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The urbanization and frequency of extreme rainfall events (EREs) have considerably increased over the recent decade in several cities in India. Forecasting of these EREs remains a significant challenge not only in urban environments but also across diverse geographical regions. However, there is a particularly pressing need for improved rainfall forecasts in urban areas where the impacts of cities and human activities are profound. Using the Weather Research and Forecasting (WRF) model, a series of sensitivity experiments have been carried out by changing the various parameterization schemes to establish an improved model configuration for predicting EREs across the city of Bhubaneswar, Odisha- one of the most vulnerable cities to heavy rainfall in the recent decades. The study examines the influence of the urban heat island (UHI) effect on EREs, focusing on two specific EREs that occurred over Bhubaneswar and neighboring regions during the summer and post-monsoon season on July 19–21, 2018, and October 19–21, 2017. A total of thirty-two combinations of various cumulus, microphysics, and land surface sensitivity experiments are carried out for each ERE (a total of 64 for the two events). The results show that the combinations of Noah + + Nocumulus and Noah-MP + Thompson + Kain-Fritsch are the most effective in capturing the spatial and temporal patterns of EREs with a root mean square error of 33.9 and 36.2 mm, respectively. In addition, the study successfully reproduced vertical integrated specific humidity. Moreover, it has been observed that the UHI effect reduces rainfall intensity by 4% within urban areas during extreme rainfall events. [ABSTRACT FROM AUTHOR]

The frequency and intensity of extreme precipitation events are on the rise worldwide. Despite extensive efforts, regional climate models still show significant biases for extreme precipitation events, often due to factors like improper physics, the choice of land surface model, and spatial domain. Thus, this study uses a Coupled Land–Atmosphere Regional Climate Model version 4.7 (RegCM4.7) to explore how the choice of land surface models (LSMs) and domain extent affects the simulation of extreme precipitation over India. In this regard, a total of four sensitivity experiments have been carried out using two LSMs (CLM4.5 and BATS) over each of the two domains (one over the bigger South Asia CORDEX domain and another for the smaller domain over the Indian region). The main objective is to provide a holistic idea for obtaining an optimum model domain and LSMs for precipitation extremes over India. The model performance is demonstrated for extreme precipitation and associated processes. The result shows the systematic discrepancy in simulating extreme precipitation with a strong inter-simulation spread, indicating the strong sensitivity of extreme precipitation on the LSMs as well as the model domain. The BATS configuration shows a significant overestimation of consecutive wet days and very low precipitation, partially associated with a deficiency in convection. By contrast, the considerable underestimation of intense precipitation can be attributed to the presence of frequent, light drizzle, which hinders the accumulation of moisture in the atmosphere to a sufficient degree to prevent extreme rainfall. Despite significant improvement, the best-configured model (CLM with Indian domain) still indicates substantial bias for extreme precipitation. This deficiency in the model could potentially be mitigated by enhancing both horizontal and vertical resolutions. Nevertheless, further research is needed to explore other physics parameterizations and dynamic mechanisms to address this issue. [ABSTRACT FROM AUTHOR]

Abstract: Land surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2 and higher by 14.86 W·m−2 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.

