Land Surface Processes Research Articles

Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley

Soil moisture is an important variable controlling many land surface processes and is used to quantify precipitation, drought, flooding, irrigation and other factors that influence decision making and risk-assessment. This paper presents the retrieval of high resolution (∼1 km) soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1) previously only tested for scatterometer data. The model is applied over a diverse set of landcover types across the entire Po-Valley in Italy over a 4-year time-period from 2016 to 2019. The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The model parameterisation and retrieval method are chosen such as to constitute a trade-off between a physically plausible and a computationally feasible modeling approach. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.

Multi-temporal assessment of land surface temperatures as an indicator of land use/cover changes and climate variability in the Develi Basin, Turkey.

Land surface temperature (LST) is an important parameter that reflects land surface processes of water and energy balance and has been used in assessment of land use/cover changes. However, the use of LST in monitoring changes in non-urban areas such as agricultural areas and wetlands is still limited. In this study, we aim to determine the spatial and temporal changes in LST in a semi-arid agricultural basin in Turkey (Develi Basin), where land use/cover and climatic conditions showed considerable variability since 1980s. Irrigated agriculture have expanded in the basin since 1987, after the construction of a large irrigation project. The basin hosts an internationally important wetland, called the Sultan Marshes, affected negatively by irrigation expansion. The study covers a 39-year period from 1984 to 2022. Four Landsat Thematic Mapper (TM) images acquired in 1984, 1987, 2003, and 2007 and two Landsat 8 OLI/TIRS images acquired in 2014 and 2022 were used in the analyses. The land use/cover changes were evaluated based on Normalized Difference Vegetation Index (NDVI). LST was estimated through top-of-atmosphere brightness temperature from thermal bands of Landsat images. Climate variability from 2014 to 2022 was analyzed with statistical methods. The results indicated that Develi Basin faced both spatial and temporal land use/cover changes. The area covered with natural steppe vegetation and water bodies decreased in the basin. In contrast, the sparsely and densely vegetated soil covers, which mostly denote agricultural areas, increased. Changes in LST values were observed from 1984 to 2022 as a result of climatic factors and land use/cover changes. LST changes were variable across different land use/cover types; LST decreased in irrigated areas and increased in lakes that went dry over years. LST changes proved useful for evaluating land use/cover changes and climatic variations in agricultural basins.

Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network

ABSTRACT Monitoring and mapping vegetation dynamics using remote sensing data are essential for our understanding of land surface processes. Most current satellite-based methods process vegetation index time-series data from a series of images to retrieve key points that correspond to vegetation phenophases. As deep learning approaches have been found to be powerful in processing individual images, we tested the applicability of convolutional neural network (CNN) in mapping vegetation growth days (VGD) and the start of growing season (SOS) from each Landsat image at fine-spatial-resolution. To provide references for both model training and testing, we applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse image pairs of Landsat 8 Operational Land Imager (OLI) and Moderate-resolution Imaging Spectroradiometer (MODIS) for four Landsat tiles in China. We then applied a first derivative method to retrieve VGD for the fused satellite data at fine-spatial-resolution. The CNN model was trained using each fused individual image as the model inputs and derived VGD as the targets. The trained model was further used to map VGD from individual Landsat image. The result could match the reference map well as indicated by the evaluation metrics. In terms of VGD, the method achieved a coefficient of determination of 0.85 and a root mean squared error of 8.17 days. In terms of SOS, the method achieved a coefficient of determination of 0.75 and a root mean squared error of 4.09 days. Compared with existing methods that require time series of satellite data spanning the entire growth cycles to retrieve phenological metrics, this study provides an alternative method to map VGD as well as SOS using individual Landsat image. Our study highlights the power of deep learning models in extracting phenological features from individual remote sensing images. Researchers can use our methods to predict near real-time VGD and SOS in the future.

Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental‐Scale

AbstractUnprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, motivating the development of large‐scale hydrologic modeling for solutions. Water age/quality is as important as water quantity for understanding the terrestrial water cycle. However, scientific progress in tracking water parcels at large‐scale with high spatiotemporal resolutions is far behind that in simulating water balance/quantity owing to the lack of powerful modeling tools. EcoSLIM is a particle tracking model working with ParFlow‐CLM that couples integrated surface‐subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework on distributed, multi‐Graphics Processing Unit platforms with Compute Unified Device Architecture‐Aware Message Passing Interface for accelerating EcoSLIM to continental‐scale. In tests from catchment‐, to regional‐, and then to continental‐scale using 25‐million to 1.6‐billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where the parallelization is inadequate, and a standard parallel framework is also absent. The parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth System Models.

