Ben Livneh

A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions*

AbstractThis paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset h...

Noah LSM Snow Model Diagnostics and Enhancements

Abstract A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United ...

Soil Moisture, Snow, and Seasonal Streamflow Forecasts in the United States

AbstractLand surface model experiments are used to quantify, for a number of U.S. river basins, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Snow initialization has a major impact on skill during the spring melting season. Soil moisture initialization has a smaller but still statistically significant impact during this season, and in other seasons, its contribution to skill dominates. Realistic soil moisture initialization can contribute to skill at long leads (over 6 months) for certain basins and seasons. Skill levels in all seasons are found to be related to the ratio of initial total water storage (soil water plus snow) variance to the forecast period precipitation variance, allowing estimates of the potential for skill in areas outside the verification basins.

A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013

A data set of observed daily precipitation, maximum and minimum temperature, gridded to a 1/16° (~6 km) resolution, is described that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53° N for the period 1950–2013. The dataset improves previous products in spatial extent, orographic precipitation adjustment over Mexico and parts of Canada, and reduction of transboundary discontinuities. The impacts of adjusting gridded precipitation for orographic effects are quantified by scaling precipitation to an elevation-aware 1981–2010 precipitation climatology in Mexico and Canada. Differences are evaluated in terms of total precipitation as well as by hydrologic quantities simulated with a land surface model. Overall, orographic correction impacts total precipitation by up to 50% in mountainous regions outside CONUS. Hydrologic fluxes show sensitivities of similar magnitude, with discharge more sensitive than evapotranspiration and soil moisture. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities as compared with a commonly used reanalysis product, making it suitable for estimating large-scale hydrometeorologic phenomena.

Assessing the Impacts of Global Warming on Snowpack in the Washington Cascades

Abstract The decrease in mountain snowpack associated with global warming is difficult to estimate in the presence of the large year-to-year natural variability in observations of snow-water equivalent (SWE). A more robust approach for inferring the impacts of global warming is to estimate the temperature sensitivity (λ) of spring snowpack and multiply it by putative past and future temperature rises observed across the Northern Hemisphere. Estimates of λ can be obtained from (i) simple geometric considerations based on the notion that as the seasonal-mean temperature rises by the amount δT, the freezing level and the entire snowpack should rise by the increment δT/Γ, where Γ is the mean lapse rate; (ii) the regression of 1 April SWE measurements upon mean winter temperatures; (iii) a hydrological model forced by daily temperature and precipitation observations; and (iv) the use of inferred accumulated snowfall derived from daily temperature and precipitation data as a proxy for SWE. All four methods yiel...

Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation

AbstractThe 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 snow depth 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 translat...

How Has Human-Induced Climate Change Affected California Drought Risk?

AbstractThe current California drought has cast a heavy burden on statewide agriculture and water resources, further exacerbated by concurrent extreme high temperatures. Furthermore, industrial-era global radiative forcing brings into question the role of long-term climate change with regard to California drought. How has human-induced climate change affected California drought risk? Here, observations and model experimentation are applied to characterize this drought employing metrics that synthesize drought duration, cumulative precipitation deficit, and soil moisture depletion. The model simulations show that increases in radiative forcing since the late nineteenth century induce both increased annual precipitation and increased surface temperature over California, consistent with prior model studies and with observed long-term change. As a result, there is no material difference in the frequency of droughts defined using bivariate indicators of precipitation and near-surface (10 cm) soil moisture, bec...

High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California*

AbstractGridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the...

Snowmelt rate dictates streamflow

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 (BSA) using Variable Infiltration Capacity model-simulated evapotranspiration, potential evapotranspiration, and estimated precipitation at 1/16° resolution from 1950-2013. 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.

Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study

Correctly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow-atmosphere-soil transfer, Land Ecosystem-Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land-atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below-canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land-atmosphere interactions.