Simulated Soil Temperature Research Articles

Numerical simulation based on two-directional freeze and thaw algorithm for thermal diffusion model

Freeze-thaw processes significantly modulate hydraulic and thermal characteristics of soil. The changes in the frost and thaw fronts (FTFs) affect the water and energy cycles between the land surface and the atmosphere. Thus, the frozen soil comprising permafrost and seasonally frozen soil has important effects on the land surface hydrology in cold regions. In this study, a two-directional freeze and thaw algorithm is incorporated into a thermal diffusion equation for simulating FTFs. A local adaptive variable-grid method is used to discretize the model. Sensitivity tests demonstrate that the method is stable and FTFs can be tracked continuously. The FTFs and soil temperature at the Qinghai-Tibet Plateau D66 site are simulated hourly from September 1, 1997 to September 22, 1998. The results show that the incorporated model performs much better in the soil temperature simulation than the original thermal diffusion equation, showing potential applications of the method in land-surface process modeling.

Assessing an Enhanced Version of SWAT on Water Quantity and Quality Simulation in Regions with Seasonal Snow Cover

Winter soil-temperature simulations with soil and water assessment tool (SWAT) are generally inaccurate in snow-dominated areas of the word, because the existing empirically-based soil-temperature module in SWAT does not account for snow-insulation effects. This problem, in turn, leads to all subsequent projections including water flow and nutrient loading biased. To address this issue, a physically-based soil-temperature module was developed and incorporated into SWAT as an alternative to the empirical formulation, which was found generate better overall estimates of winter soil temperature in a previous study. In the current study, we continue to examine the modified version of SWAT performance on watershed modeling by making pairwise comparisons between the outputs from the original and modified versions of SWAT against related field measurements in a testing watershed. Results show that the physically-based soil-temperature formulation helps to dramatically improve SWAT estimations of base flow discharge and NO3-N loading for the watershed as a result of improvement in modeled winter soil temperatures. Compared with the original version of SWAT, the new version of SWAT predicted overall lower surface runoff and soil moisture content, as well as higher percolation and lateral flow in winter, resulting in clear differences in modeled flow paths and fate of chemical pollutants. Adding a physically-based treatment of soil temperature in SWAT is an important generalization that has potential of making the model more relevant to snow-dominated areas of the world.

Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance

This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R2 > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R2 < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R2 < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.

Comparison of different soil temperature algorithms in permafrost regions of Qinghai-Xizang (Tibet) Plateau of China

Soil thermal diffusivity is a crucial physical parameter that affects soil temperature. By applying sinusoidal boundary conditions, an analytical solution using separated variables for the heat conduction-convection equation was developed. The thermal diffusivity and liquid water flux density were calculated with data collected at field observation sites in permafrost regions of Qinghai-Xizang (Tibet) Plateau (QXP). By taking the soil layer at the depth of 5cm as the upper boundary, the soil temperature at a depth of 10cm was modeled by the thermal conduction-convection method, amplitude method and phase method. The statistical analysis of the standard error of the estimate (SEE), the normalized standard error (NSEE) and the root mean square error (RMSE) demonstrated that the thermal conduction-convection method provided the most accurate prediction of soil temperature, with average SEE, NSEE, and RMSE of 0.72°C, 9.26% and 0.72°C, respectively. The thermal conduction-convection method provides a useful tool for calculating soil thermal parameters, simulating soil temperature and land surface processes parameterization for permafrost changes modelling under global warming.

