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Glaciers are the ambassadors of climate change. Their worldwide decline has serious impacts on natural hazards, the water cycle, and sea level rise. The monitoring of glaciers documents the increasingly rapid melting, which has culminated in a suite of extreme years—for example, 2023 in North America and the European Alps. Numerical simulations of future glacier change, supported by a large basis of observational data, allow us to understand the enormous changes ahead and prepare us for a world without glaciers. Technical approaches to reduce glacier melting have been developed and are applicable locally—for example, in connection with tourist activities—but they clearly fail at the larger scale. [ABSTRACT FROM AUTHOR]

Ciénegas are desert wetlands that are strongly correlated with the stability of the hydrologic cycle in arid landscapes. However, these environments are particularly vulnerable to climate change, drought, water diversion, and fire suppression which all have contributed to the degradation of ciénegas along the U.S.-Mexican borderlands. Therefore, identifying the timing of precipitation patterns in this region is of particular interest because of its relationship to many ecological responses of the landscape such as groundwater recharge and fire. Here, we present the first-ever fire paleoenvironmental record from Ciénega Chimeneas, a ciénega complex in the Sierra de Juárez of Baja California. We explore how changes in the amount and seasonality of moisture affected ciénega complexes and fire activity from across the southwestern North American region over the past 45,000 years. Our record suggests that during the late glacial period, 41,000–21,000 cal yr BP, the increase of Larrea and Quercus indicate an increase in summer precipitation. Subsequently, when summer precipitation increased, pollen preservation and pollen accumulation rates (PAR) also increased. Increased vegetation allowed for increased fire activity during the late glacial period. Regionally, most of the comparison sites also indicated wet conditions during the full glacial period. However, around 21,000 cal yr BP, effective moisture decreased resulting in a dramatic change in the vegetation assemblage. Specifically, the summer wet taxa disappeared, while pollen preservation and PAR decreased, suggesting a dry period until ∼14,000 cal yr BP. Little fire activity is recorded after 21,000 cal yr BP, likely due to the absence of fuels. At ∼14,000 cal yr BP, Larrea and Quercus reappear suggesting increased summer precipitation. However, the appearance of Cyperaceae and increases in grasses suggest increased winter precipitation. We suggest that the combination of summer wet and winterwet taxa ∼14,000 cal yr BP represents an increase in ENSO conditions, while the summer moisture controls remained relatively constant. [ABSTRACT FROM AUTHOR]

The ocular condition known as age-related macular degeneration (AMD) affects the retina and impairs vision in the elderly. For both controlling and detecting retinal illnesses like AMD, optical coherence tomography (OCT) is an important investigative tool. The accurate segmentation of retinal layers is critical, as accurately segmenting the layers helps ophthalmologists for early diagnosis of AMD. An accurate and effective detection technique is created using the proposed Water Cycle Corona Virus Optimization-based Deep Residual Network (WCCVO-based DRN) to address these problems and identify AMD at the early stages. In the first step, the active contour model is used to segment the layers, and features such as reflectivity, thickness, curvature, statistical features, and the devised Local Directional Gradient Pattern Histogram (LDGPH) are retrieved. The LDGPH is designed based on the concept of Local Directional pattern (LDP) and Local Gradient pattern (LGP). At last, for the AMD detection DRN classifier is used, which is trained by the devised WCCVO. The accuracy, sensitivity, and specificity metrics for the WCCVO-based DRN achieved satisfactory performance with values of 0.916, 0.923, and 0.919. [ABSTRACT FROM AUTHOR]

Quantifying leaf photosynthetic response to nitrogen (N) deposition under contrasting water conditions is important for reliably modelling terrestrial carbon and water cycles, a topic that has not been well understood. Here, we analysed 737‐paired observations from 102 publications to assess the response of 11 leaf photosynthesis‐related properties to N addition under different water conditions. Our research includes global experiments, with 19 conducted in the field and 83 in greenhouses. Treatments without water reduction were classified as ‘no water change’, while those with reduced water or precipitation causing physiological drought were categorized as ‘drought’. We found that, compared to the control group, N addition significantly increased leaf photosynthetic rate (Pn; 20.9%), leaf transpiration (E; 8.3%) and stomatal conductance (gs; 14.1%). However, the decrease in Pn (−11.6%), E (−24.7%) and gs (−23.9%) under the combination of N addition and drought indicated that N addition could not offset the negative effects of drought. Furthermore, N addition significantly enhanced water‐use efficiency (WUE) by 19.8% under no water change conditions and by 21.1% under drought conditions. Within plant functional groups, herbaceous species exhibited greater susceptibility to N addition than woody species, especially under drought conditions. The observed patterns of increase in Pn with longer experimental duration and WUE with higher N rate under drought conditions showed that plants would gradually adapt to long‐term water stress in the context of N deposition. Furthermore, our results showed that drought could strengthen the correlations between leaf photosynthetic properties. Lastly, our study demonstrated that N addition and drought significantly impacted leaf nitrogen content and SPAD, respectively, and further affected gs, Pn and WUE. Synthesis. Our results emphasize the crucial role of water conditions in shaping the response of leaf photosynthesis to nitrogen (N) enrichment and also acknowledge the significance of leaf functional traits in regulating the dynamics of leaf photosynthetic processes. [ABSTRACT FROM AUTHOR]

The concentrations and fluxes of arsenic (As) and cadmium (Cd) were examined in the hydrological cycle, litterfall and soils in a maquis broadleaved evergreen forest stand in western Greece. The concentrations of the metals in the hydrological cycle ranged from 0.076 to 0.306 μg L−1 and 0.040–0.050 μg L−1 for As and Cd, respectively. It was found that the enrichment of As in the atmosphere was both due to suspended geogenic material and long range transfer, whereas for Cd the long range transfer was the predominant way for deposition. Two models were assessed to find the volumes of percolation water to compare the fluxes with the estimated ones derived from soil solution measurements. The daily deterministic forest-hydrological model (WBS3) was found to perform better than the physically based 1D semi-distributed soil-plant-atmosphere model (BROOK90). When calculating the total amounts of the heavy metals in soils, the statistical uncertainty derived from every soil layer has to be taken into account. The residence time of Cd in the forest floor (L + FH) of the soils was found 6.4 years, whereas that of As 36.3 years. This difference shows the mobility of Cd in comparison to As in the soils of the area. [ABSTRACT FROM AUTHOR]

Revealing the subtle variations in thermal structure in a reservoir is crucial for accurately understanding the interaction between water and the carbon cycle. This study provides a detailed description of the thermal structure of Dalongdong reservoir, a representative subtropical stratified reservoir in Guangxi, China. It unveils the three stable state changes in water – mixing, microstratification, and stratification – along with their influencing factors. The study emphasizes air temperature (AT) as the primary factor influencing water temperature (WT) stability, followed by water level. Cross-correlation analysis shows that adjacent WTs experience a prolonged response hysteresis during the stratification period, while the mixing period exhibits a shorter hysteresis. Wavelet analysis revealed that AT and surface-layer WT exhibited a dominant oscillation period of 16–32 h. The periodicity of other water layers is not obvious. This framework can help to enhance our understanding of material cycles and protection of the water environment. [ABSTRACT FROM AUTHOR]

