Common Land Model Research Articles

Representing permafrost properties in CoLM for the Qinghai–Xizang (Tibetan) Plateau

Abstract Most land surface models (LSMs) used in climate models do not perform well in modeling the permafrost processes. Due to the complex permafrost distribution characteristics and landscapes of the Qinghai–Tibet Plateau (QTP), the LSMs simulations over QTP are even worse. In this study, we revised the permafrost scheme in the original Common Land Model (CoLM) to improve its capability of simulating permafrost processes. We adopted a new frozen soil parameterization scheme, in which maximum unfrozen water content is defined as a function of soil matric potential. In addition, we extended the model's bottom to a depth below that without annual variations in temperature and replaced the zero-flux lower boundary condition with a constant geothermal heat flux based on literature and temperature gradient measurements in a 34.5-m-deep borehole. What's more, we revised the original snow cover fraction parameterization scheme of CoLM according to the special snow cover distribution characteristics over QTP. We calibrated and validated the modified model against observations from 2005 to 2008. The results indicate that the modified model produced more reasonable simulations of radiation balance components and significantly improved the simulation of soil liquid water content. It also shows an improved capability of reproducing soil temperatures from the top to the bottom of soil layers. The modified CoLM provides a useful tool for understanding and predicting the fate of permafrost in QTP under a warming climate.

Sobol’ sensitivity analysis of parameters in the common land model for simulation of water and energy fluxes

The water and energy transfer of land surface is complex due to its large spatial and temporal variability. The modeling and simulation is an important means to study land water and energy transfer, but most selection and analysis of model parameters are empirical and qualitative. This paper has proposed a method of quantitatively identifying the most influential parameters of Common Land Model through Sobol’ sensitivity analysis. Considering sensible heat flux as the model output, the first order and total sensitivity indices of 25 model input parameters are estimated using an improved Sobol’ method. The simulated results are resampled using a bootstrapping method and the corresponding sensitivity indices are calculated. Confidence intervals for the bootstrapping sensitivity indices are estimated by using a percentile method. The results show that the parameters phi0 and porsl are the most important parameters, followed by ref(2,1), tran(2,1) and bsw. Five out of 25 parameters need to have an accurate evaluation, while the other parameters are fixed to a certain value. The sensitivity indices of parameters phi0 and porsl are decreasing after precipitation, while the sensitivity indices of parameters tran(2, 1) and ref(2, 1) are increasing after precipitation.

Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116° 36′ E; latitude: 36° 57′ N) and Arou (longitude: 100° 27′ E; latitude: 38° 02′ N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.

Sensitivity and Parameters Optimization Method of Soil Parameters to Soil Moisture in Common Land Model

Sensitivity and Parameters Optimization Method of Soil Parameters to Soil Moisture in Common Land Model

Estimating net radiation flux in the Tibetan Plateau by assimilating MODIS LST products with an ensemble Kalman filter and particle filter

Net radiation flux is a key physical variable of energy and water exchange processes in land–atmosphere interactions. Among the methods to estimate net radiation flux, measurements and estimates from modeling have their limitations. Data assimilation methods have potential to estimate net radiation flux more accurately by merging model dynamics and observations. In this paper, two data assimilation schemes are developed based on the Common Land Model (CoLM) using the ensemble Kalman filter (EnKF) and particle filter (PF) algorithms, respectively, to improve predictions of surface net radiation flux in the Tibetan Plateau. First, Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products were used to update the LAI in CoLM. It was found that sparse vegetation in the Tibetan Plateau has an obvious influence on the surface temperature. Then, the experiments of assimilating MODIS land surface temperature (LST) products were carried out with the EnKF and PF algorithms, respectively. The two schemes were tested and validated with observations from two automatic weather stations (AWSs) in Muztaga in the Tibetan Plateau during the period of October 1, 2009–September 30, 2010. The results show that the two assimilation schemes are both feasible and an improvement over the predictions of cases with no assimilation for the estimations of surface temperature and net radiation flux. Furthermore, the different surface types and the accuracy of MODIS LST products have an important influence on the assimilation results. In addition, the performances of EnKF and PF are almost same in the study.

