Water Cycle Research Articles

The diurnal variation of dust and water ice aerosol optical depth at Jezero crater observed by MEDA/TIRS over a full Martian year

The Thermal InfraRed Sensor (TIRS) on the Perseverance rover has provided nearly two full Mars years of systematic monitoring of the total aerosol optical depth above Jezero Crater. These observations span a wide range of timescales, capturing seasonal patterns, diurnal variations, and minute-to-minute fluctuations in aerosol loading. By combining TIRS retrievals with orbital observations, the relative contributions of dust and water ice aerosols can be estimated, revealing their different seasonal and diurnal behaviors. The TIRS record shows distinct periods of dust storm activity, including strong regional storms during the perihelion season as well as short-lived but intense dust events outside the typical dust storm season. Water ice clouds exhibit pronounced seasonal and diurnal variability, with peak activity occurring during the aphelion season but with a presence throughout the year. The diurnal variation of clouds differs significantly between the aphelion and perihelion seasons, with clouds persisting throughout the night during the aphelion season, while largely absent outside of specific periods after sunrise and sunset during the perihelion season. These results provide new insights into the complex behavior of aerosols at Jezero Crater and their connections to atmospheric dynamics and the Martian dust and water cycles.

Rainwater shocks and economic growth: The role of the water cycle partition

This paper improves our understanding of how rainwater impacts economic growth by investigating the effects of overlooked properties of the water cycle. First, we consider the natural separation of rainwater into flows of blue water (i.e., the water that runs off towards rivers) and green water (i.e., that remaining in the soil). Second, we account for the presence of surface and groundwater stocks. These considerations allow us to comprehensively address the whole partition of rainwater, which, upon reaching the ground, splits into distinct water resources that determine water availability inland. Our analyses on a global panel coupling sub-national economic and hydrological data show that rainwater does increase growth, but do so differently depending on its partition. Specifically, blue water leads to more economic growth than green water at the margin, but, because two thirds of terrestrial water is green, the latter contributes more to growth in total. By missing this crucial partition, we find that commonly used rainwater measurements overstate rainwater’s contribution to growth (by about two). Our analyses further indicate that, although groundwater reserves always mitigate the impacts of rainwater reduction on growth, surface water reserves sometimes amplify regional dependence to rainwater (depending on sector, income and reserve types).

Eco-Friendly synergistic energy generation: Co-Ni2S4 nanoparticles with S-g-C3N4 as a dual-action electrocatalyst for sustainable water splitting

The most appealing technique for producing hydrogen fuel is electrochemical water splitting, which uses natural water cycle capabilities from renewable sources. The transition metal sulfide family includes nickel sulfide (NiS), which is promising for electrochemical water splitting as an exceptionally efficient, stable, and active electrocatalyst. Nickel sulfide (NiS), a series of cobalt-doped nickel sulfide (CoNi2S4) by varying weight percentages of Co (2, 4, 6, 8, wt %) and a series of sulfur-doped graphitic carbon nitride (SGCN) composite with 6 % CoNi2S4 by varying weight percentage of SGCN (10, 30, 50, 70, 90, wt %) were all synthesized using one-pot hydrothermal method. Structural morphologies and composition of the synthesized nanoparticles (NPs) and nanocomposites (NCs) were investigated using SEM, EDX, FTIR, and XRD characterization techniques. The electrochemical performance was examined via voltammetric, electrochemical impedance, and chronoamperometric studies. Outcomes suggest that composite 70 % SGCN@6 % CNS is the best electrocatalyst with the smaller Tafel slope (107 mVdec-1), lowest overpotentials (440 mV@10 mAcm−2), and minor charge transfer resistance (15 Ω) as electrode materials for electrochemical water splitting. The present research provides a workable approach for creating efficient bifunctional electrocatalysts for total water splitting, it can be inferred from the data.