Horizontal and vertical moisture advection in the lower troposphere of the Arctic under progressing global warming is examined using a large‐scale ensemble model data set. Advection is decomposed into terms related to the basic state of the atmosphere and transient eddies and compared against a non‐warming experiment. During summer, horizontal moisture advection increases mainly by transient eddies advecting moisture from the lower latitudes. During winter, enhanced evaporation due to reduced sea ice becomes a source of moisture diminishing the role of transient eddies moistening the atmosphere. This effect intensifies under extreme global warming, turning the change in total horizontal advection in the lower troposphere negative. Diminished horizontal advection during winter is counteracted by vertical advection accompanied with enhanced evaporation and upper‐level horizontal advection maintaining the increase in column moisture. These results improve our understanding of how the water cycle in the Arctic responds via atmospheric processes under global warming. Plain Language Summary: This study uses a data set with a large number of members to investigate how the atmosphere is moistened in the Arctic (north of 70˚N) under various stages of global warming. The movement of moisture is summarized in terms of horizontal and vertical components of advection, a measure of how the atmosphere is moistened in a given specific humidity gradient and wind, which is decomposed into parts related to the average state of the atmosphere and parts related to high‐frequency phenomena, such as cyclones. It is found that (a) during summer, moisturization of the Arctic is mainly driven by horizontal movement of moisture from a remote source in the lower latitude, (b) during winter, this moisturization due to horizontal movement of moisture is suppressed and is driven more by vertical movement of moisture due to reduction of sea ice and increased evaporation. As global warming progresses, the role or horizontal movement of moisture is taken over by the local process of evaporation and vertical movement of moisture. This research helps us understand how the water cycle in the Arctic changes with severe global warming and improves our knowledge of the atmospheric processes that govern the climate system through various feedbacks. Key Points: Moisture advection in the Arctic under progressing global warming is examined using a large‐scale ensemble model simulation data setMoistening effect of horizontal advection in the lower troposphere is enhanced for summer and diminished for winter under extreme warmingEnhanced horizontal advection in the upper layer and surface evaporation are consistent with increased column moisture during winter [ABSTRACT FROM AUTHOR]

The dynamical environment of the stratosphere, during the summer over North America, provides a natural integrator of the impact of convection in the lower stratosphere, as air can be confined for periods of a few days to more than a week. In situ data obtained during the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign show increasing water vapor mixing ratios in background air as a function of time the air parcel spent within the North American Monsoon Anticyclone region. We find that water vapor added to the stratosphere by convection decreases with altitude and tends to drop below detectable limits by the 415 K isentrope in 2021 and the 430 K isentrope in 2022. Integrating between potential temperatures of 380 and 460 K we find that convection added between 20 and 32 Tg per summer to the stratosphere in 2021 and 2022. While the total amount is only 1%–4% of the amount ascending in the tropics across the tropical tropopause, small changes in the annual flux of water can have a significant effect on the radiation budget of the atmosphere. Locally, over North America we find that convection increased the water vapor mixing ratio at 380 K by as much as 40%. Tropopause‐penetrating convection is part of the yearly cycle of stratospheric water vapor and we suggest that it must be included in extratropical models to accurately predict future trends in stratospheric water vapor. Plain Language Summary: Water vapor in the stratosphere is important for regulating Earth's climate and also influences the amount of ozone in the stratosphere. Most air rises into the stratosphere through the tropical tropopause where the excess water freezes out leaving very dry air. But air and water can also be injected into the stratosphere via strong convective storm systems. During the summer months, strong storms over the Sierra Madre Occidental and the central United States are known to produce many overshoots that can be seen with radar and satellite. A recent NASA airborne campaign called Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) aimed to study this region. The aircraft was equipped with 12 different sensors measuring gases, aerosols, and meteorological variables. We use these measurements to look at the cumulative effect these storms have on the background air in the stratosphere and specifically calculate the amount of water vapor added by convection as a function of altitude above the tropopause. We find that water added by storms over North America is 1%–4% of the total amount ascending in the tropics. Over North America convection may account for as much as a 40% increase in lower stratospheric water vapor. Key Points: Deep convection over North America lofts significant amounts of water to the lower stratosphereThe North American Monsoon Anticyclone (NAMA) provides a natural integrator of convective perturbations to the stratosphereConvective detrainment of water vapor and ice is quantified by looking at changes in background air as a function of time spent in the NAMA [ABSTRACT FROM AUTHOR]

Precipitation δ18O has offered valuable insights into the evolution of the Asian monsoon. Recent researches focusing on precipitation Δ′17O has enhanced our understanding by offering new perspectives beyond those of δ18O, revealing insights into vapor sources and continental recycling. Nevertheless, there remains a lack of interannual triple oxygen isotope data, particularly in the Asian monsoon region. In this study, we analyzed the triple oxygen isotopes and hydrogen isotopes in monthly precipitation samples collected from Chongqing in Southwest China between 2019 and 2022 A.D. Seasonal variations in δD, δ18O, δ17O, and d‐excess values were observed, with lower values during the rainy season and higher values during the dry season, highlighting the impact of changes in moisture sources and local meteorological conditions on seasonal shifts in δD, δ18O, and δ17O. While, mean Δ′17O values were higher in rainy season and lower in dry season. Notably, during rainy season, there is a negative correlation between monthly Δ′17O values and the RH of the vapor source area, as well as a positive correlation with d‐excess. Recalculated Δ′17O values based on RH of oceanic moisture source, are higher than the measured values for this period, indicating the contribution of terrigenous moisture to precipitation in SW China. Precipitation Δ′17O values provide a more precise reflection of changes in moisture source, continental recycling, and evapotranspiration processes that drive water cycling compared Integrating modeling works in future will facilitate the use of precipitation Δ′17O values to quantify the impact of different moisture source on precipitation. Plain Language Summary: In the context of climatic warming, changes in water vapor source areas have substantial implications for the stability of regional water resources. Despite the importance of these changes, few studies have analyzed them using precipitation data from SW China. We conducted monthly measurements of isotopic compositions in precipitation samples collected in monsoonal SW China between 2019 and 2022 A.D. Seasonal variations in δD, δ18O, δ17O, and d‐excess revealed low values during the rainy season (May–October) and significantly higher values during the dry season (November–April). These variations are primarily attributed to different water vapor sources in distinct seasons. Moreover, we observed a negative correlation between precipitation Δ′17O and relative humidity in the source area of the water vapor during the rainy season, suggesting that Δ′17O contains valuable information about the water vapor's source area. Additionally, the relatively higher Δ′17O values observed in the rainy season imply the influence of continental recycling and evapotranspiration processes. Key Points: Characteristics of triple oxygen and hydrogen isotopes in precipitation were analyzed based on a 4‐year monitoring period in Southwest ChinaTriple oxygen isotopes in precipitation serve as indicators of the extent of isotopic fractionation in water vapor between different seasonsChanges in precipitation Δ′17O show a negative correlation with relative humidity from the moisture source area during the rainy season [ABSTRACT FROM AUTHOR]

Tropical oceans are the main global water vapor and latent heat sources, but their responses to radiative forcing remain unclear. Here, we investigate oceanic moisture dynamics of the western tropical Pacific (WTP) over the past 210,000 years through an approach of planktonic foraminiferal triple oxygen isotope (Δ′17O). The Δ′17O record is dominated by the precession cycles (~23,000 years), with lower values reflecting higher humidity in concert with higher Northern Hemisphere summer insolation. Our empirical and modeling results, combined with other geological archives, suggest that the enhanced moisture convergence over the WTP largely intensifies changes in the meridional and zonal hydrological cycles, affecting rainfall patterns in East Asia and northern South America. We propose that the insolation-driven WTP moisture dynamics play a pivotal role in regulating tropical hydroclimate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary 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 (Gwmax$$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$) and maximum carboxylation capacity (Vcmax$$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$). We found that leaves in cold‐temperate forests displayed ‘fast’ carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, and Vcmax$$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$, as an adaptation to the shorter growing season. However, these leaves exhibited ‘slow’ hydraulic traits, with larger but fewer stomata and similar Gwmax$$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$, 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. [ABSTRACT FROM AUTHOR]