An improved model for soil surface temperature from air temperature in permafrost regions of Qinghai-Xizang (Tibet) Plateau of China

Soil temperature plays a key role in hydro-thermal processes in environments and is a critical variable linking surface structure to soil processes. There is a need for more accurate temperature simulation models, particularly in Qinghai-Xizang (Tibet) Plateau (QXP). In this study, a model was developed for the simulation of hourly soil surface temperatures with air temperatures. The model incorporated the thermal properties of the soil, vegetation cover, solar radiation, and water flux density and utilized field data collected from Qinghai-Xizang (Tibet) Plateau (QXP). The model was used to simulate the thermal regime at soil depths of 5 cm, 10 cm and 20 cm and results were compared with those from previous models and with experimental measurements of ground temperature at two different locations. The analysis showed that the newly developed model provided better estimates of observed field temperatures, with an average mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) of 1.17 °C, 1.30 °C and 13.84 %, 0.41 °C, 0.49 °C and 5.45 %, 0.13 °C, 0.18 °C and 2.23 % at 5 cm, 10 cm and 20 cm depths, respectively. These findings provide a useful reference for simulating soil temperature and may be incorporated into other ecosystem models requiring soil temperature as an input variable for modeling permafrost changes under global warming.

Climate-Induced Shifts in Global Soil Temperature Regimes

Soil temperature is a key factor of plant growth and biological enzyme activities occurring in the soil, affected by the land cover, the evapotranspiration rate, the albedo, and the energy budget of the soil surface. In recent decades, efforts have been made to conserve soils against nonsustainable anthropogenic pressures. Changes in climate can impose additional threats on soil sustainability, as global scale soil temperature regime alterations are expected under global warming. Here, data from three well-established global climate models, spanning from 1981 to as far as 2120, are used to force the JULES (Joint UK Land Environment Simulator) model and produce simulations of soil temperature, calculating the water and energy budgets of the land surface. Modeled soil temperature data are used to estimate the climate-induced changes in the global soil temperature regimes at three different global warming levels. The results show significant shifts in the soil temperature regime for extended areas of the world, especially in the northern hemisphere. Pergelic and Cryic areas are reduced, whereas the Mesic and Thermic soils gain large areas in all three studied scenarios. Implications of the warming patterns might indicate the northward shift of various croplands in regions that until now their cultivation was not possible.

Importance of soil thermal regime in terrestrial ecosystem carbon dynamics in the circumpolar north

In the circumpolar north (45–90°N), permafrost plays an important role in vegetation and carbon (C) dynamics. Permafrost thawing has been accelerated by the warming climate and exerts a positive feedback to climate through increasing soil C release to the atmosphere. To evaluate the influence of permafrost on C dynamics, changes in soil temperature profiles should be considered in global C models. This study incorporates a sophisticated soil thermal model (STM) into a dynamic global vegetation model (LPJ-DGVM) to improve simulations of changes in soil temperature profiles from the ground surface to 3m depth, and its impacts on C pools and fluxes during the 20th and 21st centuries. With cooler simulated soil temperatures during the summer, LPJ-STM estimates ~0.4PgCyr−1 lower present-day heterotrophic respiration but ~0.5PgCyr−1 higher net primary production than the original LPJ model resulting in an additional 0.8 to 1.0PgCyr−1 being sequestered in circumpolar ecosystems. Under a suite of projected warming scenarios, we show that the increasing active layer thickness results in the mobilization of permafrost C, which contributes to a more rapid increase in heterotrophic respiration in LPJ-STM compared to the stand-alone LPJ model. Except under the extreme warming conditions, increases in plant production due to warming and rising CO2, overwhelm the e nhanced ecosystem respiration so that both boreal forest and arctic tundra ecosystems remain a net C sink over the 21st century. This study highlights the importance of considering changes in the soil thermal regime when quantifying the C budget in the circumpolar north.