Eco-friendly tea waste magnetite nanoparticles for enhanced adsorptive removal of norfloxacin and paroxetine from water

Traces of pharmaceuticals have been detected in water cycle raising concerns regarding the potential risks to human health and aquatic environment. Accordingly, removal of such compounds from water samples is a major concern. Herein, a facile and green NaOH modified tea waste magnetite nanoparticles have been fabricated for the adsorptive removal of a commonly used antibiotic (norfloxacin) and antidepressant (paroxetine) from water samples. The synthesized nanoparticles were characterized using TEM, FT-IR, DLS and VSM. Factors affecting the adsorption efficiency have been investigated with respect to pH (5, 7 and 9), adsorbent amount (0.125, 0.25 and 0.5 g), initial drug concentration (25, 50 and 100 µg/mL) and contact time (0.5, 1 and 2 h). The adsorption isotherms have been calculated. A percentage removal of 98 % and 99 % were obtained for simultaneous removal of 50 µg/mL norfloxacin and paroxetine, respectively in 2 h. Monitoring norfloxacin and paroxetine concentrations was performed through a validated HPLC-DAD method. The reusability of the nanoparticles has been studied for 3 adsorption–desorption cycles. No significant loss in adsorption efficiency was observed offering a sustainable water treatment. This study would offer a facile, green and economic protocol for the removal of complex organic compounds from water resources.

Study on performance deterioration and life prediction of reed straw-recycled brick aggregate concrete after freeze-thaw cycle

The current rate of carbon dioxide emissions poses a significant threat to global climate, making it increasingly important and urgent to lead the carbon-intensive construction industry to achieve carbon neutrality. Currently, the most practical method involves reusing waste materials. In this study, waste red bricks were used to replace 75 % of natural aggregates in concrete, and three different forms of reed stalks (including reed straw ash, reed straw strips, and reed straw powder) were added to prepare the recycled reed straw brick aggregate concrete (RSAC). Freeze-thaw tests were carried out for 25, 50, 75, and 100 cycles in water and sodium sulfate solution to establish a freeze-thaw deterioration model and perform life prediction. The results showed that the degree of freeze-thaw deterioration of the specimens in sulfuric acid solution was small, and the degree of deterioration of reed powder concrete (RSAC-F) and reed ash concrete (RSAC-H) was more serious than reed strip concrete (RSAC-T) and additive-free concrete (RSAC-0). After 100 water freeze-thaw cycles, the strength losses of the specimens were 21.55 %, 21.7 %, 18.71 %, and 20.54 %, and the relative dynamic elastic modulus losses were 6.11 %, 7.25 %, 4.39 %, and 5.79 %, and the mass losses were 1.79 %, 2.04 %, 1.4 %, and 1.5 %. The fitting results showed that the predicted values of the parabolic model and two-parameter Weibull distribution damage model were closer to the experimental values, and the fitting rates are both greater than 90 %. The life prediction results show that the freeze-thaw resistance of the four types of reed-brick aggregate recycled concrete can reach more than 80 years in southern China, meeting the durability requirements, and the RSAC-T and RSAC-0 group concrete could also be used in projects with lower frost resistance requirements in the north region of China.

Large variations of dissolved carbon occurred in small ponds within an agricultural watershed

Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) significantly affect the aquatic carbon budget and ecosystem functions. Small ponds are abundant globally and widely distributed especially in agricultural watersheds, however, the variability of DOC and DIC, along with their driving factors, remains poorly understood, which likely hampers the understanding of carbon cycle of inland waters. The presented study was designed to fill the knowledge gap based on a detailed year-long field investigation via examining DOC and DIC concentrations across ponds with differing functionalities (e.g. sewage ponds, irrigation ponds, and natural ponds) of a typical agricultural watershed in eastern China. Our results found a pronounced impact of human activities on pond DOC and DIC, with higher DOC occurring in sewage ponds (10.84 ± 2.83 mg L−1) and irrigation ponds (9.09 ± 2.57 mg L−1) and peak DIC in irrigation ponds (20.36 ± 2.49 mg L−1) compared to that at natural ponds (DOC: 7.54 ± 2.55 mg L−1; DIC: 11.16 ± 3.85 mg L−1) with less human activity. The positive correlations between DOC/DIC and key environmental variables (e.g. nutrients and chlorophyll-a) further demonstrated that human activity can either directly increase the carbon concentrations via pollutant discharge, or indirectly increase DOC concentration via stimulating primary production. Meanwhile, field measurements found precipitation and temperature play roles in determining the carbon variability. Specifically, precipitation increased the DOC of these ponds via enhancing land-based carbon inputs, and decrease the DIC of irrigation ponds via diluting. Temperature can influence the carbon dynamics through increasing primary productivity and metabolism. Our study underscores the roles of human and natural influences in determining the large variations of DOC and DIC in small ponds, which should be considered to better understand the carbon dynamic variability of human-impacted small aquatic systems.

