Search Results (367)

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Surface all-wave net radiation ($R_n$) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of $R_n$ from satellite data is gaining more and more attention. In order to obtain the daily $R_n$ ($R_{nd}$) from the instantaneous satellite observations, a parameter $C_d$, which is defined as the ratio between the $R_n$ at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for $C_d$ estimation, namely New Model, was proposed based on the comprehensive clear-sky $R_n$ measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate $C_d$ at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the $R_{nd}$ obtained by multiplying $C_d$ from New Model had the best accuracy, yielding an overall $R^2$ of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model $C_d$ estimate was applied with MODIS data to calculate $R_{nd}$. After validation, the $R_{nd}$ computed from $C_d$ was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with $R_{nd}$-validated $R^2$ of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new $C_d$ model only works for clear-sky, it has the strong potential to be used in estimating $R_{nd}$ from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

Urban thermal environments, characterized by the interplay between indoor and outdoor conditions, pose growing challenges in high-density coastal cities. This study proposes a multi-scale, integrative framework that couples a satellite-derived land surface temperature (LST) analysis with microscale building performance simulations to holistically evaluate the high-density urban thermal environment in subtropical climates. The results reveal that compact, high-density morphologies reduce outdoor heat stress (UTCI) through self-shading but lead to significantly higher cooling loads, energy use intensity (EUI), and poorer daylight autonomy (DA) due to restricted ventilation and limited sky exposure. In contrast, more open, vegetation-rich forms improve ventilation and reduce indoor energy demand, yet exhibit higher UTCI values in exposed areas and increased lighting energy use in poorly oriented spaces. This study also proposes actionable design strategies, including optimal building spacing (≥15 m), façade orientation (30–60° offset from west), SVF regulation (0.4–0.6), and the integration of vertical greenery to balance solar access, ventilation, and shading. These findings offer evidence-based guidance for embedding morphological performance metrics into planning policies and building design codes. This work advances the integration of outdoor and indoor performance evaluation and supports climate-adaptive urban form design through quantitative, policy-relevant insights. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

The use of a rock-bed accumulator for a short-term heat storage and air exchange in a building facility is an economical and energy-efficient technological solution to balance and optimize the energy supplied to the facility. Existing scientific studies have not addressed, as yet, the environmental impacts of using a rock bed for heat storage. The purpose of the research is the environmental life cycle assessment (LCA) of a heat storage system in a rock-bed accumulator supported by a photovoltaic installation. The boundaries of the analyzed system include manufacturing the components of the storage device, land preparation for the construction of the accumulator, the entire construction process, including transportation of materials, and its operation in cooperation with a horticultural facility (foil tunnel) during one growing season, as well as the photovoltaic installation. The functional unit in the analysis is 1 square meter of rock-bed accumulator surface area. SimaPro 8.1 software and Ecoinvent database were used to perform the LCA, applying the ReCiPe model to analyze environmental impact. The analysis showed the largest negative environmental impact occurs during raw materials extraction and component manufacturing (32.38 Pt). The heat stored during one season (April to October) at a greenhouse facility reduces this negative impact by approx. 7%, mainly due to the reduction in the use of fossil fuels to heat the facility. A 3 °C increase in average air temperature results in an average reduction of 0.7% per year in the negative environmental impact of the rock-bed thermal energy storage system. Full article

Investigating soil temperature and the heat transfer process is essential for understanding water–heat changes and energy balance in farmland. The conversion from upland fields (UFs) to paddy fields (PFs) alters the land cover, irrigation regimes, and soil properties, leading to differences in soil temperature, thermal properties, and heat fluxes. Our study aimed to quantify the effects of converting UFs to PFs on soil temperature and heat transfer processes, and to elucidate its underlying mechanisms. A long-term cultivated UF and a newly developed PF (converted from a UF in May 2015) were selected for this study. Soil water content (SWC) and temperature were monitored hourly over two years (June 2017 to June 2019) in five soil horizons (i.e., 10, 20, 40, 60, and 90 cm) at both fields. The mean soil temperature differences between the UF and PF at each depth on the annual scale varied from −0.1 to 0.4 °C, while they fluctuated more significantly on the seasonal (−0.9~1.8 °C), monthly (−1.5~2.5 °C), daily (−5.6~4.9 °C), and hourly (−7.3~11.3 °C) scales. The SWC in the PF was significantly higher than that in the UF, primarily due to differences in tillage practices, which resulted in a narrower range of soil temperature variation in the PF. Additionally, the SWC and soil physicochemical properties significantly altered the soil’s thermal properties. Compared with the UF, the volumetric heat capacity (C_s) at the depths of 10, 20, 40, 60, and 90 cm in the PF changed by 8.6%, 19.0%, 5.5%, −4.3%, and −2.9%, respectively. Meanwhile, the thermal conductivity (λ_θ) increased by 1.5%, 18.3%, 19.0%, 9.0%, and 25.6%, respectively. Moreover, after conversion from the UF to the PF, the heat transfer direction changed from downward to upward in the 10–20 cm soil layer, resulting in a 42.9% reduction in the annual average soil heat flux (G). Furthermore, the differences in G between the UF and PF were most significant in the summer (101.9%) and most minor in the winter (12.2%), respectively. The conversion of the UF to the PF increased the C_s and λ_θ, ultimately reducing the range of soil temperature variation and changing the direction of heat transfer, which led to more heat release from the soil. This study reveals the effects of farmland use type conversion on regional land surface energy balance, providing theoretical underpinnings for optimizing agricultural ecosystem management. Full article