Improving the CoLM in Taklimakan Desert hinterland with accurate key parameters and an appropriate parameterization scheme

Improving and validating land surface models based on integrated observations in deserts is one of the challenges in land modeling. Particularly, key parameters and parameterization schemes in desert regions need to be evaluated in-situ to improve the models. In this study, we calibrated the land-surface key parameters and evaluated several formulations or schemes for thermal roughness length (z0h) in the common land model (CoLM). Our parameter calibration and scheme evaluation were based on the observed data during a torrid summer (29 July to 11 September 2009) over the Taklimakan Desert hinterland. First, the importance of the key parameters in the experiment was evaluated based on their physics principles and the significance of these key parameters were further validated using sensitivity test. Second, difference schemes (or physics-based formulas) of z0h were adopted to simulate the variations of energy-related variables (e.g., sensible heat flux and surface skin temperature) and the simulated variations were then compared with the observed data. Third, the z0h scheme that performed best (i.e., Y07) was then selected to replace the defaulted one (i.e., Z98); the revised scheme and the superiority of Y07 over Z98 was further demonstrated by comparing the simulated results with the observed data. Admittedly, the revised model did a relatively poor job of simulating the diurnal variations of surface soil heat flux, and nighttime soil temperature was also underestimated, calling for further improvement of the model for desert regions.

Evapotranspiration models of different complexity for multiple land cover types

AbstractA comparison between half‐hourly and daily measured and computed evapotranspiration (ET) using three models of different complexity, namely, the Priestley–Taylor (P‐T), the reference Penman–Monteith (P‐M) and the Common Land Model (CLM), was conducted using three AmeriFlux sites under different land cover and climate conditions (i.e. arid grassland, temperate forest and subhumid cropland). Using the reference P‐M model with a semiempirical soil moisture function to adjust for water‐limiting conditions yielded ET estimates in reasonable agreement with the observations [root mean square error (RMSE) of 64–87 W m−2 for half‐hourly and RMSE of 0.5–1.9 mm day−1 for daily] and similar to the complex Common Land Model (RMSE of 60–94 W m−2 for half‐hourly and RMSE of 0.4–2.1 mm day−1 for daily) at the grassland and cropland sites. However, the semiempirical soil moisture function was not applicable particularly for the P‐T model at the forest site, suggesting that adjustments to key model variables may be required when applied to diverse land covers. On the other hand, under certain land cover/environmental conditions, the use of microwave‐derived soil moisture information was found to be a reliable metric of regional moisture conditions to adjust simple ET models for water‐limited cases. Further studies are needed to evaluate the utility of the simplified methods for different landscapes. Copyright © 2011 John Wiley & Sons, Ltd.

Implementation of a topographically controlled runoff scheme for land surface parameterizations in regional climate models

The Common Land Model (CLM), the land component of the Community Climate System Model (CCSM), for simulating water and energy exchanges between land and atmosphere has water and energy biases resulting from deficiencies in some parameterizations related to hydrological processes. This paper presents the implementation of modified parameterizations in the terrestrial hydrological scheme of the CLM and their effects on the runoff prediction. In particular, the new formulation for topographically controlled baseflow developed in this study can represent effects of the surface macropores and the vertical change of hydraulic conductivity on baseflow. To assess the performance of the new improved parameterizations, we compare runoff results from a set of offline simulations using the consistent North American Regional Reanalysis (NARR) meteorological forcing dataset and realistic Surface Boundary Conditions (SBCs) with observations from the U.S. Geological Survey (USGS) gauge station for a study catchment around the Ohio Valley region. The new modified scheme especially incorporating topographically controlled baseflow plays a significant role in generating and partitioning surface runoff and subsurface runoff. It is also observed that the new model-simulated subsurface runoff makes a significant contribution to improve the runoff predictability in simulating declining recession curves due to the role of baseflow.