This study examines the sensitivity of regional climate model version 4.7 (RegCM4.7) to the choice of the land surface scheme (LSS) and model domain in simulating the Indian summer monsoon rainfall (ISMR). The RegCM4.7 shows improved performance in simulating the ISMR on using the Community Land Model version 4.5 (CLM) LSS as compared to the Biosphere–Atmosphere Transfer Scheme (BATS) over the most part of India. However, the (CLM) LSS as compared to the BATS over most parts of India. However, the value addition of using CLM over BATS varies spatially and also the choice of domain. For example, a more significant improvement in simulating mean precipitation is seen over the eastern (western) flank of central India on using the larger Coordinated Regional Climate Downscaling Experiment (CORDEX-SA) domain, while western and southern India shows a larger improvement in the Indian (IND) model domain. The model performance greatly improves on using the smaller Indian (IND) model domain as compared to the larger CORDEX-SA domain for the precipitation intensity greater than 15 mm/day. The best model performance in simulating the ISMR and its associated features is obtained when CLM LSS is used in combination with the IND domain (e.g., CLM IND model setup). It is found that the impact of the model domain is higher as compared to the LSS towards improved ISMR simulation. The smaller model domain size helps in the better simulation of the convective precipitation. The CORDEX-SA domain produces imperfect low-level jets (shifted westwards), which tend to reduce the moisture supply towards the Indian land region, thus leading to dry rainfall bias over the central Indian region. The BATS scheme also performs poorly in this regard as compared to the CLM scheme. The inclusion of CLM scheme in a model setup that uses the IND domain largely overcomes the problem of dry bias over the Indian region. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of a non‐hydrostatic Regional Climate Model (RegCM) with two land surface parameterizations, namely Biosphere‐Atmosphere Transfer (BAT) scheme and Community Land Model (CLM) scheme on the simulation of summer monsoon over India. The initial and boundary conditions are taken from ERA‐Interim (ERA‐I) reanalysis available at a 0.75° spatial grid. The RegCM is designed with 25 km horizontal resolution and 23 vertical levels, and it integrated for the period 1st January 1982 to 30th September 2016. We used the NOAA Optimum Interpolation Weekly sea surface temperatures, the topography and land‐use data from the United States Geological Survey, and Global Land Cover Characterization. Our results reveal that the summer monsoon precipitation anomaly is reasonably well simulated with BAT and CLM schemes compared to those of India Meteorological Department (IMD) observations. The spatial distributions of temperature anomaly over India with BAT compared to CRU shows cold bias over most of the Indian sub‐continent, whereas the CLM shows large warm bias over central India and monsoon trough region. The low‐level and upper‐level winds are in good agreement with ERA‐I. Further, the analysis of extreme monsoon events (i.e., excess and deficit seasons) reveals that CLM depicts less bias in simulating the excess and deficit precipitation regions as compared to those of BAT. Conversely, BAT simulates temperature well over India in both excess and deficit monsoon seasons. The Taylor's diagram analysis, Added Value Index based on Mean Square Error, and Modified Brier Skill Score are also applied for the extreme rainfall seasons. Our quantitative analysis demonstrates that the CLM have resolved better rainfall features with more skills with respect to that of BAT. The results suggest that CLM have added value in Indian Summer Monsoon (ISM) regional climate simulation than the BAT, particularly on simulation of the rainfall characteristics during extreme rainfall seasons. [ABSTRACT FROM AUTHOR]

The land surface scheme is crucial for the performance of regional climate models in dynamic downscaling application. In this study, we explore the sensitivity of the simulation with 10‐km resolution Weather Research and Forecasting (WRF) model to the land surface schemes in simulating South Asian summer monsoon. The WRF simulations for 19 summers from 2000 to 2018 are conducted with four different land surface schemes including Exp‐CLM4, Exp‐Noah‐MP, Exp‐PX, and Exp‐SSiB, with that the initial and boundary conditions for the WRF model simulations are provided from the NCEP‐FNL data. The ERAI reanalysis data, GHCN‐CAMS, and CRU gridded data are used to comprehensively evaluate the WRF simulations. Compared with verification data, the WRF model can reasonably capture the spatial patterns of summer mean large‐scale atmospheric circulation, 2‐m temperature and precipitation. The simulation results, however, are sensitive to the option of land surface scheme. The performance of Exp‐CLM4 and Exp‐SSiB is better than that of Exp‐Noah‐MP and Exp‐PX assessed by multivariable integrated evaluation (MVIE) method. To explore the dynamic and physical mechanisms behind the WRF model sensitivity to land surface schemes, the differences in the surface energy balance between the ensemble means Ens‐CLM4‐SSiB and Ens‐NoanMP‐PX are analysed in detail. The results illustrate that the intensity of the simulated sensible heat flux over South Asia is weaker in Ens‐CLM4‐SSiB than that in Ens‐NoahMP‐PX. The simulated wind fields are influenced due to the geostrophic adjustment process caused by large differences in geopotential height; thus, the simulations of 2‐m temperature and precipitation are all affected. [ABSTRACT FROM AUTHOR]

Regional climate models with high-resolution simulation are particularly useful for providing a detailed representation of land surface processes, and for studying the relationship between land surface processes and heat events. However, large differences and uncertainties exist among different land surface schemes (LSSs). This study comprehensively assesses the sensitivity to different LSSs based on two extreme heat events in eastern China using the Weather Research and Forecasting (WRF) model. Among the five LSSs (i.e., 5TD, CLM4, Noah, Noah-MP and RUC), Noah is closest to observations in reproducing the temperatures and energy fluxes for both two heat events. The modeled warm biases result mainly from the underestimation of evapotranspirative cooling. Our results show that how each LSS partitions the evapotranspiration (ET) and sensible heat largely determines the relationship between the temperature and turbulent fluxes. Although the simulated two extreme heat events manifest similar biases in the temperatures and energy fluxes, the land surface responses (ET and soil moisture) are different.