In semi-arid regions, seasonally dry tropical forests are essential for regulating the surface energy balance, which can be analyzed by examining air heating processes and water availability control. The objective of this study was to evaluate the ability of the Brazilian Developments on the Regional Atmospheric Modelling System (BRAMS) model in simulating the seasonal variations of the energy balance components of the Caatinga biome. The surface measurements of meteorological variables, including air temperature and relative humidity, were also examined. To validate the model, we used data collected in situ using an eddy covariance system. In this work, we used the BRAMS model version 5.3 associated with the Joint UK Land Environment Simulator (JULES) version 3.0. The model satisfactorily represented the rainfall regime over the northeast region of Brazil (NEB) during the wet period. In the dry period, however, the coastal rainfall pattern over the NEB region was underestimated. In addition, the results showed that the surface fluxes linked to the energy balance in the Caatinga were impacted by the effects of rainfall seasonality in the region. The assessment of the BRAMS model’s performance demonstrated that it is a reliable tool for studying the dynamics of the dry forest in the region, providing valuable support for sustainable management and conservation efforts. Full article

The formation of avalanche hazards in mountainous regions is largely influenced by slope insolation and albedo. This paper presents a quantitative analysis of how solar radiation, surface reflectivity (albedo), temperature, and snow cover affect avalanche formation depending on slope aspect (north-, south-, east-, and west-facing). This study is based on remote sensing data from MODIS, ERA5-Land, CHIRPS, and a digital terrain model for the winter periods from 2000 to 2024. The results show that north-facing slopes have higher albedo values (up to 0.95) and greater snow cover stability (30–50%), which contributes to increased avalanche risk, especially at temperatures above −5 °C. South-facing slopes are characterized by lower albedo values (around 0.20–0.40) and more intense snowmelt, which reduces the likelihood of avalanches. Regression analysis revealed a strong positive correlation between snow depth and avalanche risk (r = 0.87), as well as a moderate negative correlation between temperature and snow cover stability (r = −0.25). The influence of albedo on avalanche risk was found to be indirect, acting through its impact on the surface energy balance. The resulting avalanche risk map demonstrated high accuracy (overall agreement: 86%; Kappa coefficient: 0.72), highlighting the effectiveness of an integrated approach based on geophysical and climatic parameters. The data obtained can be used to support avalanche safety management and slope assessment in the context of climate change. Full article

The aim of this study is to present a methodology for diagnosing cities in terms of hydrological and meteorological threats, with the goal of improving water management and helping cities adapt to changing conditions. Urbanisation is expected to progress unevenly across countries and cities, influenced by factors such as climatic conditions, economic disparities, and governance structures. Consequently, urban landscapes should strive for a balanced approach that integrates safety and risk management, commercial spaces, emotional well-being, and the promotion of biodiversity. Cities play a pivotal role in addressing climate change, as they account for a significant share of global energy consumption and greenhouse gas emissions. In Poland, numerous national and international projects are being implemented to help cities mitigate the impacts of climate change. Among these, the City with Climate project aimed to enhance residents’ quality of life while facilitating a pro-climate transition for cities. A holistic and multifaceted approach was adopted, incorporating the analysis of historical flood events based on archival documents and rescue service reports, detailed GIS data such as soil sealing, non-drained basins, NDVI, NDBI, and a multi-criteria analysis targeting hydrological and water management factors to develop effective solutions for urban retention challenges. The main findings indicate that: (1) combining insightful analyses using well-established methods provides a robust foundation for informed decision-making by city authorities; (2) overlaying information layers, such as local flooding interventions, non-drained areas, drainage networks, and soil sealing, helps identify areas requiring large-scale, technical, or nature-based solutions; and (3) regardless of city size, there is a concerning trend of increasing impervious surfaces replacing green areas, alongside urban sprawl altering land use in flood-prone regions, including mountainous, forested, and floodplain areas that should be protected. These findings illustrate that employing a structured project methodology alongside a comprehensive approach can significantly contribute to urban landscape planning, addressing the challenges of climate change while enhancing urban biodiversity through blue and green infrastructure. Full article

Soil moisture is a core climate variable in land surface processes and has a strong influence on the energy balance and water exchange between the land surface–vegetation–atmosphere columns. However, the low spatial resolution of soil moisture remote sensing products cannot satisfy the requirements of research and applications based on hydro-meteorological and eco-hydrological simulations and the management of water resources at the watershed scale. A feasible solution is to downscale soil moisture products derived from microwave remote sensing, which often requires the support of auxiliary variables. Meanwhile, during the validation process of remote sensing products, the spatial scales between in situ observations and remote sensing pixel retrievals are inconsistent; thus, in situ observations should be translated to ground truths at a pixel scale via reasonable upscaling methods. Many auxiliary variables can serve as proxies in the scaling of soil moisture, although few studies have analyzed their feasibility and application conditions. In this paper, a SiB2 (Simple Biosphere Model-II) simulation for the Daman superstation from 1 May to 30 September 2013, was employed to calculate seven auxiliary variables related to soil moisture: ATIs and ATIc (Apparent Thermal Inertias based on surface soil temperature and canopy temperature), E (Evaporation), E/ET_a (Ratio of Evaporation and Actual Evapotranspiration), E/ET_p (Ratio of Evaporation and Potential Evapotranspiration), EF (Evaporative Fraction) and AEF (Actual Evaporative Fraction). The applicability of these variables was then evaluated via a correlation analysis between the variables and soil moisture. The results indicated that E is highly sensitive to soil moisture at Phase I (R² ≥ 0.67), whereas ATIs is the greatest indicator of soil moisture at Phase II (R² ≥ 0.51). Considering both the correlation and computability of these auxiliary variables, the EF (R² ≥ 0.56) and AEF (R² ≥ 0.54) are recommended as proxies for Phase I, while ATIs (R² ≥ 0.51) is also recommended for Phase II. Full article

Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions has not been comprehensively evaluated. This study investigates spatiotemporal trends of five SSM products—MERRA-2, ESACCI, GLEAM, GLDAS, and ERA5—from 1980 to 2023. The performance of these products was evaluated using in situ station data and the three-cornered hat (TCH) method, followed by partial correlation analysis to assess the influence of environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET), vapor pressure deficit (VPD), and leaf area index (LAI), on SSM from 1981 to 2018. The results showed consistent SSM patterns: higher values in India, the North China Plain, and Russia, and lower values in the Arabian Peninsula, the Iranian Plateau, and Central Asia. Regionally, MAT, PET, VPD, and LAI increased significantly (0.04 °C yr⁻¹, 1.66 mm yr⁻¹, 0.004 kPa yr⁻¹, and 0.003 m² m⁻² yr⁻¹, respectively; p < 0.05), while MAP rose non-significantly (0.29 mm yr⁻¹). ERA5 exhibited the strongest correlation with in situ station data (R² = 0.42), followed by GLEAM (0.37), ESACCI (0.28), MERRA2 (0.19), and GLDAS (0.17). Additionally, ERA5 showed the highest correlation (correlation = 0.72), while GLEAM had the lowest bias (0.03 m³ m⁻³) and ESACCI exhibited the lowest ubRMSE (0.03 m³ m⁻³). The three-cornered hat method identified ERA5 and GLDAS as having the lowest uncertainties (<0.03 m³ m⁻³), with ESACCI exceeding 0.05 m³ m⁻³ in northern regions. Across land cover types, cropland had the lowest uncertainty among the five SSM products, while forest had the highest. Partial correlation and dominant factor analysis identified MAP as the primary driver of SSM. This study comprehensively evaluated SSM products, highlighting their strengths and limitations. It underscored MAP’s crucial role in SSM dynamics and provided insights for improving SSM datasets and water resource management in drylands, with broader implications for understanding the hydrological impacts of climate change. Full article

Evapotranspiration (ET) is a crucial parameter for agricultural management and the hydrologic cycle, and instantaneous satellite images are the primary data source for regional ET. The constant evaporative fraction method (EFO) is a common approach for converting short-time ET (ET_st) to daily ET (ET_day). However, EFO has some limitations due to simple assumptions, including the following: the short-time evaporative fraction (EF_st) equals the daily evaporative fraction (EF_day). This study proposed an improved evaporative fraction method (EFI) through theoretical derivation and data analysis without additional data requirements, enabling the accurate upscaling of ET_st to ET_day. The vapor pressure deficit and available energy were considered in EFI to describe the main effect factor and estimate the deviation between EF_st and EF_day, defining the deviation coefficient and potential deviation between EF_st and EF_day. EFI was tested through four aspects: different agricultural systems, various sites, two growth stages, and different sources of EF_st, comparing estimated ET_day from EFI and measured ET_day. EFI reduced the mean absolute percentage error (MAPE) of ET_day estimation from 23% to 16% when EF_st is derived from measured data compared to EFO. Similarly, the MAPE of ET_day estimation reduced from 38% to 31% when EF_st is derived from a remote sensing model (Surface Energy Balance Algorithm for Land, SEBAL). EFI performs better during the growing period than the fallow season, providing critical information for irrigation practices. Crop type is not a main control factor for the relationship between η (ratio between VPD and R_n-G) and EF_st, and EFI is adaptable to various agricultural systems. The encouraging results of EFI in different scenarios demonstrate its accuracy and robustness. Therefore, EFI is anticipated to upscale EF_st to EF_day, generating a more accurate ET on a regional scale through remote sensing technology. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article