Simulation and improvement of common land model on the bare soil of Loess Plateau underlying surface

Climate model has become an irreplaceable tool for the study and prediction of climate changes. The land surface process, as one of the important parts of all climate models, must be considered so that the simulative ability of climate models could be improved. Using the common land model (CoLM) that is driven by the LOPEX experiment data, the characteristics of land surface processes of the Loess Plateau are simulated. Furthermore, based on the comparison of the field observation data with the simulated results, the simulative performance of CoLM in the Loess Plateau region is also examined. The results show that, CoLM can be used in the Loess Plateau, and it perfectly simulates net radiations and net short-wave radiations. However, the simulated land-surface temperature is slightly higher than actual measured value, while the simulated soil temperature values in lower layers (5, 10, 20, 40 cm) are relatively less and the variety phase of these also lag. Moreover, the simulated sensible heat flux is a little larger, while the simulated soil thermal conductivity value is obviously lower. By modifying the calculation plan of soil thermal conductivity, the simulated result has been greatly improved. As a whole, if CoLM is applied in the Loess Plateau of Northwestern China, the parameterization of soil thermal conductivity should be ameliorated, which can improve its simulative capacity in the Loess Plateau regions.

Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model

Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult. This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to the measurements, with a root mean square error (RMSE) between 115 and 130 W m–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.

Coupling a terrestrial biogeochemical model to the common land model

A terrestrial biogeochemical model (CASACNP) was coupled to a land surface model (the Common Land Model, CoLM) to simulate the dynamics of carbon substrate in soil and its limitation on soil respiration. The combined model, CoLM CASACNP, was able to predict long-term carbon sources and sinks that CoLM alone could not. The coupled model was tested using measurements of belowground respiration and surface fluxes from two forest ecosystems. The combined model simulated reasonably well the diurnal and seasonal variations of net ecosystem carbon exchange, as well as seasonal variation in the soil respiration rate of both the forest sites chosen for this study. However, the agreement between model simulations and actual measurements was poorer under dry conditions. The model should be tested against more measurements before being applied globally to investigate the feedbacks between the carbon cycle and climate change.

A new approach for parameter optimization in land surface model

In this study, a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyu station in Jilin Province, China, combined with a sophisticated LSM (common land model, CoLM). Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification, salination, and degradation since late 1960s. In this study, three key land-surface parameters, namely, soil color, proportion of sand or clay in soil, and leaf-area index were chosen as parameters to be optimized. Our study comprised three experiments: First, a single-parameter optimization was performed, while the second and third experiments performed triple- and six-parameter optimizations, respectively. Notable improvements in simulating sensible heat flux (SH), latent heat flux (LH), soil temperature (TS), and moisture (MS) at shallow layers were achieved using the optimized parameters. The multiple-parameter optimization experiments performed better than the single-parameter experminent. All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM. Moreover, clear mathematical meaning, simple design structure, and rapid computability give this method great potential for further application to parameter optimization in LSMs.

Evaluation of a Conjunctive Surface–Subsurface Process Model (CSSP) over the Contiguous United States at Regional–Local Scales

AbstractThis study presents a comprehensive evaluation on a Conjunctive Surface–Subsurface Process Model (CSSP) in predicting soil temperature–moisture distributions, terrestrial hydrology variations, and land–atmosphere exchanges against various in situ measurements and synthetic observations at regional–local scales over the contiguous United States. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface–subsurface flow interactions, and bedrock constraint on water table depths. As compared with the AmeriFlux tower measurements, the CSSP and CLM3.5 reduce surface sensible and latent heat flux errors from CoLM by 10 W m−2 on average, and have much higher correlations with observations for daily latent heat variations. The CSSP outperforms the CLM3.5 over the crop, grass, and shrub sites in depicting the latent heat annual cycles. While retaining the improvement for soil moisture in deep layers, the CSSP shows further advantage over the CLM3.5 in representing seasonal and interannual variations in root zones. The CSSP reduces soil temperature errors from the CLM3.5 (CoLM) by 0.2 (0.7) K at 0.1 m and 0.3 (0.6) K at 1 m; more realistically captures seasonal–interannual extreme runoff and streamflow over most regions and snow depth anomalies in high latitude (45°–52°N); and alleviates climatological water table depth systematic bias (absolute error) by about 1.2 (0.4) m. Clearly, the CSSP performance is overall superior to both the CoLM and CLM3.5. The remaining CSSP deficiencies and future refinements are also discussed.

