Use In Land Surface Models Research Articles

Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications

The article presents a revue of the estimates of soil surface moisture, soil water content, and evapotranspiration as elements of water and heat regimes of land surface areas at various spatial scales, made with the use of remote sensing data for Earth in various spectral ranges. In most cases considered in the study, such estimates were obtained with the use of land surface models. A special section is focused on the results of estimating soil surface moisture and water content using satellite data from microwave range, including radar data. Estimates of soil surface moisture content obtained with the use of neural networks are presented. A brief description is given to international hydrological–atmosphere experiments carried out under world research projects aimed to obtain data on the processes of moisture and heat exchange between the land surface and the surface atmosphere layer. Land surface, satellite, and model databases that have been formed using the results of studies in the field under consideration since the mid-1980s are reviewed. Prospects of further studies based on the development of new multispectral instrumentation, the creation of new databases, and the use of a new generation of satellites—global-coverage microsatellites with high-resolution sensors are presented.

A deep‐learning‐based experiment for benchmarking the performance of global terrestrial vegetation phenology models

AbstractAimVegetation phenology that characters the periodic life cycles of plants is indicative of the interactions between the biosphere and the atmosphere. Robust modelling of vegetation phenology metrics that correspond to canopy development events is essential to our understanding of how plants and ecosystems respond to a changing climate. Given considerable uncertainties associated with vegetation phenology modelling using numerical models, we explore the deep learning approach to predicting the timing of global vegetation phenology metrics.LocationGlobal.Time period2001–2015.Major taxa studiedDeciduous vegetation (DV), stressed deciduous vegetation (SDV), evergreen vegetation (EV).MethodsWe developed a one‐dimensional convolutional neural network regression (1D‐CNNR) model with 10 hierarchical structures to model global vegetation phenology using meteorological variables as inputs. The developed deep learning model was evaluated using satellite‐derived phenology metrics (i.e., green‐up, maturity, senescence, and dormancy) and compared with the terrestrial ecosystem model Biome‐BGC (BioGeochemical Cycles).ResultsOur experimental results show that the 1D‐CNNR model well captures both the spatial pattern and inter‐annual variation of satellite‐derived multiyear vegetation phenology metrics on a global scale. The median root‐mean‐square errors (RMSEs) and standard deviations between phenology metrics derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data and predicted by the 1D‐CNNR model on a global scale from 2001 to 2015 are 4.1 ± 5.9, 4.2 ± 12.1, 3.0 ± 6.8, and 3.4 ± 4.3 days for green‐up, maturation, senescence, and dormancy, respectively, for the DV type; 13.3 ± 29.6, 8.4 ± 29.1, 8.1 ± 21.3, and 9.1 ± 21.6 days for green‐up, maturation, senescence, and dormancy, respectively, for the SDV type; and 13.9 ± 17.4, 17.7 ± 34.6, 18.8 ± 42.9, and 12.1 ± 17.7 days for green‐up, maturation, senescence, and dormancy, respectively, for the EV type.Main conclusionsThis research demonstrates that the 1D‐CNNR model has the potential for large‐scale modelling of vegetation phenology. Results from the deep learning model suggest that there is room to improve numerical vegetation phenology models for use in land surface models.

L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records

The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.

Evaluation of Soil Thermal Conductivity Schemes for Use in Land Surface Modeling

AbstractSoil thermal conductivity (STC) is an important physical parameter in modeling land surface processes. Previous studies on evaluations of STC schemes are mostly based on direct measurements of local conditions, and their recommendations cannot be used as a reference in selecting STC schemes for land modeling use. In this work, seven typical STC schemes are incorporated into the Common Land Model to evaluate their applications in land surface modeling. Statistical analyses show that the Johansen (1975) scheme and its three derivatives, Côté and Konrad (2005, https://doi.org/10.1139/t04‐106), Balland and Arp (2005, https://doi.org/10.1139/s05‐007), and Lu et al. (2007, https://doi.org/10.2136/sssaj2006.0041), are significantly superior to the other schemes with respect to both STC estimations and their applications in modeling soil temperature and the partitioning of surface available energy in the Common Land Model. The Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme ranks at the top among the selected schemes. Uncertainty analyses based on single‐point and global simulations both show that the differences in STC estimations can induce significant differences in simulated soil temperature in arid/semiarid and seasonally frozen regions, especially at deep layers. The hydrology‐related variables are slightly affected by STC variations, but slight changes in these variables can induce notable changes in soil temperature by altering soil thermal properties. These results emphasize the important role of STC in modeling soil thermodynamics and suggest the necessity of further developing STC schemes based on land modeling applications. According to the evaluation analyses, we recommend the Balland and Arp (2005, https://doi.org/10.1139/s05‐007) scheme as a more suitable selection for use in land surface models.

Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

Spatiotemporal soil moisture variations associated with hydro‐meteorological factors over the Yarlung Zangbo River basin in Southeast Tibetan Plateau

AbstractSoil moisture (SM) is a key factor in the exchanging process of the hydrological cycle, which is rather difficult to be directly observed. In situ measurements on SM, however, are subjected to the point scales. Use of land surface models has been a promising way to explore SM variations, especially for poorly gauged high mountain regions such as the Yarlung Zangbo River (YZR) basin located in Southeast Tibetan Plateau. This study made an attempt to investigate the spatiotemporal variations of SM and discuss hydro‐meteorological factors impacting SM evolution based on the Global Land Data Assimilation Systems (GLDAS) outputs during the period 1970–2009. Results show that (a) GLDAS data sets have high agreement and low bias with in situ measurements and consistent spatial distribution with ERA reanalysis data sets; (b) an abrupt change of SM is detected in 1992 and a significantly decreasing trend happens during 1970–2009 and 1993–2009; (c) precipitation is the dominant climatic factor controlling SM during the period 1970–2009, whereas surface air temperature is the critical factor for the significant change of SM. Owing to the significant increasing of surface air temperature since 1992, its impact on SM increased by ∼91% than that before 1992. Evapotranspiration (ET) and snow water equivalent (SWE) are also taken into consideration, showing relatively weak influence on SM, which may be due to the dynamic process of coupled SM‐ET or low snow cover area fraction across the YZR basin. Findings in this study have important implications for SM variations in poorly gauged high mountain regions which may largely influences downstream water availability.

Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil.

Evaluating stomatal models and their atmospheric drought response in a land surface scheme: A multibiome analysis

AbstractStomatal conductance (gs) is a key variable in Earth system models as it regulates the transfer of carbon and water between the terrestrial biosphere and the lower atmosphere. Various approaches have been developed that aim for a simple representation of stomatal regulation applicable at the global scale. These models differ, among others, in their response to atmospheric humidity, which induces stomatal closure in a dry atmosphere. In this study, we compared the widely used empirical Ball‐Berry and Leuning stomatal conductance models to an alternative empirical approach, an optimization‐based approach, and a semimechanistic hydraulic model. We evaluated these models using evapotranspiration (ET) and gross primary productivity (GPP) observations derived from eddy covariance measurements at 56 sites across multiple biomes and climatic conditions. The different models were embedded in the land surface model JSBACH. Differences in performance across plant functional types or climatic conditions were small, partly owing to the large variations in the observational data. The models yielded comparable results at low to moderate atmospheric drought but diverged under dry atmospheric conditions, where models with a low sensitivity to air humidity tended to overestimate gs. The Ball‐Berry model gave the best fit to the data for most biomes and climatic conditions, but all evaluated approaches have proven adequate for use in land surface models. Our findings further encourage future efforts toward a vegetation‐type‐specific parameterization of gs to improve the modeling of coupled terrestrial carbon and water dynamics.

Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Comprehensive assessment of the FVC products is critical for their improvement and use in land surface models. This study investigates the performances of two major long time serial FVC products: GEOV1 and Australian MODIS. The spatial and temporal consistencies of these products were compared during the 2000–2012 period over the main biome types across the Australian continent. Their accuracies were validated by 443 FVC in-situ measurements during the 2011–2012 period. Our results show that there are strong correlations between the GEOV1 and Australian MODIS FVC products over the main Australian continent while they exhibit large differences and uncertainties in the coastal regions covered by dense forests. GEOV1 and Australian MODIS describe similar seasonal variations over the main biome types with differences in magnitude, while Australian MODIS exhibit unstable temporal variations over grasslands and shifted seasonal variations over evergreen broadleaf forests. The GEOV1 and Australian MODIS products overestimate FVC values over the biome types with high vegetation density and underestimate FVC in sparsely vegetated areas and grasslands. Overall, the GEOV1 and Australian MODIS FVC products agree with in-situ FVC values with a RMSE around 0.10 over the Australian continent.

Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models

Accurate estimates of how soil water stress affects plant transpiration are crucial for reliable land surface model (LSM) predictions. Current LSMs generally use a water stress factor, β, dependent on soil moisture content, θ, that ranges linearly between β=1 for unstressed vegetation and β=0 when wilting point is reached. This paper explores the feasibility of replacing the current approach with equations that use soil water potential as their independent variable, or with a set of equations that involve hydraulic and chemical signaling, thereby ensuring feedbacks between the entire soil–root–xylem–leaf system. A comparison with the original linear θ-based water stress parameterization, and with its improved curvi-linear version, was conducted. Assessment of model suitability was focused on their ability to simulate the correct (as derived from experimental data) curve shape of relative transpiration versus fraction of transpirable soil water. We used model sensitivity analyses under progressive soil drying conditions, employing two commonly used approaches to calculate water retention and hydraulic conductivity curves. Furthermore, for each of these hydraulic parameterizations we used two different parameter sets, for 3 soil texture types; a total of 12 soil hydraulic permutations. Results showed that the resulting transpiration reduction functions (TRFs) varied considerably among the models. The fact that soil hydraulic conductivity played a major role in the model that involved hydraulic and chemical signaling led to unrealistic values of β, and hence TRF, for many soil hydraulic parameter sets. However, this model is much better equipped to simulate the behavior of different plant species. Based on these findings, we only recommend implementation of this approach into LSMs if great care with choice of soil hydraulic parameters is taken.

