A Coupled Land‐Atmosphere Simulation Program (CLASP): Calibration and validation

Gutowski, William J.; Vörösmarty, Charles J; Person, Mark; Ötles, Zekai; Fekete, B M; York, J.

doi:10.1029/2001jd000392

Cited by 65 publications

(80 citation statements)

References 44 publications

(60 reference statements)

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“…Luo et al, 2013), surface water and groundwater interactions (e.g. Winter, 2001;Gutowski et al, 2002;Maxwell and Miller, 2005;Yeh and Eltahir, 2005;Fan et al, 2007;Niu et al, 2007), and the interactions among these processes and with other existing processes in current LSMs (e.g. .…”

Section: Bounding Complexity: the Use Of Multiple Constraintsmentioning

confidence: 99%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land-atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land-atmosphere fluxes of carbon dioxide (CO 2 ), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "thirdgeneration" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by speciesdiverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.

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Section: Bounding Complexity: the Use Of Multiple Constraintsmentioning

confidence: 99%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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“…Fan et al (2013) found that 22%-32% of land globally is affected by shallow groundwater. Groundwater provides a source of water for evapotranspiration even during dry seasons or drought conditions (Yeh et al 1998;Gutowski et al 2002;Miguez-Macho and Fan 2012). In fact, some plants are highly reliant on groundwater (Orellana et al 2012;Rossatto et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Some models (e.g., Gutowski et al 2002;York et al 2002) explicitly model the three-dimensional flow of groundwater, but these are computationally expensive to implement into GCMs . These have largely been implemented into regional models with finer horizontal resolution since horizontal transport is more important at these smaller scales Yeh and Eltahir 2005).…”

Section: Introductionmentioning

confidence: 99%

Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)

et al. 2016

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One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.98 latitude 3 1.258 longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.

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“…More recently, three-dimensional (e.g., Gutowski et al 2002) and one-dimensional (i.e., models of independent soil column) (e.g., Liang et al 2003;Chen and Hu 2004;Maxwell and Miller 2005;Niu et al 2006;Fan et al 2007;Miguez-Macho et al 2007) groundwater models have been implemented in land surface models to simulate groundwater table and recharge/ discharge dynamically. Most studies reported results based on offline simulations driven by observed atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

Climate–soil–vegetation control on groundwater table dynamics and its feedbacks in a climate model

et al. 2010

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Among the three dynamically linked branches of the water cycle, including atmospheric, surface, and subsurface water, groundwater is the largest reservoir and an active component of the hydrologic system. Because of the inherent slow response time, groundwater may be particularly relevant for long time-scale processes such as multi-years or decadal droughts. This study uses regional climate simulations with and without surface watergroundwater interactions for the conterminous US to assess the influence of climate, soil, and vegetation on groundwater table dynamics, and its potential feedbacks to regional climate. Analyses show that precipitation has a dominant influence on the spatial and temporal variations of groundwater table depth (GWT). The simulated GWT is found to decrease sharply with increasing precipitation. Our simulation also shows some distinct spatial variations that are related to soil porosity and hydraulic conductivity. Vegetation properties such as minimum stomatal resistance, and root depth and fraction are also found to play an important role in controlling the groundwater table.Comparing two simulations with and without groundwater table dynamics, we find that groundwater table dynamics mainly influences the partitioning of soil water between the surface (0-0.5 m) and subsurface (0.5-5 m) rather than total soil moisture. In most areas, groundwater table dynamics increases surface soil moisture at the expense of the subsurface, except in regions with very shallow groundwater table. The change in soil water partitioning between the surface and subsurface is found to strongly correlate with the partitioning of surface sensible and latent heat fluxes. The evaporative fraction (EF) is generally higher during summer when groundwater table dynamics is included. This is accompanied by increased cloudiness, reduced diurnal temperature range, cooler surface temperature, and increased cloud top height. Although both convective and non-convective precipitation are enhanced, the higher EF changes the partitioning to favor more non-convective precipitation, but this result could be sensitive to the convective parameterization used. Compared to simulations without groundwater table dynamics, the dry bias in the summer precipitation is slightly reduced over the central and eastern US Groundwater table dynamics can provide important feedbacks to atmospheric processes, and these feedbacks are stronger in regions with deeper groundwater table, because the interactions between surface and subsurface are weak when the groundwater table is deep. This increases the sensitivity of surface soil moisture to precipitation anomalies, and therefore enhances land surface feedbacks to the atmosphere through changes in soil moisture and evaporative fraction. By altering the groundwater table depth, land use change and groundwater withdrawal can alter land surface response and feedback to the climate system.

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A Coupled Land‐Atmosphere Simulation Program (CLASP): Calibration and validation

Cited by 65 publications

References 44 publications

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)

Climate–soil–vegetation control on groundwater table dynamics and its feedbacks in a climate model

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