Temporal and spatial dynamics of carbon and water use efficiency on the Qinghai-Tibet plateau and their ecosystem responses

Research on the carbon and water use efficiency (CUE, WUE) in the Qinghai-Tibet Plateau (QTP) holds significant importance due to its implications in understanding and addressing global environmental challenges, particularly climate change. This study comprehensively explored the spatiotemporal distribution patterns and trends of WUE and CUE on the plateau from 2001 to 2018 using linear regression analysis and geostatistical methods. The results revealed a gradual decrease in WUE from southeast to northwest, showing a non-linear variation trend during the study period. CUE exhibited relatively minor spatial variations but demonstrated a significant decrease trend in the eastern regions. The results from the Random Forest algorithm and path analysis indicated that ecosystem respiration (Re) was the predominant controlling factor for WUE and CUE in the cold alpine (PA) region. In the temperate, warm temperate (Te&WT) region, temperature emerged as the primary determinant for WUE, while Leaf Area Index (LAI) served as the absolute dominant factor for CUE. Additionally, LAI also played a crucial role in determining WUE and CUE in the tropical and subtropical (Tr&S) region. Partial correlation analysis highlighted the heterogeneity in the responses of WUE and CUE to environmental changes across different regional climates and vegetation types. The findings emphasized the significant impact of biological factors on terrestrial carbon and water cycling processes (WUE and CUE) within the context of global climate change. Furthermore, the study identified a potential linkage between WUE and CUE, with their relationship to vegetation dynamics showing significant variations across various ecosystems. WUE and CUE showed a positive correlation in the western regions, particularly in high-altitude areas, while a negative correlation was observed in eastern regions with high SOC. In the Te&WT and the Tr&S region, the increase of NDVI (Normalized difference vegetation index) suppressed the rise of both WUE and CUE, establishing a negative correlation between them. Conversely, in the PA region, a significant positive correlation trend between WUE and CUE was observed, indicating a positive response to the decrease in NDVI. These findings are of significant importance for advancing the understanding of and effectively addressing climate change, maintaining ecosystem stability, and promoting sustainable socio-economic development.

Research on a zero water consumption operation model and feasibility for office buildings

ABSTRACT According to Taiwan's government policy on net zero emissions by 2050, building designs are moving toward zero energy and zero water consumption to achieve these challenge goals. Among these goals, establishing an independent water cycle within building systems is a primary objective. The present study examined office buildings as the research subject due to their crucial water usage characteristics, which can serve as a reference for other building types. We developed a water-use estimation model by conducting a literature review and data collection from existing green office building cases in Taiwan. The methods involved calculating the median annual water consumption per unit building area and discussing the current water-saving design status and water-saving rate. The findings indicate a median water-saving rate of 53%, which is far short of the goal of achieving a zero water building. This finding is primarily attributed to the infrequent use of water compensation in building designs. The feasibility of a zero water building is validated and determines its crucial operation. Consequently, design engineers can employ this methodology to compute water conservation rates for their designs, aiming for the construction of a zero water consumption building.

Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize

Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.

Application of water cycle algorithm with demand follows green level and nonlinear power pattern of the product for an inventory system

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.

Stochastic Approach to the Evolution of the Global Water Cycle: Results of Historical Experiments on the CMIP-6 Models

Stochastic Approach to the Evolution of the Global Water Cycle: Results of Historical Experiments on the CMIP-6 Models

Assessment of the Restoration Potential of Forest Vegetation Coverage in the Alxa Desert Region of China.

To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship model. This model links evapotranspiration (ET) with meteorological variables and the Enhanced Vegetation Index (EVI). Furthermore, the study estimated the recovery thresholds and potential of forest and grassland vegetation coverage in the Alxa Desert region under various precipitation scenarios. The findings reveal that ET exhibited an increasing trend in 84.17% of the Alxa Desert region, with a significant increase observed in 61.53% of the area, indicating positive outcomes from the implementation of the Three-North Shelterbelt Forest Program. Notably, however, ET in the southeastern plain region demonstrated a decreasing trend, which is strongly associated with human activities. The response relationship model demonstrated that linear relationship areas constituted 47.52%, while nonlinear relationship areas accounted for 45.51% of the total. The overall model exhibited an R2 value of 0.69, indicating a high level of predictive accuracy. Analysis of forest and grassland coverage revealed that, under wet year scenarios, the vegetation coverage showed a significant trend of recovery, with an average recovery threshold of (75.4 ± 12.5)% and an average recovery potential of (8.5 ± 3.6)%. It is noteworthy that the vegetation coverage in 31.25% of the area had already surpassed the recovery threshold. The outcomes of this study provide a theoretical foundation for the formulation of more scientifically rigorous ecological restoration strategies in the future.

A Study on Total Mercury Content in Surface Water and Backwater Fishes of Periyar

Aquatic systems are extensively contaminated with heavy metals released due to anthropogenic activities. Mercury is one of toxic elements and its toxicity to humans has been established. The concern about mercuric pollution in the environments started with the incident of ‘Minamata’ in Japan in 1950’s. During the study period, flowing mercury electrode was used for the preparation of caustic at Travancore Cochin Chemicals Ltd (TCC), Eloor and effluents of small industries at Edayar were the sources of mercuric pollution. Surface water samples were collected from 1Km apart from TCC, near to Indian Rare Earth Ltd (IRE), Muttinakam and Mannamthuruth and fishes (male and female) were collected from this region with the help of local fishermen. This study implies the amount of mercury in aquatic system and its influence in different body components of three fishes. Total mercury content in both the samples analysed using cold vapour atomic absorption using Mercury Analyser MA-5840 and loss on mercury on heating processes (fishes) was decreased by the use of Bethge Apparatus. Fishes were Oreochromis mossambicus (Thilapia), Mugil cephalus (Mullet) and Arius arius (Cat fish) which were living in surface to near shore, middle of the river and bottom. The proximate composition (AOAC, 2000) of fishes showed that they were low fat (0.57-4.24%) with high protein content (18.4-21.96%). The total mercury content in surface water varied from 1.667- 3.334ng/ml and it was above tolerance level (1ng/ml) while in fishes followed the order A. arius> M. cephalus> O. mossambicus. Relatively higher concentrations of mercury were noticed in gut and liver than muscle, and also male fishes predominated over female. This study shows that mercury cycle in the habitat water did not influence to any hazardous level in these three fishes.

A first assessment of airborne HyTES-based land surface temperature and evapotranspiration

The Hyperspectral Thermal Emission Spectrometer (HyTES) offers high spatial and spectral resolution thermal infrared (TIR) airborne measurements, which are crucial for deriving land surface temperature and emissivity (LST&E). These measurements have wide-ranging applications, particularly in understanding water stress and plant water use. One critical application of TIR satellite-sensor systems is the estimation of evapotranspiration (ET), which can be derived from LST. ET is essential for modeling water fluxes from the land surface, and various algorithms leverage LST as a key boundary condition for this purpose. In this study, we apply an ET algorithm to HyTES LST data for the first time, using an analytical surface energy balance model, the Surface Temperature Initiated Closure (STIC) version 1.3. We provide an overview of the STIC model, detailing its application to HyTES data, including the integration of ancillary datasets. We demonstrate the practicality of this approach by presenting ET and LST calculations for HyTES flightlines from three field campaigns conducted in 2019, 2021, and 2023. To validate our results, we compare the derived ET and LST against available in situ measurements, including eddy covariance-derived latent heat flux and radiometer-derived LST. While this study focuses on HyTES data, the same methodology is applicable to any instantaneous LST dataset. Advancing TIR mapping of ET is crucial for applications in agriculture, water management and for understanding the evolving water cycle.