Modelling and mapping permafrost at high spatial resolution using Landsat and Radarsat images in northern Ontario, Canada: part 1 – model calibration

Permafrost is an important ground thermal condition that has significant biophysical and socio-economic impacts. In order to better understand the distribution and dynamics of permafrost, there is a need to map permafrost at high spatial resolution. This study is part of a research project that aims to model and map permafrost using remote sensing images and the Northern Ecosystem Soil Temperature (NEST) model in the central part of the Hudson Bay Lowland in northern Ontario, Canada. The study area is near the southern margin of permafrost region where permafrost exists only in isolated patches. In this study, we ran the NEST model from 1932 to 2012 using a climate data set compiled from station observations and grid data sources. The model outputs were then compared to field observations acquired during 2009–2012 at seven peat monitoring stations and two flux towers, which represent three major types of peatland in the study area (bog, fen, and palsa). The simulated soil temperatures at various depths show good agreement with the observations, and the simulated latent and sensible heat fluxes and net radiation are similar to the observations at the two flux towers. The model accurately shows the existence of permafrost only at palsa sites. Based on the general range of climate and ground conditions in this area, sensitivity tests indicate that the modelled permafrost conditions are sensitive to leaf area index, air temperature, precipitation, and soil texture. Therefore, the NEST model is capable of simulating ground temperature and permafrost conditions in where permafrost occurs only sporadically. A companion paper (part 2) uses the model with Landsat and Radarsat imagery to map the distribution and dynamics of permafrost in this area.

Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

Most current land surface models (LSMs) show poor performance in soil water and temperature simulations for cold regions because of the inappropriate model structures and ill-parameterizations. This study aims to explore how to parameterize soil organic carbon (SOC) in term of soil moisture simulations using the Community Land Model version 4.0 (CLM4.0) model and the Dynamic Land Model version 1.0 (DLM1.0) model. Firstly, we discuss the sensitivities of modeled soil moisture to SOC based on observations. The SOC parameterizations in both CLM4.0 and DLM1.0 were proved poor against the measured SOC through sensitivity analysis for the Heihe River watershed area (cold but with high SOC). A SOC parameterization was developed and tested based on multiple year observations. Our findings are twofold: (i) the soil organic parameterizations in CLM4.0 and DLM1.0 are inappropriate for simulating soil moisture in cold regions with high SOC, and the modified SOC parameterization significantly improves the soil moisture simulations; (ii) the impacts of SOC on soil moisture vary significantly with seasons, which is largest for JJA (June, July and August) followed by SON (September, October and November), MAM (March, April and May) and DJF (December, January and February). This study highlights that the impacts of SOC should be fully considered for LSMs soil moisture simulations, especially for cold areas with high SOC.

The effect of different evapotranspiration methods on portraying soil water dynamics and ET partitioning in a semi-arid environment in Northwest China

Abstract. Different methods for assessing evapotranspiration (ET) can significantly affect the performance of land surface models in portraying soil water dynamics and ET partitioning. An accurate understanding of the impact a method has is crucial to determining the effectiveness of an irrigation scheme. Two ET methods are discussed: one is based on reference crop evapotranspiration (ET0) theory, uses leaf area index (LAI) for partitioning into soil evaporation and transpiration, and is denoted as the ETind method; the other is a one-step calculation of actual soil evaporation and potential transpiration by incorporating canopy minimum resistance and actual soil resistance into the Penman–Monteith model, and is denoted as the ETdir method. In this study, a soil water model, considering the coupled transfer of water, vapor, and heat in the soil, was used to investigate how different ET methods could affect the calculation of the soil water dynamics and ET partitioning in a crop field. Results indicate that for two different ET methods this model varied concerning the simulation of soil water content and crop evapotranspiration components, but the simulation of soil temperature agreed well with lysimeter observations, considering aerodynamic and surface resistance terms improved the ETdir method regarding simulating soil evaporation, especially after irrigation. Furthermore, the results of different crop growth scenarios indicate that the uncertainty in LAI played an important role in estimating the relative transpiration and evaporation fraction. The impact of maximum rooting depth and root growth rate on calculating ET components might increase in drying soil. The influence of maximum rooting depth was larger late in the growing season, while the influence of root growth rate dominated early in the growing season.