Estimation of net surface shortwave radiation from land surface temperature in regional scale

The method to estimate NSSR (net surface shortwave radiation) from LST (land surface temperature) in regional scale is discussed. First, an elliptical model between the time series of normalized LST and NSSR was developed using the daily evolution of LST and NSSR. Second, time series of LST and NSSR were simulated by common land model (CoLM) and were proved to be of high accuracy. On the basis of these, a non-linear least square ellipse fitting using the genetic algorithm method was used to fit the normalized LST and NSSR. Finally, LST was inverted using MODIS (moderate resolution imaging spectroradiometer) data with the split-window algorithm, and the regional NSSR was then estimated with LST and an elliptical model. The validation result shows that the derived average NSSR of 50×50 pixels of MODIS data was quite close to the observed data, and the distribution was reasonable, which indicates that the proposed method was capable of estimating NSSR on a regional scale.

Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites: Advanced Microwave Scanning Radiometer E, land surface model, and ground measurements

AbstractIn the past few decades, there have been great developments in remotely sensed soil moisture, with validation efforts using land surface models (LSMs) and ground‐based measurements, because soil moisture information is essential to understanding complex land surface–atmosphere interactions. However, the validation of remotely sensed soil moisture has been very limited because of the scarcity of the ground measurements in Korea. This study validated Advanced Microwave Scanning Radiometer E (AMSR‐E) soil moisture data with the Common Land Model (CLM), one of the most widely used LSMs, and ground‐based measurements at two Korean regional flux monitoring network sites. There was reasonable agreement regarding the different soil moisture products for monitoring temporal trends except National Snow and Ice Data Centre (NSIDC) AMSR‐E soil moisture, albeit there were essential comparison limitations by different spatial scales and soil depths. The AMSR‐E soil moisture data published by the National Aeronautics and Space Administration and Vrije Universiteit Amsterdam (VUA) showed potential to replicate temporal variability patterns (root‐mean‐square errors = 0·10–0·14 m3 m−3 and wet BIAS = 0·09 − 0·04 m3 m−3) with the CLM and ground‐based measurements. However, the NSIDC AMSR‐E soil moisture was problematic because of the extremely low temporal variability and the VUA AMSR‐E soil moisture was relatively inaccurate in Gwangneung site characterized by complex geophysical conditions. Additional evaluations should be required to facilitate the use of recent and forthcoming remotely sensed soil moisture data from Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive missions at representative future validation sites. Copyright © 2011 John Wiley & Sons, Ltd.

Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter

[1] In this study, a data assimilation scheme is developed on the basis of the ensemble Kalman filter algorithm and the common land model (CoLM); soil moisture and model parameters are simultaneously optimized to improve the estimates of turbulent fluxes. Land surface temperature (LST) is retrieved from geostationary operational environmental satellites (GOES) data, validated, and then assimilated into the model. The data assimilation results are validated at six observation sites in the United States that include grassland, cropland, and forestland cover types. Data assimilation results indicate that in addition to improvements in the prediction of turbulent fluxes, model uncertainties are also reduced as a result of the assimilation of GOES LST retrieval data. The average reductions in root mean square error (RMSE) values are 47.5 and 31.1 W m−2. The effects of simultaneous optimization of soil moisture and model parameters are compared with those resulting from separate optimization; simultaneous optimization is found to yield smaller RMSE values. Further, in this study, the effects of Moderate Resolution Imaging Spectroradiometer (MODIS) and GOES temporal resolution data on data assimilation results are studied. The assimilation results indicate that the average RMSE values for GOES temporal resolution data are smaller than that for MODIS temporal resolution data. During the assimilation time period, the soil moisture obtained from assimilation closely agrees with the observed values, and the four vegetation parameters show distinct seasonal variations. However, the lack of sufficient information makes it difficult to estimate the true value of these variables and parameters.

Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model

AbstractFour data assimilation scheme combinations derived from two strategies and two optimization algorithms [the ensemble Kalman filter (EnKF) and the shuffled complex evolution method developed at The University of Arizona (SCE-UA)] are developed based on the Common Land Model (CLM) to improve predictions of water and heat fluxes. The first strategy is constructed through adjusting the soil temperature, while the second strategy adjusts the soil moisture. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are compared with ground-measured surface temperature, and assimilated into the CLM. The relationship equation between the MODIS LST products and CLM surface temperature is taken as the observation operator and the root-mean-square error (RMSE) is applied as the observation error. The assimilation results are validated by measurements from six observation sites located in Germany, the United States, and China. Results indicate that the developed data assimilation schemes can improve estimates of water and heat fluxes. Overall, strategy 2 is superior to strategy 1 when using the same optimization algorithm. The EnKF algorithm performs slightly better than the SCE-UA algorithm when using the same strategy. Strategy 2 combined with the EnKF algorithm performs best for water and heat fluxes, and the reductions in the RMSE are found to be 24.0 and 15.2 W m−2 for sensible and latent heat fluxes, respectively. The joint assimilation of the MODIS LST and soil moisture observations can produce better results for strategy 2 with the SCE-UA. Since preprocessing model parameters are used in this study, the uncertainties in the model parameters may have resulted in suboptimal assimilation results. Therefore, model calibrations should be conducted in the future.

Using conditional nonlinear optimal perturbation method in parameter optimization of land surface processes model

In this paper, we attempted to entend the application of conditional nonlinear optimal perturbation(CNOP) to the optimization of parameters in land surface model. We used the common land model and data of Tongyu station,which is a reference site of the CEOP in the semi-arid regions, and used three key parameters (soil color, soil sand/lay proportion and leaf area index) as parameters to be optimized. Two experiments are designed in our work, namely the single-parameter optimization and the triple-parameter optimization. Notable improvements in simulating sensible heat flux (SH), latent heat flux (LH), soil temperature (TS) and moisture (MS) at shallow layers were achieved by using the optimized parameters. In addition, the latter experiment shows a better performance than the former. All results above illustrate that the application of CNOP method can be extended to parameters optimization of land surface model. And what is more, due to its other advantages, such as the clear mathematical meaning, the simple design structure, and the fast computing speed, it shows a great potential for further applications in parameters optimization of related problems.

Coupling groundwater and land surface processes: Idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes

This work investigates the role of terrain and subsurface heterogeneity on the interactions between groundwater dynamics and land surface energy fluxes using idealized simulations. A three‐dimensional variably saturated groundwater code (ParFlow) coupled to a land surface model (Common Land Model) is used to account for both vertical and lateral water and pressure movement. This creates a fully integrated approach, coupling overland and subsurface flow while having an explicit representation of the water table and all land surface processes forced by atmospheric data. Because the water table is explicitly represented in these simulations, regions with stronger interaction between water table depth and the land surface energy balance (known as critical zones) can be identified. This study uses simple terrain and geologic configurations to demonstrate the importance of lateral surface and subsurface flows in determining land surface heat and moisture fluxes. Strong correlations are found between the land surface fluxes and water table depth across all cases, including terrain shape, subsurface heterogeneity, vegetation type, and climatological region. Results show that different land forms and subsurface heterogeneities produce very different water table dynamics and land surface flux responses to atmospheric forcing. Subsurface formation and properties have the greatest effect on the coupling between the water table and surface heat and moisture fluxes. Changes in landform and land surface slope also have an effect on these interactions by influencing the fraction of rainfall contributing to overland flow versus infiltration. This directly affects the extent of the critical zone with highest coupling strength along the hillside. Vegetative land cover, as seen in these simulations, has a large effect on the energy balance at the land surface but a small effect on streamflow and water table dynamics and thus a limited impact on the land surface‐subsurface interactions. Although climate forcing has a direct effect on water table dynamics and feedbacks to the land surface, in this study it does not overcome that of subsurface heterogeneity and terrain.