Role In Land-atmosphere Interactions Research Articles

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

How do groundwater dynamics influence heatwaves in southeast Australia?

Groundwater plays an integral role in land-atmosphere interactions by connecting the sub-surface storage of water to transpiration via interactions with the root zone. The role of groundwater in moderating heatwave intensity has rarely been examined in coupled climate simulations, as many models overlook this key component of the water cycle. We use a coupled land-atmosphere model with an explicit representation of groundwater dynamics to analyze three major heatwave events in southeast Australia. Groundwater moistens and cools the land surface, with impacts extending to the top of the atmospheric boundary layer during heatwaves. Associated with the cooling, an increase of net radiation and a reduction in the atmospheric boundary layer height occurred, primarily over areas with a shallow water table depth and woody vegetation. The maximum air temperatures were reduced by up to 3 °C at the surface and up to 1 °C through the atmospheric boundary layer. Our results point to an important influence of groundwater on heatwave intensity, implying the necessity of considering groundwater dynamics in climate models in the future heatwave predications.

Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain

Soil moisture is an important component of the Earth system and plays a key role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several satellite-based coarse spatial resolution soil moisture datasets have been produced and widely used for various applications in climate science, hydrology, ecosystem research and agriculture. Owing to the strong demand for soil moisture data with high spatial resolution for regional applications, much effort has recently been devoted to the generation of high spatial resolution soil moisture data from either high-resolution satellite observations or by downscaling existing coarse-resolution satellite-based soil moisture datasets. In addition, land surface models provide an alternative way to obtain consistent high-resolution soil moisture information when forced with high-resolution inputs. The aim of this study is to create and evaluate high-resolution soil moisture products derived from multiple sources including satellite observations and land surface model simulations. The JULES-CHESS simulated soil moisture and satellite-based soil moisture datasets including SMAP L3E, SMAP L4, SMOS L4, Sentinel 1, ASCAT, and Sentinel 1/SMAP combined products were first validated against observed soil moisture from COSMOS-UK, a network of in-situ cosmic-ray based sensors. Second, an approach based on triple collocation was applied to compare these satellite products in the absence of a known reference dataset. Third, a combined soil moisture product was generated to integrate the better-performing soil moisture estimates based on triple collocation error estimation and a least-squares merging scheme. From further evaluation, it is found that the merged soil moisture integrates the characteristics of model simulation and satellite observations and particularly improves the limited temporal variability of the JULES-CHESS simulation. Therefore, we conclude that the triple collocation merging scheme is a simple and reliable way to combine satellite-based soil moisture products with outputs from the JULES-CHESS simulation for estimating model-data fused high-resolution soil moisture for the British mainland.

Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, which is the longest available satellite-based SM data record. The methods used were linear, cubic and autoregressive interpolation and support vector machines (SVMs). This study focused on Southern Europe and spanned the years 2003–2015. The different methods were applied in the temporal and spatial domains and evaluated using the holdout cross-validation technique. A set of variables was introduced in the SVM model to estimate SM, namely, land surface temperature, precipitation, normalized difference vegetation index (NDVI), potential evaporation, soil texture and geographical coordinates. For the SVMs, several combinations of these variables were considered, including a principal component analysis (PCA) containing all of them. Although the different methods show a generally good performance, the SVM method outperforms the rest. Using the SM of the precedent day (SMt-1) is key to obtain good estimates. The median value of the correlation coefficient (R) obtained with the SVM and the SMt-1 series in the temporal analysis was 0.83, and the RMSE was 0.025 m3m−3. Similar results were obtained in the spatial analysis, with the best performance (R = 0.88; RMSE = 0.024 m3m−3) obtained by the SVM using the SMt-1 series and the static variables. The application of PCA to input variables was not beneficial, and the interpolation methods failed when dealing with large spatial or temporal gaps. A validation of the CCI SM series with in situ SM data from four networks located in Spain, France, Germany and Italy was also performed and no substantial differences were observed between results obtained with the original and with the reconstructed series. In addition, best inputs obtained with SVM were used to evaluate the random forest (RF) method in the temporal and spatial domain. This method showed a good ability to estimate soil moisture values in the temporal domain but to a lesser extent than SVM while for the spatial domain it did not seem to be as accurate. Our results confirm that we can efficiently deal with spatio-temporal gaps on observational SM databases using the SVM method and the past time series and soil texture as supporting information.