Responses of hydrological processes to vegetation greening and climate change in subtropical watersheds

Study regionXiangjiang River Basin (XRB) in subtropical China. Study focusVegetation changes have a substantial impact on the water cycle in watersheds. However, the responses of hydrological processes to vegetation changes in subtropical regions have not yet been thoroughly clarified. The primary objectives of this study were to quantify the sensitivity of hydrological variables to vegetation changes and to elucidate the contributions of vegetation greening and climatic changes to alterations in hydrological variables in the Xiangjiang River Basin (XRB). New hydrological insights for the regionEvapotranspiration (ET) exhibits a significant positive sensitivity to alterations in Leaf Area Index (LAI) in the XRB, while soil water storage (SW) and flow depth (Q) showed a significant negative sensitivity to LAI changes. Changes in LAI primarily result in increased ET and reduced SW and Q, while increased in precipitation leads to augmented ET, SW, and Q. Alterations in potential evapotranspiration have been associated with heightened ET and reduced SW and Q. From the perspective of relative contributions, LAI predominantly influences alterations in ET within the XRB; P and PET are the primary driver of shifts in SW; and P chiefly governs the variations in Q. Our results highlight the important impact of vegetation changes on hydrological variables in subtropical watersheds.

A monitoring platform based on electrical impedance and AI techniques to enhance the resilience of the built environment

Structural Health Monitoring (SHM) and early warning systems (EWSs) play a pivotal role in enhancing seismic resilience for both buildings and occupants. This paper introduces a monitoring platform that collects electrical impedance data from scaled concrete beams undergoing load and accelerated degradation tests. Artificial Intelligence (AI) algorithms are employed for predictive analysis, scrutinizing historical impedance data, and forecasting future trends. These algorithms adapt to environmental parameters, becoming valuable tools in data-driven decision-making processes. In particular, the study investigates concrete specimens in different test conditions, utilizing a distributed sensor network based on electrical impedance as well as temperature and relative humidity sensors. Real-time data are transmitted to a cloud infrastructure during accelerated degradation tests (both in water and in chloride-rich solution) and in room conditions. An AI-based forecasting approach using Prophet is proposed, ingesting electrical impedance and temperature data, and tested to predict electrical impedance corresponding to approximately 10 % of the time series balancing responsiveness with predictive accuracy, crucial for effective EWS operations and management requirements. The performance of the tested models is evaluated employing metrics such as Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The proposed approach surpasses statistical methods and deep learning techniques, reporting a MAPE always lower than 3.20 % and a correlation higher than 81.65 % (in wet-dry cycles in water these values are 0.65 Ω and 91.85 %, respectively). This proves to be a promising step towards transparent SHM, which integrates AI models facilitating self-monitoring and early maintenance prediction, thus enhancing the resilience of the built environment.

A novel solar-driven vacuum desalination unit: Simultaneously venturi-induced self-evaporation and self-condensation with fresh water cycle

Integrating solar energy in desalination processes offers a cost-effective and efficient solution. Moreover, reducing the saturated temperature of water vapor through pressure reduction can lower energy consumption and improve evaporation rates. This study investigates the performance of a novel venturi system integrated with an evacuated tube collector (ETC) that simultaneously conducts low-pressure evaporation and condensation. The research evaluates the system’s response to variations in flow rate and explores the potential for reducing freshwater pump activity to conserve energy. Increasing the flow rate led to a greater pressure differential across the venturi, causing a decrease in throat pressure. The system achieves its peak water production at a flow rate of 75 L per minute (LPM), yielding 9.02 kg/m2 with a system pressure of 0.297 bar. This production signifies a notable surge of 37.01 % compared to the recorded minimum flow rate. The cost analysis indicates that the cost of producing one kilogram of water per unit area is $0.045. The system achieved its maximum efficiency of 40.87 % by decreasing the water pump operation time to 1.5 h/day while following a cycling pattern of 5 min on-15 min off.

