Search Results (393)

Dye wastewater pollution has become a critical environmental issue, while conventional silica adsorbents are often limited by high production costs and the use of chemical silicon sources. In this study, a black-liquor-derived silica-based adsorbent was prepared from bamboo alkaline black liquor through calcium hydroxide precipitation, acid leaching, washing, and calcination. The obtained product was systematically characterized by ICP-OES, XRD, SEM, and BET analysis. Phase analysis showed that the product was mainly composed of amorphous SiO₂, together with a small amount of residual CaSiO₃. The product was semi-quantitatively estimated to contain approximately 64.6 wt.% total SiO₂ and 14.7 wt.% total CaSiO₃, and the estimated SiO₂ yield was about 4.6 wt.% based on dry black liquor solids. The adsorbent exhibited a mesoporous structure with a specific surface area of 60.04 m²/g, a pore volume of 0.23 cm³/g, and an average pore diameter of 7.5 nm. The effects of adsorbent dosage, initial methylene blue concentration, solution pH, and temperature on adsorption performance were investigated. Under the selected conditions of 0.5 g adsorbent, 50 mg/L methylene blue, pH 7, and 20 °C, the removal efficiency reached 82.94%. Adsorption isotherm analysis showed that the Langmuir equation provided the best fit among the tested models over the studied concentration range, and the linear Langmuir fit gave an apparent capacity parameter of 840.3 mg/g. However, this fitted value should not be interpreted as a physically realizable ideal monolayer uptake. This work provides a feasible route for converting silicon-containing bamboo alkaline black liquor into a mesoporous silica-based adsorbent for dye removal. Full article

Substantial declines in acidifying emissions across Europe have led to pronounced chemical recovery of Norwegian surface waters. In recent decades, however, changes in water chemistry have increasingly coincided with climate change, complicating the attribution of observed trends to individual drivers. This study assesses whether ongoing climate change has produced detectable effects on freshwater chemistry in Norway and how these effects vary among catchments with differing sensitivities to acidification. In this study, the Model of Acidification of Groundwater In Catchments (MAGIC), which is based on current understanding of the processes governing acid–base chemistry in soils and waters, was used to simulate the effects of declining acid deposition. Deviations between observed and modelled water chemistry were provisionally interpreted as climate-related effects. However, these residuals may also reflect model or parameter uncertainty and other unaccounted-for processes. The analysis draws on long-term monitoring data (1986–2022) from 59 acid-sensitive Trend Lakes distributed across Norway, together with four Field Research Stations (1986–2020) representing contrasting hydroclimatic and biogeochemical conditions. Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, while relationships between inferred climate effects and climatic variables were examined using Pearson’s correlation analysis. Across the Trend Lakes, inferred climate effects were predominantly positive for acid-neutralising capacity (ANC) and weathering-derived cations, suggesting that climate change may contribute to accelerated chemical recovery, particularly in catchments less sensitive to acidification. The inferred climate effects varied substantially among the Field Research Stations. Higher temperatures were generally associated with enhanced recovery, possibly through intensified silicate weathering, whereas increased precipitation and runoff appeared to dampen recovery. Overall, the results suggest that climate change exerts a measurable influence on freshwater chemistry in Norway, although the magnitude and direction of the response are strongly modulated by catchment-specific characteristics. While previous studies have identified climate-related influences on individual chemical variables, quantitative attempts to separate climate- and acid-deposition-related effects across a large number of acid-sensitive catchments remain rare. Here, we use deviations between observed water chemistry and MAGIC simulations of acid deposition recovery as a screening approach to investigate whether climate-related signals can be detected at the national scale and whether these signals vary among catchments with differing sensitivities to acidification. Full article

The increasing frequency and intensity of extreme weather are profoundly affecting the ecological carbon cycle in arid regions, yet there remains a lack of quantitative understanding regarding the nonlinear response of net ecosystem productivity (NEP) in Central Asia to changes in extreme weather conditions. This study focused on the five Central Asian countries and China’s Xinjiang region, where NEP was estimated using MODIS Net Primary Productivity and daily meteorological data, and 12 extreme climate indices (ECIs) were constructed. By combining the XGBoost model with the SHapley Additive exPlanations method, the key ECIs of NEP were identified for different regions, and their nonlinear responses and threshold characteristics were quantified. The results show that from 2000 to 2023, Central Asia overall acted as a weak carbon source, with NEP exhibiting a spatial pattern of increasing in the east and decreasing in the west. Extreme precipitation indices showed an overall declining trend, whereas extreme temperature indices increased significantly. There is significant spatial heterogeneity in the importance of ECIs across different regions. Specifically, the annual total precipitation (PRCPTOT) is most important in Kazakhstan, Kyrgyzstan, and Tajikistan, while the annual maximum of daily maximum temperature (TXx) shows greater importance in Turkmenistan and Xinjiang. The responses of NEP to ECIs exhibited significant nonlinear threshold characteristics. PRCPTOT showed a positive saturation effect in Kazakhstan, Kyrgyzstan, and Tajikistan, with a threshold range of 700–1000 mm in Kyrgyzstan. TXx exhibited a pronounced negative high-temperature effect in Turkmenistan and Xinjiang, with thresholds of approximately 42 °C and 30 °C, respectively. In Uzbekistan, Diurnal Temperature Range (DTR) showed a response trough near 10 °C. The study reveals the nonlinear response and spatial heterogeneity of the NEP in Central Asia to extreme weather change, offering theoretical support for ecosystem restoration and sustainable carbon management in arid regions. Full article

