Ben Livneh

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Larry J Zimmerman

Indiana University Indianapolis

Saeed Golian

Shahrood University of Technology

DEBAPRASAD BANDYOPADHYAY

Saint-Petersburg State University

Thomas BURELLI

University of Ottawa | Université d'Ottawa

Solichin Manuri

The Australian National University

Normah Awang Besar

Universiti Malaysia Sabah

Joana Cabral de Oliveira

Universidade Estadual de Campinas

Harshavardhan Deshmukh

INDIRA GANDHI NATIONAL OPEN UNIVERSITY

Eduardo Chávarri

Universidad Nacional Agraria La Molina

Shipra Shah

Fiji National University

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New Interest in Reservoir Evaporation in Western United States

Eos, 2016

Assimilation of AMSR-E snow products in mountainous basins

Agu Fall Meeting Abstracts, Dec 1, 2011

Water storage in the form of snowpacks is a significant term in the inter-seasonal and inter-annu... more Water storage in the form of snowpacks is a significant term in the inter-seasonal and inter-annual water budgets of many mountainous regions. Accurate estimation of snow water equivalence (SWE) is also important for prediction of water supply, and flood forecasting in snow-dominant river basins. We aim to improve SWE estimation in such regions by assimilating AMSR-E satellite data into the Variable Infiltration Capacity (VIC) hydrologic model. We apply the model at high resolution (1 km) In order to more accurately represent topographic variability. We combine the VIC SWE simulation with a forward microwave emissions model, the Dense Media Radiative Transfer (DMRT) model. The Ensemble Kalman Filter (EnKF) is used to produce advanced and updated SWE simulations. The observations are the AMSR-E brightness temperatures. The SWE estimates resulting from the data assimilation scheme are evaluated using surface measurements from SNOTEL sites in the Salmon River Basin, Idaho. In addition, ground measurements were conducted in February, 2009 and March 2010 to evaluate the sensitivity of the brightness temperature to relatively deep snowpacks. The measurement data shows consistency through two years and also comparable with satellite observation by tuning the grain size and adding forest effect.

The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

This talk describes two recent studies that address the potential contributions of soil moisture ... more This talk describes two recent studies that address the potential contributions of soil moisture information to hydrological and meteorological prediction. Although the studies utilize soil moisture derived from the integration of large-scale land surface models with observations-based meteorological data, the results directly illustrate the potential usefulness of satellite-derived soil moisture information (e.g., from SMOS and SMAP) for applications in prediction. The first study, the GEWEX- and CLIVAR-sponsored GLACE-2 project, quantifies the contribution of realistic soil moisture initialization to skill in subseasonal forecasts of precipitation and air temperature (out to two months). The multi-model study shows that soil moisture information does indeed contribute skill to the forecasts, particularly for air temperature, and particularly when the initial local soil moisture anomaly is large. Furthermore, the skill contributions tend to be larger where the soil moisture initialization is more accurate, as measured by the density of the observational network contributing to the initialization. The second study focuses on streamflow prediction. The relative contributions of snow and soil moisture initialization to skill in streamflow prediction at seasonal lead, in the absence of knowledge of meteorological anomalies during the forecast period, were quantified with several land surface models using uniquely designed numerical experiments and naturalized streamflow data covering multiple decades over the western United States. In several basins, accurate soil moisture initialization is found to contribute significant levels of predictive skill. Depending on the date of forecast issue, the contributions can be significant out to leads of six months. Both studies suggest that improvements in soil moisture initialization would lead to increases in predictive skill. The relevance of SMOS and SMAP satellite-based soil moisture information to prediction are discussed in the context of these studies.