Constraining Parameter Uncertainty in Simulations of Water and Heat Dynamics in Seasonally Frozen Soil Using Limited Observed Data

Water and energy processes in frozen soils are important for better understanding hydrologic processes and water resources management in cold regions. To investigate the water and energy balance in seasonally frozen soils, CoupModel combined with the generalized likelihood uncertainty estimation (GLUE) method was used. Simulation work on water and heat processes in frozen soil in northern China during the 2012/2013 winter was conducted. Ensemble simulations through the Monte Carlo sampling method were generated for uncertainty analysis. Behavioral simulations were selected based on combinations of multiple model performance index criteria with respect to simulated soil water and temperature at four depths (5 cm, 15 cm, 25 cm, and 35 cm). Posterior distributions for parameters related to soil hydraulic, radiation processes, and heat transport indicated that uncertainties in both input and model structures could influence model performance in modeling water and heat processes in seasonally frozen soils. Seasonal courses in water and energy partitioning were obvious during the winter. Within the day-cycle, soil evaporation/condensation and energy distributions were well captured and clarified as an important phenomenon in the dynamics of the energy balance system. The combination of the CoupModel simulations with the uncertainty-based calibration method provides a way of understanding the seasonal courses of hydrology and energy processes in cold regions with limited data. Additional measurements may be used to further reduce the uncertainty of regulating factors during the different stages of freezing–thawing.

Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.

Validation of the global land data assimilation system based on measurements of soil temperature profiles

Soil temperature is a key parameter in the soil–vegetation–atmosphere system. It plays an important role in the land surface water and energy cycles, and has a major influence on vegetation growth and other hydrological aspects. We evaluated the accuracy of the soil temperature profiles from the Global Land Data Assimilation System (GLDAS) using nine observational networks across the world and aimed to find a reliable global soil temperature profile dataset for future hydrological and ecological studies. In general, the soil temperature profile data generated by the Noah model driven by the GLDAS forcing data (GLDAS_Noah10 and GLDAS_Noah10_v2) were found to have high skills in terms of daily, monthly, and mean seasonal variations, indicated by smaller bias and root-mean-square-error (RMSE) (both <3°C) and correlation coefficients larger than 0.90. Conversely, the Community Land Model (CLM) results (GLDAS_CLM10) generally showed larger bias and RMSE (both >4°C). Further analysis showed that the overestimation by GLDAS_CLM10 was mainly caused by overestimation of the ground heat flux, determined by the thermal conductivity parameterization scheme, whereas the underestimation by GLDAS_Noah10 was due to underestimation of downward longwave radiation from the forcing data. Thus, more accurate forcing data should be required for the Noah model and an improved thermal parameterization scheme should be developed for the CLM. These approaches will improve the accuracy of simulated soil temperatures. To our knowledge, it is the first study to evaluate the GLDAS soil temperatures with comprehensive in situ observations across the world, and has a potential to facilitate an overall improvement of the GLDAS products (not only soil temperatures but also the related energy and water fluxes) as well as a refinement of the land surface parameterization used in GLDAS.

New Fourier-series-based analytical solution to the conduction–convection equation to calculate soil temperature, determine soil thermal properties, or estimate water flux

Temperature is an important physical variable of soil. Heat transfer in soils predominantly occurs due to conduction and convection. In this paper, we present a new analytical solution, based upon the Fourier series boundary conditions for soil surface temperature, in which the separation of variables for the heat conduction–convection equation was established. Data that had been collected from the Qinghai-Xizang (Tibet) Plateau (QXP) were used to calculate the thermal diffusivity and the liquid water flux density using different methods. The results of the soil thermal diffusivity using 5cm as the upper boundary for the soil depth were used to calculate the soil temperature using both the single sine wave conduction and conduction–convection model and the Fourier series conduction–convection model. These results were then compared with the values for the temperature of the field soil measured at a depth of 10cm. The average standard error of the estimate (SEE), the normalized standard error (NSEE) and Bias were 0.16°C, 2.70% and 0.11°C for the Fourier series conduction–convection method. The results indicate that the Fourier series model provides a better estimate of observed field temperatures than the sine wave model. This method provides a useful tool for determining soil thermal parameters, simulating soil temperature and the parameterization of land surface processes for modeling permafrost changes under global warming conditions.