Vegetation-heatwave correlations and contrasting energy exchange responses of different vegetation types to summer heatwaves in the Northern Hemisphere during the 1982–2011 period

Heatwave is a serious threat to society and can lead to grave consequences. Although it is well known that persistent large-scale circulation anomalies are the key to generate heatwaves, land-atmosphere interactions have also been suggested to intensify and propagate heatwaves. Vegetation plays a vital role in land-atmosphere interactions, modulating energy and water exchange through various pathways. However, vegetation impacts on surface energy exchange during extreme events such as heatwaves, and the attribution of effects to different vegetation types, is complex and poorly understood. In this study, we found strong interannual correlations between summer heatwaves and various plant function types (PFTs), based on the Global Heatwave and Warm-spell Record (GHWR) and leaf area index (LAI) products from satellites during 1982–2011. In Central Europe and the southern and southeastern part of North America, where the land cover is dominated by grasslands, temperate deciduous forests or temperate needleleaf forests, heatwaves tend to occur more frequently in years with lower LAI. In contrast, in the northwestern and northeastern part of North America, where boreal evergreen forests dominate, higher-than-normal LAI is associated with an increase in heatwave occurrence. These findings are in general supported by composite analyses of extreme LAI years in these PFT regions and heatwave characteristics therein.We speculate that the different surface heat flux responses over different PFTs during heatwaves may explain the above relationships. Focusing on North America, and using various datasets including those generated by the Global Land Data Assimilation System (GLDAS) with three different land surface models (CLM, MOS, NOAH), three reanalysis datasets (MERRA-2, NOAA-CIRES-DOS, NCEP/NCAR), and also observations from an extensive network of flux towers, we found that when temperate and boreal evergreen forests are greener, positive sensible heat anomalies increase significantly during heatwaves. Meanwhile, over boreal evergreen forests, changes in latent heat anomalies are much smaller than the positive sensible heat anomalies, suggesting that a greener boreal evergreen forest may prolong and amplify heatwaves significantly. This generates a positive feedback mechanism that begins with higher LAI in generally warmer years in these temperature-limited regions, thus sustaining the strong positive heatwave-LAI correlation. In contrast, for temperate needleleaf forests, temperate deciduous forests and grasslands, strong positive latent heat anomalies with cloud, precipitation and evaporative cooling feedback during high-LAI years appear to suppress heatwaves regionally. Our study revealed the interannual relationships between heatwaves and vegetation as well as the underlying energy exchange processes for different vegetation types and background climate conditions, with implications for the management of forest resources in view of worsening heatwave severity under the future climate.

Declining Soil Moisture Threatens Water Availability in the U.S. Great Plains

HighlightsFuture water security in the U.S. Great Plains is threated by a drying trend in average annual soil moisture.Agricultural management will become increasingly challenging due to declines in surface water storage.Alternative management strategies are needed to meet future environmental and anthropogenic surface water demand.Abstract. Spatiotemporal trends in soil moisture are of considerable importance within the food-energy-water nexus. Soil moisture dictates the productivity of ecosystems, plays a major role in land-atmosphere interactions, influences climate change projections, and shapes future water security. Monitoring of long-term soil moisture trends has proven useful for managing water resource allocation and developing solutions to global water security challenges. Thus, we examine annual trends in surface soil moisture throughout the U.S. Great Plains region from 1987 to 2016 using data from NASA’s observation-driven SPoRT-LIS land surface model at 3 km resolution. Results reveal a drying trend in soil moisture for a majority of the U.S. Great Plains, although wetter conditions have been realized in the northernmost region of the Missouri River basin. These results, when coupled with climate change-driven increases in temperature and evapotranspiration, will inevitably drive baseline soil moisture to drier conditions in the future. Under drying soil moisture conditions, future agricultural production and water resource management in the U.S. Great Plains will become increasingly complicated, thereby threatening future food and water security. However, these results can be applied to inform and improve climate change adaptation strategies in order to ensure adequate volumes of freshwater to meet future human and environmental water demand. Keywords: Climate change, SPoRT-LIS, Trend analysis, Water security.

