Climate change and human activities increasingly threaten lakes that store 87% of Earth's liquid surface fresh water. Yet, recent trends and drivers of lake volume change remain largely unknown ...globally. Here, we analyze the 1972 largest global lakes using three decades of satellite observations, climate data, and hydrologic models, finding statistically significant storage declines for 53% of these water bodies over the period 1992-2020. The net volume loss in natural lakes is largely attributable to climate warming, increasing evaporative demand, and human water consumption, whereas sedimentation dominates storage losses in reservoirs. We estimate that roughly one-quarter of the world's population resides in a basin of a drying lake, underscoring the necessity of incorporating climate change and sedimentation impacts into sustainable water resources management.
The semiarid U.S. Great Plains is prone to severe droughts having major consequences for agricultural production, livestock health, and river navigation. The recent 2012 event was accompanied by ...record deficits in precipitation and high temperatures during the May–August growing season. Here the physics of Great Plains drought are explored by addressing how meteorological drivers induce soil moisture deficits during the growing season. Land surface model (LSM) simulations driven by daily observed meteorological forcing from 1950 to 2013 compare favorably with satellite-derived terrestrial water anomalies and reproduce key features found in the U.S. Drought Monitor. Results from simulations by two LSMs reveal that precipitation was directly responsible for between 72% and 80% of the soil moisture depletion during 2012, and likewise has accounted for the majority of Great Plains soil moisture variability since 1950. Energy balance considerations indicate that a large fraction of the growing season temperature variability is itself driven by precipitation, pointing toward an even larger net contribution of precipitation to soil moisture variability.
To assess robustness across a larger sample of drought events, daily meteorological output from 1050 years of climate simulations, representative of conditions in 1979–2013, are used to drive two LSMs. Growing season droughts, and low soil moisture conditions especially, are confirmed to result principally from rainfall deficits. Antecedent meteorological and soil moisture conditions are shown to affect growing season soil moisture, but their effects are secondary to forcing by contemporaneous rainfall deficits. This understanding of the physics of growing season droughts is used to comment on plausible Great Plains soil moisture changes in a warmer world.
Despite the importance of precipitation phase to global hydroclimate simulations, many land surface models use spatially uniform air temperature thresholds to partition rain and snow. Here we show, ...through the analysis of a 29-year observational dataset (n = 17.8 million), that the air temperature at which rain and snow fall in equal frequency varies significantly across the Northern Hemisphere, averaging 1.0 °C and ranging from -0.4 to 2.4 °C for 95% of the stations. Continental climates generally exhibit the warmest rain-snow thresholds and maritime the coolest. Simulations show precipitation phase methods incorporating humidity perform better than air temperature-only methods, particularly at relative humidity values below saturation and air temperatures between 0.6 and 3.4 °C. We also present the first continuous Northern Hemisphere map of rain-snow thresholds, underlining the spatial variability of precipitation phase partitioning. These results suggest precipitation phase could be better predicted using humidity and air temperature in large-scale land surface model runs.
Water is one of the most critical resources derived from natural systems. While it has long been recognized that forest disturbances like fire influence watershed streamflow characteristics, ...individual studies have reported conflicting results with some showing streamflow increases post-disturbance and others decreases, while other watersheds are insensitive to even large disturbance events. Characterizing the differences between sensitive (e.g. where streamflow does change post-disturbance) and insensitive watersheds is crucial to anticipating response to future disturbance events. Here, we report on an analysis of a national-scale, gaged watershed database together with high-resolution forest mortality imagery. A simple watershed response model was developed based on the runoff ratio for watersheds (n = 73) prior to a major disturbance, detrended for variation in precipitation inputs. Post-disturbance deviations from the expected water yield and streamflow timing from expected (based on observed precipitation) were then analyzed relative to the abiotic and biotic characteristics of the individual watershed and observed extent of forest mortality. The extent of the disturbance was significantly related to change in post-disturbance water yield (p < 0.05), and there were several distinctive differences between watersheds exhibiting post-disturbance increases, decreases, and those showing no change in water yield. Highly disturbed, arid watersheds with low soil: water contact time are the most likely to see increases, with the magnitude positively correlated with the extent of disturbance. Watersheds dominated by deciduous forest with low bulk density soils typically show reduced yield post-disturbance. Post-disturbance streamflow timing change was associated with climate, forest type, and soil. Snowy coniferous watersheds were generally insensitive to disturbance, whereas finely textured soils with rapid runoff were sensitive. This is the first national scale investigation of streamflow post-disturbance using fused gage and remotely sensed data at high resolution, and gives important insights that can be used to anticipate changes in streamflow resulting from future disturbances.