Comparison of root water uptake functions to simulate surface energy fluxes within a deep‐rooted desert shrub ecosystem

AbstractRoot water uptake (RWU) is a unique process whereby plants obtain water from soil, and it is essential for plant survival. The mechanisms of RWU are well understood, but their parameterization and simulation in current Land Surface Models (LSMs) fall short of the requirements of modern hydrological and climatic modelling research. Though various RWU functions have been proposed for potential use in LSMs, none was proven to be applicable for dryland ecosystems where drought was generally the limiting factor for ecosystem functioning. This study investigates the effect of root distribution on the simulated surface energy fluxes by incorporating the observed vertical root distribution. In addition, three different RWU functions were integrated into the Common Land Model (CLM) in place of the default RWU function. A comparison of the modified model's results with the measured surface energy fluxes measured by eddy covariance techniques in a Central Asian desert shrub ecosystem showed that both RWU function and vertical root distribution were able to significantly impact turbulent fluxes. Parameterizing the root distribution based on in‐situ measurement and replacing the default RWU function with a revised version significantly improved the CLM's performance in simulating the latent and sensible heat fluxes. Sensitivity analysis showed that varying the parameter values of the revised RWU function did not significantly impact the CLM's performance, and therefore, this function is recommended for use in the CLM in Central Asian desert ecosystems and, possibly, other similar dryland ecosystems. Copyright © 2013 John Wiley & Sons, Ltd.

A New Characterization of the Land Surface Heterogeneity over Africa for Use in Land Surface Models

AbstractInformation related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database—constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993—was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000–07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34% instead of 64% in comparison with the original version of ECOCLIMAP.

Global terrestrial water storage capacity and flood potential using GRACE

Terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) and precipitation observations from the Global Precipitation Climatology Project (GPCP) are applied at the regional scale to show the usefulness of a remotely sensed, storage‐based flood potential method. Over the GRACE record length, instances of repeated maxima in water storage anomaly that fall short of variable maxima in cumulative precipitation suggest an effective storage capacity for a given region, beyond which additional precipitation must be met by marked increases in runoff or evaporation. These saturation periods indicate the possible transition to a flood‐prone situation. To investigate spatially and temporally variable storage overflow, a monthly storage deficit variable is created and a global map of effective storage capacity is presented for possible use in land surface models. To highlight a flood‐potential application, we design a monthly global flood index and compare with Dartmouth Flood Observatory flood maps.

An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF

Interactions between the atmosphere and the land surface have considerable influences on weather and climate. Coupled land–atmosphere systems that can realistically represent these interactions are thus critical for improving our understanding of the atmosphere-biosphere exchanges of energy, water, and their associated feedbacks. NASA's Land Information System (LIS) is a high-resolution land data assimilation system that integrates advanced land surface models, high-resolution satellite and observational data, data assimilation techniques, and high performance computing tools. LIS has been coupled to the Weather Research and Forecasting (WRF) model, enabling a high-resolution land–atmosphere modeling system. Synthetic simulations using the coupled LIS–WRF system demonstrates the interoperable use of land surface models, high-resolution land surface data and other land surface modeling tools through LIS. Real case study simulations for a June 2002 International H2O Project (IHOP) day is conducted by executing LIS first in an uncoupled manner to generate high-resolution soil moisture and soil temperature initial conditions. During the case study period, the land surface (LIS) and the atmospheric (WRF) models are executed in a coupled manner using the LIS–WRF system. The results from the simulations illustrate the impact of accurate, high-resolution land surface conditions on improving the prediction of clouds and precipitation. Thus, the coupled LIS–WRF system provides a testbed to enable studies in improving our understanding and predictability of regional and global water and energy cycles.

A simple surface temperature assimilation scheme for use in land surface models

This paper examines the utilization of surface temperature as a variable which can be assimilated in off‐line land surface hydrological models. The connection between the surface temperature and evapotranspiration is utilized in making adjustments to the model‐computed surface soil moisture. This adjustment is a function of the difference between the model‐computed and the observed surface temperature. Comparisons between the model‐computed and satellite‐observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the A.M. and P.M. overpass of the NOAA 10) over the Red‐Arkansas basin in the southwestern United States (31°50′N‐36°N, 94°30′W‐104°30′W) for a period of 1 year (August 1987 to July 1988). The soil moisture estimates resulting from the assimilation of surface temperature have a closer agreement with the values derived from the special sensor microwave imager than those from simulations without surface temperature assimilation. Assimilation reduces the effect of errors in precipitation and/or shortwave radiation on simulated soil moistures.