High-resolution simulation and validation of soil moisture in the arid region of Northwest China

Soil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0–10, 0–20, and 0–50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.

Quantifying the Effects of Climate and Vegetation on Soil Moisture in an Arid Area, China

Soil moisture plays a critical role in land-atmosphere interactions. Quantifying the controls on soil moisture is highly valuable for effective management of water resources and climatic adaptation. In this study, we quantified the effects of precipitation, temperature, and vegetation on monthly soil moisture variability in an arid area, China. A non-linear Granger causality framework was applied to examine the causal effects based on multi-decadal reanalysis data records. Results indicate that precipitation had effects on soil moisture in about 91% of the study area and explained up to 40% of soil moisture variability during 1982–2015. Temperature and vegetation explained up to 8.2% and 3.3% of soil moisture variability, respectively. Climatic extremes were responsible for up to 10% of soil moisture variability, and the importance of climatic extremes was low compared to that of the general climate dynamics. The time-lagged analysis shows that the effects of precipitation and temperature on soil moisture were immediate and dissipated shortly. In addition, the effects of precipitation on soil moisture decreased with the increase of precipitation, soil moisture, and elevation. This study provides deep insight for uncovering the drivers of soil moisture variability in arid regions.

Pairing FLUXNET sites to validate model representations of land-use/land-cover change

Abstract. Land surface energy and water fluxes play an important role in land–atmosphere interactions, especially for the climatic feedback effects driven by land-use/land-cover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah Multi-Parameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (Rnet) and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H, G, and Rnet change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations.

Diagnosing the strength of soil temperature in the land atmosphere interactions over Asia based on RegCM4 model

This study aims to investigate the soil temperature atmosphere (ST Atm) coupling strength and their influence on the subsequent climate anomalies over Asia based on a regional climate model RegCM4 with both statistical analysis method and numerical experiments. The major findings are as follows: (1) The soil temperature precipitation (ST P) coupling is quite weak and spatially scattered from both RegCM4 output and GLDAS data in each season with the only obvious signals existing over the north India region in summer season according to the RegCM4 output; the soil temperature 2m air temperature (ST T2m) coupling is strong and spatially continuous with the India (in spring, summer and autumn seasons), South Indochina (in spring and summer seasons) and part of North China (in summer season) standing out as hotspots. (2) The soil temperature evapotranspiration (ST ET) and soil temperature evaporative fraction (ST EF) coupling almost resembles the same spatial patterns and magnitude in each season. Both the ET and EF play an important role in linking ST P coupling in each season, but not in the ST T2m coupling. The hotspots of ST T2m coupling outweigh the areas with strong ST ET and ST EF coupling. Only the India and Indochina in each season are recognized by the RegCM4 while the Indochina and west Asia in spring, north India, Indochina and part of mid-high latitudes in summer season pinpointed by GLDAS data show consistent strong ST T2m coupling with significant ST ET and ST EF coupling. This finding demonstrates the complicated ST Atm coupling. (3) The ST anomalies prescribed at the first step of each season can only persist a short time (within 5days) over most part of the study area while the fixed one-month anomalies can last longer (approximately 2months with colder anomalies and 45days with warmer anomalies in tropical regions). (4) The impact of ST anomalies on subsequent precipitation is quite weak and can be ignored over most part of the study area, while the impact on subsequent T2m is obvious and spatially continuous. The spatial patterns for the changes of climate variables (e.g., latent heat flux (LH), sensible heat flux (SH), ET, T2m) are almost consistent with the changes of ST over most part of the study area (except India and Indochina in spring and autumn seasons) where warmer (colder) ST anomalies lead to increase (decrease) of LH, SH, ET and T2m, demonstrating the important role of land atmosphere interactions in the locale climate. Over the India and Indochina, the large-scale circulation dominates the local climate in spring and autumn seasons. (5) The impact of colder ST anomalies can last longer than the warmer ones. Among the spring, summer and autumn seasons, the impact persists shortest in summer and longest in autumn, especially over the India region.

Surface heterogeneity impacts on boundary layer dynamics via energy balance partitioning

Abstract. The role of land-atmosphere interactions under heterogeneous surface conditions is investigated in order to identify mechanisms responsible for altering surface heat and moisture fluxes. Twelve coupled land surface – large eddy simulation scenarios with four different length scales of surface variability under three different horizontal wind speeds are used in the analysis. The base case uses Landsat ETM imagery over the Cloud Land Surface Interaction Campaign (CLASIC) field site for 3 June 2007. Using wavelets, the surface fields are band-pass filtered in order to maintain the spatial mean and variances to length scales of 200 m, 1600 m, and 12.8 km as lower boundary conditions to the model (approximately 0.25, 1.2 and 9.5 times boundary layer height). The simulations exhibit little variation in net radiation. Rather, there is a pronounced change in the partitioning of the surface energy between sensible and latent heat flux. The sensible heat flux is dominant for intermediate surface length scales. For smaller and larger scales of surface heterogeneity, which can be viewed as being more homogeneous, the latent heat flux becomes increasingly important. The simulations showed approximately 50 Wm−2 difference in the spatially averaged latent heat flux. The results reflect a general decrease of the Bowen ratio as the surface conditions transition from heterogeneous to homogeneous. Air temperature is less sensitive to variations in surface heterogeneity than water vapor, which implies that the role of surface heterogeneity may be to maximize convective heat fluxes through modifying and maintaining local temperature gradients. More homogeneous surface conditions (i.e. smaller length scales), on the other hand, tend to maximize latent heat flux. The intermediate scale (1600 m) this does not hold, and is a more complicated interaction of scales. Scalar vertical profiles respond predictably to the partitioning of surface energy. Fourier spectra of the vertical wind speed, air temperature and specific humidity (w~, T~ and q~) and associated cospectra (w~T~, w~q~ and T~q~), however, are insensitive to the length scale of surface heterogeneity, but the near surface spectra are sensitive to the mean wind speed.

China land soil moisture EnKF data assimilation based on satellite remote sensing data

Soil moisture plays an important role in land-atmosphere interactions. It is an important geophysical parameter in research on climate, hydrology, agriculture, and forestry. Soil moisture has important climatic effects by influencing ground evapotranspiration, runoff, surface reflectivity, surface emissivity, surface sensible heat and latent heat flux. At the global scale, the extent of its influence on the atmosphere is second only to that of sea surface temperature. At the terrestrial scale, its influence is even greater than that of sea surface temperatures. This paper presents a China Land Soil Moisture Data Assimilation System (CLSMDAS) based on EnKF and land process models, and results of the application of this system in the China Land Soil Moisture Data Assimilation tests. CLSMDAS is comprised of the following components: 1) A land process model—Community Land Model Version 3.0 (CLM3.0)—developed by the US National Center for Atmospheric Research (NCAR); 2) Precipitation of atmospheric forcing data and surface-incident solar radiation data come from hourly outputs of the FY2 geostationary meteorological satellite; 3) EnKF (Ensemble Kalman Filter) land data assimilation method; and 4) Observation data including satellite-inverted soil moisture outputs of the AMSR-E satellite and soil moisture observation data. Results of soil moisture assimilation tests from June to September 2006 were analyzed with CLSMDAS. Both simulation and assimilation results of the land model reflected reasonably the temporal-spatial distribution of soil moisture. The assimilated soil moisture distribution matches very well with severe summer droughts in Chongqing and Sichuan Province in August 2006, the worst since the foundation of the People’s Republic of China in 1949. It also matches drought regions that occurred in eastern Hubei and southern Guangxi in September.

Surface soil water content regimes: opportunities in soil science

With the qualities and properties of soil not being uniformly distributed across continents, soils are classified according to their morphological features, genesis and soil-forming processes. Because properties of soils vary across the landscape, a relationship observed at one location may not be applicable for other locations. Hence, measured data and deduced relationships are location specific and should be interpreted with information regarding the particular soil type according to the discipline of soil science. To better understand the role of water in land-atmosphere interactions and the role of land-atmosphere interactions in regional and global climate, spatial and temporal observations of water in the soil surface can be more comprehensively analyzed with a knowledge of soil science. On the other hand, an opportunity to strengthen the discipline of soil science (with its various branches of soil physics, soil chemistry, soil microbiology, etc.) exists if spatial and temporal observations of water in the soil surface at different scales are consudered. Anticipating that such observations of soil water will eventually become more abundantly available through satellite imagery, we discuss how they can be used to improve our understanding and application of soil science.