Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling

Soil moisture is a key variable in hydrological modelling, which could be estimated by land surface modelling. However the previous studies have focused on evaluating these soil moisture estimates by using point-based measurements, and there is a lack of attention for their appraisal over basin scales particularly for hydrological applications. In this study, we carry out for the first time, a detailed evaluation of five sources of soil moisture products (NLDAS-2 multi-model simulated soil moistures: Noah, VIC, Mosaic and SAC; and a ground observation), against a widely used hydrological model Xinanjiang (XAJ) as a benchmark at a U.S. basin. Generally speaking, all products have good agreements with the hydrological soil moisture simulation, with superior performance obtained from the SAC model and the VIC model. Furthermore, the results indicate that the in-situ measurements in deeper soil layer are still usable for hydrological applications. Nevertheless further improvement is still required on the definition of land surface model layer thicknesses and the related data fusion with the remotely sensed soil moisture. The potential usage of the NLDAS-2 soil moisture datasets in real-time flood forecasting is discussed.

[1]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[3]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[4]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[5]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[6]  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 .

[7]  Cédric H. David,et al.  Hydrological evaluation of the Noah‐MP land surface model for the Mississippi River Basin , 2014 .

[8]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[9]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[10]  Dawei Han,et al.  Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model , 2013, Water Resources Management.

[11]  Q. Duana,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

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

[13]  K. Mitchell,et al.  A parameterization of snowpack and frozen ground intended for NCEP weather and climate models , 1999 .

[14]  Y. Kerr,et al.  Operational readiness of microwave remote sensing of soil moisture for hydrologic applications , 2007 .

[15]  Lotta Andersson,et al.  Soil-moisture deficit simulations with models of varying complexity for forest and grassland sites in Sweden and the U.K. , 1991 .

[16]  Victor Koren,et al.  Physically-based modifications to the Sacramento Soil Moisture Accounting model. Part A: Modeling the effects of frozen ground on the runoff generation process , 2014 .

[17]  Y. Kerr,et al.  State of the Art in Large-Scale Soil Moisture Monitoring , 2013 .

[18]  P. Dirmeyer,et al.  A study of land surface processes using land surface models over the Little River Experimental Watershed, Georgia , 2008 .

[19]  Mustafa Tombul,et al.  Mapping Field Surface Soil Moisture for Hydrological Modeling , 2007 .

[20]  D. Vidal-Madjar,et al.  Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region , 1994 .

[21]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[22]  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 .

[23]  M. Shao,et al.  Modeling Effects of Land use and Vegetation Density on Soil Water Dynamics: Implications on Water Resource Management , 2014, Water Resources Management.

[24]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[25]  Dawei Han,et al.  Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate , 2013 .

[26]  Xuesong Zhang,et al.  Evaluating the SWAT Model for Hydrological Modeling in the Xixian Watershed and a Comparison with the XAJ Model , 2011 .

[27]  M. Ek,et al.  Evaluation of multi-model simulated soil moisture in NLDAS-2 , 2014 .

[28]  J. Qin,et al.  Improving the Noah Land Surface Model in Arid Regions with an Appropriate Parameterization of the Thermal Roughness Length , 2010 .

[29]  Giuseppe Mendicino,et al.  Integrated Drought Watch System: A Case Study in Southern Italy , 2007 .

[30]  Chong-Yu Xu,et al.  Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows , 2012, Water Resources Management.

[31]  Lifeng Luo,et al.  The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill , 2011 .

[32]  A. Ganji,et al.  A Modified Constrained State Formulation of Stochastic Soil Moisture for Crop Water Allocation , 2010 .

[33]  M. Shinoda,et al.  Seasonal change of soil moisture in Mongolia: its climatology and modelling , 2011 .

[34]  Jack McConchie,et al.  In situ measurement of soil moisture and pore-water pressures in an 'incipient' landslide: Lake Tutira, New Zealand. , 2011, Journal of environmental management.

[35]  Dawei Han,et al.  Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application , 2013, Water Resources Management.

[36]  A. Robock,et al.  The Global Soil Moisture Data Bank , 2000 .

[37]  S. Halder,et al.  Performance of Noah land surface model over the tropical semi-arid conditions in western India , 2011 .

[38]  John Kochendorfer,et al.  U.S. Climate Reference Network Soil Moisture and Temperature Observations , 2013 .

[39]  Vijay P. Singh,et al.  Evaluation of the SCS-CN-Based Model Incorporating Antecedent Moisture , 2004 .

[40]  T. Schmugge,et al.  Passive microwave remote sensing system for soil moisture: some supporting research , 1989 .

[41]  Jesse,et al.  U.S. Climate Reference Network after One Decade of Operations: Status and Assessment , 2013 .

[42]  Randal D. Koster,et al.  The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle , 1994 .

[43]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[44]  E. Engman Remote sensing in hydrology , 1990 .

[45]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

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

[47]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins , 2011 .

[48]  Zong-Liang Yang,et al.  Evaluating Enhanced Hydrological Representations in Noah LSM over Transition Zones: Implications for Model Development , 2009 .

[49]  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 .

[50]  M. Borga,et al.  Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins , 2008 .

[51]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[52]  K. Mitchell,et al.  Assessment of the Land Surface and Boundary Layer Models in Two Operational Versions of the NCEP Eta Model Using FIFE Data , 1997 .

[53]  Robert S. Webb,et al.  Global Soil Texture and Derived Water-Holding Capacities (Webb et al.) , 2000 .

[54]  Jeffrey P. Walker,et al.  In situ measurement of soil moisture: a comparison of techniques | NOVA. The University of Newcastle's Digital Repository , 2004 .