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Aaron Boone
and
Pierre Etchevers

Abstract

The Interactions between Soil, Biosphere, and Atmosphere land surface scheme is currently used coupled both to atmospheric models and to a distributed hydrological model. There are two snow-scheme options available for hydrological modeling: the baseline force–restore approach, which uses a composite snow–soil–vegetation energy budget and a single snow layer; and a multilayer detailed internal-process snow model. Only the force–restore method is routinely used in atmospheric modeling applications. Recent studies have shown that hydrological simulations for mountainous catchments within the Rhone basin, France, are significantly improved using the detailed snow scheme. The main drawback is that the scheme is computationally expensive, and it is not currently feasible for routine application in atmospheric models. For these reasons, a third new intermediate-complexity scheme has been developed that includes certain key physical processes from the complex model for improved snowpack realism and hydrological depiction while attemping to keep computational requirements similar to those of the simple default scheme. In the current study, the new scheme is described, evaluated, and compared with the results from the two other schemes at a local scale at an alpine site located within the Rhone basin for two contrasting (weather) years. All schemes are able to model the basic features of the snow cover with similar errors averaged over the 2-yr period; however, there are important differences on shorter timescales. When compared with the more complex scheme, it was found that differing surface energy budget parameterizations (turbulent transfer, albedo) were the cause for the largest differences in total snowpack snow water equivalent (SWE) simulated by the models. When compared with the simple scheme, the ability for the intermediate model to simulate snow ripening resulted in the largest differences in simulated SWE and snow temperature during melt and runoff.

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Peter J. Wetzel
and
Aaron Boone

Abstract

This paper presents a general description of, and demonstrates the capabilities of, the Parameterization for Land–Atmosphere–Cloud Exchange (PLACE). The PLACE model is a detailed process model of the partly cloudy atmospheric boundary layer and underlying heterogeneous land surfaces. In its development, particular attention has been given to three of the model's subprocesses: the prediction of boundary layer cloud amount, the treatment of surface and soil subgrid heterogeneity, and the liquid water budget. The model includes a three-parameter nonprecipitating cumulus model that feeds back to the surface and boundary layer through radiative effects. Surface heterogeneity in the PLACE model is treated both statistically and by resolving explicit subgrid patches. The model maintains a vertical column of liquid water that is divided into seven reservoirs, from the surface interception store down to bedrock.

Five single-day demonstration cases are presented, in which the PLACE model was initialized, run, and compared to field observations from four diverse sites. The model is shown to predict cloud amount well in these while predicting the surface fluxes with similar accuracy. A slight tendency to underpredict boundary layer depth is noted in all cases.

Sensitivity tests were also run using anemometer-level forcing provided by the Project for Inter-comparison of Land-surface Parameterization Schemes (PILPS). The purpose is to demonstrate the relative impact of heterogeneity of surface parameters on the predicted annual mean surface fluxes. Significant sensitivity to subgrid variability of certain parameters is demonstrated, particularly to parameters related to soil moisture. A major result is that the PLACE-computed impact of total (homogeneous) deforestation of a rain forest is comparable in magnitude to the effect of imposing heterogeneity of certain surface variables, and is similarly comparable to the overall variance among the other PILPS participant models. Were this result to be bourne out by further analysis, it would suggest that today's average land surface parameterization has little credibility when applied to discriminating the local impacts of any plausible future climate change.

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Augusto Getirana
,
Aaron Boone
,
Christophe Peugeot
, and
ALMIP2 Working Groupa

Abstract

Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

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Aaron Boone
,
Jean-Christophe Calvet
, and
Joël Noilhan

Abstract

The inclusion of a third soil layer in the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model is presented in this paper. The soil water content between the base of the root zone and the deep soil layer is described using a generalized form of the force–restore method. The new force–restore coefficient is calibrated using a detailed high-resolution soil water transfer model and then is related to the soil textural properties using simple regression relationships. It is shown that the use of a calibrated coefficient gives better results, in general, than a direct solution method when using similar model geometry with the same number of layers.

In the initial two-layer version of ISBA, it was not possible to distinguish the root zone and subroot zone soil water reservoirs. With the three-layer version, the deep soil layer may provide water to the system through capillary rises only, and the available water content (for transpiration) is clearly defined. Three test cases are examined in which atmospheric forcing, a good description of the soil properties and vegetation cover, and measured soil moisture profile data are present for an annual cycle. Use of the three-layer version of ISBA gives general improvement in modeling results, and values for key parameters that relate evapotranspiration to soil moisture are more consistent with those inferred from observations, compared with the two-layer version.

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Reto Stöckli
,
Pier Luigi Vidale
,
Aaron Boone
, and
Christoph Schär

Abstract

Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.

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Augusto C. V. Getirana
,
Aaron Boone
, and
Christophe Peugeot

Abstract

Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Ouémé River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum–Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005–08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five gauges within the basin. Results demonstrate that the RRS simulates streamflow at all gauges with relative errors varying from −20% to 3% and Nash–Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the subbasin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated streamflows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.

