hydrologic cycle

A first analysis of new daily discharge data for 111 northern rivers from 1936–1999 and 1958–1989 finds an overall pattern of increasing minimum daily flows (or “low flows”) throughout Russia. These increases are generally more abundant than are increases in mean flow and appear to drive much of the overall rise in mean flow observed here and in previous studies. Minimum flow decreases have also occurred but are less abundant. The minimum flow increases are found in summer as well as winter and in nonpermafrost as well as permafrost terrain. No robust spatial contrasts are found between the European Russia, Ob\u27, Yenisey, and Lena/eastern Siberia sectors. A subset of 12 unusually long discharge records from 1935–2002, concentrated in south central Russia, suggests that recent minimum flow increases since ∼1985 are largely unprecedented in the instrumental record, at least for this small group of stations. If minimum flows are presumed sensitive to groundwater and unsaturated zone inputs to river discharge, then the data suggest a broad‐scale mobilization of such water sources in the late 20th century. We speculate that reduced intensity of seasonal ground freezing, together with precipitation increases, might drive much of the well documented but poorly understood increases in river discharge to the Arctic Ocean

The effects of spatial resolution and dimensionality on modeling regional-scale hydraulics in a multichannel river

As modeling capabilities at regional and global scales improve, questions remain regarding the appropriate process representation required to accurately simulate multichannel river hydraulics. This study uses the hydrodynamic model LISFLOOD-FP to simulate patterns of water surface elevation (WSE), depth, and inundation extent across a ∼90 km, anabranching reach of the Tanana River, Alaska. To provide boundary conditions, we collected field observations of bathymetry and WSE during a 2 week field campaign in summer 2013. For the first time at this scale, we test a simple, raster-based model's capabilities to simulate 2-D, in-channel patterns of WSE and inundation extent. Additionally, we compare finer resolution (≤25 m) 2-D models to four other models of lower dimensionality and coarser resolution (100–500 m) to determine the effects of simplifying process representation. Results indicate that simple, raster-based models can accurately simulate 2-D, in-channel hydraulics in the Tanana. Also, the fine-resolution, 2-D models produce lower errors in spatiotemporal outputs of WSE and inundation extent compared to coarse-resolution, 1-D models: 22.6 cm versus 56.4 cm RMSE for WSE, and 90% versus 41% Critical Success Index values for simulating inundation extent. Incorporating the anabranching channel network using subgrid representations for smaller channels is important for simulating accurate hydraulics and lowers RMSE in spatially distributed WSE by at least 16%. As a result, better representation of the converging and diverging multichannel network by using subgrid solvers or downscaling techniques in multichannel rivers is needed to improve errors in regional to global-scale models

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

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 >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

Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables

Development of the Global Width Database for Large Rivers

River width is a fundamental parameter of river hydrodynamic simulations, but no global-scale river width database based on observed water bodies has yet been developed. Here we present a new algorithm that automatically calculates river width from satellite-based water masks and flow direction maps. The Global Width Database for Large Rivers (GWD-LR) is developed by applying the algorithm to the SRTM Water Body Database and the HydroSHEDS flow direction map. Both bank-to-bank river width and effective river width excluding islands are calculated for river channels between 60S and 60N. The effective river width of GWD-LR is compared with existing river width databases for the Congo and Mississippi Rivers. The effective river width of the GWD-LR is slightly narrower compared to the existing databases, but the relative difference is within ±20% for most river channels. As the river width of the GWD-LR is calculated along the river channels of the HydroSHEDS flow direction map, it is relatively straightforward to apply the GWD-LR to global- and continental-scale river modeling

Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing