Land Surface Water Balance Research Articles

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Towards uninterrupted canopy-trait time-series: A Bayesian radiative transfer model inversion using multi-sourced satellite observations

Canopy traits play an important role in plant growth, carbon sequestration, and the land surface water and energy balance, and thus it is important to understand and monitor how these traits change over time. Canopy radiative transfer models, which use canopy traits to predict spectra, provide a powerful means to understand canopy trait variation and these models can be inverted, allowing us to use remotely sensed data to monitor canopy traits. However, a single inversion represents a snapshot in time for a single sensor. In this study, we used a Bayesian approach to assimilate snow-free observations from three different sensors (Landsat 8, Sentinel-2A and MODIS) over the course of 2019 by inverting the ProSAIL radiative transfer model, producing a robust estimate of six canopy traits and their joint uncertainties at each time point. Next, we used a state-space time series model to integrate observations from multiple sensors to capture variation in vegetation optical traits throughout the year. The results demonstrate that our model can capture seasonal variation of surface reflectance with less than 5% RMSE compared with observed reflectance. Moreover, linking observations in time narrows the uncertainty intervals compared to the posterior distributions for individual time points. We are also able to make statistical inferences on dates when no data is available, and provide reasonable and reliable time-series inversion results compared with LAI ground measurements.

Spatiotemporal variation characteristics of hourly soil temperature in different layers in the low-latitude plateau of China

Soil temperature change has considerable impact on land surface energy and water balances, and hence on changes in weather/climate, surface/subsurface hydrology, and ecosystems. However, little is known regarding the spatiotemporal variations and influencing factors of changes in hourly soil temperature (depth: 5–320 cm) in low-latitude highland areas. This study analyzed the hourly soil temperature at each hour during 2004–2020 and at 08:00, 14:00, and 20:00 (Beijing Time) during 1961–2020. The results revealed the following. 1) As soil depth increased, average soil temperature increased in autumn and winter, and decreased annually and in spring and summer. It exhibited significant increase during 00:00–23:00 annually, seasonally, and monthly, especially at depths of 40–320 cm during 2004–2020. Average soil temperature increased at 08:00 and decreased at 14:00 and 20:00 with increasing soil depth, but the opposite trend was found annually, seasonally, and monthly at 08:00, 14:00, and 20:00 during 1961–2020. 2) With increasing elevation, average soil temperature decreased at 08:00, 14:00, and 20:00 at depths of 5–20 cm, and showed significant increase trend at 08:00 and 14:00 at depths of 10–20 cm (except at 14:00 at 10-cm depth). 3) At 5-cm depth, the critical accumulated soil temperature of ≥12°C and 14°C extended the potential growing season during 1961–2020. 5) Significant uptrend of hourly soil temperature annually, seasonally, and monthly potentially leads to additional release of carbon to the atmosphere and increased soil respiration, reinforcing climate warming. These findings contribute to better understanding of the variation of shallow soil temperatures and land–atmosphere interactions in low-latitude highland areas.

Assessment of Model-Derived Mass Flux Relative to Satellite Gravimetry via a Water Balance in North America

Abstract GRACE and GRACE Follow-On (GRACE-FO) mission data are utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002–21). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from Center for Space Research (CSR) GRACE RL06 mass concentrations (mascons), and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect. Significance Statement Measurements of Earth’s gravity field from the GRACE and GRACE-FO satellite missions are utilized to create estimates of water storage changes in 18 North American river basins that are compared to changes in water storage calculated from an atmospheric model reanalysis (NARR) and a land surface model (NLDAS-2 Noah). The resulting comparison demonstrates that certain basin characteristics can have a slight effect on model accuracy, while climatic conditions such as drought can have a major impact on model accuracy. This work provides useful quantification of when and where modeled water transport loses accuracy, which is integral to our understanding of the present and future distribution of this crucial resource and the natural processes that affect it.

Upscaling Hydrological Processes for Land Surface Models With a Two‐Hydrologic‐Variable Model: Application to the Little Washita Watershed

AbstractLand Surface Models (LSMs) are key components of Earth System Models, which the Intergovernmental Panel on Climate Change relies on in many of their studies. However, these models either neglect or oversimplify basin‐scale hydrological processes that produce the land surface water balance. Bringing the dominant physical processes from the local scale up to the LSMs presents a significant challenge for improving the models. This article presents an upscaling (or bottom‐up) approach to identify the basin‐scale driving variables that need to be exported into LSMs. The approach is developed on the Little Washita Watershed (OK, USA) using 20‐year hydrology (1993–2013). A physically based 3D model built with HydroGeoSphere first produces a reference simulation. An equivalent hillslope model is then able to capture both 3D simulated water balance and local water table dynamics with reasonable accuracy. The physical analysis of the water balance in the different hillslope compartments leads to the identification of two driving variables: seepage face extension and water table slope. The two variables are then implemented in a conceptual model. Results show a good capacity of this model to capture the water balance of hillslopes having different lengths and slopes. Moreover, the model is able to capture the water balance of the reference simulation with reasonable accuracy. The proposed approach thus reduces the 3D watershed model to a two‐variable conceptual model that constitutes a basis for developing an improved LSM hydrology.

Remote-Sensing Inversion Method for Evapotranspiration by Fusing Knowledge and Multisource Data

Evapotranspiration (ET) is the main process parameter of the land surface heat and water balance. Evapotranspiration remote-sensing inversion can be divided into two types of methods, process-driven and data-driven, according to the model power. This paper presents a comprehensive and systematic review of the research progress of data-driven ET remote-sensing inversion methods and their products; reviews the basic principles, advantages, and disadvantages of related methods/products from three perspectives: empirical regression, machine learning, and data fusion; and finally indicates the development direction of data-driven ET remote-sensing inversion research.

Irrigated cropland expansion exacerbates the urban moist heat stress in northern India

Agricultural irrigation has significantly reshaped the land surface energy and water balance. Previous studies have well investigated its cooling effect on air temperature (T air). However, its effect on increasing air humidity which can intensify the humid heat was often overlooked, particularly over urban areas with high population density, high T air, and consequently greater exposure to moist heat stress. In this study, using state-of-the-art reanalysis data at a high spatial resolution (∼9 km), we evaluated how changes in area equipped for irrigation (AEI) around a city affect urban moist heat stress (UMHS) in more than 1000 cities in China and India. In addition to T air, wet-bulb temperature (T WB) and wet-bulb globe temperature (T WBG), which consider humidity and are closer to the perceived temperature, were assessed. We found that although AEI expansion lowers urban T air, irrigation increases T WB and T WBG due to increased air humidity, thereby exacerbating the UMHS. This ‘warming’ effect of irrigation is more evident in northern India where AEI has expanded significantly in recent decades, and is prominent in the pre-monsoon and post-monsoon seasons, when precipitation and air humidity are low. However, this effect is not evident in China due to the lower intensity of AEI expansion and differing climatic conditions. Overall, this study highlights the side-effect of irrigation on regional climate, providing crucial information for better understanding urban heat stress and for future city planning.

Multi–Proxy Reconstruction of Drought Variability in China during the Past Two Millennia

Drought imposes serious challenges to ecosystems and societies and has plagued mankind throughout the ages. To understand the long-term trend of drought in China, a series of annual self-calibrating Palmer drought severity indexes (scPDSI), which is a semi-physical drought index based on the land surface water balance, were reconstructed during AD 56~2000. Multi-proxy records of tree-ring width and stalagmite oxygen isotope δ18O were used for this reconstruction, along with random forest regression. The spatiotemporal characteristics of the reconstruction results were analyzed, and comparisons were made with previous studies. Results showed that (1) China witnessed a drought-based state during the past 2000 years (mean value of scPDSI was −0.3151), with an average annual drought area of 85,000 km2; 4 wetting periods, i.e., the Han Dynasty (AD 56~220), the Tang Dynasty (AD 618~907), the Ming Dynasty (AD 1368~1644), and the Qing Dynasty (AD 1644~1912); and 2 drying periods, i.e., the Era of Disunity (AD 221~580) and the Song Dynasty (AD 960~1279). (2) Three different alternating fluctuation dry-wet modes (i.e., interannual, multidecadal, and centennial scales) in China were all significantly (p-value < 0.001) correlated with the amplitude and frequency of temperature in the Northern Hemisphere. (3) According to the spatial models disassembled from the rotated empirical orthogonal function, China was divided into nine dry-wet regions: northwestern China, Xinjiang, southwestern China, southeastern China, the Loess plateau, central China, southwestern Tibet, eastern China, and northeastern China. (4) The random forest (RF) was found to be accurate and stable for the reconstruction of drought variability in China compared with linear regression.

HydroPy (v1.0): a new global hydrology model written in Python

Abstract. Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy.

Spatiotemporal imputation of MODIS land surface temperature using machine learning techniques (Case study: New Mexico's Lower Rio Grande Valley)

Land surface temperature (LST) is one of the main factors in the physical processes of land surface energy and water balance at the global scale. The importance of LST is being progressively increased in a variety of water resource fields. However, the time series of LST observations regularly have missing values because of various reasons such as cloudiness. Missing value imputation is a technique to use a realistic value to estimate or replace the missing value. The objective of this study was to investigate and compare the potential of machine learning (ML) models including K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Boosted Regression Trees (BRT), and Extreme Learning Machine (ELM) for spatiotemporal imputation of LST satellite images in New Mexico's Lower Rio Grande Valley (LRGV) where LST is a critical variable for water resource studies. The cross-validation approach was applied to obtain the optimized parameters for each model separately. The model comparison results showed that the SVR model is the most accurate model compared to other models from missing ratio 0.1 to 0.8 (0.24 < mean RMSE (°C) < 0.33). The BRT and ELM models showed the same performance level in general with almost the same mean CV-RMSE of 0.5°C. However, the ELM imputed maps showed noisy estimations particularly when the missing ratios were increased. The BRT model showed a better performance compared to ELM with a lower missing ratio from 0.1 to 0.5. The KNN model found to be the least reliable ML model for spatiotemporal imputation of LST among the all models with mean RMSE of 0.982°C. Also, blurring effects were more obvious in the KNN imputed maps than other models particularly in larger missing ratios. Overall, the findings confirmed the superiority of the SVR and BRT over the ELM and KNN for spatiotemporal imputations of LST.

Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach

Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions.

A Comprehensive Evaluation of 4-Parameter Diurnal Temperature Cycle Models with In Situ and MODIS LST over Alpine Meadows in the Tibetan Plateau

Diurnal variation of land surface temperature (LST) is essential for land surface energy and water balance at regional or global scale. Diurnal temperature cycle (DTC) model with least parameters and high accuracy is the key issue in estimating the spatial–temporal variation of DTC. The alpine meadow is the main land cover in the Tibetan Plateau (TP). However, few studies have been reported on the performance of different DTC models over alpine meadows in the TP. Four semi-empirical types of DTC models were used to generate nine 4-parameter (4-para) models by fixing some of free parameters. The performance of the nine 4-para DTC models were evaluated with four in situ and MODIS observations. All models except GOT09-dT-ts (dT means the temperature residual between T0 and T (t→∞); ts means the time when free attenuation begins) had higher correlation with in situ data (R2 &gt; 0.9), while the INA08-ts model performed best with NSE of 0.99 and RMSE of 2.04 K at all sites. The GOT09-ts-τ (τ is the total optical thickness), VAN06-ts-ω1 (ω1 means the half-width of the cosine term in the morning), and GOT01-ts models had better performance, followed by GOT09-dT-τ, GOT01-dT, and VAN06-ts-ω2 (ω2 means the half-width of the cosine term in the afternoon) models. All models had higher accuracy in summer than in other seasons, while poorer performance was produced in winter. The INA08-ts model showed best performance among all seasons. Models with fixing ts could produce higher accuracy results than that with fixing dT. The comparison of INA08-ts model driven by in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated that the simulation accuracy mainly depended on the accuracy of MODIS LST. The daily maximum temperature generated by the nine models had high accuracy when compared with in situ data. The sensitivity analysis indicated that the INA08-dT and GOT09-dT-ts models were more sensitive to parameter dT, while all models were insensitive to parameter ts, and all models had weak relationship with parameters ω and τ. This study provides a reference for exploring suitable DTC model in the TP.

