A land data assimilation system for sub-Saharan Africa food and water security applications

McNally, Amy; Arsenault, Kristi R.; Kumar, Sujay V.; Shukla, Shraddhanand; Peterson, Peter Y.; Wang, Shugong; Funk, Chris; Peters‐Lidard, Christa D.; Verdin, J. P.

doi:10.1038/sdata.2017.12

Cited by 344 publications

(177 citation statements)

References 79 publications

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“…At the same time, a better representation of surface soil moisture in atmospheric models has shown to improve weather forecasts (e.g., Bisselink et al, 2011;Orth et al, 2016;Quesada et al, 2012;van den Hurk et al, 2012) and constrain predictions of future climate variability (Sippel et al, 2016;van den Hurk et al, 2016;Vogel et al, 2017). This has further implications as land surface models (LSMs) are increasingly used to assess drought conditions (Dai, 2013;Prudhomme et al, 2014;Ukkola et al, 2016) and develop early-warning systems (McNally et al, 2017). However, intercomparing results from past soil moisture-temperature coupling studies is not straightforward: they are not only commonly based on a single land surface model and coupling metric, and rarely contrasted against observational data, but are also affected by the particular choice of atmospheric forcing.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture‐Temperature Coupling in a Set of Land Surface Models

Gevaert

Miralles

Jeu³

et al. 2018

JGR Atmospheres

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The land surface controls the partitioning of water and energy fluxes and therefore plays a crucial role in the climate system. The coupling between soil moisture and air temperature, in particular, has been shown to affect the severity and occurrence of temperature extremes and heat waves. Here we study soil moisture‐temperature coupling in five land surface models, focusing on the terrestrial segment of the coupling in the warm season. All models are run off‐line over a common period with identical atmospheric forcing data, in order to allow differences in the results to be attributed to the models' partitioning of energy and water fluxes. Coupling is calculated according to two semiempirical metrics, and results are compared to observational flux tower data. Results show that the locations of the global hot spots of soil moisture‐temperature coupling are similar across all models and for both metrics. In agreement with previous studies, these areas are located in transitional climate regimes. The magnitude and local patterns of model coupling, however, can vary considerably. Model coupling fields are compared to tower data, bearing in mind the limitations in the geographical distribution of flux towers and the differences in representative area of models and in situ data. Nevertheless, model coupling correlates in space with the tower‐based results ( r = 0.5–0.7), with the multimodel mean performing similarly to the best‐performing model. Intermodel differences are also found in the evaporative fractions and may relate to errors in model parameterizations and ancillary data of soil and vegetation characteristics.

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Section: Introductionmentioning

confidence: 99%

Soil Moisture‐Temperature Coupling in a Set of Land Surface Models

Gevaert

Miralles

Jeu³

et al. 2018

JGR Atmospheres

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“…At least 21 of the 55 groups participating in the SMAP Early Adopter Program (Moran et al, 2015) use the ensemble Kalman filter (EnKF; Evensen, 2003) as a primary method for extracting information from SMAP data products. Soil moisture DA is used routinely in Land Data Assimilation Systems (LDAS; Kumar et al, 2008;McNally et al, 2017;Rodell et al, 2004;Xia et al, 2011) for hydrological and hydrometeorological modeling (Maggioni & Houser, 2017), as well as in many other hydrology-related remote sensing applications (Mladenova et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of Data Assimilation

Nearing

Yatheendradas

Crow

et al. 2018

Water Resources Research

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Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real‐world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of trade‐offs between improving (usually expensive) remote sensing observing systems versus improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the ensemble Kalman filter used to assimilate remote sensing soil moisture retrievals from Advanced Microwave Scattering Radiometer for Earth (AMSR‐E) into the Noah land surface model.

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“…The objectives of this study are to 1) identify the most suitable combination of LSMs, reanalysis and precipitation data for a water balance study in the upper Blue Nile basin; and 2) support the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS;McNally et al, 2017) by evaluating water budget components to ensure high quality of drought monitoring products. This study provides comparisons of multi-model inputs (i.e.…”

Section: Introductionmentioning

confidence: 99%

Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

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a b s t r a c tImproved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, insitu streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

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A land data assimilation system for sub-Saharan Africa food and water security applications

Cited by 344 publications

References 79 publications

Soil Moisture‐Temperature Coupling in a Set of Land Surface Models

Soil Moisture‐Temperature Coupling in a Set of Land Surface Models

The Efficiency of Data Assimilation

Upper Blue Nile basin water budget from a multi-model perspective

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