Literature DB >> 32908457

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment.

Michael F Jasinski1, Jordan S Borak2,1, Sujay V Kumar1, David M Mocko3,4,1, Christa D Peters-Lidard5, Matthew Rodell1, Hualan Rui6,7, Hiroko K Beaudoing2,1, Bruce E Vollmer6, Kristi R Arsenault3,1, Bailing Li2,1, John D Bolten1, Natthachet Tangdamrongsub2,1.   

Abstract

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125°× 1258° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm yr-1 in the upper Great Plains and Northeast to -1 to -9 mm yr-1 in the West and South, net radiation flux trends range from 10.05 to 10.20 W m-2 yr-1 in the East to -0.05 to -0.20 W m-2 yr-1 in the West, and U.S.-wide temperature trends average about +0.03 K yr-1. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days yr-1 while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W m-2 yr-1, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm yr-1 while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Entities:  

Year:  2019        PMID: 32908457      PMCID: PMC7477810          DOI: 10.1175/jhm-d-17-0234.1

Source DB:  PubMed          Journal:  J Hydrometeorol        ISSN: 1525-7541            Impact factor:   4.349


  6 in total

1.  The application of multi-mission satellite data assimilation for studying water storage changes over South America.

Authors:  M Khaki; J Awange
Journal:  Sci Total Environ       Date:  2018-08-08       Impact factor: 7.963

2.  Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence.

Authors:  Seyed Hamed Alemohammad; Bin Fang; Alexandra G Konings; Filipe Aires; Julia K Green; Jana Kolassa; Diego Miralles; Catherine Prigent; Pierre Gentine
Journal:  Biogeosciences       Date:  2017-09-20       Impact factor: 4.295

3.  Benefits and Pitfalls of GRACE Data Assimilation: a Case Study of Terrestrial Water Storage Depletion in India.

Authors:  Manuela Girotto; Gabriëlle J M De Lannoy; Rolf H Reichle; Matthew Rodell; Clara Draper; Soumendra N Bhanja; Abhijit Mukherjee
Journal:  Geophys Res Lett       Date:  2017-04-24       Impact factor: 4.720

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

Authors:  Amy McNally; Kristi Arsenault; Sujay Kumar; Shraddhanand Shukla; Pete Peterson; Shugong Wang; Chris Funk; Christa D Peters-Lidard; James P Verdin
Journal:  Sci Data       Date:  2017-02-14       Impact factor: 6.444

5.  Global land moisture trends: drier in dry and wetter in wet over land.

Authors:  Huihui Feng; Mingyang Zhang
Journal:  Sci Rep       Date:  2015-12-11       Impact factor: 4.379

6.  Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA Climate Division data plus comparisons to reanalyses.

Authors:  Richard Grotjahn; Jonathan Huynh
Journal:  Sci Rep       Date:  2018-07-24       Impact factor: 4.379

  6 in total
  1 in total

1.  Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction.

Authors:  Hamed Ghaedi; Allison C Reilly; Hiba Baroud; Daniel V Perrucci; Celso M Ferreira
Journal:  PLoS One       Date:  2022-08-03       Impact factor: 3.752

  1 in total

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