On the Sensitivity of the Precipitation Partitioning Into Evapotranspiration and Runoff in Land Surface Parameterizations
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Jiangfeng Wei | Peirong Lin | Lingcheng Li | Zong‐Liang Yang | Jiangfeng Wei | P. Lin | Wen‐Ying Wu | Hui Zheng | Zong‐Liang Yang | Wen‐Ying Wu | Long Zhao | Shu Wang | H. Zheng | Lingcheng Li | Long Zhao | Shu Wang | Hui Zheng
[1] J. Famiglietti,et al. Global Estimates of River Flow Wave Travel Times and Implications for Low‐Latency Satellite Data , 2018 .
[2] R. Dewar,et al. New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis. , 2018, The New phytologist.
[3] 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 .
[4] Ming Ye,et al. A new process sensitivity index to identify important system processes under process model and parametric uncertainty , 2017 .
[5] Atul K. Jain,et al. Challenging terrestrial biosphere models with data from the long‐term multifactor Prairie Heating and CO2 Enrichment experiment , 2017, Global change biology.
[6] Magnus Strand,et al. COMPARISON AND ANALYSIS , 2017 .
[7] Zong-Liang Yang,et al. Effects of soil‐type datasets on regional terrestrial water cycle simulations under different climatic regimes , 2016 .
[8] M. Clark,et al. Towards simplification of hydrologic modeling: identification of dominant processes , 2016 .
[9] J. H. Cushman,et al. Model Validation: Testing Models Using Data and Sensitivity Analysis , 2016 .
[10] Sabine Attinger,et al. The impact of standard and hard‐coded parameters on the hydrologic fluxes in the Noah‐MP land surface model , 2016 .
[11] T. Hoar,et al. Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4-RTM-DART System , 2016 .
[12] Fei Chen,et al. Assessing uncertainties in the Noah‐MP ensemble simulations of a cropland site during the Tibet Joint International Cooperation program field campaign , 2016 .
[13] R. Maxwell,et al. Connections between groundwater flow and transpiration partitioning , 2016, Science.
[14] Gil Bohrer,et al. Tree level hydrodynamic approach for resolving aboveground water storage and stomatal conductance and modeling the effects of tree hydraulic strategy , 2016 .
[15] Yuanchao Fan. Modeling oil palm monoculture and its associated impacts on land-atmosphere carbon, water and energy fluxes in Indonesia , 2016 .
[16] Lifeng Luo,et al. Basin‐scale assessment of the land surface water budget in the National Centers for Environmental Prediction operational and research NLDAS‐2 systems , 2016 .
[17] J. Freer,et al. Improving the theoretical underpinnings of process‐based hydrologic models , 2016 .
[18] Sujay V. Kumar,et al. Basin‐scale assessment of the land surface energy budget in the National Centers for Environmental Prediction operational and research NLDAS‐2 systems , 2016 .
[19] H. Gupta,et al. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory , 2016 .
[20] H. Gupta,et al. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application , 2016 .
[21] G. Mirfenderesgi. Tree-level hydrodynamic approach for modeling aboveground water storage and stomatal conductance illuminates the effects of tree hydraulic strategy , 2016 .
[22] Sabine Attinger,et al. Accelerating advances in continental domain hydrologic modeling , 2015 .
[23] M. Ek,et al. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part II: Impact of Soil Texture Classification and Vegetation Type Mismatches , 2015 .
[24] Steven M. Quiring,et al. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis , 2015 .
[25] Sabine Attinger,et al. Computationally inexpensive identification of noninformative model parameters by sequential screening , 2015 .
[26] D. Lawrence,et al. Improving the representation of hydrologic processes in Earth System Models , 2015 .
[27] G. Katul,et al. Turbulent Energy Spectra and Cospectra of Momentum and Heat Fluxes in the Stable Atmospheric Surface Layer , 2015, Boundary-Layer Meteorology.
[28] Gabriel G. Katul,et al. Revisiting the Turbulent Prandtl Number in an Idealized Atmospheric Surface Layer , 2015 .
[29] Saman Razavi,et al. What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models , 2015 .
[30] Francesca Pianosi,et al. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions , 2015, Environ. Model. Softw..
[31] R. Koster. “Efficiency Space”: A Framework for Evaluating Joint Evaporation and Runoff Behavior* , 2015 .
[32] M. Clark,et al. Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts , 2015 .
[33] Dmitri Kavetski,et al. A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .
[34] Dmitri Kavetski,et al. A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .
[35] R. Maxwell,et al. A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3 , 2015 .
[36] Fei Chen,et al. The effect of groundwater interaction in North American regional climate simulations with WRF/Noah-MP , 2015, Climatic Change.
