Land Surface Datasets Research Articles

Systematic Evaluation of a High-Resolution CLM5 Simulation over Continental China for 1979–2018

Abstract The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application. Significance Statement The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application.

Earth observation for exposome mapping of Germany: analyzing environmental factors relevant to non-communicable diseases

• Human health is influenced by the exposome – exposure to environmental factors. • Earth Observation is a powerful tool to map this exposome at local to national scale. • Mapped cumulative exposure impact is relevant to myocardial infarctions. • Using the same methodology for other non-communicable diseases, NCDs, is feasible. • Exposure impact is “high” to “very high” for 13 % of the nation’s built-up areas. Non-communicable diseases – NCDs – (e.g., asthma, cancer, or diabetes) are a major concern for society and medicine. According to the World Health Organization, NCDs are responsible for > 70 % of global premature deaths. Apart from increasing mortality, these diseases strain one’s immune system which leads to higher susceptibility to transmittable diseases. NCD-susceptibility depends on the genome (genetic predisposition), behavior (lifestyle), and exposome of a person. The exposome is a composition of environmental parameters such as exposure to air pollution, noise, extreme temperatures, or surrounding greenness. Using Earth Observation data, the majority of factors making up the exposome can be monitored over long periods of time at high resolution and with nearly global coverage. Still, exposome maps and products communicating NCD risk are not widely available. In this study, we utilize eight land surface datasets (distance to green spaces, distance to blue spaces, temperature, noise from industry, as well as road, rail, and air traffic, and light pollution) as well as two air pollution datasets (PM 2.5 and NO 2 ) to map health-relevant environmental exposure. We use an established cumulative approach and incorporate exposure-response relationships from scientific literature to map environments that impact public health for the complete area of Germany. We present results communicating exposure relevant to myocardial infarction risk. The methodology is transferable to other NCDs and other areas of interest. In the context of the global health burden from NCDs and ongoing global change, this approach supplies findings for communicating health-relevant exposure.

Quantifying Flood Frequency Associated with Clustered Mesoscale Convective Systems in the United States

Abstract Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

Development of an Integrated Global Land Surface Dataset from 1901 to 2018

Development of an Integrated Global Land Surface Dataset from 1901 to 2018

Flood Mapping from Dam Break Due to Peak Inflow: A Coupled Rainfall–Runoff and Hydraulic Models Approach

In this study, we conducted flood mapping of a hypothetical dam break by coupling the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS) models under different return periods of flood inflow. This study is presented as a case study on the Kesem embankment dam in Ethiopia. Hourly hydrological and meteorological data and high-resolution land surface datasets were used to simulate the design floods for piping dam failure with empirical dam breach methods. Based on the extreme inflows and the dam physical characteristics, the dam failure was simulated by a two-dimensional, unsteady flow hydrodynamic model. As a result, the dam will remain safe for up to 50-year return-period inflows, but it breaks for 100- and 200-year return periods and floods the downstream area. For the 100-year peak inflow, a 208 km2 area will be inundated by a maximum depth of 20 m and for a maximum duration of 46 h. The 200-year inflow will inundate a 240 km2 area with a maximum depth of 31 m for a maximum duration of 93 h. The 2D flood map provides satisfactory spatial and temporal resolution of the inundated area for evaluation of the affected facilities.

Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

Abstract. The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

On the cooling potential of irrigation to mitigate urban heat island

Urban overheating is the most documented phenomenon of climate change, causing severe energy problems. The study aims to evaluate the mitigation potential of irrigation as a response to the urban overheating and heatwaves in large cities, using simulation-based techniques. Mesoscale simulations are conducted for a 32.5 km × 22.5 km metropolitan region of Sydney, Australia, and it is modelled based on the moderate resolution imaging spectroradiometer (MODIS) 500 m resolution land surface dataset. To better represent the Sydney metropolitan area, the New South Wales Land Zoning dataset is used to remap the land surface. The weather research forecast model (WRF) combined with the urban canopy model (UCM) is used as the simulation tool. The results show that a daily irrigation scheme results in a maximum daily temperature drop of approximately 1.3 °C and an average daily air temperature decrease close to 0.5 °C. It is also found that the cooling effect due to the additional irrigation is strongly correlated to the ambient temperature, urban fraction, and the soil moisture state before irrigation. The irrigation induced cooling effect tends to be higher in the urban areas where the soil is drier before irrigation, or in a larger portion of the area that is covered with vegetation, or where the ambient temperature is higher.

