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Dive into the research topics where Lindsey E. Gulden is active.

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Featured researches published by Lindsey E. Gulden.


Journal of Geophysical Research | 2007

Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data

Guo Yue Niu; Zong-Liang Yang; Robert E. Dickinson; Lindsey E. Gulden; Hua Su

Received 17 May 2006; revised 24 October 2006; accepted 26 December 2006; published 7 April 2007. [1] Groundwater interacts with soil moisture through the exchanges of water between the unsaturated soil and its underlying aquifer under gravity and capillary forces. Despite its importance, groundwater is not explicitly represented in climate models. This paper developed a simple groundwater model (SIMGM) by representing recharge and discharge processes of the water storage in an unconfined aquifer, which is added as a single integration element below the soil of a land surface model. We evaluated the model against the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (DS) data. The modeled total water storage (including unsaturated soil water and groundwater) change agrees fairly well with GRACE estimates. The anomaly of the modeled groundwater storage explains most of the GRACE DS anomaly in most river basins where the water storage is not affected by snow water or frozen soil. For this reason, the anomaly of the modeled water table depth agrees well with that converted from the GRACE DS in most of the river basins. We also investigated the impacts of groundwater dynamics on soil moisture and evapotranspiration through the comparison of SIMGM to an additional model run using gravitational free drainage (FD) as the model’s lower boundary condition. SIMGM produced much wetter soil profiles globally and up to 16% more annual evapotranspiration than FD, most obviously in arid-to-wet transition regions.


Geophysical Research Letters | 2007

Improving land-surface model hydrology: Is an explicit aquifer model better than a deeper soil profile?

Lindsey E. Gulden; Enrique Rosero; Zong-Liang Yang; Matthew Rodell; Charles S. Jackson; Guo Yue Niu; Pat J.-F. Yeh; James S. Famiglietti

We use Monte Carlo analysis to show that explicit representation of an aquifer within a land-surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997–2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation-based monthly changes in state-averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single-criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.


Journal of Hydrometeorology | 2009

Evaluating Enhanced Hydrological Representations in Noah LSM over Transition Zones: Implications for Model Development

Enrique Rosero; Zong-Liang Yang; Lindsey E. Gulden; Guo Yue Niu; David J. Gochis

Abstract The authors introduce and compare the performance of the unified Noah land surface model (LSM) and its augments with physically based, more conceptually realistic hydrologic parameterizations. Forty-five days of 30-min data collected over nine sites in transition zones are used to evaluate (i) their benchmark, the standard Noah LSM release 2.7 (STD); (ii) a version equipped with a short-term phenology module (DV); and (iii) one that couples a lumped, unconfined aquifer model to the model soil column (GW). Their model intercomparison, enhanced by multiobjective calibration and model sensitivity analysis, shows that, under the evaluation conditions, the current set of enhancements to Noah fails to yield significant improvement in the accuracy of simulated, high-frequency, warm-season turbulent fluxes, and near-surface states across these sites. Qualitatively, the versions of DV and GW implemented degrade model robustness, as defined by the sensitivity of model performance to uncertain parameters. Q...


Journal of Hydrometeorology | 2011

Ensemble Evaluation of Hydrologically Enhanced Noah-LSM: Partitioning of the Water Balance in High-Resolution Simulations over the Little Washita River Experimental Watershed

Enrique Rosero; Lindsey E. Gulden; Zong-Liang Yang; Luis Gustavo Gonçalves de Gonçalves; Guo Yue Niu; Yasir H. Kaheil

Abstract The ability of two versions of the Noah land surface model (LSM) to simulate the water cycle of the Little Washita River experimental watershed is evaluated. One version that uses the standard hydrological parameterizations of Noah 2.7 (STD) is compared another version that replaces STD’s subsurface hydrology with a simple aquifer model and topography-related surface and subsurface runoff parameterizations (GW). Simulations on a distributed grid at fine resolution are compared to the long-term distribution of observed daily-mean runoff, the spatial statistics of observed soil moisture, and locally observed latent heat flux. The evaluation targets the typical behavior of ensembles of models that use realistic, near-optimal sets of parameters important to runoff. STD and GW overestimate the ratio of runoff to evapotranspiration. In the subset of STD and GW runs that best reproduce the timing and the volume of streamflow, the surface-to-subsurface runoff ratio is overestimated and simulated streamfl...


Journal of Geophysical Research | 2005

A simple TOPMODEL‐based runoff parameterization (SIMTOP) for use in global climate models

Guo Yue Niu; Zong-Liang Yang; Robert E. Dickinson; Lindsey E. Gulden


Journal of Geophysical Research | 2010

Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season

Enrique Rosero; Zong-Liang Yang; Thorsten Wagener; Lindsey E. Gulden; Soni Yatheendradas; Guo Yue Niu


Agricultural and Forest Meteorology | 2014

Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado

Bradley Christoffersen; Natalia Restrepo-Coupe; M. Altaf Arain; Ian T. Baker; Bruno Paraluppi Cestaro; P. Ciais; Joshua B. Fisher; David Galbraith; Xiaodan Guan; Lindsey E. Gulden; Bart van den Hurk; Kazuhito Ichii; Hewlley Maria Acioli Imbuzeiro; Atul K. Jain; Naomi M. Levine; Gonzalo Miguez-Macho; Ben Poulter; Débora Regina Roberti; Koichi Sakaguchi; A. K. Sahoo; Kevin Schaefer; Mingjie Shi; Hans Verbeeck; Zong-Liang Yang; Alessandro C. Araújo; Bart Kruijt; Antonio O. Manzi; Humberto R. da Rocha; Celso von Randow; Michel Nobre Muza


Geophysical Research Letters | 2008

Model performance, model robustness, and model fitness scores: A new method for identifying good land‐surface models

Lindsey E. Gulden; Enrique Rosero; Zong-Liang Yang; Thorsten Wagener; Guo Yue Niu


Journal of Geophysical Research | 2007

Interannual variation in biogenic emissions on a regional scale

Lindsey E. Gulden; Zong-Liang Yang; Guo Yue Niu


Atmospheric Environment | 2008

Sensitivity of biogenic emissions simulated by a land-surface model to land-cover representations

Lindsey E. Gulden; Zong-Liang Yang; Guo Yue Niu

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Zong-Liang Yang

University of Texas at Austin

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Enrique Rosero

University of Texas at Austin

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Charles S. Jackson

University of Texas at Austin

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Matthew Rodell

California Institute of Technology

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David J. Gochis

National Center for Atmospheric Research

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James S. Famiglietti

California Institute of Technology

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Robert E. Dickinson

University of Texas at Austin

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Soni Yatheendradas

Goddard Space Flight Center

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