Snowmelt rate dictates streamflow Barnhart, Theodore B.; Molotch, Noah P.; Livneh, Ben ...
Geophysical research letters,
16 August 2016, Letnik:
43, Številka:
15
Journal Article
Recenzirano
Odprti dostop
Declining mountain snowpack and earlier snowmelt across the western United States has implications for downstream communities. We present a possible mechanism linking snowmelt rate and streamflow ...generation using a gridded implementation of the Budyko framework. We computed an ensemble of Budyko streamflow anomalies (BSAs) using Variable Infiltration Capacity model‐simulated evapotranspiration, potential evapotranspiration, and estimated precipitation at 1/16° resolution from 1950 to 2013. BSA was correlated with simulated baseflow efficiency (r2 = 0.64) and simulated snowmelt rate (r2 = 0.42). The strong correlation between snowmelt rate and baseflow efficiency (r2 = 0.73) links these relationships and supports a possible streamflow generation mechanism wherein greater snowmelt rates increase subsurface flow. Rapid snowmelt may thus bring the soil to field capacity, facilitating below‐root zone percolation, streamflow, and a positive BSA. Previous works have shown that future increases in regional air temperature may lead to earlier, slower snowmelt and hence decreased streamflow production via the mechanism proposed by this work.
Key Points
Snowmelt rate drives subsurface flow and streamflow production across the western U.S.
Western U.S. ecoregions have varying streamflow sensitivities to changes in snowmelt rate
Earlier, slower snowmelt usually produces less streamflow than more rapid melt
This is the second part of a study on continental‐scale water and energy flux analysis and validation conducted in phase 2 of the North American Land Data Assimilation System project (NLDAS‐2). The ...first part concentrates on a model‐by‐model comparison of mean annual and monthly water fluxes, energy fluxes and state variables. In this second part, the focus is on the validation of simulated streamflow from four land surface models (Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC‐SMA), and Variable Infiltration Capacity (VIC) models) and their ensemble mean. Comparisons are made against 28‐years (1 October 1979–30 September 2007) of United States Geological Survey observed streamflow for 961 small basins and 8 major basins over the conterminous United States (CONUS). Relative bias, anomaly correlation and Nash‐Sutcliffe Efficiency (NSE) statistics at daily to annual time scales are used to assess model‐simulated streamflow. The Noah (the Mosaic) model overestimates (underestimates) mean annual runoff and underestimates (overestimates) mean annual evapotranspiration. The SAC‐SMA and VIC models simulate the mean annual runoff and evapotranspiration well when compared with the observations. The ensemble mean is closer to the mean annual observed streamflow for both the 961 small basins and the 8 major basins than is the mean from any individual model. All of the models, as well as the ensemble mean, have large daily, weekly, monthly, and annual streamflow anomaly correlations for most basins over the CONUS, implying strong simulation skill. However, the daily, weekly, and monthly NSE analysis results are not necessarily encouraging, in particular for daily streamflow. The Noah and Mosaic models are useful (NSE > 0.4) only for about 10% of the 961 small basins, the SAC‐SMA and VIC models are useful for about 30% of the 961 small basins, and the ensemble mean is useful for about 42% of the 961 small basins. As the time scale increases, the NSE increases as expected. However, even for monthly streamflow, the ensemble mean is useful for only 75% of the 961 small basins.
Key Points
First time to validate long‐term simulated streamflow for NLDAS
A typical exmaple to evaluate NLDAS products
Significant progress for NLDAS project
Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS‐2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, ...Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large‐scale floods. NLDAS‐2 builds upon the framework of the first phase of NLDAS (NLDAS‐1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3‐year (1997–1999) to a 30‐year (1979–2008) time window. As the first of two parts, this paper details the configuration of NLDAS‐2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model‐to‐model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS‐1. The second part of the two‐part article focuses on the validation of model‐simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS‐2 LSMs that were initiated following NLDAS‐1. However, large inter‐model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub‐surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS‐1 to NLDAS‐2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.
Key Points
Important progress of North American Land Data Assimilation System
Long‐term high‐resolution hydrometeorological data set
Application for operational drought monitoring
Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave ...radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature T
surf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234mm (57% of peak SWE), mean differences up to 6.2°C in T
surf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.
In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly ...understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters.
Plain Language Summary
Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future.
Key Points
High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity losses
Estimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacity
Potential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters
The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and ...streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snowdepth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States.A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.
PDF