Transferability of land surface model parameters using remote sensing and in situ observations

Assessing the Contributions of East African and West Pacific Warming to the 2014 Boreal Spring East African Drought

Bulletin of the American Meteorological Society, 2015

Evaporative partitioning in a unified land model

Accurate partitioning of precipitation into evapotranspiration and runoff, and more generally est... more Accurate partitioning of precipitation into evapotranspiration and runoff, and more generally estimation of the surface water balance, is crucial both for hydrologic forecasting and numerical weather and climate prediction. One important aspect of this issue is the partitioning of evapotranspiration into soil evaporation, canopy evaporation, and plant transpiration, which in turn has implications for other terms in the surface water balance. In the first part of the study, we tested several well known land surface models in multi-year simulations over the continental U.S. Among the models, which included the Variable Infiltration Capacity (VIC) model, the Community Land Model (CLM), the Noah Land Surface Model (Noah LSM), and the NASA Catchment model, there were substantial variations in the partitioning. These results motivated a more detailed evaluation, using data for two catchments that were a part of the second phase of the Distributed Model Intercomparison Project (DMIP-2), the East Fork Carson River Basin and the Illinois River Basin. In this portion of the study, we evaluated a unified land model (ULM) which is a merger of the NWS Sacramento Soil Moisture Accounting model (SAC-SMA), which is used operationally for flood and seasonal streamflow prediction, and the Noah LSM, which is the land scheme used in NOAA’s suite of weather and climate prediction models. Our overall objective is to leverage the operational strengths of each model, specifically to improve streamflow prediction and soil moisture states within the Noah LSM framework, and to add a vegetation component to the SAC-SMA model. Partitioning of evapotranspiration into its three components is a key part of the ULM performance, and controls our ability to use calibrated SAC-SMA parameters within the ULM framework. In our evaluations at the DMIP-2 sites, we examined sensitivities of soil moisture and evaporative components in ULM to changes in vegetation cover, root zone depth, canopy interception capacity, and aerodynamic resistance. Using observations of evaporative fluxes above and below the canopy, we compare model performance and suggest physics upgrades to advance model performance.

Land Surface Model parameter regionalization with remote sensing and observations

Development of a unified land model with multi-criteria observational data for the simulation of regional hydrology and land-atmosphere interaction

NCEP/NLDAS Drought Monitoring and Prediction

by Ben Livneh and J. Schaake

Agu Spring Meeting Abstracts, May 1, 2009

The NCEP Environmental Modeling Center (EMC) collaborated with its CPPA (Climate Prediction Progr... more The NCEP Environmental Modeling Center (EMC) collaborated with its CPPA (Climate Prediction Program of the Americas) partners to develop a North American Land Data Assimilation System (NLDAS, http://www.emc.ncep.noaa.gov/mmb/nldas) to monitor and predict the drought over the Continental United States (CONUS). The realtime NLDAS drought monitor, executed daily at NCEP/EMC, including daily, weekly and monthly anomaly and percentile of six fields (soil moisture, snow water equivalent, total runoff, streamflow, evaporation, precipitation) outputted from four land surface models (Noah, Mosaic, SAC, and VIC) on a common 1/8th degree grid using common hourly land surface forcing. The non-precipitation surface forcing is derived from NCEP's retrospective and realtime North American Regional Reanalysis System (NARR). The precipitation forcing is anchored to a daily gauge-only precipitation analysis over CONUS that applies a Parameter-elevation Regressions on Independent Slopes Model (PRISM) correction. This daily precipitation analysis is then temporally disaggregated to hourly precipitation amounts using radar and satellite precipitation. The NARR- based surface downward solar radiation is bias-corrected using seven years (1997-2004) of GOES satellite- derived solar radiation retrievals. The uncoupled ensemble seasonal drought prediction utilizes the following three independent approaches for generating downscaled ensemble seasonal forecasts of surface forcing: (1) Ensemble Streamflow Prediction, (2) CPC Official Seasonal Climate Outlook, and (3) NCEP CFS ensemble dynamical model prediction. For each of these three approaches, twenty ensemble members of forcing realizations are generated using a Bayesian merging algorithm developed by Princeton University. The three forcing methods are then used to drive the VIC model in seasonal prediction mode over thirteen large river basins that together span the CONUS domain. One to nine month ensemble seasonal prediction products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation and streamflow are derived for each forcing approach. The anomalies and percentiles of the predicted products for each approach may be used for CONUS drought prediction. This system is executed at the beginning of each month and distributes its products by the 10th of each month. The prediction products are evaluated using corresponding monitoring products for the VIC model and are compared with the prediction products from other research groups (e.g., University of Washington at Seattle, NASA Goddard) in the CONUS.