Statistical modelling of spatial patterns of the urban heat island intensity in the urban environment of Augsburg, Germany

Spatial and temporal variability of meteorological variables across urban areas due to differences in land surface characteristics is a common phenomenon. Most pronounced is the effect of land cover on air temperature. In this study, parametric and non-parametric statistical approaches (stepwise multiple linear regression, random forests) were applied in order to model sub-daily and daily spatial patterns of the urban heat island intensity in the major city of Augsburg, Southern Germany, and its rural surroundings. A large number of model setups utilizing variables from different land surface data sets as predictors and taking into account different seasonal, daily and meteorological situations was examined. The results were compared concerning different measures of model performance (mean squared skill score, mean squared error, explained variance). For individual setups and situations considerable skill with a mean squared skill score of up to 0.85 was reached. The best performing models were obtained from multiple linear regression for situations with low wind speeds and cloud cover in the morning and evening. Selected models were utilized to derive continuous spatial distributions of the air temperature deviations from a rural reference station. The resulting maps can be useful for various applications, e.g. in the context of urban planning.

Accuracy of Simulated Diurnal Valley Winds in the Swiss Alps: Influence of Grid Resolution, Topography Filtering, and Land Surface Datasets

We evaluate the near-surface representation of thermally driven winds in the Swiss Alps in a numerical weather prediction model at km-scale resolution. In addition, the influence of grid resolution (2.2 km and 1.1 km), topography filtering, and land surface datasets on the accuracy of the simulated valley winds is investigated. The simulations are evaluated against a comprehensive set of surface observations for an 18-day fair-weather summer period in July 2006. The episode is characterized by strong diurnal wind systems and the formation of shallow convection over the mountains, which transitions to precipitating convection in some areas. The near-surface winds (10 m above ground level) follow a typical diurnal pattern with strong daytime up-valley flow and weaker nighttime down-valley flow. At a 2.2 km resolution the valley winds are poorly simulated for most stations, while at a 1.1 km resolution the diurnal cycle of the valley winds is well represented in most large (e.g., Rhein valley at Chur and Rhone valley at Visp) and medium-sized valleys (e.g., Linth valley at Glarus). In the smaller valleys (e.g., Maggia valley at Cevio), the amplitude of the valley wind is still significantly underestimated, even at a 1.1 km resolution. Detailed sensitivity experiments show that the use of high-resolution land surface datasets, for both the soil characteristics as well as for the land cover, and reduced filtering of the topography are essential to achieve good performance at a 1.1 km resolution.

Impact of updating land surface data on micrometeorological weather simulations from the WRF model

Land surface processes play a critical role in governing the surface energy partitioning and the atmospheric circulation within a climate system. Improper representations of present land state, particularly spatially specific fields such as land cover, topographical and biophysical parameters contribute to the uncertainty in the model’s weather simulations extending from local to regional scales. The present study investigates the impact of superior land surface datasets on the performance of the Weather Research and Forecasting (WRF) model in simulating micrometeorological/near-surface weather, particularly sensible variables such as temperature, relative humidity, solar radiation and wind speed. The hypothesis is that the updated land surface datasets would help in improving micrometeorological forecasts over the domain comprising of Punjab, Haryana and Uttarakhand states in India. A land use land cover (LULC) dataset derived from Advanced Wide Field Sensor (AWiFS); an elevation dataset from the Shuttle Radar Topography Mission (SRTM), and a Leaf Area Index (LAI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS), are used in model initialization. Performance evaluation of the model’s simulation is done for controlled (default) and modified land boundary conditions with in situ weather from a network of automatic weather stations (AWS) operated by the Indian Space Research Organization (ISRO). In the modified run, the model more closely captured the temporal evolution of surface level temperature, relative humidity, wind speed, surface pressure and solar radiation. Improvement in 24-hr forecast ranges from 15 to 30% for these near-surface weather variables. Further testing of the model’s performance on its capability to forecast 8-day micrometeorological weather variables revealed that the modified run gave consistent results. The average RMSE values for minimum and maximum temperature, wind speed, relative humidity and precipitation are 2.5 and 3 oC, 2 m s –1 , 18% and 3.5 mm, respectively. The modification helped in increasing the lead-time of the model’s forecast by reducing the propagation error. Thus, this study emphasizes the fact that improved representation of land surface parameters has a definite effect on weather simulations at local to regional scales. For a country like India, where the feedback mechanisms between land and atmosphere are more prominent due to inherent climatic characteristics, it is critical to concentrate and improve on the inputs that represent the initial land state.

Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China

This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident.

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Assessment of a Long-Term High-Resolution Hydroclimatic Dataset for the U.S. Midwest

Abstract A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.