Integration of a Three-Dimensional Process-Based Hydrological Model into the Object Modeling System

The integration of a spatial process model into an environmental modeling framework can enhance the model’s capabilities. This paper describes a general methodology for integrating environmental models into the Object Modeling System (OMS) regardless of the model’s complexity, the programming language, and the operating system used. We present the integration of the GEOtop model into the OMS version 3.0 and illustrate its application in a small watershed. OMS is an environmental modeling framework that facilitates model development, calibration, evaluation, and maintenance. It provides innovative techniques in software design such as multithreading, implicit parallelism, calibration and sensitivity analysis algorithms, and cloud-services. GEOtop is a physically based, spatially distributed rainfall-runoff model that performs three-dimensional finite volume calculations of water and energy budgets. Executing GEOtop as an OMS model component allows it to: (1) interact directly with the open-source geographical information system (GIS) uDig-JGrass to access geo-processing, visualization, and other modeling components; and (2) use OMS components for automatic calibration, sensitivity analysis, or meteorological data interpolation. A case study of the model in a semi-arid agricultural catchment is presented for illustration and proof-of-concept. Simulated soil water content and soil temperature results are compared with measured data, and model performance is evaluated using goodness-of-fit indices. This study serves as a template for future integration of process models into OMS.

An analytical model for estimating soil temperature profiles on the Qinghai-Tibet Plateau of China

Soil temperature is a key variable in the control of underground hydro-thermal processes. To estimate soil temperature more accurately, this study proposed a solution method of the heat conduction equation of soil temperature (improved heat conduction model) by applying boundary conditions that incorporate the annual and diurnal variations of soil surface temperature and the temporal variation of daily temperature amplitude, as well as the temperature difference between two soil layers in the Tanggula observation site of the Qinghai-Tibet Plateau of China. We employed both the improved heat conduction model and the classical heat conduction model to fit soil temperature by using the 5 cm soil layer as the upper boundary for soil depth. The results indicated that the daily soil temperature amplitude can be better described by the sinusoidal function in the improved model, which then yielded more accurate soil temperature simulating effect at the depth of 5 cm. The simulated soil temperature values generated by the improved model and classical heat conduction model were then compared to the observed soil temperature values at different soil depths. Statistical analyses of the root mean square error (RMSE), the normalized standard error (NSEE) and the bias demonstrated that the improved model showed higher accuracy, and the average values of RMSE, bias and NSEE at the soil depth of 10-105 cm were 1.41°C, 1.15°C and 22.40%, respectively. These results indicated that the improved heat conduction model can better estimate soil temperature profiles compared to the traditional model.

Augmentations to the Noah Model Physics for Application to the Yellow River Source Area. Part I: Soil Water Flow

Abstract This is the first part of a study focusing on evaluating the performance of the Noah land surface model (LSM) in simulating surface water and energy budgets for the high-elevation source region of the Yellow River (SRYR). A comprehensive dataset is utilized that includes in situ micrometeorological and profile soil moisture and temperature measurements as well as laboratory soil property measurements of samples collected across the SRYR. Here, the simulation of soil water flow is investigated, while Part II concentrates on the surface heat flux and soil temperature simulations. Three augmentations are proposed: 1) to include the effect of organic matter on soil hydraulic parameterization via the additivity hypothesis, 2) to implement the saturated hydraulic conductivity as an exponentially decaying function with soil depth, and 3) to modify the vertical root distribution to represent the Tibetan conditions characterized by an abundance of roots in the topsoil. The diffusivity form of Richards’ equation is further revised to allow for the simulation of soil water flow across soil layers with different hydraulic properties. Usage of organic matter for calculating the porosity and soil suction improves the agreement between the estimates and laboratory measurements, and the exponential function together with the Kozeny–Carman equation best describes the in situ . Through implementation of the modified hydraulic parameterization alone, the soil moisture underestimation in the upper soil layer under wet conditions is resolved, while the soil moisture profile dynamics are better captured by also including the modified root distribution.