Characteristics of soil moisture transport in the aeration zone of subsidence areas under the disturbance of coal seam mining

High-intensity coal mining has induced a series of ecological and environmental problems issues, including surface subsidence, the development of ground cracks, and the deterioration of vegetation. The disruption of water circulation systems induced by mining, such as perched groundwater, groundwater of aeration zone, and phreatic water, is the root cause of vegetation withering. The aeration zone serves as a crucial nexus in the process of water cycling and exerts a significant influence on soil fertility. To explore the characteristics of soil moisture transport in subsidence areas under the mining disturbance, on-site monitoring of the size and morphology characteristics of subsidence areas and ground cracks was conducted in typical mining areas in Inner Mongolia, China. Subsequently, a typical soil moisture transport model was constructed in subsidence areas, the soil moisture transport patterns under the influence of different types of subsidence and cracks were analyzed, and the influence law of soil damage on soil moisture transport in the aerated zone was clarified. The results indicate that (1) Based on the occurrence and distribution characteristics of subsidence cracks, the subsidence area can be divided into tension zone, compression zone, and neutral zone; the ground cracks are divided into permanent tension cracks and dynamic cracks. (2) The drought stress effect of soil in the subsidence area is significant. Under the influence of soil structure variation, the water-holding capacity of the soil in the subsidence area decreases, and the soil moisture dissipation is strong. The soil moisture transport rate in the aeration zone of the subsidence area is ranked as follows: tension zone > neutral zone > compression zone. (3) Ground cracks can exacerbate the soil moisture transport rate in the aeration zone. After 15 d of crack appearance, the soil moisture transport reaches a relatively stable state, and the soil moisture transport rate in the surface layer of the crack is the fastest, and the loss of soil moisture is the most significant. The crack effect is not significant beyond 100 cm from the crack. This study provides a theoretical and data support for soil and vegetation remediation in mining subsidence areas.

Leaf stomatal configuration and photosynthetic traits jointly affect leaf water use efficiency in forests along climate gradients.

Water use efficiency (WUE) represents the trade-off between carbon assimilation and water loss in plants. It remains unclear how leaf stomatal and photosynthetic traits regulate the spatial variation of leaf WUE in different natural forest ecosystems. We investigated 43 broad-leaf tree species spanning from cold-temperate to tropical forests in China. We quantified leaf WUE using leaf δ13C and measured stomatal traits, photosynthetic traits as well as maximum stomatal conductance ( ) and maximum carboxylation capacity ( ). We found that leaves in cold-temperate forests displayed 'fast' carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, and , as an adaptation to the shorter growing season. However, these leaves exhibited 'slow' hydraulic traits, with larger but fewer stomata and similar , resulting in higher leaf WUE. By contrast, leaves in tropical forests had smaller and denser stomata, enabling swift response to heterogeneous light conditions. However, this stomatal configuration increased potential water loss, and coupled with their low photosynthetic capacity, led to lower WUE. Our findings contribute to understanding how plant photosynthetic and stomatal traits regulate carbon-water trade-offs across climatic gradients, advancing our ability to predict the impacts of climate changes on forest carbon and water cycles.

All weather land surface temperature estimation by combining thermal infrared and passive microwave radiometry: a study over India

ABSTRACT Land Surface Temperature (LST) plays a crucial role in water and energy cycle studies. However, clouds pose a significant challenge in obtaining continuous LST time series from Thermal Infrared (TIR) sensors. To overcome this challenge, this study leverages the potential of Passive Microwave Radiometry (PMR), which offers all-weather observation capabilities, albeit at a coarser spatial resolution to estimate all-weather LST over India. In this study, we trained a Random Forest (RF) model using clear sky LST from either Moderate Resolution Spectro Radiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) and passive microwave observations from Advanced Microwave Scanning Radiometer-2 (AMSR2), enabling LST estimation at a 1 km spatial resolution under clear and cloudy conditions. The performance of the models was evaluated by comparing the RF simulated LST with observed LST data from MODIS and VIIRS satellites. The Root Mean Square Error (RMSE) for the RF models was found to be 2.17 K and 2.29 K respectively, with coefficient of determination (R2) values of 0.97 for both the models. Furthermore, comparisons with in-situ observations resulted in RMSE values of 2.24–3.70 K and 2.40–4.07 K for clear sky and cloudy sky LST, respectively, for MODIS. Similarly, for the VIIRS LST prediction model, the RMSE values were 2.61–3.51 K and 2.60–3.97 K for clear and cloudy sky conditions. These findings demonstrate the potential of using passive microwave radiometers to estimate LST and highlight the applicability of the RF model for LST estimation under overcast conditions.