The intensification of water resource usage, correlated with changes in land use and cover as well as climate variability, has led to significant alterations in the hydrological cycle, impacting water availability in basins. This study assesses hydrological trends in the section of the Janeiro River basin located in the Brazilian savannah (Cerrado biome), focusing on the influence of anthropogenic activities and climate variability between 1985 and 2017. Using precipitation, actual evapotranspiration, streamflow, land use and cover, and water use authorization data, we applied statistical tests (Mann–Kendall, Sen's slope, Pettitt, and RHO Spearman) to identify trends, abrupt changes, and correlations. The results show a decreasing trend in average and minimum flows, with reductions of 30 to 40%, respectively, compared to the historical series average, not attributable to significant changes in precipitation but rather to an expansion of agricultural areas and an intensification of water consumption for irrigation. There was a reduction from 76.5% to the sum of Natural and Forest Formation areas and an increase of 71.1% in Agricultural areas. The correlation between land use changes and streamflows suggests that the conversion of natural vegetation into agricultural lands is directly associated with the decline in water availability. This study highlights the need for sustainable planning and management of water resources, considering the seasonality of water availability and agricultural demands, to mitigate the negative impacts on the hydrological cycle and ensure water sustainability in the Brazilian savannah region. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts water cycle processes and water resource management in the upper Yellow River (UYR), China. Understanding the influence of meteorological factors on low-flow changes is crucial, but the optimal number of antecedent days and the specific contributions of different factors remain unclear. In this study, we use a structural equation model and path analysis to dissect the direct and indirect effects of selected meteorological factors (daily precipitation, P; average temperature, AT; average wind velocity, AWV; average relative humidity, ARH; and total radiation, TR) on four low-flow indices in the UYR. We employ data from 1958 to 2017, collected from six meteorological stations and eight hydrological stations above Lanzhou hydrological station. Our findings reveal that: (1) meteorological factors have varying direct and indirect impacts on low-flow changes at corresponding and cumulative scales. For instance, at the corresponding scale, P, AT, AWV, ARH, and TR have direct impacts of 42%, 54%, 74%, 79%, and 59%, respectively. At the cumulative scale, these values change to 67%, 59%, 67%, 64%, and 60%, respectively. (2) Cumulative effects of meteorological factors enhance the significance and goodness of fit of the analysis model, decreasing residual path coefficients and elevating the contribution of independent variables to the model. (3) The dominant components of meteorological factors affecting low-flow changes differ between corresponding and cumulative scales, explaining the variations in direct and indirect impacts. These insights are valuable for sustainable water resource management in drought-prone regions with water scarcity. [ABSTRACT FROM AUTHOR]

Drought is caused by imbalances in the hydrological cycle's variables, especially lack of rainfall, which is frequently brought on by climate change and can occur anywhere on the Earth. This study aims to conduct a detailed seasonal drought analysis through the seasonal precipitation statistics, groundwater level, and soil moisture in Bankura District during 1991–2022. The study utilized the standardized precipitation index, Standardized water-level Index, and Standardized soil moisture index to assess the meteorological drought, hydrological drought, and agricultural drought at multiple time scales. The three drought indices have been calculated by the 'Standardized Precipitation-Evapotranspiration Index' package using 'R' programming, which make it possible to compare the drought situations in various climatic zones. The three months standardized precipitation index was used in the seasonal analysis of different types of drought. The modified Mann–Kendall trend analysis was used to acquire information on the course of the drought and rains. Correlation analysis was also done to evaluate the dependency of agricultural drought upon the meteorological drought and hydrological drought. Seasonal droughts in meteorological drought, hydrological drought, and agricultural drought have been compared to the production of the four major seasonal crops. The trend of rainfall showed -0.184, -1.149, -1.263 and -3.598 mm decreases in pre-monsoon, winter, post-monsoon and monsoon season respectively. The occurrence of drought with negative standardized precipitation index, standardized water-level index, and standardized soil moisture index values frequently depicted dry events in the study area. The results show that the drought harms the productivity of food grains, with production losses of 122.77 thousand tonnes and yield rate losses of 292.37 kg per hectare from the average, respectively. This study also considered non-structural and structural efforts from the governmental, stakeholder, and research communities to mitigate the seasonal drought, frame drought-resilient agriculture, and promote sustainability. [ABSTRACT FROM AUTHOR]

Satellite-based observations of free-tropospheric water vapour isotopologue ratios (HDO / H 2 O, expressed in form of the δ value δ D) with good global and temporal coverage have become available recently. We investigate the potential of these observations for constraining the uncertainties of the atmospheric analyses fields of specific humidity (q), temperature (T), and δ D and of variables that capture important properties of the atmospheric water cycle, namely the vertical velocity (ω), the latent heating rate (Q2), and the precipitation rate (Prcp). Our focus is on the impact of the δ D observations relative to the impact achieved by the observation of q and T , which are much more easily observed by satellites and are routinely in use for atmospheric analyses. For our investigations we use an Observing System Simulation Experiment; i.e. we simulate the satellite observations of q , T , and δ D with known uncertainties and coverage (e.g. observations are not available for cloudy conditions, i.e. at locations where the atmosphere is vertically unstable). Then we use the simulated observations within a Kalman-filter-based assimilation framework in order to evaluate their potential for improving the quality of atmospheric analyses. The study is made for low latitudes (30° S to 30° N) and for 40 d between mid-July and the end of August 2016. We find that q observations generally have the largest impacts on the analyses' quality and that T observations have stronger impacts overall than δ D observations. We show that there is no significant impact of δ D observations for stable atmospheric conditions; however, for very unstable conditions, the impact of δ D observations is significant and even slightly stronger than the respective impact of T observations. Very unstable conditions are rare but are related to extreme events (e.g. storms, flooding); i.e. the δ D observations significantly impact the analyses' quality of the events that have the largest societal consequences. The fact that no satellite observations are available at the location and time of the unstable conditions indicates a remote impact of δ D observations that are available elsewhere. Concerning real-world applications, we conclude that the situation of δ D satellite observations is very promising but that further improving the model's linkage between convective processes and the larger-scale δ D fields might be needed for optimizing the assimilation impact of real-world δ D observations. [ABSTRACT FROM AUTHOR]

Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions. [ABSTRACT FROM AUTHOR]

Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet forecasting its spatio-temporal distribution remains challenging due to complex climate interactions and extreme events. We developed a method combining spatio-temporal clustering and deep learning neural networks to improve multi-site, multi-year precipitation δ18O predictions. Using a comprehensive dataset from 33 German sites (1978–2012), our model considers precipitation δ18O and its controlling factors, including precipitation and temperature distribution. We applied the K-means ++ method for classification and divided data into training and prediction sets. The convolutional neural network (CNN) model extracted spatial features, while the bi-directional long short-term memory (Bi-LSTM) model focused on temporal features. Spatio-temporal clustering using K-means ++ improved forecast accuracy and reduced errors. This study highlights the potential of deep learning and clustering techniques for forecasting complex spatio-temporal data and offers insights for future research on isotope distributions. [ABSTRACT FROM AUTHOR]

In recent decades, the annual mean temperature has increased, with unusual alternations of hot and cold years. In addition, the changes in temporal precipitation patterns are caused by complex interactions between temperature change, the global water cycle, and other components of the Earth's systems. To construct a statistical model of these temporal patterns in terms of temperature and precipitation, we propose a logistic mixture of spatial multivariate penalized regression splines for temporal profiles and apply this model to the contiguous United States climate data over 123 years (1900 to 2022) at 252 weather stations. The results reveal that the proposed model identifies climatologically meaningful clusters of weather stations in the contiguous United States with two important meteorological variables, temperature and precipitation, identifying the climate change patterns of each climate zone. The surface air temperature increased in the Northeast and West (Mountain and Pacific) regions, where the climate is affected by the continental Arctic air. A notable increment of precipitation also occurred in the Northeast. In contrast, the South region, where the climate is affected by the tropical Atlantic Ocean, is more stable than other regions in terms of year-to-year variations in temperature and precipitation. [ABSTRACT FROM AUTHOR]

