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

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.

[1]  M. Rodell,et al.  Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model , 2016 .

[2]  H. B. Mann Nonparametric Tests Against Trend , 1945 .

[3]  J. Thepaut,et al.  Toward a Consistent Reanalysis of the Climate System , 2014 .

[4]  A. Bondeau,et al.  Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model , 2009 .

[5]  Minha Choi,et al.  Estimation of evapotranspiration from ground-based meteorological data and global land data assimilation system (GLDAS) , 2014, Stochastic Environmental Research and Risk Assessment.

[6]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[7]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[8]  Markus Reichstein,et al.  Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data , 2011 .

[9]  Sujay V. Kumar,et al.  A land data assimilation system for sub-Saharan Africa food and water security applications , 2017, Scientific Data.

[10]  Toshio Koike,et al.  Simultaneous estimation of both hydrological and ecological parameters in an ecohydrological model by assimilating microwave signal , 2014 .

[11]  Dara Entekhabi,et al.  Tests of sequential data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness , 1999, IEEE Trans. Geosci. Remote. Sens..

[12]  Michael A. Palecki,et al.  Trend Identification in Twentieth-Century U.S. Snowfall: The Challenges , 2007 .

[13]  Luca Brocca,et al.  Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Sarah M. Champion,et al.  Trends and Extremes in Northern Hemisphere Snow Characteristics , 2016, Current Climate Change Reports.

[15]  Witold F. Krajewski,et al.  An analysis of small-scale rainfall variability in different climatic regimes , 2003 .

[16]  B. Braswell,et al.  Trends in wintertime climate in the northeastern United States: 1965–2005 , 2008 .

[17]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[18]  G. Senay,et al.  A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET , 2013 .

[19]  P. Brooks,et al.  Changes in snowpack accumulation and ablation in the intermountain west , 2012 .

[20]  G. Hegerl,et al.  Indices for monitoring changes in extremes based on daily temperature and precipitation data , 2011 .

[21]  A. V. Vecchia,et al.  Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods and Droughts in the United States: State of Knowledge , 2013 .

[22]  M. Rodell,et al.  Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin , 2008 .

[23]  Huihui Feng,et al.  Global land moisture trends: drier in dry and wetter in wet over land , 2015, Scientific Reports.

[24]  Sujay V. Kumar,et al.  Land information system: An interoperable framework for high resolution land surface modeling , 2006, Environ. Model. Softw..

[25]  Juan B. Valdés,et al.  On the influence of the spatial distribution of rainfall on storm runoff , 1979 .

[26]  T. Koike,et al.  A land data assimilation system for simultaneous simulation of soil moisture and vegetation dynamics , 2015 .

[27]  Guiling Wang,et al.  Understanding evapotranspiration trends and their driving mechanisms over the NLDAS domain based on numerical experiments using CLM4.5 , 2016 .

[28]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[29]  Wade T. Crow,et al.  Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations , 2009 .

[30]  M. Sharifi,et al.  Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model , 2018 .

[31]  J. Awange,et al.  The application of multi-mission satellite data assimilation for studying water storage changes over South America. , 2019, The Science of the total environment.

[32]  Alina Barbu,et al.  Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area , 2017 .

[33]  Chris Derksen,et al.  Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes , 2010 .

[34]  Giorgio Boni,et al.  Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation , 2001 .

[35]  K. Mo,et al.  Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products , 2012 .

[36]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[37]  W. Wagner,et al.  Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing , 2013 .

[38]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[39]  R. Moore,et al.  Rainfall and sampling uncertainties: A rain gauge perspective , 2008 .

[40]  Michael A. Palecki,et al.  Trends in Twentieth-Century U.S. Snowfall Using a Quality-Controlled Dataset , 2009 .

[41]  K. Cowtan,et al.  Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network , 2016 .

[42]  S. Hagemann,et al.  Can climate trends be calculated from reanalysis data , 2004 .

[43]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[44]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[45]  Wouter Dorigo,et al.  Homogeneity of a global multisatellite soil moisture climate data record , 2016 .

[46]  Yudong Tian,et al.  Estimating evapotranspiration with land data assimilation systems , 2011 .

[47]  D. Toll,et al.  Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data , 2010 .

[48]  Randal D. Koster,et al.  Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models , 2010 .

[49]  Bailing Li,et al.  NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment , 2017, Journal of Hydrometeorology.

[50]  N. Knowles Trends in Snow Cover and Related Quantities at Weather Stations in the Conterminous United States , 2015 .

[51]  R. Grotjahn,et al.  Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA Climate Division data plus comparisons to reanalyses , 2018, Scientific Reports.

[52]  M. Ek,et al.  Continental‐scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS‐2): 2. Validation of model‐simulated streamflow , 2012 .

[53]  Libo Wang,et al.  A multi‐data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008 , 2010 .

[54]  Dick Dee,et al.  Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets , 2010 .

[55]  Filipe Aires,et al.  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. , 2017, Biogeosciences.

[56]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[57]  B. Forman,et al.  The spatial scale of model errors and assimilated retrievals in a terrestrial water storage assimilation system , 2013 .

[58]  Martin Wild,et al.  Enlightening Global Dimming and Brightening , 2012 .

[59]  Teruo Aoki,et al.  A 38-year (1978–2015) Northern Hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors , 2017 .

[60]  M. Clark,et al.  Snow Data Assimilation via an Ensemble Kalman Filter , 2006 .

[61]  James E. Hocker,et al.  Hydro-Climatological Drought Analyses and Projections Using Meteorological and Hydrological Drought Indices: A Case Study in Blue River Basin, Oklahoma , 2012, Water Resources Management.

[62]  Abhijit Mukherjee,et al.  A Data Assimilation Perspective on India’s Terrestrial Water Storage Trends , 2017 .

[63]  J. Curry,et al.  Berkeley Earth Temperature Averaging Process , 2013 .

[64]  Lifeng Luo,et al.  Basin‐Scale Assessment of the Land Surface Energy Budget in the NCEP Operational and Research NLDAS‐2 Systems , 2015 .

[65]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[66]  Xubin Zeng,et al.  Intercomparison of Seven NDVI Products over the United States and Mexico , 2014, Remote. Sens..

[67]  Benjamin F. Zaitchik,et al.  Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model , 2009 .

[68]  Russell S. Vose,et al.  Reanalyses Suitable for Characterizing Long-Term Trends , 2010 .

[69]  Ning Ma,et al.  A Systematic Evaluation of Noah‐MP in Simulating Land‐Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States , 2017 .

[70]  R. Lund,et al.  Trends in Extreme U.S. Temperatures , 2014 .

[71]  V. Kousky,et al.  Assessing objective techniques for gauge‐based analyses of global daily precipitation , 2008 .

[72]  Alina Barbu,et al.  Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France , 2013 .

[73]  P. Sen Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[74]  Taha B. M. J. Ouarda,et al.  Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study , 2003 .

[75]  Wade T. Crow,et al.  Dual Forcing and State Correction via Soil Moisture Assimilation for Improved Rainfall–Runoff Modeling , 2014 .