Land surface schemes (LSSs) in Earth System Models simulate how vegetation regulates land‐atmosphere fluxes of heat, water, carbon, and momentum. Despite advances in the spatial resolution of regional climate modeling, significant land cover changes occured at sub‐grid scale still not properly treated. Here, we investigate the response of evapotranspiration, representing the fluxes of heat and water, to deforestation in Southeast Asian Massif by employing three LSSs (Noah mosaic, Noah‐MP and Community Land Model (CLM)) with different approaches in representing sub‐grid variability, implemented in the Weather Research and Forecasting model. Two experiments, with and without satellite‐observed deforestation, were performed for each scheme. Results show that the simulations are highly sensitive to the subgrid‐scale approaches embedded in the LSSs. Compared with the observed historical climate, CLM outperforms other schemes and Noah mosaic shows the largest bias. However, if we target the simulation of the climate impacts of land cover change, the Noah mosaic scheme taking the sub‐grid approach can better capture the response of evapotranspiration to deforestation. The NoahMP and CLM schemes underestimate the evapotranspiration response in the grid cells where the dominant land cover type has not changed, but overestimate the response in those grid cells with dominant type changed, which is a characteristic of dominant‐grid approach. To improve our understanding of climate impacts induced by fine‐scale land cover change, future efforts to better represent subgrid‐scale variability and land‐atmosphere flux exchange in climate models are desirable. Key Points: Employing land surface schemes (LSSs) that take a sub‐grid approach is critical in simulating the climate impacts of fine‐scale land cover changeLSSs that take a dominant approach can largely misestimate the climate response to land cover changeThe theoretical tiling approach of Community Land Model was not active when it was coupled into the Weather Research and Forecasting model [ABSTRACT FROM AUTHOR]

The effects of different land surface scheme (LSS) implementations on the simulated climate of the Middle East and North Africa (MENA) have been investigated with the Weather Research and Forecasting (WRF) regional model. Six simulations were carried out using four different LSSs (Noah, NoahMP, CLM, RUC) for the period 2000–2010, driven by ERA-Interim meteorological reanalyses at a horizontal resolution of 50 km. Deviations of key surface climate variables, radiation, and turbulent fluxes from the different LSS runs are presented relative to the default Noah scheme. The simulated annual mean climate variables in the MENA (indicating uncertainty) range from 0.7 to 2.4 ∘C for air temperature, 2.0 to 3.4 ∘C for land temperature, and 5 to 25 mm/month (54–65%) for precipitation. The Noah scheme deviates less than − 1 W/m2 from the domain-wide surface energy balance and the NoahMP less than − 2 W/m2, while for CLM and RUC the deviation is 3–4 W/m2. Considering the differences among the surface energy balance from the various LSSs compared to the reference Noah, a surface climate response is calculated, and average LSS-induced climate sensitivity is derived for the air (and land) temperature of 0.1 ∘C per W/m2 and − 6 mm per W/m2 for precipitation. The LSS-induced range in the modelled climate is of similar magnitude to the climate change projection estimates for the region, which highlights the importance of carefully selecting a land surface scheme in the regional climate simulations. [ABSTRACT FROM AUTHOR]