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Aerosol–cloud interactions in mixed-phase clouds (MPCs) are one of the most uncertain drivers of the hydrological cycle and climate change. A synergy of in situ, remote-sensing and modelling experiments were used to determine the source of ice-nucleating particles (INPs) for MPCs at Mount Helmos in the eastern Mediterranean. The influences of boundary layer turbulence, vertical aerosol distributions and meteorological conditions were also examined. When the observation site is in the free troposphere (FT), approximately 1 in ×106 aerosol particles serve as INPs around -25 °C. The INP abundance spans 3 orders of magnitude and increases in the following order: marine aerosols; continental aerosols; and, finally, dust plumes. Biological particles are important INPs observed in continental and marine aerosols, whereas they play a secondary, although important, role during Saharan dust events. Air masses in the planetary boundary layer (PBL) show both enriched INP concentrations and a higher proportion of INPs to total aerosol particles, compared with cases in the FT. The presence of precipitation/clouds enriches INPs in the FT but decreases INPs in the PBL. Additionally, new INP parameterizations are developed that incorporate the ratio of fluorescent-to-nonfluorescent or coarse-to-fine particles and predict >90 % of the observed INPs within an uncertainty range of a factor of 10; these new parameterizations exhibit better performance than current widely used parameterizations and allow ice formation in models to respond to variations in dust and biological particles. The improved parameterizations can help MPC formation simulations in regions with various INP sources or different regions with prevailing INP sources. [ABSTRACT FROM AUTHOR]

Plant rooting patterns in bioswales, raingardens and other vegetated infiltration systems are essential, as they contribute biopores which maintain the infiltration function over time. However, fluctuating hydrological conditions, ranging from flooded to drained, can have a heavy impact on plant rooting, as well as consequences for plant contributions to other ecosystem services and ecological functions. This study tested the biomass allocation to roots and the vertical root profile of four plant species, alone or in competition with a grass, and their responses to the experimental manipulation of soil hydrology in soil column microcosms. The hydrological regimes were combinations of flooded and drained conditions, respectively, including Wet cycles (72 and 96 h), Dry cycles (24 and 144 h), Wet-dry cycles (72 and 264 h), and Control group (watered twice per week). When the species were exposed to repeated wet-dry cycling hydrological regimes, we found a clear shift in vertical root distribution and shallower rooting in wetter regimes. It was also found that alongside this shallower rooting, there were no changes to total biomass and only moderate adjustments to biomass investment in roots. Overall, differences in rooting patterns between hydrological regimes and species were moderate when the dicot species were grown alone. The addition of the grass Festuca rubra contributed to a strong increase in total root mass density that evened out the differences in rooting patterns but also gave a deeper rooting. Accordingly, mixed species systems may be a robust approach to vegetated infiltration systems. [ABSTRACT FROM AUTHOR]

As a key factor influencing the cloud life cycle and radiative properties, liquid‐ice mass partitioning remains a major source of uncertainties in modeling mixed‐phase clouds. One of the unresolved problems is that liquid‐ice mixing is highly inhomogeneous, but it has not been well understood and quantified for parameterization. In this study, the liquid‐ice mixing homogeneity (χ) is quantified using the information‐theoretic entropy based on airborne measurements. It is demonstrated that χ is positively correlated with the condensed water content (CWC). Cloud regions with low χ are consist of liquid and ice clusters. With the increase in χ, the size and frequency of continuous mixed‐phase clusters increase. For a given CWC, χ is lower at relatively warm temperatures as sedimentation of large ice crystals can enhance the inhomogeneity. The strong positive relationship between CWC and χ indicates CWC should be considered when parameterizing the liquid‐ice mixing in models. Plain Language Summary: Mixed‐phase clouds consist of water vapor, supercooled droplets, and ice particles. The complex liquid‐ice interactions have a substantial influence on the macrophysical and microphysical characteristics of the clouds, which ultimately affect the global water cycle and radiative balance. Observations and model experiments have shown that liquid and ice are not uniformly mixed, and the liquid‐ice mixing homogeneity also strongly affects the cloud glaciation processes. However, the liquid‐ice mixing homogeneity is still not well understood and is poorly represented in numerical models. In this study, based on airborne measurements, the liquid‐ice mixing homogeneity is quantified. It is found that there is a positive correlation between mixing homogeneity and the condensed water content, while the latter is a predictable value in models. These results provide a new idea to constrain the liquid‐ice mixing in models and are potentially helpful to improve the model simulation of mixed‐phase clouds. Key Points: Liquid‐ice mixing homogeneity is quantified using information‐theoretic entropy and airborne measurementsCondensed water content is positively related to liquid‐ice mixing homogeneity in mixed‐phase cloudsCondensed water content and temperature should be considered in parameterization of liquid‐ice mixing homogeneity [ABSTRACT FROM AUTHOR]

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

Rainfall plays an important role in maintaining the water cycle by replenishing aquifers, lakes, and rivers, supporting aquatic life, and sustaining terrestrial ecosystems. Accurate prediction is crucial given the intricate interplay of atmospheric and oceanic phenomena, especially amidst contemporary challenges. In this study, to predict rainfall, 12,852 data points from opensource global weather data for three cities in Indonesia were utilized, incorporating input variables such as maximum temperature (°C), minimum temperature (°C), wind speed (m/s), relative humidity (%), and solar radiation (MJ/m²). Three novel and robust Deep Learning models were used: Recurrent Neural Network (DRNN), Deep Gated Recurrent Unit (DGRU), and Deep Long Short-Term Memory (DLSTM). Evaluation of the results, including statistical metrics like Root-Mean-Square Errors and Correction Coefficient (R2), revealed that the Deep Long Short-Term Memory model outperformed DRNN and Deep Gated Recurrent Unit with values of 0.1289 and 0.9995, respectively. DLSTM networks offer several advantages for rainfall prediction, particularly in sequential data like time series prediction, excelling in handling long-term dependencies important for capturing weather patterns over extended periods. Equipped with memory cell architecture and forget gates, DLSTM networks effectively retain and retrieve relevant information. Furthermore, DLSTM networks enable parallelization, enhancing computational efficiency, and offer flexibility in model design and regularization techniques for improved generalization performance. Additionally, the results indicate that maximum temperature and solar radiation parameters exhibit an indirect influence on rainfall, while minimum temperature, wind speed, and relative humidity parameters have a direct relationship with rainfall. [ABSTRACT FROM AUTHOR]

The detection and characterization of Earth-like planets around Sun-like stars is an important goal of exoplanetary research, given their promise for hosting potentially habitable conditions. Key orbital parameters, such as eccentricity, can influence a planet's climate response and, as a consequence, affect its potential habitability. Utilizing the Earth System Model – the Whole Atmosphere Community Climate Model (WACCM6), we simulated Earth-like exoplanets with two different orbital parameters: one circular (⁠|$e = 0$|⁠) and another highly eccentric (⁠|$e = 0.4$|⁠), both with zero obliquity but fixing the annual mean insolation. The highly eccentric case exhibits a 1.9 K warmer surface temperature due to lower surface and cloud albedo and a weaker longwave cloud forcing. Exploring the annual global mean climate difference, we analysed latitudinal and seasonal variations in hydrological cycle variables, such as sea ice, land snow, and clouds. Land habitability metrics based on temperature and precipitation reveal that the |$e=0.4$| case has over 25 per cent more habitable land area for more than 80 per cent of its orbit, compared with the |$e=0$| case. Additionally, the global circulation pattern shifts from a three-cell to a two-cell system in the |$e=0.4$| case, expanding the Hadley cell to higher latitudes, enhancing meridional latent heat transport, and improving land habitability at higher latitudes. Our study suggests that Earth-like exoplanets with high eccentricity orbiting Sun-like stars may have greater land habitability than their circular counterparts, due to seasonally warmer surface temperatures and more evenly distributed precipitation over land. [ABSTRACT FROM AUTHOR]