High-latitude regions are witnessing unprecedented wildfires and accelerated warming. This study explored wildfire patterns and land changes within the high-latitude Indigenous Tłı̨chǫ territory in the Northwest Territories, Canada. It used the Two-Eyed Seeing approach, which combines Western science, or Scientific Ecological Knowledge (SEK), with Indigenous knowledge, or Traditional Ecological Knowledge (TEK). This method integrated Earth observation data with insights from Tłı̨chǫ Elders and officials. We analyzed spatiotemporal variations in burned areas, land surface temperature (LST), albedo, snow cover, soil moisture, and land-cover types. A listening and storytelling session with community Elders provided an in-depth Indigenous perspective. Our findings indicate a concerning shift in wildfire activity on Tłı̨chǫ land, primarily driven by the interplay between climate change and land-cover changes. Land-cover estimates over the past fifteen years indicate that nearly half of the forested areas on Tłı̨chǫ-owned land have been converted to other land-cover types, with shrublands increasing twofold and grasslands expanding tenfold. We observed significant increases in spring and summer LSTs (p < 0.05), alongside decreases in precipitation and snow cover (p < 0.05), consistent with the Elders’ observations. The decline in topsoil moisture, coupled with rising temperatures, has triggered a positive feedback loop in forested areas, intensifying future wildfire risk. The study’s implications extend beyond the Tłı̨chǫ territory, suggesting a broader significance for climate resilience and Indigenous stewardship. It highlights the significance of place-based, integrated research for understanding complex wildfire behavior and land-cover transformations. The study indicates that the Two-Eyed Seeing approach, which weaves local Indigenous knowledge with quantitative Earth observations, not only improves analytical precision but also provides a collaborative framework for developing targeted strategies to mitigate the effects of increasingly severe fire regimes and land-cover changes. Full article

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application. Full article

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data. Full article

To clarify the anomalous corrosion behavior in Cu-containing CoCrNi-based medium-entropy alloys, in which an enhanced corrosion driving force is accompanied by a reduced overall corrosion rate, the phase constitution, microstructure, electrochemical behavior, post-corrosion morphology, and surface chemical states of CoCrNi, CoCrNiCu, and CoCrNiCuFe alloys were systematically compared. The results show that Cu addition induces pronounced phase separation in the CoCrNi matrix, leading to the formation of a Cu-depleted FCC1 phase, a continuous Cu-rich FCC2 intergranular network, and dispersed nanoscale Cu-rich precipitates, with an FCC2 area fraction of about 0.145. In 3.5 wt.% NaCl solution, CoCrNiCu exhibits a stronger thermodynamic tendency for corrosion, whereas its overall corrosion rate does not increase, but instead shows the lowest corrosion current density and higher impedance, indicating an anomalous electrochemical response. Post-corrosion SEM morphology, EDS elemental mapping, and XPS valence-state analyses further reveal that corrosion is mainly concentrated in the Cu-rich phases and their adjacent narrow regions, while the Cu-rich phases themselves remain relatively stable as non-sacrificial cathodes. Semi-quantitative thermodynamic and mass-transport calculations indicate that although Cu-induced phase separation enhances the micro-galvanic corrosion driving force, with an estimated interphase potential difference of about 0.337 V, the overall corrosion rate remains constrained by the oxygen diffusion supply during cathodic oxygen reduction on the Cu-rich regions. Therefore, the anomalous corrosion response of CoCrNiCu can be attributed to the synergistic effect of the enhanced micro-galvanic corrosion driving force caused by Cu-induced phase separation and the restricted cathodic oxygen supply. Full article