Divergent sensitivity of surface water and energy variables to precipitation product uncertainty in the Tibetan Plateau

Precipitation is a major driving factor for land surface water and energy balances. Uncertainty in global precipitation products over observation sparse regions such as the Tibetan Plateau (TP) is generally large. Sensitivity of surface water and energy variables to precipitation uncertainty can provide clues for confidence that can be assigned to simulated water and energy variables in such regions. In this study, the sensitivities of surface water and energy variables to global precipitation product uncertainty over four large river basins in the TP are quantified and inter-compared based on a newly developed sensitivity analysis approach. A water and energy budget-based distributed hydrological model including biosphere is utilized after calibration and validation against observed runoff and Land Surface Temperatures (LSTs) from Moderate Resolution Imaging Spectroradiometer (MODIS). Eight global precipitation products are used to represent the precipitation uncertainty. Results show that Canopy interception loss (CIE) and runoff are highly sensitive to the uncertainty in general, whereas LSTs are not sensitive. Therefore, confidence in simulated CIE and runoff can be considered relatively low when using global precipitation products in the four basins. These results imply that other simulated variables may have large uncertainty even when LSTs simulation performs well, and accurate simulations of CIE and runoff require high accuracy in precipitation. Because CIE has profound influence on local hydrological cycle, the results also imply that utilizing the most accurate precipitation product is critical for local scale hydrological cycle research.

A Framework for Global Multicategory and Multiscalar Drought Characterization Accounting for Snow Processes.

Drought indices do not always provide the most relevant information for water resources management as most of them neglect the role of snow in the land surface water balance. In this study, a physically based drought index, the Standardized Moisture Anomaly Index (SZI), was modified and improved by incorporating the effects of snow dynamics for drought characterization at multiple time scales. The new version of the SZI, called SZIsnow, includes snow in both the water supply and demand in drought characterization by using the water‐energy budgets from the Global Land Data Assimilation Systems product. We compared and evaluated the performance of SZIsnow and SZI in drought identification globally across various time scales using observed multicategory drought evidences from several sources. Results show that the SZIsnow agrees better with the observed changes in hydrological and agricultural droughts than the SZI, particularly over basins with high snow accumulation. Furthermore, the SZIsnow is more consistent with the residual water‐energy ratio than the SZI over snow‐influenced regions. Overall, the SZIsnow can be either a complement or an improvement over the SZI for identifying, monitoring, and characterizing hydrological and agricultural droughts at various scales (e.g., 1–48 months) over high‐latitude and high‐elevation regions that receive snow.

Detecting hotspots of interactions between vegetation greenness and terrestrial water storage using satellite observations

Changes in water availability strongly affect vegetation growth, and vegetation can also modify land water storage by changing the land surface water balance. Here, based on the terrestrial water storage (TWS) data retrieved from the Gravity Recovery and Climate Experiment (GRACE)satellites mission and the normalized difference vegetation index (NDVI) from Jan. 2003 to Dec. 2015, we investigate the interplay between land water and vegetation greenness at a global scale. The results reveal a coherent trend with statistical significance between the terrestrial water storage anomaly (TWSA) and the NDVI in 20.90% of global vegetated lands in contrast to a non-coherent trend of 20.87% in global vegetated lands. Vegetation greenness exhibits a common 0- to 1-month delayed response to the TWSA, and significant positive TWSA-NDVI relationships appear in approximately 43.17% of global vegetated areas. A comparison study suggests that the response of vegetation greenness to the TWSA is more rapid than that to precipitation. Interactions between the TWSA and NDVI are further investigated by using the Granger causality test technique. Globally, a strong interaction between the TWSA and NDVI occurs in over 16.75% of vegetated areas. Simultaneously, vegetation greenness is found to be the Granger cause of the TWSA in over 40.34% of global vegetated areas, indicating widespread impacts of vegetation change on variations in land water storage. A case study in China suggests that vegetation greenness increase is an important reason for the decrease in the TWSA in North and Northwest China, which are traditionally water-limited-growth regions. In two humid regions, Southwest and South China, the influence of the TWSA on vegetation greenness seems to be stronger than that of vegetation greenness on the TWSA. Our study suggests that the GRACE TWS is a useful tool for investigations of interactions between vegetation greenness and land water conditions.