The exchanges of water, energy and carbon between the land surface and the atmosphere are tightly coupled, so that errors in simulating evapotranspiration lead to errors in simulating both the water and carbon balances. Areas with seasonally frozen soils present a particular challenge due to the snowmelt‐dominated hydrology and the impact of soil freezing on the soil hydraulic properties and plant root water uptake. Land surface schemes that have been applied in high latitudes often have reported problems with simulating the snowpack and runoff. Models applied at the Boreal Ecosystem Research and Monitoring Sites in central Saskatchewan have consistently over‐predicted evapotranspiration as compared with flux tower estimates. We assessed the performance of two Canadian land surface schemes (CLASS and CLASS‐CTEM) for simulating point‐scale evapotranspiration at an instrumented jack pine sandy upland site in the southern edge of the boreal forest in Saskatchewan, Canada. Consistent with past reported results, these models over‐predicted evapotranspiration, as compared with flux tower observations, but only in the spring period. Looking systematically at soil properties and vegetation characteristics, we found that the dominant control on evapotranspiration within these models was the canopy conductance. However, the problem of excessive spring ET could not be solved satisfactorily by changing the soil or vegetation parameters. The model overestimation of spring ET coincided with the overestimation of spring soil liquid water content. Improved algorithms for the infiltration of snowmelt into frozen soils and plant‐water uptake during the snowmelt and soil thaw periods may be key to addressing the biases in spring ET. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) model was employed to simulate the water and energy process over the near surface in the Yellow River source region from July to September 2015. The Rapid Update Cycle (RUC), Noah, Noah-Multi-Physics (Noah-MP), and Community Land Model (CLM) version 4 land surface schemes were selected and evaluated in our experiments. The simulated results were also compared with the Global Land Data Assimilation System (GLDAS) outputs, the ground-based automatic weather stations (AWSs), and the Zoige plateau wetlands ecosystem research flux station data. There was substantial agreement between the results of the simulations and observations in terms of 2-m temperature and relative humidity. However, the simulated values of the 2-m temperature were mostly lower than observations, while the values of relative humidity were higher than the observations. The correlation coefficients (R) between the simulations and observations of air temperature in four experiments were all higher than 0.94, and simulated results of the WRF-RUC experiment had the minimum bias compared with other experiments. The root mean square error (RMSE) and bias of 2-m relative humidity between the WRF-CLM4 simulations and observations were lower than other experiments. Moreover, the simulation of the 10-cm soil temperature was lower than the observations. WRF-CLM4 simulations agreed better with the observations, and WRF-Noah-MP simulations had the largest bias in all of the experiments. The soil moisture increased rapidly when precipitation was occurring and decreased slowly after the precipitation. The four experiments overestimate precipitation leading to higher soil moisture compared with the real situations. The peak value of net radiation was appropriately 850 W/m2 during the summertime in the Zoige plateau area. The simulated net radiation agreed well with the observation except in the WRF-RUC scheme. During the summertime, vegetation grew rapidly, and the latent heat flux transport increased and played a dominant role in surface energy transport in the Zoige area. The RMSE between the simulation and observation of latent heat flux had the minimum value of 98.0 W/m2 in the WRF-CLM4 scheme, and the bias of sensible heat flux had the minimum value of 15.4 W/m2 in the WRF-Noah scheme. This study used the WRF model to explore the applicability of the different land surface schemes in the Zoige plateau area. The research related to this study can be used to understand the water-energy cycle process over the Qinghai-Tibet Plateau wetland and can also provide references for the application of regional meteorological models over this area. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a general interface for coupling general circulation models (GCMs) to land surface schemes (LSS) in order to achieve a plug compatibility between these complex models. As surface parameterizations include more processes, they have moved from being subroutines of GCMs to independent schemes which can also be applied for other purposes. This evolution has raised the problem within climate modeling groups of coupling these schemes to GCMs in a simple and flexible way. As LSS reaches a larger independence, a general interface is needed to enable exchange within the community. This paper discusses the tasks LSS have to fulfill when coupled to a GCM after a review of the current state of the art and the likely future evolutions of both components. The numerical schemes used for the processes which couple the land surfaces to the atmosphere are reviewed to ensure that the interface can be applied to all LSS and GCMs after only minor changes.

Three land surface schemes available in the regional climate model RegCM4 have been examined to understand the coupling between land and atmosphere for simulation of the Indian summer monsoon rainfall. The RegCM4 is coupled with biosphere-atmosphere transfer scheme (BATS) and the National Center for Atmospheric Research (NCAR) Community Land Model versions 3.5, and 4.5 (CLM3.5 and CLM4.5, respectively) and model performance is evaluated for recent drought (2009) and normal (2011) monsoon years. The CLM4.5 has a more distinct category of surface and it is capable of representing better the land surface characteristics. National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) reanalysis version 2 (NNRP2) datasets are considered as driving force to conduct the experiments for the Indian monsoon region (30°E-120°E; 30°S-50°N). The NNRP2 and India Meteorological Department (IMD) gridded precipitation data are used for verification analysis. The results indicate that RegCM4 simulations with CLM4.5 (RegCM4-CLM4.5) and CLM3.5 (RegCM4-CLM3.5) surface temperature (at 2 ms) have very low warm biases (~1 °C), while with BATS (RegCM4-BATS) has a cold bias of about 1-3 °C in peninsular India and some parts of central India. Warm bias in the RegCM4-BATS is observed over the Indo-Gangetic plain and northwest India and the bias is more for the deficit year as compared to the normal year. However, the warm (cold) bias is less in RegCM4-CLM4.5 than other schemes for both the deficit and normal years. The model-simulated maximum (minimum) surface temperature and sensible heat flux at the surface are positively (negatively) biased in all the schemes; however, the bias is higher in RegCM4-BATS and lower in RegCM4-CLM4.5 over India. All the land surface schemes overestimated the precipitation in peninsular India and underestimated in central parts of India for both the years; however, the biases are less in RegCM4-CLM4.5 and more in RegCM4-CLM3.5 and RegCM4-BATS. During both the years, BATS scheme in RegCM4 failed to represent low precipitation over the leeward than windward side of the Western Ghats, while CLM schemes (both versions) in the RegCM4 are able to depict this feature. In the topographic regions, such as the Western Ghats, northeast India and state of Jammu and Kashmir, RegCM4-BATS overestimates the rainfall amount with higher bias. Statistical analysis using anomaly correlation coefficient, root mean square error, equitable threat score, and critical success index confirms that RegCM4-CLM performs better than RegCM4-BATS in the simulation of the Indian summer monsoon. [ABSTRACT FROM AUTHOR]