Diagnosis of performance of the Noah LSM snow model

Agu Fall Meeting Abstracts, Dec 1, 2007

Quantifying snow water equivalent (SWE) and runoff timing are vital in hydrologic modeling, espec... more Quantifying snow water equivalent (SWE) and runoff timing are vital in hydrologic modeling, especially for snowmelt driven regimes such as the western U.S. and the pan-Arctic. The Noah land-surface model, used in the National Center for Environmental Prediction's operational suite of weather and climate forecast models, has shown a tendency toward ablation of snowpacks earlier than in observations in off-line tests. We report a diagnosis of the sensitivities of various components of the snow scheme in the Noah model that might affect snow ablation. We find that issues with the model's turbulent exchange schemes, and its radiative exchange formulations appear to be the main contributors to the negative SWE bias during the ablation season. Using modified albedo and surface exchange schemes, we test alternative formulations both using point observations at observing sites in mountain maritime locations in the Pacific Northwest, and in the interior of the western U.S. and Canada, as well as in simulations of seasonal variations of snow extent over the entire pan-Arctic drainage. Our results suggest that the a modest change in the snow albedo formulation results in the greatest improvement in model performance, although under some conditions an alternation in the turbulent exchange parameterization produces improved results as well.

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

by Ben Livneh and K. Arsenault

Http Dx Doi Org 10 1175 Jhm D 13 0132 1, Dec 2, 2014

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How does availability of meteorological forcing data impact physically-based snowpack simulations?

Journal of Hydrometeorology, 2015

Bark Beetles: Cause for Concern in Snowy Western Watersheds?

http://utilityii.com/bark-beetles-cause-for-concern-in-snowy-western-watersheds/

Forest disturbances, management actions, and a changing climates implications for carbon and water supplies

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

by Ben Livneh and Theodore Bohn

Scientific Data, 2015

A data set of observed daily precipitation, maximum and minimum temperature, gridded to a 1/16° (... more 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.

The relative value of MRED winter seasonal forecasts vs. statistically downscaled CFS forecasts for seasonal hydrological forecasting

The relative value of dynamical vs. statistical downscaling of Climate Forecast System (CFS) fore... more The relative value of dynamical vs. statistical downscaling of Climate Forecast System (CFS) forecasts for seasonal hydrologic forecasting is assessed. The dynamically downscaled retrospective climate forecasts were produced by the MRED ("Multi-RCM Ensemble Downscaling of NCEP CFS Seasonal Forecasts") project. In MRED, multiple Regional Climate Models (RCMs) were used to downscale CFS wintertime seasonal forecast from original spatial resolution of 2.5 degree to 0.375 degree (dynamical downscaling). We used 10 common ensemble members among all RCMs (initialization dates at 0000 UTC Nov. 21-25, Nov 29-Dec. 3) for the forecast period of Dec. 1-Apr. over 22-year period (1982-2003). The spatial domain is the Conterminous United States (CONUS). We assessed the value of the MRED forecasts in comparison with a much simpler bias correction and spatial downscaling (BCSD) (statistical downscaling); specifically in terms of the resultant seasonal forecast skill of hydrologic variable...

Comparison and Analysis of 30-year NCEP/NLDAS Products for the Winter Season

Noah snow physics is upgraded based on the collaboration between NCEP Environmental Model Center ... more Noah snow physics is upgraded based on the collaboration between NCEP Environmental Model Center Land-Hydrology Group and Hydrological Group of University of Washington by constraining sublimation on snow surfaces, increasing maximum snow albedo and including snow age. The upgraded Noah is used to run 30-year (1979-2008) North-America Land Data Assimilation System (NLDAS) forcing and generate 30-year NLDAS products. Besides the Noah, the NASA Mosaic, the OHD SAC and the Princeton VIC land models were also run for the same period using a common 1/8th degree grid using common hourly land surface forcing. The non-precipitation surface forcing is derived from NCEP's retrospective and realtime North American Regional Reanalysis System (NARR). The precipitation forcing is anchored to a daily gauge-only precipitation analysis over CONUS that applies a Parameter-elevation Regression on Independent Slopes Model (PRISM) correction. This daily precipitation analysis is then temporally disa...