Effects of climate and terrestrial storage on temporal variability of actual evapotranspiration

Knowledge of the temporal variability in actual evapotranspiration (E) is essential for a better understanding of the interaction and feedback between atmospheric and land surface hydrologic processes under various natural and anthropogenic conditions. Recently, Zeng and Cai (2015) proposed a decomposition framework of the E variance, based on water balance and the Budyko hypothesis. On the basis of a long-term (1960–2008) land surface dataset, this study applies the theoretical framework to assess the effects of climate and terrestrial storage factors on the interannual and intra-annual variance in E across China. An error decomposition framework is developed to quantify the key factors in the error of the predicted E variance. The results show that the prediction of the E variance is more accurate in arid climates than in humid climates, and the corresponding error is primarily controlled by the variability of precipitation (P) and runoff (R). Climate is the primary source for the E variance, and the dominant sources shift from potential evaporation (PET) in humid climates to P in arid climates. The interactions between P and PET tend to dampen the E interannual variance and enhance the E intra-annual variance, and this effect is especially significant in humid climates. Terrestrial storage change is more capable of accommodating climatic fluctuations at the intra-annual scale than at the interannual scale, and for some arid regions it is the dominant factor influencing the E variance. The response of terrestrial storage to P is more significant than its response to PET, especially for regions with strong human impact. Neglecting the effects of terrestrial storage would possibly underestimate or overestimate the E variance in both humid and arid climates, due to the interactions between climate and the change in the terrestrial storage.

Production of a combined land surface data set and its use to assess land‐atmosphere coupling in China

AbstractLand‐atmosphere interactions play an important role in shaping regional climate and its variability. In land‐atmosphere coupling study, a fundamental challenge is data limitation, such as the sparsity of long‐term land observations and uncertainty in individual model simulations. This study produces a multisource combined land surface data set using a Bayesian model averaging method, for the assessment of land‐atmosphere coupling across China. We employ the newly produced soil moisture and evapotranspiration, together with satellite‐derived soil moisture and observation‐based evapotranspiration to assess spatiotemporal characteristics of the coupling with observed precipitation and temperature. We also define a coupling index to identify region‐specific regimes. The results have shown that strong coupling occurs over northern China, particularly in the transition zone between dry and wet climate. Here summer coupling is dominated by land evaporative water storage. Over the southern humid regions and regions at high altitudes, land‐atmosphere coupling in summer is characterized by an energy‐limited regime. Estimated coupling strengths vary with season and variable used. Precipitation‐related couplings are generally stronger in summer; temperature‐related couplings are stronger in summer in dry areas but stronger in winter in humid areas. These findings provide a multisource combined representation and cross validation of spatial and temporal characteristics of land‐atmosphere coupling across China. The implications are that northern China is a critical region for climate change/variability impact and adaptation assessment.

Content and Composition of Global Artificial Land Surface Dataset at 30 m Resolution (2010)

Content and Composition of Global Artificial Land Surface Dataset at 30 m Resolution (2010)

Real-Time Simulation of the GOES-R ABI for User Readiness and Product Evaluation

Abstract In support of the Geostationary Operational Environmental Satellite R series (GOES-R) program, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison is generating high quality simulated Advanced Baseline Imager (ABI) radiances and derived products in real time over the continental United States. These data are mainly used for testing data-handling systems, evaluating ABI-derived products, and providing training material for forecasters participating in GOES-R Proving Ground test bed activities. The modeling system used to generate these datasets consists of advanced regional and global numerical weather prediction models in addition to state-of-the-art radiative transfer models, retrieval algorithms, and land surface datasets. The system and its generated products are evaluated for the 2014 Pacific Northwest wildfires; the 2013 Moore, Oklahoma, tornado; and Hurricane Sandy. Simulated aerosol optical depth over the Front Range of Colorado during the Pacific Northwest wildfires was validated using high-density Aerosol Robotic Network (AERONET) measurements. The aerosol, cloud, and meteorological modeling system used to generate ABI radiances was found to capture the transport of smoke from the Pacific wildfires into the Front Range of Colorado and true-color imagery created from these simulated radiances provided visualization of the smoke plumes. Evaluation of selected simulated ABI-derived products for the Moore tornado and Hurricane Sandy cases was done using real-time GOES sounder/imager products produced at CIMSS. Results show that simulated ABI moisture and atmospheric stability products, cloud products, and red–green–blue (RGB) airmass composite imagery are well suited as proxy ABI data for user preparedness.