Using the evaporative stress index to monitor flash drought in Australia

Flash drought is a term and concept that has gained increasing attention in the research literature and media since it was first coined in the United States in the early 2000s to describe a drought that has undergone rapid intensification. In Australia the term has recently been used in reference to the 2017/18 drought in eastern Australia. Due to its rapid intensification, the impacts of flash droughts will likely occur too quickly for many of the usual drought-coping mechanisms to be deployed. This study proposes the use of the evaporative stress index (ESI), the standardized anomaly of the actual evapotranspiration to potential evapotranspiration ratio, to identify flash droughts in Australia computed using daily outputs from the Bureau of Meteorology’s land surface water balance model AWRA-L. The case study of the January 2018 flash drought in eastern Australia is used to assess and demonstrate the suitability of the ESI. Results show that the ESI accurately highlighted the event and offered potential for flash drought pre-warning by a few weeks. In addition, the availability of long term high-resolution outputs from AWRA-L offers the ability to investigate multiple flash drought events in detail for greater understanding and to inform stakeholders.

A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems

Accurate estimation of ecosystem-scale land surface energy and water balance has great importance in weather and climate studies. This paper summarizes seasonal and interannual fluctuations of energy and water balance components in two distinctive semiarid ecosystems, sagebrush (SB) and lodgepole pine (LP) in the Snake River basin of Idaho. This study includes 6 years (2011–2016) of eddy covariance (EC) along with modeled estimates. An analysis of the energy balance indicated a higher energy balance ratio (0.88) for SB than for LP (0.86). The inclusion of canopy storage (CS) increased the association between turbulent fluxes and available energy in LP. Green vegetation fraction (GVF) significantly controlled evapotranspiration (ET) and surface energy partitioning when available energy and soil moisture were not limited. Seasonal water balance in the Budyko framework showed severe water-limited conditions in SB (6–9 months) compared to LP (6–7 months). Based on the validated Noah land surface model estimates, direct soil evaporation (ESoil) is the main component of ET (62 to 79%) in SB due to a large proportion of bare soil (60%), whereas at the lodgepole pine site, it was transpiration (ETran, 42–52%). A complementary ratio (CR) analysis on ET and potential ET (PET) showed a strong asymmetric CR in SB, indicating significant advection. Both SG and LP showed strong coupling between soil moisture (SM) and air temperature (Ta). However, a weak coupling was observed in SB when the soil was dry and Ta was high. This weak coupling was due to the presence of net advection. The results presented here have a wider application: to help us understand and predict the survival, productivity, and hydroclimatology of water-limited ecosystems.

Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

Spatiotemporal variations of annual shallow soil temperature on the Tibetan Plateau during 1983–2013

Soil temperature changes in cold regions can have great impacts on the land surface energy and water balance, and hence changes in weather and climate, surface and subsurface hydrology and ecosystem. We investigate the spatiotemporal variations of annual soil temperature at depths of 0, 5, 10, 15, 20, and 40 cm during 1983–2013 using observations at 85 stations on the Tibetan Plateau (TP). Our results show that the climatological soil temperatures exhibit a similar spatial pattern among different depths and they are generally higher than surface air temperature at the individual stations. Spatially averaged soil temperature show that the TP has experienced significant warming trends at all six depths during 1983–2013, and the soil at 0-cm depth has the fastest warming rate among all the six layers and the surface air temperature. The first leading mode of joint empirical orthogonal function (EOF) analysis exhibits a spatially prevailing warming pattern across the six depths. This plateau-wide soil warming correlates very well with surface air temperature and sea surface temperature in response to increasing radiative forcing caused by greenhouse gases. The joint EOF2 displays a southeastern-northwestern dipole pattern on the TP in the interannual-decadal variability of soil temperature at all layers, which appears to be related to the warm season precipitation and anomalous atmospheric circulations. The spatial difference of soil warming rates across stations on the TP is associated primarily with the spatial distribution of precipitation (mainly rainfall), with vegetation, snowfall and elevation playing a rather limited role.