Physically based distributed and semi-distributed hydrological models have become some of the primary tools for water resources studies and management over the past decades owing to increased computational capabilities and advances in data measurements. Representation of the existing heterogeneity in nature still remains one of the main challenges in these models and is accomplished primarily via watershed discretization, subdividing watersheds into hydrologically similar land parcels. Discretization decisions in distributed modelling studies are often ad hoc and determined with little or no quantitative analysis to support these decisions. In this work, we present a quantitative methodology for assessing alternative watershed discretization schemes in terms of their corresponding model performance in ungauged basins. The effect of the computational time spent for calibrating each scheme (calibration budget) is considered as part of the assessment. Here, these schemes differ in how they represent landscape attributes and range from a simple lumped scheme to more complex ones by adding spatial land cover and then soil information. The methodology is demonstrated using the Modélisation Environmentale-Surface et Hydrologie (MESH) model as applied to the Nottawasaga River basin in Ontario, Canada. Results reveal that model performance in ungauged basins depends upon the location of the validation sub-basin (i.e. upstream or downstream) with respect to the calibration sub-basins. Also, using a more complex scheme did not necessarily lead to improved performance in validation, when constrained by calibration budget. Therefore, the calibration budget also should be considered as a factor in the assessment process. This methodology was also implemented using a shorter sub-period for calibration, which leads to substantial computational saving. Results of the sub-period test were promising and consistent particularly when sufficient budget is spent to calibrate the model. Other strategies utilized for reducing the computational burden of the proposed analyses are also discussed in this study. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as drier than the other three LSMs (NDVI values are mostly greater than 0). Finally, the unsupervised clustering of global land surface based on long-term mean temperature and precipitation, soil texture and land slope reveals that areas classified as dry by the UNEP index but not by the LSMs do not have dry region characteristics. The dominant cluster for these areas has high water holding capacity. We conclude that the LSM-based aridity index may identify dryland areas more effectively than the UNEP aridity index because the former incorporates the role of vegetation and soil in the partitioning of precipitation into evaporation, runoff and infiltration. [Copyright &y& Elsevier]

The article presents a research which examined the performance of a general circulation model (GCM) land surface scheme (LSS) using a fine-scale calibrated hydrological approach within the Nile River. LSS depicts the interface between land surface and the atmosphere in GCM. Researchers used two GCM gridboxes to test the capacity of the hydrological model to provide a quasi-observed runoff series for direct comparison with simulated runoff from LSS at GCM scale. Results revealed that the high sensitivity of runoff and evapotranspiration to radiation and precipitation inputs raise the issues on temporal disaggregation of climatic data.

Present-day land surface schemes used in weather prediction and climate models include parameterizations of physical processes whose complex nonlinear interactions can lead to models of unknown spatial and temporal characteristics. This paper describes the timescales of the evapotranspiration response of 16 land surface schemes which participated in the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red-Arkansas River experiment. The basins were represented by 61, 1°×1° grid boxes. Ten years of hourly meteorological data were used to force 16 land surface schemes off line. The evapotranspiration responses of the models are characterized by an impulse response function (or unit kernel) which is described by a two-parameter model, representing the fast response of evaporation from the canopy surface or bare soil and a slower one due to transpiration. The analysis of the results shows significant differences among the various LSS in their characteristic timescales across the basins. [Copyright &y& Elsevier]