[37] Zong-Liang Yang,et al. Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed , 2014 .
[38] R. Dickinson,et al. Impact of mesophyll diffusion on estimated global land CO2 fertilization , 2014, Proceedings of the National Academy of Sciences.
[39] 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 .
[40] 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 .
[41] 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 .
[42] R. Dickinson,et al. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .
[43] Jan Polcher,et al. Acceleration of Land Surface Model Development over a Decade of Glass , 2011 .
[44] Robert Leconte,et al. Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin , 2011 .
[45] P. Dirmeyer. A History and Review of the Global Soil Wetness Project (GSWP) , 2011 .
[46] Dmitri Kavetski,et al. Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .
[47] 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 .
[48] Kevin W. Manning,et al. The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins , 2011 .
[49] D. Shahsavani,et al. Variance-based sensitivity analysis of model outputs using surrogate models , 2011, Environ. Model. Softw..
[50] S. Seneviratne,et al. Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations , 2011 .
[51] S. Seneviratne,et al. Global intercomparison of 12 land surface heat flux estimates , 2011 .
[52] Dennis P. Lettenmaier,et al. Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow , 2010 .
[53] Zong-Liang Yang,et al. Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season , 2010 .
[54] Paola Annoni,et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..
[55] 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 .
[56] Hubert H. G. Savenije,et al. Analytical derivation of the Budyko curve based on rainfall characteristics and a simple evaporation model , 2009 .
[57] Zong-Liang Yang,et al. Impacts of vegetation and groundwater dynamics on warm season precipitation over the Central United States , 2009 .
[58] Peter E. Thornton,et al. The Partitioning of Evapotranspiration into Transpiration, Soil Evaporation, and Canopy Evaporation in a GCM: Impacts on Land–Atmosphere Interaction , 2007 .
[59] Dagang Wang,et al. Quantifying the Strength of Soil Moisture–Precipitation Coupling and Its Sensitivity to Changes in Surface Water Budget , 2007 .
[60] Zong-Liang Yang,et al. Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data , 2007 .
[61] Naota Hanasaki,et al. GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface , 2006 .
[62] Zong-Liang Yang,et al. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models , 2005 .
[63] Tod A. Laursen,et al. Finite element tree crown hydrodynamics model (FETCH) using porous media flow within branching elements: A new representation of tree hydrodynamics , 2005 .
[64] 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 .
[65] D. Mocko,et al. Simulation of high-latitude hydrological processes in the Torne-Kalix basin: PILPS phase 2(e) - 1: Experiment description and summary intercomparisons , 2003 .
[66] X. Zeng,et al. How does the partitioning of evapotranspiration and runoff between different processes affect the variability and predictability of soil moisture and precipitation? , 2003 .
[67] A. Pitman. The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .
[68] 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 .
[69] J. Dudhia,et al. Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .
[70] K. Alapaty,et al. Comparison of four different stomatal resistance schemes using FIFE data. Part II: Analysis of terrestrial biospheric-atmospheric interactions , 1998 .
[71] K. Mitchell,et al. Impact of Atmospheric Surface-layer Parameterizations in the new Land-surface Scheme of the NCEP Mesoscale Eta Model , 1997 .
[72] Keith Beven,et al. Bayesian estimation of uncertainty in land surface‐atmosphere flux predictions , 1997 .
[73] Sethu Raman,et al. Comparison of Four Different Stomatal Resistance Schemes Using FIFE Observations , 1997 .
[74] Randal D. Koster,et al. The Interplay between Transpiration and Runoff Formulations in Land Surface Schemes Used with Atmospheric Models , 1997 .
[75] H. Mooney,et al. Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.
[76] Zong-Liang Yang,et al. Description of the Biosphere-Atmosphere Transfer Scheme (BATS) for the Soil Moisture Workshop and evaluation of its performance , 1996 .
[77] K. Mitchell,et al. Simple water balance model for estimating runoff at different spatial and temporal scales , 1996 .
[78] Y. Xue,et al. Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .
[79] Andrew J. Pitman,et al. Assessing the Sensitivity of a Land-Surface Scheme to the Parameter Values Using a Single Column Model , 1994 .
[80] R. Dickinson,et al. The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .
[81] Ann Henderson-Sellers,et al. Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .
[82] G. Collatz,et al. Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .
[83] G. Collatz,et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .
[84] Piers J. Sellers,et al. A Simplified Biosphere Model for Global Climate Studies , 1991 .
[85] I. E. Woodrow,et al. A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .
[86] W. Brutsaert. Evaporation into the atmosphere , 1982 .
[87] P. Jarvis. The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .
[88] Fan Yuanchao. Modeling oil palm monoculture and its associated impacts on land-atmosphere carbon, water and energy fluxes in Indonesia , 2022 .