Impact of refined land surface properties on the simulation of a heavy convective rainfall process in the Pearl River Delta region, China

The location and occurrence time of convective rainfalls have attracted great public concern as they can lead to terrible disasters. However, the simulation results of convective rainfalls in the Pearl River Delta region often show significant discrepancies from the observations. One of the major causes lies in the inaccurate geographic distribution of land surface properties used in the model simulation of the heavy precipitation. In this study, we replaced the default soil and vegetation datasets of Weather Research and Forecasting (WRF) model with two refined datasets, i.e. the GlobCover 2009 (GLC2009) land cover map and the Harmonized World Soil Database (HWSD) soil texture, to investigate the impact of vegetation and soil on the rainfall patterns. The result showed that the simulation patterns of convective rainfalls obtained from the coupled refined datasets are more consistent with the observations than those obtained from the default ones. By using the coupled refined land surface datasets, the overlap ratio of high precipitation districts reached 36.3% with a variance of 28.5 km from the observed maximum rainfall position, while those of the default United States Geological Survey (USGS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset are 17.0%/ 32.8 km and 24.9%/49.0 km, respectively. The simulated total rainfall amount and occurrence time using the coupled refined datasets are the closest to the observed peak values. In addition, the HWSD soil data has improved the accuracy of the simulated precipitation amount, and the GLC2009 land cover data also did better in catching the early peak time.

Interactive coupling of regional atmosphere with biosphere in the new generation regional climate system model REMO-iMOVE

Abstract. The main objective of this study is the coupling of the regional climate model REMO with a new land surface scheme including dynamic vegetation phenology, and the evaluation of the new model version called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. First, we focus on the documentation of the technical aspects of the new model constituents and the coupling mechanism. The representation of vegetation in iMOVE is based on plant functional types (PFTs). Their geographical distribution is prescribed to the model which can be derived from different land surface data sets. Here, the PFT distribution is derived from the GLOBCOVER 2000 data set which is available on 1 km × 1 km horizontal resolution. Plant physiological processes like photosynthesis, respiration and transpiration are incorporated into the model. The vegetation modules are fully coupled to atmosphere and soil. In this way, plant physiological activity is directly driven by atmospheric and soil conditions at the model time step (two minutes to some seconds). In turn, the vegetation processes and properties influence the exchange of substances, energy and momentum between land and atmosphere. With the new coupled regional model system, dynamic feedbacks between vegetation, soil and atmosphere are represented at regional to local scale. In the evaluation part, we compare simulation results of REMO-iMOVE and of the reference version REMO2009 to multiple observation data sets of temperature, precipitation, latent heat flux, leaf area index and net primary production, in order to investigate the sensitivity of the regional model to the new land surface scheme and to evaluate the performance of both model versions. Simulations for the regional model domain Europe on a horizontal resolution of 0.44° had been carried out for the time period 1995–2005, forced with ECMWF ERA-Interim reanalyses data as lateral boundary conditions. REMO-iMOVE is able to simulate the European climate with the same quality as the parent model REMO2009. Differences in near-surface climate parameters can be restricted to some regions and are mainly related to the new representation of vegetation phenology. The seasonal and interannual variations in growth and senescence of vegetation are captured by the model. The net primary productivity lies in the range of observed values for most European regions. This study reveals the need for implementing vertical soil water dynamics in order to differentiate the access of plants to water due to different rooting depths. This gets especially important if the model will be used in dynamic vegetation studies.

Comparison of varied complexity models simulating recharge at the field scale

AbstractNumerical models are frequently used for the regional quantification of groundwater recharge. However there is a wide range of potential models available that represent the land surface with varying degrees of complexity, but which are rarely tested against observations at the field scale. We compared four models that simulate potential recharge at four intensively monitored sites with different vegetation and soil types in two adjacent catchments. These models were: Penman–Grindley, UN Food and Agricultural Organization, SPAtial Distributed Evaporation and Joint UK Land Environment Simulator. Standardized, unoptimized land surface datasets and pertinent literature were used for parameterization to reflect practice in regional water resource management and planning in the UK. The models were validated against soil moisture observations at all sites, as well as observed transpiration and interception and calculated total evaporation over a year at a woodland site. Soil moisture observations were generally reproduced well, but there were significant differences in how the models apportioned precipitation through the hydrological cycle. This demonstrates that soil moisture data alone are not a good diagnostic for groundwater recharge models. Significant differences in potential recharge were produced by models at both grassland sites, although simulated average annual potential recharge varied by only 15% at the grassland site on permeable soil. At the woodland sites, soil moisture contents were reproduced least accurately, and there were large differences in potential recharge at both woodland sites. This predominantly resulted from varied and inaccurate simulation of evaporation, particularly in the form of interception losses where this was explicitly represented in models. Differences in model structure, such as runoff representation, and parameter selection also influenced all results. Hydrological Processes © 2013 John Wiley & Sons, Ltd.