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Karen I. Mohr
,
James S. Famiglietti
,
Aaron Boone
, and
Patrick J. Starks

Abstract

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE), a typical surface–vegetation–atmosphere transfer (SVAT) parameterization, was used in a case study of a 2500 km2 area in southwestern Oklahoma for 9–16 July 1997. The research objective was to assess PLACE’s simulation of the spatial variability and temporal evolution of soil moisture and heat fluxes without optimization for this case study. Understanding PLACE’s performance under these conditions may provide perspective on results from more complex coupled land–atmosphere simulations involving similar land surface schemes in data-poor environments. Model simulations were initialized with simple initial soil moisture and temperature profiles tied to soil type and forced by standard meteorological observations. The model equations and parameters were not adjusted or tuned to improve results.

For surface soil moisture, 5- and 10-cm soil temperature, and surface fluxes, the most accurate simulation (5% error for soil moisture and 2 K for 5- and 10-cm soil temperature) occurred during the 48 h following heavy rainfall on 11 and 15 July. The spatial pattern of simulated soil moisture was controlled more strongly by soil texture than was observed soil moisture, and the error was correlated with rainfall. The simplifications of the subsurface soil moisture, soil texture, and vegetation cover initialization schemes and the uncertainty in the rainfall data (>10%) could account for differences between modeled and observed surface fluxes that are on the order of 100 W m−2 and differences in soil moisture that are greater than 5%. It also is likely that the soil thermal conductivity scheme in PLACE damped PLACE’s response to atmospheric demand after 13 July, resulting in reduced evapotranspiration and warmer but slower-drying soils. Under dry conditions, the authors expect that SVATs such as PLACE that use a similar simple initialization also would demonstrate a strong soil texture control on soil moisture and surface fluxes and limited spatial variability.

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Eric Brun
,
Vincent Vionnet
,
Aaron Boone
,
Bertrand Decharme
,
Yannick Peings
,
Rémi Valette
,
Fatima Karbou
, and
Samuel Morin

Abstract

The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

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Augusto C. V. Getirana
,
Aaron Boone
,
Dai Yamazaki
,
Bertrand Decharme
,
Fabrice Papa
, and
Nelly Mognard

Abstract

Recent advances in global flow routing schemes have shown the importance of using high-resolution topography for representing floodplain inundation dynamics more reliably. This study presents and evaluates the Hydrological Modeling and Analysis Platform (HyMAP), which is a global flow routing scheme specifically designed to bridge the gap between current state-of-the-art global flow routing schemes by combining their main features and introducing new features to better capture floodplain dynamics. The ultimate goals of HyMAP are to provide the scientific community with a novel scheme suited to the assimilation of satellite altimetry data for global water discharge forecasts and a model that can be potentially coupled with atmospheric models. In this first model evaluation, HyMAP is coupled with the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model in order to simulate the surface water dynamics in the Amazon basin. The model is evaluated over the 1986–2006 period against an unprecedented source of information, including in situ and satellite-based datasets of water discharge and level, flow velocity, and floodplain extent. Results show that the model can satisfactorily simulate the large-scale features of the water surface dynamics of the Amazon River basin. Among all stream gauges considered, 23% have Nash–Sutcliffe coefficients (NS) higher than 0.50 and 68% above zero. About 28% of the stations have volume errors lower than 15%. Simulated discharges at Óbidos had NS = 0.89. Time series of simulated floodplains at the basin scale agrees well with satellite-based estimates, with a relative error of 7% and correlation of 0.89. These results indicate nonnegligible improvements in comparison to previous studies for the same region.

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R. David Baker
,
Barry H. Lynn
,
Aaron Boone
,
Wei-Kuo Tao
, and
Joanne Simpson

Abstract

Idealized numerical simulations of Florida convection are performed with a coupled atmosphere–land surface model to identify the roles of initial soil moisture, coastline curvature, and land-breeze circulations on sea-breeze-initiated precipitation. The 3D Goddard Cumulus Ensemble cloud-resolving model is coupled with the Goddard Parameterization for Land–Atmosphere–Cloud Exchange land surface model, thus providing a tool to simulate more realistically land surface–atmosphere interaction and convective initiation. Eight simulations are conducted with either straight or curved coastlines, initially homogeneous soil moisture or initially variable soil moisture, and initially homogeneous horizontal winds or initially variable horizontal winds (land breezes). An additional simulation is performed to assess the role of Lake Okeechobee on convective development.

All model simulations capture the diurnal evolution and general distribution of sea-breeze-initiated precipitation over central Florida. The distribution of initial soil moisture influences the timing and location of subsequent precipitation. Soil moisture acts as a moisture source for the atmosphere, increases the convectively available potential energy, and thus preferentially focuses heavy precipitation over existing wet soil. Soil moisture–induced mesoscale circulations do not produce heavy precipitation. Coastline curvature has a major impact on the timing and location of precipitation. Earlier low-level convergence occurs inland of convex coastlines, and subsequent heavy precipitation occurs earlier in simulations with curved coastlines. Early-morning land breezes influence the timing of precipitation by modifying low-level convergence. Because of nonlinear interaction between coastline curvature and soil moisture, the highest peak accumulated rainfall and highest peak rain rates occur in simulations with both coastline curvature and initial soil moisture variations. Lake Okeechobee influences the timing and location of precipitation because of strong lake-breeze circulations.

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