Impact of climate-driven oasis evolution on human settlement in the Baiyang River Basin, northwest China, Hami, during the middle to late Holocene

Oases are major foci of human activities in arid regions. Research on the interactions between past climate change, oasis evolution, and human activities may provide an important historical frame of reference for evaluating current and future societal responses to global climate changes. Here, we use optically stimulated luminescence (OSL) and radiocarbon (14C) dating of a sedimentary sequence, combined with analyses of various environmental proxies, to reconstruct the environmental evolution of the Baiyang River Basin, northwest China, during the middle to late Holocene. Additionally, we use a compilation of radiocarbon-dated archaeological sites from the Bronze Age to the historical period to construct a timeline of human activity in the study area. The results show that a flourishing oasis environments were concentrated during the periods of 4.14–2.41 ka BP and after 1.68 ka BP, which corresponded to increases in human settlements during the Bronze Age and after the Tang Dynasty, respectively. By contrast, increased environmental instability during 5.71–4.14 ka BP and 2.41–1.68 ka BP may have hindered human settlement in this area, and the 14C dates show that human activity in the Baiyang River Basin was reduced during those periods. A comparison with regional paleoclimatic records revealed that the evolution of oases was closely related to changes in temperature and precipitation. Higher temperatures in the middle Holocene (∼6–4 ka) caused increases in glacier meltwater production and river discharge, which resulted in a more hydrogenic environment in the downstream oases. Increased humidity in the area during the late Holocene resulted in the re-emergence of oases and therefore of human settlements. Our findings highlight the important relationship between human settlement and land surface processes. We suggest that future warming may cause increased meltwater production and extreme precipitation events, which could adversely impact human societies in arid regions due to more frequent flooding in the downstream oases.

Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic model

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.

Impact of Soil Moisture in the Monsoon Region of South America during Transition Season

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

Evolution of the Convective Boundary Layer in a WRF Simulation Nested Down to 100 m Resolution During a Cloud‐Free Case of LAFE, 2017 and Comparison to Observations

AbstractThe Weather Research and Forecasting model was applied in a nested configuration from the convection‐permitting resolution of 2.5 km, via an intermediate resolution of 500 m down to the 100 m turbulence‐permitting scale to investigate the spatial and temporal evolution of the convective boundary layer and their interaction with the underlying land surface. This included detailed comparisons with observations collected during the Land‐Atmosphere Feedback Experiment, performed at the Central Facility in Oklahoma. The simulation was driven by the operational analysis of the European Center for Medium‐range Weather Forecasting for a cloud‐free case on 23 August 2017 for which the measurement operation was extended into the following night to include the evening decay of the daytime convective boundary layer. The mesoscale 2.5 km resolution was capable to represent the correct boundary layer height and its temporal evolution. Details of the morning and evening transitions between the nighttime and daytime boundary layer as well as its internal structure were only simulated by the 100 m turbulence‐permitting simulation. Although systematic differences between the simulation and lidar observations were found, the model captured the temporal and spatial structure and the statistics of turbulence rather well. Comparison with data from Eddy‐Covariance stations showed that the model was able to reproduce the evolution of many near‐surface meteorological fields. Systematically higher surface temperatures and related differences in the surface fluxes suggest weaknesses in the representation of land surface processes although the simulation was initialized with accurate and high‐resolution initial fields.

Land surface processes are the dominant driver of global runoff increase

Land surface processes are the dominant driver of global runoff increase

Role of land surface processes on Indian summer monsoon rainfall: Understanding and impact assessment

Indian Summer Monsoon is a synoptic-scale atmospheric circulation system manifested by the boundary forcing from both continents and tropical oceans. Unlike oceans, the land surface processes are complex in nature due to the heterogeneities in land surface characteristics and its associated feedbacks, thereby constraining theaccurate representation of the land surface in NWP models. Thus, understanding the land-atmosphere interaction becomes increasingly crucial especially during the Indian summer monsoon season due to the underlying warm and moist surface layer conducive forevapotranspiration, thereby fueling land atmosphere coupling during the season. The representation of surface heterogeneity and variability are constrained due to lack of surface measurements which necessitate development of land surface analysis. The major aimof the present studyisthree-fold;firstly, understanding land surfaces processes associated with the monsoonal rainfall events, secondly, preparation of a state-of-art high-resolution land surface data over India, andfinally, impact assessment of high-resolution land surface initialization on simulation monsoonalrainfall events. This study has implications for developing improved prediction system associated with the Indian Summer Monsoon.
 Indian Summer Monsoon is a synoptic-scale atmospheric circulation system manifested by the boundary forcing from both continents and tropical oceans. Unlike oceans, the land surface processes are complex in nature due to the heterogeneities in land surface characteristics and its associated feedbacks, thereby constraining theaccurate representation of the land surface in NWP models. Thus, understanding the land-atmosphere interaction becomes increasingly crucial especially during the Indian summer monsoon season due to the underlying warm and moist surface layer conducive forevapotranspiration, thereby fueling land atmosphere coupling during the season. The representation of surface heterogeneity and variability are constrained due to lack of surface measurements which necessitate development of land surface analysis. The major aimof the present studyisthree-fold;firstly, understanding land surfaces processes associated with the monsoonal rainfall events, secondly, preparation of a state-of-art high-resolution land surface data over India, andfinally, impact assessment of high-resolution land surface initialization on simulation monsoonalrainfall events. This study has implications for developing improved prediction system associated with the Indian Summer Monsoon.

Climate change-induced urban heat Island trend projection and land surface temperature: A case study of Thailand's Bangkok metropolitan

This research projects future climate change-induced urban heat island (UHI) trends (2022–2099) using three dynamically downscaled regional climate models (RCM1 – RCM3) under two representative concentration pathways: RCP4.5 and 8.5. RCP 4.5 is a moderate scenario in which emissions peak around 2040 and then decline, while RCP 8.5 is the highest baseline emissions scenario in which emissions continue to rise throughout the twenty-first century. An RCM is a numerical climate prediction model forced by specified lateral and ocean conditions from observation-based dataset that simulates atmospheric and land surface processes. In the study, the RCMs are based on three respective global climate models: ICHEC-EC-EARTH, MPI-M-MPI-ESM-MR and NOAA-GFDL-GFDL-ESM2M. The study area is Thailand's capital Bangkok, and the UHI trends are determined by the Mann-Kendall trends and Sen's slope estimates of 13 future temperature-based extreme climate indexes, consisting of nine warm extreme indexes and four cold extreme indexes. The findings reveal that the majority of the warm extreme indexes are trending upward, while most of the cold indexes exhibit a downward trend. In essence, the Bangkok metropolitan area highly likely experiences warmer temperatures in the future, contributing to higher land surface temperature and UHI intensity.

Tundra cryogenic land surface processes and CO2–C balance in sub-Arctic alpine environment withstand winter and spring warming

Cryogenic land surface processes (CLSPs), such as cryoturbation, are currently active in landscapes covering 25% of our planet where they dictate key functions, such as carbon (C) cycling, and maintain patterned landscape features. While CLSPs are expected to diminish in the near future due to milder winters especially in the southern parts of the Arctic, the shifts in C cycling in these landscapes may be more complex, since climate change can affect C cycling directly but also indirectly via CLSPs. Here, we study the effects of changing winter and spring climate on CLSPs and C cycling in non-sorted circles consisting of barren frost boils and their vegetated rims. We do this by measuring cryoturbation and ecosystem CO2 fluxes repeatedly in alpine subarctic tundra where temperatures during naturally snow covered period have been experimentally increased with snow-trapping fences and temperatures during winter and spring period after snowmelt have been increased with insulating fleeces. Opposite to our hypothesis, warming treatments did not decrease cryoturbation. However, winter warming via deeper snow increased ecosystem C sink during summer by decreasing ecosystem CO2 release in the frost boils and by counterbalancing the negative effects of cryoturbation on plant CO2 uptake in the vegetated rims. Our results suggest that short-term changes in winter and spring climate may not alter cryoturbation and jeopardize the tundra C sink.

Impact of land surface processes on convection over West Africa in convection‐permitting ensemble forecasts: A case study using the MOGREPS ensemble

AbstractSoil moisture (SM) affects weather through its impact on surface flux partitioning, influencing vertical atmospheric profiles and circulations driven by differential surface heating. In West Africa, observational studies point to a dominant negative SM‐precipitation feedback, where dry soils help to initiate and maintain convection. In this context, serious concerns exist about the ability of models with parameterised convection to simulate this observed sensitivity of daytime convection to SM. Here, we evaluate the effect of initial SM perturbations in a short‐range ensemble forecast over West Africa, comparing the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) with parameterised convection (GLOB‐ENS) to its regional convection‐permitting counterpart (CP‐ENS). Results from both models suggest SM perturbations introduce considerable spread into daytime evaporative fraction (EF) and near‐surface temperatures. This spread is still evident on Day 3 of the forecast. Both models also show a tendency to increased afternoon rainfall frequency over negative EF anomalies, reproducing the observed feedback. However, this effect is more pronounced in CP‐ENS than GLOB‐ENS, which illustrates the potential for process‐based forecast improvements at convection‐permitting scales. Finally, we identify persistent biases in rainfall caused by land cover mapping issues in the operational GLOB‐ENS setup, emphasising the need for careful evaluation of different mapping strategies for land cover.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52–0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Dynamic changes in permafrost distribution over China and their potential influencing factors under climate warming