Impacts of grid resolution on surface energy fluxes simulated with an integrated surface-groundwater flow model

Abstract. The hydrological component of the Terrestrial Systems Modeling Platform (TerrSysMP), which includes integrated surface-groundwater flow, was used to investigate the grid resolution dependence of simulated soil moisture, soil temperature, and surface energy fluxes over a sub-catchment of the Rur, Germany. The investigation was motivated by the recent developments of new earth system models, which include 3-D physically based groundwater models for the coupling of land–atmosphere interaction and subsurface hydrodynamics. Our findings suggest that for grid resolutions between 100 and 1000 m, the non-local controls of soil moisture are highly grid resolution dependent. Local vegetation, however, strongly modulates the scaling behavior, especially for surface fluxes and soil temperature, which depends on the radiative transfer property of the canopy. This study also shows that for grid resolutions above a few 100 m, the variation of spatial and temporal patterns of sensible and latent heat fluxes may significantly affect the resulting atmospheric mesoscale circulation and boundary layer evolution in coupled runs.

The reliability of single precision computations in the simulation of deep soil heat diffusion in a land surface model

Climate models need discretized numerical algorithms and finite precision arithmetic to solve their differential equations. Most efforts to date have focused on reducing truncation errors due to discretization effects, whereas rounding errors due to the use of floating-point arithmetic have received little attention. However, there are increasing concerns about more frequent occurrences of rounding errors in larger parallel computing platforms (due to the conflicting needs of stability and accuracy vs. performance), and while this has not been the norm in climate and forecast models using double precision, this could change with some models that are now compiled with single precision, which raises questions about the validity of using such low precision in climate applications. For example, processes occurring over large time scales such as permafrost thawing are potentially more vulnerable to this issue. In this study we analyze the theoretical and experimental effects of using single and double precision on simulated deep soil temperature from the Canadian LAnd Surface Scheme (CLASS), a state-of-the-art land surface model. We found that reliable single precision temperatures are limited to depths of less than about 20–25 m while double precision shows no loss of accuracy to depths of at least several hundred meters. We also found that, for a given precision level, model accuracy deteriorates when using smaller time steps, further reducing the usefulness of single precision. There is thus a clear danger of using single precision in some climate model applications, in particular any scientifically meaningful study of deep soil permafrost must at least use double precision. In addition, climate modelling teams might well benefit from paying more attention to numerical precision and roundoff issues to offset the potentially more frequent numerical anomalies in future large-scale parallel climate applications.

Comparisons of the Noah-MP land surface model simulations with measurements of forest and crop sites in Amazonia

This study evaluates the simulations of the Noah-MP surface processes over a crop and a forest sites in Amazonia using tower observations. Soil moisture simulations agree with the observations in both land covers, mainly during the rainy season. However, simulations show cold biases in the soil temperature at both sites. The magnitude and seasonal cycle of the surface energy fluxes are better simulated at the crop site, although the model significantly underestimates the sensible heat flux at this site. The model reproduces the seasonal pattern of surface runoff at both sites. The Noah-MP model does not adequately simulate the base flow at the crop site, while the simulated total runoff at the forest site is closer to the observation than at the crop site. The results show that, in general, the Noah-MP model simulations for the two sites in Amazonia exhibit fairly realistic performance, particularly over the crop site. However, there are cold biases in soil temperature simulations, which could be related with the parameterization of the equilibrium relationship between soil moisture and soil temperature.