This study analyzes H2O and HDO vertical profiles in the Venus mesosphere using Venus Express/Solar Occultation in the InfraRed data. The findings show increasing H2O and HDO volume mixing ratios with altitude, with the D/H ratio rising significantly from 0.025 at ~70 km to 0.24 at ~108 km. This indicates an increase from 162 to 1,519 times the Earth's ratio within 40 km. The study explores two hypotheses for these results: isotopic fractionation from photolysis of H2O over HDO or from phase change processes. The latter, involving condensation and evaporation of sulfuric acid aerosols, as suggested by previous authors [X. Zhang et al., Nat. Geosci. 3, 834-837 (2010)], aligns more closely with the rapid changes observed. Vertical transport computations for H2O, HDO, and aerosols show water vapor downwelling and aerosols upwelling. We propose a mechanism where aerosols form in the lower mesosphere due to temperatures below the water condensation threshold, leading to deuterium-enriched aerosols. These aerosols ascend, evaporate at higher temperatures, and release more HDO than H2O, which are then transported downward. Moreover, this cycle may explain the SO2 increase in the upper mesosphere observed above 80 km. The study highlights two crucial implications. First, altitude variation is critical to determining the Venus deuterium and hydrogen reservoirs. Second, the altitude-dependent increase of the D/H ratio affects H and D escape rates. The photolysis of H2O and HDO at higher altitudes releases more D, influencing long-term D/H evolution. These findings suggest that evolutionary models should incorporate altitude-dependent processes for accurate D/H fractionation predictions. [ABSTRACT FROM AUTHOR]

Climate models indicate that a warmer environment will increase low‐level moisture, potentially intensify extreme precipitation. However, its impact on different rainfall types remains unclear. Using satellite data, we examined changes in light (0‐95th percentile, ≤5.28 mm hr−1) and heavy (95‐100th percentile, >5.28 mm hr−1) precipitation in the tropics from 1998 to 2019. Our findings show a −9 ± 2% (23 ± 2%) change in heavy (light) rain intensity and a 13 ± 2% (−24 ± 1%) change in heavy (light) rain frequency. These changes link to warmer sea surface temperatures, increased atmospheric stability and water vapor, and weakened upward velocity. These insights shed light on how heavy and light precipitation patterns respond to changing climate, emphasizing the complexities within the hydrological cycle. Plain Language Summary: The hydrologic cycle depends greatly on precipitation. With the expectation of climate warming causing substantial changes to the global water cycle, comprehending these shifts becomes pivotal in foreseeing how climate changes will affect society. Climate models suggest that as temperatures rise, low‐level moisture will increase. However, the specific impacts of this heightened moisture on the intensity and frequency of precipitation events remain uncertain. Analyzing data from the Tropical Rainfall Measuring Mission (1998–2019), we found notable changes: a 23 ± 2% increase in light rain intensity, a −9 ± 2 % decrease in heavy rain intensity, a 13 ± 2 % rise in heavy rain frequency, and a significant −24 ± 1% decrease in light rain frequency. These changes result diverse factors like warming oceans, stabilized atmospheric conditions, increased water vapor, and weakened upward velocity. This underscores the intricate interplay between different rainfall types and how they respond to the evolving climate. Key Points: In the tropics from 1998 to 2019, heavy rain intensity decreased and light rain intensity increasedIn the tropics from 1998 to 2019, heavy rain frequency increased and light rain frequency decreasedRain shifting patterns linked to increased SST, atmospheric stability, and water vapor and decreased upward velocity [ABSTRACT FROM AUTHOR]

Efforts to detect long‐term changes in global mean evaporation minus precipitation over the ocean remain ambiguous. Here we define an ad hoc sea surface salinity index to assess the observed and simulated intensification of the freshwater flux pattern over the global ocean and, thus, of the overall water cycle. A recent salinity reconstruction shows a long‐term amplification of the climatological patterns, thereby supporting the popular "fresh gets fresher, salty gets saltier" paradigm. Unlike in a previous study, no systematic underestimation of this amplification is found in the latest generation of global climate models. Yet, the "fresh gets fresher" paradigm is much more robust than its "salty gets saltier" counterpart and the proposed salinity index does not yet provide a strong constraint on the model‐dependent projected intensification of the global water cycle intensification along the 21st century. Plain Language Summary: Recent changes in continental mean precipitation and evaporation remain poorly observed and thus poorly constrained in both atmospheric reanalyzes and global climate models. Here we propose a new index based on global sea surface salinity contrasts as a surrogate of changes in metric of the water cycle intensity. Overall, both observed and simulated indices support the "fresh gets fresher, salty gets saltier" paradigm, widely used to depict the overall intensification of the water cycle. Unlike in a previous study, no systematic underestimation of the observed amplification in salinity patterns is found in the latest generation of global climate models. The "salty gets saltier" response is however less robust than the "fresh gets fresher" and the proposed salinity index is not yet very useful to constrain the future intensity of the global water cycle. Key Points: Both observations and climate models evidence an amplification of the global ocean climatological contrasts in sea surface salinity (SSS)Yet, the "fresh gets fresher" paradigm is more robust than its salty counterpart, which is more sensitive to the ocean domain's definitionGlobal SSS observations do not yet provide a strong constraint on the projected intensification of the global water cycle [ABSTRACT FROM AUTHOR]

Surface solar radiation is fundamental for terrestrial life. It provides warmth to make our planet habitable, drives atmospheric circulation, the hydrological cycle and photosynthesis. Europe has experienced an increase in surface solar radiation, termed "brightening," since the 1980s. This study investigates the causative factors behind this brightening. A novel algorithm from the EUMETSAT satellite application facility on climate monitoring (CM SAF) provides the unique opportunity to simulate surface solar radiation under various atmospheric conditions for clouds (clear‐sky or all‐sky), aerosol optical depth (time‐varying or climatological averages) and water vapor content (with or without its direct influence on surface solar radiation). Through a multiple linear regression approach, the study attributes brightening trends to changes in these atmospheric parameters. Analyzing 61 locations distributed across Europe from 1983 to 2020, aerosols emerge as key driver during 1983–2002, with Southern Europe and high elevations showing subdued effects (0%/decade–1%/decade) versus more pronounced impacts in Northern and Eastern Europe (2%/decade–6%/decade). Cloud effects exhibit spatial variability, inducing a negative effect on surface solar radiation (−3%/decade–−2%/decade) at most investigated locations in the same period. In the period 2001–2020, aerosol effects are much smaller, while cloud effects dominate the observed brightening (2%/decade–5%/decade). This study therefore finds a substantial decrease in the cloud radiative effect over Europe in the first two decades of the 21st century. Water vapor exerts negligible influence in both sub‐periods. Plain Language Summary: This study explores the recent increase in surface solar radiation in Europe, commonly referred to as "brightening," which began in the mid‐1980s. Surface solar radiation is crucial for life on Earth, fostering warmth, shaping wind patterns, and fueling the hydrological cycle and photosynthesis. Utilizing data derived from satellites, we simulated surface solar radiation under different atmospheric conditions regarding clouds, aerosols, and water vapor. This allowed us to separate the individual effects and analyze their contributions to the observed brightening across 61 locations in Europe from 1983 to 2020. We find that the decrease in aerosols was the dominant driver for the brightening during the initial period from 1983 to 2002. Southern Europe and high‐elevation areas showed modest aerosol effects, while Northern and Eastern Europe experienced higher values. Cloud effects were predominantly negative (decreasing surface solar radiation) at most locations. However, between 2001 and 2020 the aerosol effect resulted much smaller and the brightening was mostly attributed to a decrease in cloudiness during that sub‐period. Water vapor was found to have a negligible impact on surface solar radiation trends. Key Points: Satellite application facility on climate monitoring (CM SAF) Land Flux v1.0 surface solar radiation data agree well with station‐based measurements over Europe, also at high‐altitude locationsThe direct aerosol effect was the main driver of the European brightening from 1983 to 2002. Changes in cloudiness rather counteracted itFrom 2001 to 2020 the brightening in Europe was dominated by the cloud effect and the aerosol effect resulted much smaller [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) provides vital water resources for downstream regions, with spring precipitation contributing considerably to the annual totals over the southeastern TP. The added value of convection‐permitting modeling in simulating the spring climate over the TP is uncertain. Here, we conducted and compared decade‐long regional convection‐permitting (3.3 km) and convection‐parameterized (13.2 km) Icosahedral Nonhydrostatic Weather and Climate Model (ICON) simulations to reproduce the atmospheric water cycle in spring over the TP. Results indicated that 3.3 km mesh ICON (ICON_3.3 km) exhibited notable added value in simulating the spring atmospheric water cycle over the TP. ICON_3.3 km reduced the wet biases of precipitation in the ERA5 reanalysis and 13.2 km mesh ICON (ICON_13.2 km) simulations, and improved the simulation of surface evaporation over the central and eastern TP. The reduction in the simulated precipitation in ICON_3.3 km was primarily followed by a decrease in surface evaporation from March to May, second by a reduction in water vapor flux convergence in May due to decreased water vapor inflow from the southeastern TP. Furthermore, compared to ICON_13.2 km, ICON_3.3 km alleviated the "drizzling" bias, leading to drier surface soils and decreased evaporation, and lead to 3% decrease in the fraction of evaporation converted into precipitation. Sensitivity experiments conducted at resolution of 13.2 km but turning off the convection parameterization demonstrated that both explicit representation of convection and enhanced horizontal resolution were crucial for accurately representing the spring atmospheric water cycle over the TP. Our results highlighted the need to develop kilometer‐scale models for successfully reproducing the climate characteristics across the TP. Plain Language Summary: Climate modeling is evolving toward kilometer‐scale or convective‐permitting model (CPM). The added value of regional CPM in simulating the spring climate over the Tibetan Plateau (TP), known as "Asian Water Tower," is unknown. Here, we have performed and compared two decade‐long regional climate simulations at two horizontal resolutions: convection permitting (3.3 km) and convection parameterized (13.2 km), to reproduce the atmospheric water cycle in spring over the TP. Results indicated that the CPM reduced the wet biases in the latest reanalysis data and the convection‐parameterized model over the central and eastern TP. The reduction of wet biases was mainly followed by the decreased surface evaporation, which was associated with the alleviation of "drizzling" bias in CPM. We further showed evidences that both explicit representation of convection and enhanced horizontal resolution were crucial for accurately representing the spring climate over the TP. Our findings highlight the importance of developing high‐efficiency kilometer‐scale (<4 km) models for reproducing all the climate characteristics across the TP region. Key Points: Regional convection permitting model exhibited evident added value in simulating the spring atmospheric water cycle over the Tibetan Plateau (TP)Reduced precipitation by convective‐permitting model was mainly followed by decrease in surface evaporation, associated with the alleviation of drizzling biasesBoth explicit convection and enhanced resolution were crucial for well representing the spring atmospheric water cycle over the TP [ABSTRACT FROM AUTHOR]

Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles and energy exchange. However, carbon fluxes, hydrological status, and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System; NUCAS v1.0) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite-data-driven ecosystem model – the Biosphere–atmosphere Exchange Process Simulator (BEPS). Data assimilation experiments were conducted to investigate the robustness of NUCAS and to test the feasibility and applicability of assimilating carbonyl sulfide (COS) fluxes from seven sites to enhance our understanding of stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems, including evergreen needleleaf forest, deciduous broadleaf forest, C 3 grass, and C 3 crop. Comparing model simulations with validation datasets, we found that assimilating COS fluxes notably improves the model performance in gross primary productivity and evapotranspiration, with average root-mean-square error (RMSE) reductions of 23.54 % and 16.96 %, respectively. We also showed that NUCAS is capable of constraining parameters through assimilating observations from two sites simultaneously and achieving a good consistency with single-site assimilation. Our results demonstrate that COS can provide constraints on parameters relevant to water, energy, and carbon processes with the data assimilation system and opens new perspectives for better understanding of the ecosystem carbon, water, and energy exchanges. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV3h and PWV6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV3h product performed slightly better than PWV6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor is not only a key element of the global hydrological cycle but also the most abundant greenhouse gas. The phase transition and transportation of water vapor are essential for maintaining global energy balance and regulating hydrological processes. However, due to insufficient meteorological observational data, climate research in Africa faces significant limitations despite its substantial contribution to changes in global precipitable water vapor (PWV). In this study, we used MODIS near-infrared (NIR) PWV products and Berkeley temperature data to depict the spatial–temporal variability in PWV across Africa from 2001 to 2020. The results reveal a significant increasing trend in PWV over Africa, with an increase of 0.0158 cm/year. Nearly 99.96% of Africa shows an increase in PWV, with 88.95% of these areas experiencing statistically significant changes, particularly in central regions of Africa. The increase in PWV is more pronounced in high-value months compared to low-value months. The equatorial region of the Congo Basin exhibits higher PWV, which gradually decreases as latitude increases. Despite significant warming (0.0162 °C/year) in Africa, there is no consistent positive correlation between temperature and water vapor. A positive relationship between PWV and temperature is observed in western Africa, while a negative relationship is noted in eastern and southern Africa on an annual scale. Additionally, an increasing trend in precipitation (4.6669 mm/year) is observed, with a significant positive correlation between PWV and precipitation across most of Africa, although this relationship varies by month. These findings provide valuable insights into the comprehension of the hydrothermal variation in Africa amidst climate warming. [ABSTRACT FROM AUTHOR]

Global change has substantially shifted vegetation phenology, with important implications in the carbon and water cycles of terrestrial ecosystems. Various vegetation phenology datasets have been developed using remote sensing data; however, the significant uncertainties in these datasets limit our understanding of ecosystem dynamics in terms of phenology. It is therefore crucial to generate a reliable large-scale vegetation phenology dataset, by fusing various existing vegetation phenology datasets, to provide comprehensive and accurate estimation of vegetation phenology with fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the Reliability Ensemble Averaging fusion method. The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The evaluation using the ground-based PhenoCam dataset from 280 sites indicated that the accuracy of the newly merged dataset was improved substantially. The start of growing season and the end of growing season in the newly merged dataset had the largest correlation (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (12 and 17 d, respectively). Using the new dataset, we found that the start of growing season exhibits a significant (p < 0.01) advanced trend with a rate of approximately 0.24 d yr−1, and that the end of growing season exhibits a significant (p < 0.01) delayed trend with a rate of 0.16 d yr−1 over the period 1982–2022. This dataset offers a unique and novel source of vegetation phenology data for global ecology studies. [ABSTRACT FROM AUTHOR]

The current‐state of polymer‐based humidity sensors faces numerous limitations, including energy‐costly synthesis, low sensitivity, and slow response times. This study presents innovative approach to overcome these challenges, based on a robust all‐water‐borne in situ miniemulsion polymerization. The use of water throughout the entire process mitigates the negative environmental impact. Thiol‐ene polymers reinforced with reduced graphene oxide (rGO) with concentrations ranging from 0.2–1.0 wt% are selected to fabricate these chemoresistive sensors. The selected thiol‐enes present high hydrophobicity and a semicrystalline nature, suggesting resistance to early delamination even under prolonged exposure to humidity. Incorporating rGO not only imparts electrical conductivity but also enhances mechanical and water resistance of the composite films. The 0.6% rGO composite exhibits optimal resistance for humidity sensing, demonstrating rapid and consistent responses across three exposure cycles to water vapor concentrations ranging 800–5000 ppm. Moreover, the sensor exhibits remarkable selectivity toward water vapors over these of toluene, propanol, and 4‐methyl‐2‐pentanol, attributed to the high surface hydrophilicity and inherent porosity of the waterborne film, and network structuring of rGO platelets within the matrix. In summary, this study pioneers a novel approach to polymer‐based humidity sensing, addressing key limitations while offering enhanced sensitivity, rapid response times, and superior selectivity. [ABSTRACT FROM AUTHOR]