As a major component of the cryosphere, permafrost plays important roles in the climate system and land surface processes of the Earth. Owing to the rapidly warming climate, permafrost over the globe has degraded in recent decades. However, quantifying the distribution and temporal changes in permafrost is challenging. In this study, we modified the widely used surface frost number model by considering the spatial distribution of soil hydrothermal properties and then revisited the spatiotemporal patterns of permafrost distribution and its changes during the past decades (1961–2017) in China. We found that the modified surface frost number model performs well in simulating permafrost extent in China, with the overall accuracy and kappa coefficients being 0.92 and 0.78 in the calibration (1980s) and 0.94 and 0.77 in the validation period (2000s). Based on the modified model, we also found that permafrost extent in China demonstrated a significant decreasing trend over the past decades, especially on the Qinghai-Tibet Plateau (QTP), with a trend of −1.15 × 104 km2/yr (P < 0.01). Moreover, there is a significant relationship between ground surface temperature and permafrost distribution area, with the R2 being 0.41, 0.42, and 0.77 in NE and NW China and on the QTP. The sensitivity of permafrost extent to ground surface temperature in NE China, NW China, and the QTP, respectively, was −8.56 × 104, −1.97 × 104, and −34.60 × 104 km2/°C, respectively. Permafrost degradation has accelerated since the late 1980s, possibly due to increased climate warming. This study is of great significance for improving permafrost distribution simulation at large spatial scales (trans-regional) and for offering vital information for adapting to climate change in cold regions.

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.

Evaluation of CLM5.0 for simulating surface energy budget and soil hydrothermal regime in permafrost regions of the Qinghai-Tibet Plateau

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, especially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 °C, 2.07 °C, 2.02 °C, and 2.95 °C to 1.34 °C, 1.35 °C, 1.35 °C and 2.29 °C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m−3 to 0.049 m3 m−3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

Dataset of Comparative Observations for Land Surface Processes over the Semi-Arid Alpine Grassland against Alpine Lakes in the Source Region of the Yellow River

Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.

Temporal hydrological drought clustering varies with climate and land-surface processes

Recurrent hydrological droughts (streamflow deficits) are highly impactful and challenge water management. Regional studies have provided some evidence of drought-rich periods at specific time scales. However, it is yet unclear where and when droughts cluster in time. Here, we test for significant temporal hydrological drought clustering at subseasonal to multi-year time scales in different climate zones around the world using two different clustering metrics, i.e. the dispersion index and Ripley’s K. We find that (1) only 10% of the catchments show temporal hydrological drought clustering, (2) hydrological droughts cluster from seasonal to 3-year time scales with clustering being strongest at an annual time scale; (3) arid catchments with a low snow fraction are most prone to temporal drought clustering; and (4) temporal clustering is more pronounced for hydrological than for meteorological droughts. These results suggest that besides climatic drivers, land-surface processes importantly influence the temporal clustering behavior of hydrological droughts.

Comparison of environmental controls on daily actual evapotranspiration dynamics among different terrestrial ecosystems in China

Terrestrial actual evapotranspiration (ETa) is a key state variable modulating land surface processes; however, it remains challenging to recognize how daily ETa responses to environmental variables vary with ecosystems, which is relevant for understanding various ecosystem processes. To this end, comprehensive datasets were synthesized for 22 field sites equipped with eddy covariance towers from the ChinaFLUX network and the National Tibetan Plateau Data Center, which included four different ecosystems across China (e.g., barren or sparsely vegetated-BAR, grassland, forest, and cropland ecosystems). The impacts of environmental variables on daily ETa variations at each field site were assessed by the boosted regression tree (BRT) method. Overall, the BRT results showed that invariant of ecosystem types, net radiation (Rn) was the primary control on daily ETa dynamics with an average contribution of 62.9 % for all the sites, followed by leaf area index-LAI (18.4 %), vapor pressure deficit (8.2 %), soil water content-SWC (7.4 %), and wind speed (3.1 %). More importantly, it was revealed that the interactions of daily ETa with energy (Rn) and water (SWC) supplies could be significantly modified by vegetation through various regulation mechanisms. Specifically, for the BAR and grassland ecosystems, the influences of vegetation on daily ETa highly depended on local land cover and hydrological conditions (e.g., groundwater). For the forest ecosystem, the contrasting impacts of LAI between evergreen and temperate forests were largely attributed to the differences in LAI dynamics over growing seasons, while physiological differences in crops affected the daily ETa responses to LAI variations at cropland sites. This study underscored the varying interactions of daily ETa with Rn and SWC, which could be further shaped by vegetation through a variety of regulation mechanisms across different ecosystems.