Evaluation of Retrospective National Water Model Soil Moisture and Streamflow for Drought‐Monitoring Applications

Author:

Hughes M.1ORCID,Jackson D. L.12ORCID,Unruh D.3,Wang H.4,Hobbins M.12ORCID,Ogden F. L.3,Cifelli R.1,Cosgrove B.3,DeWitt D.4,Dugger A.5ORCID,Ford T. W.6ORCID,Fuchs B.7,Glaudemans M.38,Gochis D.5ORCID,Quiring S. M.9ORCID,RafieeiNasab A.5,Webb R. S.1,Xia Y.10,Xu L.411

Affiliation:

1. NOAA Physical Sciences Laboratory Boulder CO USA

2. Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA

3. NOAA Office of Water Prediction Silver Spring MD USA

4. NOAA Climate Prediction Center University Research Court College Park MD USA

5. National Center for Atmospheric Research Boulder CO USA

6. Illinois State Water Survey Prairie Research Institute University of Illinois, Urbana Champaign IL USA

7. National Drought Mitigation Center University of Nebraska Lincoln NE USA

8. Now at NOAA Analyze, Forecast, Support Office Silver Spring MD USA

9. Ohio State University Columbus OH USA

10. SAIC at NOAA Environmental Modeling Center National Centers for Environmental Prediction College Park MD USA

11. ERT, Inc Laurel MD USA

Abstract

AbstractThe National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

Funder

Climate Program Office

National Integrated Drought Information System

Cooperative Institute for Research in Environmental Sciences

Publisher

American Geophysical Union (AGU)

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