AbstractThis study explores systemic approaches in water education, focusing on the Domestic Water Cycle (DWC) as a complex educational topic. Water education aims to cultivate responsibility for water resources, engage individuals in water-related issues, and promote sustainable behaviours. Understanding water’s complexity is paramount, and systemic approaches offer a promising way to enhance this understanding. This research develops a comprehensive systemic tool for curriculum designers, educators and teachers to implement water education projects effectively. It also analyses the representation of DWC in official documents related to compulsory education in France, focusing on cycles 1–4. Using qualitative content analysis, the study examines these documents in depth. A four-step methodology was developed to create a systemic diagram of the DWC, incorporating various approaches as guiding principles. This diagram was then used to analyze educational programs. Findings reveal progressive development in DWC treatment across educational levels. Cycles 1 and 2 include some DWC elements, but the overall flow is not identified. In Cycle 3, several elements and relationships are not explicitly included. However, Cycle 4 exhibits a stronger systemic approach, addressing DWC complexity. This study demonstrates that integrating a systemic approach and utilizing a systemic tool can provide valuable support in teaching complexity effectively. Furthermore, it suggests this approach can help students develop essential skills such as problem-solving, understanding interrelationships, and making informed decisions across various domains, including environmental studies. [ABSTRACT FROM AUTHOR]

Hydrogen and oxygen-stable isotopes are widely used as tracers of the water cycle, and surface water is an integral part of the water cycle. Compared with other water bodies, surface water is more susceptible to different natural and anthropogenic factors, and an accurate understanding of surface water changes is of great significance in ensuring regional water security, maintaining ecological balance, and promoting sustainable economic and social development. Due to the influence of observation conditions and instrumental analysis, global surface water isotopes' spatial and temporal distribution could be more balanced worldwide. For this reason, we have compiled and analyzed the stable hydrogen and oxygen isotope data in surface water from 22432 sampling stations worldwide from 1956 to 2023, with 102862 data records. The results found that (1) surface water stable isotopes are gradually depleted from the equator to the poles and from the coast to the interior. However, there are significant differences in the spatial and temporal distributions of surface water isotopes in different regions. (2) The variation of stable isotopes in surface water is controlled by geographic location, topographic conditions, and meteorological factors (especially temperature), and its heterogeneity is considerable. The global stable isotope dataset of surface water provides vital information for an in-depth understanding of the water cycle and climate change. It can provide essential data references for global water resource management and research. The Global Surface Water Stable Isotope Dataset is available at https://doi.org/10.17632/fs7rwp7fpr.1 (Zhu, 2024). [ABSTRACT FROM AUTHOR]

Precipitation plays a crucial role in the hydrological cycle and is vital for water resources management, climate change research, and ecosystem conservation. Precipitation stable isotopes serve as the 'fingerprints' of precipitation, which can clearly trace the formation, transport, and subsequent processes of precipitation. However, due to the scarcity of precipitation stable isotope data, we face challenges of temporal discontinuity and spatial heterogeneity when studying it at large to medium scales. Therefore, we compiled precipitation hydrogen and oxygen stable isotope data (δ18O and δ2H) from 2059 global sites spanning from 1961 to 2023, totaling 141,624 records. Our study indicates significant variations of global precipitation stable isotopes both spatially and temporally. Spatially, the isotopic composition of precipitation in different regions varies significantly due to factors such as geographical location, underlying surface conditions, and atmospheric circulation. Temporally, δ18O and δ2H show decreasing trends, while d-excess shows an increasing trend, with the impact of global temperature rise being very apparent. This precipitation stable isotope dataset provides robust support for our understanding of global precipitation changes and climate change. Through further investigation of precipitation stable isotope data, we hope to uncover more mechanisms and influencing factors of precipitation processes, providing a more accurate basis for the assessment and prediction of climate and water resource changes. [ABSTRACT FROM AUTHOR]

Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a "common" short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m -2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this "long period", key results show (i) trends between - 1.18 ± 0.68 to 3.82 ± 3.94 kg m -2 per decade and - 0.39 ± 0.27 to 1.24 ± 0.85 kg m -2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K -1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K -1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

Rainfall redistribution through the vegetation canopy plays a key role in the hydrological cycle. Although there have been studies on the heterogeneous patterns of rainfall redistribution in some ecosystems, the understanding of this process in different stages of forest succession remains insufficient. Therefore, this study investigated the change tendency in rainfall redistribution and rainwater chemistry in a subtropical forest succession in South China, based on 22 years (2001–2022) of rainfall monitoring (740 valid events). Results showed that, at the event scale, both the throughfall ratio and the stemflow ratio in pine forest (PF) were higher than in mixed forest (MF) and broadleaf forest (BF). At the interannual scale, throughfall and stemflow of forests experienced an initial decrease followed by a subsequent increase over the entire measurement period (except stemflow of the pine forest), which reflects the trend in open rainfall. The variability in throughfall showed an increase from MF to PF to BF, and the variability in stemflow likewise showed an increase from MF to PF to BF. Changes in throughfall and stemflow in the BF are thus higher than those in the MF and PF over time. Furthermore, important differences in rainwater chemistry fluxes among the three forest types were found, changing in varying order over time. On average, total nitrogen (TN) and total phosphorus (TP) fluxes of throughfall increased from BF to MF to PF, while the potassium (K+) flux of throughfall showed a decrease from BF to MF to PF. Stemflow chemical fluxes varied less among forest types and over time, although tree species most importantly affected varying stemflow chemistry. These results show important changes in patterns of rainfall redistribution over time and that characteristic variations are driven by rainfall and forest factors. Therefore, this study provides insight into long-term rainfall redistribution processes by linking changes in rainfall spectra with a typical subtropical forest succession sequence. [ABSTRACT FROM AUTHOR]

Evapotranspiration plays a key role in the terrestrial water cycle, climate extremes, and vegetation functioning. However, the understanding of spatio-temporal variability of evapotranspiration is limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we investigate the estimation of evapotranspiration and land surface energy-balance partitioning by only using observations made by smartphone sensors. Individual variables known to effect evapotranspiration as measured by smartphone sensors generally showed a high correlation with routine observations during a multiday field test. In combination with a simple multivariate regression model fitted on observed evapotranspiration, the smartphone observations had a mean RMSE of 0.10 and 0.05 mm h -1 during validation against lysimeter and eddy covariance observations, respectively. This is comparable to an error of 0.08 mm h -1 that is associated with estimating the eddy covariance ET from the lysimeter or vice versa. The results suggests that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field. [ABSTRACT FROM AUTHOR]

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon's Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel. [ABSTRACT FROM AUTHOR]

This study investigated the distribution of sedimentary phosphorus (P) species along an area of a rapid current at the southern coast of Zhoushan Island. The objective of this study was to improve the understanding of P cycling in a zone of rapid water cycling. Results showed that the average percentage of each P form to total P (TP) was in the following order: apatite P (Ca-P; 52%) was found in the most abundant, followed by organic P (OP; 16%), exchangeable-P (Ex-P; 14%), detrital P (De-P; 11%), and iron-bound P (Fe-P; 7%). Ca-P showed a trend of an increasing concentration from a location at the west (ZS1 has mean Ca-P = 45.6 mg kg−1) toward the east (ZS2 has mean Ca-P = 82.69 mg kg−1) and south-east (ZS3 has mean Ca-P = 82.17 mg kg−1); De-P also increased from 15.12 mg kg−1 at ZS1 to 22.53 mg kg−1 at ZS2 and 27.45 mg kg−1 at ZS3, but the three bioavailable P species, OP, Ex-P, and Fe-P, decreased from the west toward the east of the coastal area. Results along the cores showed the occurrences of sediment P adsorption and release throughout the time span from the 1930s to the present, with an overall trend of decreasing Ca-P and TP from the bottom to surface sediments. There was a tendency of Ca-P formation at the expense of Ex-P and OP release during transport and organic matter decomposition. The likely impact of climate change in the coastal zone would be an increased temperature resulting in elevated organic matter decomposition and P release. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) emissions are a serious global issue, with substantial evidence indicating that hydroclimate processes significantly contribute to these emissions. Forests, covering one-third of global land, are key in the water cycle and influence hydroclimate processes, which vary with climate, latitude, and forest types. The role of hydroclimate in regulating global forest N2O emission remains largely unknown. Our global analysis shows that hydroclimate factors dominate the latitudinal gradient of forest N2O fluxes, which decrease with latitude. N2O fluxes are highest in tropical forests, followed by temperate and boreal forests. Hydroclimate factors contribute 78.2% to N2O fluxes, while soil factors contribute 21.8%. Our results urgently call for future studies to investigate the relationship between N2O flux and hydroclimate factors like radiation, evapotranspiration, and vapor pressure deficits. Collectively, these findings highlight hydroclimate significant impact on N2O emissions and suggest incorporating these factors into predictive models for greater accuracy. [ABSTRACT FROM AUTHOR]