Implications of the methodological choices for hydrologic portrayals of climate change over the Contiguous United States: statistically downscaled forcing data and hydrologic models

by Naoki Mizukami, Ben Livneh, Lauren Hay, and Ethan Gutmann

Journal of Hydrometeorology, 2015

ABSTRACT

Snowmelt rate dictates streamflow

by Ben Livneh and John Knowles

Geophysical Research Letters, 2016

Towards computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme Water Resour. Res. 49 (9), 5700 - 5714

Water Resources Research

1] We present an assessment of a framework to reduce computational expense required for hydrologi... more 1] We present an assessment of a framework to reduce computational expense required for hydrologic prediction over new domains. A common problem in computational hydrology arises when a hydrologist seeks to model a new domain and is subsequently required to estimate representative model parameters for that domain. Our focus is to extend previous development of the Multiscale Parameter Regionalization (MPR) technique, to a broader set of climatic regimes and spatial scales to demonstrate the utility of this approach. We hypothesize that this technique will be applicable for (1) improving predictions in ungauged basins, and (2) as a tool for upscaling high-fidelity hydrologic simulations closer to a general circulation model (GCM) scales, while appreciably reducing computational expense in parameter estimation. We transfer hydrologic model parameters from a single central European basin, to 80 candidate basins within the United States. The regionalization is further tested across a range of climatic and land-cover conditions to identify potential biases in transferability. The results indicate a high degree of success in transferring parameters from central Europe to North America. Parameter scaling from 1/8 up to 1 confirms that MPR can produce a set of quasi-scale independent parameters, with only modest differences in model performance across scales (<3%). Model skill generally decreases approximately 10-20% when transferring parameters toward alternate climatic and land-cover conditions. Finally, we show that the success of model parameter transfer is contingent upon soil, land-cover, and climatic regimes relative to those used during calibration, particularly going from high-to-low clay content and from dense-to-sparse forest.

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New Interest in Reservoir Evaporation in Western United States

Eos, 2016

Assimilation of AMSR-E snow products in mountainous basins

Agu Fall Meeting Abstracts, Dec 1, 2011

The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

Transferability of land surface model parameters using remote sensing and in situ observations

Assessing the Contributions of East African and West Pacific Warming to the 2014 Boreal Spring East African Drought

Bulletin of the American Meteorological Society, 2015

Evaporative partitioning in a unified land model

Land Surface Model parameter regionalization with remote sensing and observations

Development of a unified land model with multi-criteria observational data for the simulation of regional hydrology and land-atmosphere interaction

NCEP/NLDAS Drought Monitoring and Prediction

by Ben Livneh and J. Schaake

Agu Spring Meeting Abstracts, May 1, 2009

Diagnosis of performance of the Noah LSM snow model

Agu Fall Meeting Abstracts, Dec 1, 2007

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

by Ben Livneh and K. Arsenault

Http Dx Doi Org 10 1175 Jhm D 13 0132 1, Dec 2, 2014

Download

How does availability of meteorological forcing data impact physically-based snowpack simulations?

Journal of Hydrometeorology, 2015

Bark Beetles: Cause for Concern in Snowy Western Watersheds?

Forest disturbances, management actions, and a changing climates implications for carbon and water supplies

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

by Ben Livneh and Theodore Bohn

Scientific Data, 2015

The relative value of MRED winter seasonal forecasts vs. statistically downscaled CFS forecasts for seasonal hydrological forecasting

Comparison and Analysis of 30-year NCEP/NLDAS Products for the Winter Season

Implications of the methodological choices for hydrologic portrayals of climate change over the Contiguous United States: statistically downscaled forcing data and hydrologic models

by Naoki Mizukami, Ben Livneh, Lauren Hay, and Ethan Gutmann

Journal of Hydrometeorology, 2015

ABSTRACT

Snowmelt rate dictates streamflow

by Ben Livneh and John Knowles

Geophysical Research Letters, 2016

Towards computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme Water Resour. Res. 49 (9), 5700 - 5714

Water Resources Research

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