The Thar is the most densely populated desert in the world, which supports diverse ecosystems and human endeavours such as agriculture and socioeconomic activities. Water demand and supply in the Thar play an essential role in regulating the socioeconomic activities of the region. Inland water and precipitation aid the movement of water in the Thar Desert. Precipitation in the Thar is governed by the Indian Summer Monsoon (ISM), during which the winds distribute water vapour to regulate precipitation across the region. Therefore, we analyse the water vapour, its sources and its relation with precipitation using satellite measurements and reanalysis data in the Thar during ISM. Like other regions, a clear seasonal cycle of water vapour is observed in the Thar, with very high values (> 45 kg/m2) during ISM and low in winter (< 15 kg/m2). Evapotranspiration and moisture transport have significant effects on the amount of water vapour during ISM. There is a significant increase in water vapour in the troposphere, with high trends at the surface (0.032 g/kg/year) and small at the tropopause (0.00002 g/kg/year). A significant increase in column water vapour is also estimated in the Thar during ISM, with high trends in the eastern and southern areas, at about 0.15–0.35 kg/m2/year. The rise in water vapour in the Thar can be attributed to the increase in evapotranspiration (0.03–0.07 mm/day/year) and water vapour transport (> 0.5 kg/m/s/year) from the Arabian Sea and Indian Ocean. The rise in water vapour can lead to an increase in precipitation in the Thar, as it shows significant positive trends (0.05–0.1 mm/day/year) in the eastern areas during ISM. The increase in precipitation and water vapour in the arid Thar Desert can have significant implications for the regional environment and agriculture. [ABSTRACT FROM AUTHOR]

Visualizing medical images is difficult due to artifacts, poor local contrast, low soft tissue contrast, excessive noise levels, and a wide dynamic range. This has created a serious problem for physicians, resulting in unapproachable and inaccurate disease diagnoses. To circumvent this problem, this research proposes a medical image enhancement (MIE) approach based on the hybrid simulated annealing‐evaporation rate‐based water cycle algorithm (SA‐ERWCA). The ERWCA enhances distorted medical images by finding the best optimal solution for the transformation parameters according to the fitness function. The fitness function consists of three objective measurements, namely, entropy, number of edges, and sum of edge intensities. Due to its high potential for finding a globally optimal solution, SA is applied solely to determine the optimized initial population of the ERWCA, thereby enhancing its convergence characteristics. To simply put, the main objective of SA is to avoid premature convergence of the ERWCA. Thus, blending SA and ERWCA produces better‐quality medical images. The performance of the proposed algorithm was compared to histogram equalization (HE), low contrast stretching (LCS), contrast‐limited adaptive histogram equalization (CLAHE), particle swarm optimization (PSO), accelerated PSO (APSO), and the water cycle algorithm (WCA). Along with the objective function fitness, seven full reference (FR) image quality assessment (IQA) metrics were implemented to evaluate image quality and compare performance. The findings of the present study showed that the proposed strategy outperforms all of the compared methods in terms of objective function fitness and perceptual visual IQA metrics such as the Harr wavelet‐based perceptual similarity index (HaarPSI), visual signal‐to‐noise ratio (VSNR), and information content weighted structural similarity index measure (IW‐SSIM). The suggested technique also exhibited better stability and faster convergence time to the optimum solution. Furthermore, the proposed approach outperformed others by avoiding premature convergence to the best solution and providing excellent optimization results with acceptable computational efficiency. [ABSTRACT FROM AUTHOR]

Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change makes it more difficult for these ecosystems to adapt to the fluctuation in sea levels and other environmental changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. However, their divergent estimates of the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetlands (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model-data comparison, we showed the importance of using a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also found that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites in diverse environments can help reduce the model uncertainty. [ABSTRACT FROM AUTHOR]

Monitoring the LST(Land surface temperature) is crucial for understanding environmental conditions and their impact on energy exchange between the Earth and atmosphere, as well as the water cycle and overall environmental evolution. To this end, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST, 8-Day (MOD11A2,061) products were utilized to study the day and night time LST in Iran and its watersheds from 2001–2022. The findings revealed that during the day, 1.5% of Iran area had a temperature below 0 °C, 21% had an LST between 0–20 °C, 37% above 40 °C, with the largest region (40%) falling within the 20–40 °C range. At night, 16.7% had a temperature below 0 °C, 1.7% of the area had an LST above 30 °C, 39% between 15–30 °C, with the largest region (43%) falling between 0–15 °C. Furthermore, the study revealed a significant increase in LST during the summer season, with LST day showing a positive trend in ten watersheds with a confidence level of 95% (p = 0.05) and a very strong positive trend in Helle watershed with a confidence level of 99% (p = 0.01). while LST night showed a significant positive trend in 6 watersheds (p = 0.05) and a very strong significant positive trend in 18 watersheds (p = 0.01). Additionally, LST night showed a significantly greater increasing trend compared to LST day. [ABSTRACT FROM AUTHOR]

Considered the Asian water tower, the Qinghai–Tibet Plateau (QTP) processes substantial permafrost, and its hydrological environments are spatially different and can be easily disturbed by changing permafrost and melting ground ice. Permafrost degradation compels melting permafrost to become an important source of surface runoff, changes the storage of groundwater, and greatly influences the hydrological processes in permafrost regions. However, the evidence linking permafrost degradation and hydrological processes on the QTP is lacking, which increases the uncertainties in the evaluation of the results of changing permafrost on the water resources. Stable isotopes offer valuable information on the connections between changing permafrost (ground ice) and water components. It is therefore particularly important to observe the changes in the stable isotopes of different waterbodies which can vary over hourly to annual timescales and truly capture the thawing signals and reflect the influence of permafrost (ground ice) on the regional hydrological processes. The Beiluhe Basin (BLH) in the hinterland of the QTP was selected; it integrates all the water components related to hydrological cycles well and is an ideal site to study the hydrological effect of permafrost change. This paper presents the temporal data of stable isotopes (δ18 O, δ D, and d -excess) in different waterbodies (precipitation, stream water, thermokarst lake, and groundwater) in the BLH produced between 2017 and 2022. Here, the first detailed stable isotope data of ground ice at 17 boreholes and 2 thaw slumps are presented. A detailed description of the sampling processes, sample pretreating processes, and isotopic data quality control is given. The data first described the full seasonal isotope amplitude in the precipitation, stream, and thermokarst lakes and delineated the depth isotopic variability in ground ice. In total, 554 precipitation samples, 2402 lake/pond samples, 675 stream water samples, 102 supra-permafrost water samples, and 19 sub-permafrost water samples were collected during 6 years of continuous sampling work. Importantly, 359 ground ice samples at different depths from 17 boreholes and 2 profiles were collected. This first dataset provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP. It also provides support for the cryospheric study on the Northern Hemisphere. The dataset provided in this paper can be obtained at 10.5281/zenodo.10684110 (Yang, 2024). [ABSTRACT FROM AUTHOR]