The Jinsha River Basin constitutes the largest hydropower base in China. However, its complex terrain results in insufficient accurate data support for numerical forecasts, leading to low accuracy in precipitation predictions. To investigate the spatiotemporal distribution characteristics of precipitation in this basin with high precision, we evaluated the applicability of several mainstream precipitation products—GSMAP (Global Satellite Mapping of Precipitation), GPM-IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), CMORPH (Climate Prediction Center Morphing technique), and ERA5 (European Center for Medium-Range Weather Forecasts Reanalysis 5)—in the Jinsha River Basin. Based on the XGBoost algorithm, we developed a merging model that integrates satellite and reanalysis data with station observations for daily-scale applications. The results indicate that the GSMAP-Gauge precipitation product exhibits strong performance in both quantitative accuracy and precipitation event detection, with a better correlation coefficient (CC = 0.66), the lowest root mean square error (RMSE = 4.45), and higher probability of detection (POD = 0.88) and critical success index (CSI = 0.59). The ERA5 and GSMAP-Gauge products performed well in detecting light rain events (daily precipitation < 10 mm), with hit rates of 0.92 and 0.90, respectively. Meanwhile, the GPM-IMERG and CMORPH-BLD products showed higher hit rates for heavy rain events (daily precipitation > 25 mm) compared to the other two products. Specifically, the POD indices for GPM-IMERG and CMORPH-BLD were 0.45 and 0.60, respectively, while those for ERA5 and GSMAP-Gauge were below 0.4. Following the precipitation merging experiment, the multi-source precipitation merged product (MSP) substantially enhanced the accuracy of precipitation estimates, and the spatiotemporal distribution characteristics of the merged data aligned more closely with the station observations. This study analyzes the strengths and limitations of various precipitation products in the Jinsha River Basin and provides a feasible multi-source precipitation data merging scheme, offering a novel approach to constructing high-precision daily precipitation datasets in complex terrain regions. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

Radar quantitative precipitation estimation (QPE) is a crucial product for nowcasting and disaster warning. However, its accuracy is constrained by factors such as radar band, attenuation effects, and variations in the phase and microphysical properties of precipitation particles. Based on X-band phased-array radar data from Zhongshan City, Guangdong Province, this study compares and evaluates the QPE correction performance of three deep learning models: stacking ensemble learning, gated recurrent unit (GRU), and three-dimensional convolutional neural network (3D CNN). The aim is to explore the applicability of different model types under complex precipitation conditions. Data from August 2023 to August 2024 were used to construct the samples, with records from May 2024 held out as an independent test set and excluded from model training and hyperparameter tuning. Model performance was assessed under different radar combinations (three-radar, dual-radar, and single-radar configurations), temporal scales (minute and hourly), and precipitation intensities. The results show that: (1) at the minute scale, all three models improved the original QPE, reducing average relative error (RE) by approximately 24.6–29.5%, mean absolute error (MAE) by 23.2–27.7%, and root-mean-square error (RMSE) by 19.7–22.8%, while increasing correlation coefficient (CC) by approximately 20.4–20.9%. Specifically, GRU achieved the largest reduction in RE, stacking showed slight advantages in controlling MAE and RMSE, and 3D CNN and GRU showed similar improvements in CC. (2) At the hourly scale, the correction effect varied with precipitation intensity. In the light-to-moderate rainfall range ($0.1\le R<8.0\mathrm{mm}{\mathrm{h}}^{-1}$, where R denotes hourly rainfall), 3D CNN generally showed better error-control performance, whereas the advantage of GRU was less consistent among radar combinations. In the heavy-rainfall range ($R\ge 16.0\mathrm{mm}{\mathrm{h}}^{-1}$), stacking and GRU provided complementary value in some radar configurations, although model performance remained configuration dependent. (3) Case analysis shows that stacking can improve the original QPE at some extreme-precipitation stations, but correction performance in the extreme high-value range remains unstable, and GRU and 3D CNN are more prone to underestimation. Oriented toward operational applications, this study systematically evaluates the applicability and limitations of three model types under different scenarios while considering computational-resource constraints and timeliness requirements, thereby providing a reference for model selection and operational application in radar QPE correction. Full article

The spatial heterogeneity and dynamic changes in soil organic matter (SOM) are key indicators for assessing cultivated land quality and the carbon cycle. Currently, large-scale SOM monitoring relies primarily on limited ground sampling, making it difficult to capture continuous spatiotemporal variation patterns. Taking Anhui Province, China as the study area, this research integrates multi-source remote sensing and geostatistical methods to construct a multi-source collaborative SOM inversion model and analyze its spatiotemporal evolution patterns, thereby achieving high-precision, continuous spatiotemporal monitoring of SOM. A total of 3026 sampling points in Huangshan, Chuzhou and Fuyang cities in Anhui Province were selected as model training samples. The study divided the terrain into three elevation zones (<20 m, 20–40 m, >40 m) and employed the Synthetic Minority Oversampling Technique (SMOTE) method to optimize sample distribution. Based on MODIS data, this study screened spectral bands and key phenological periods significantly correlated with SOM. By integrating spectral information from Landsat 8/9 OLI imagery, meteorological data and topographic factors, a random forest (RF) inversion model incorporating multi-source environmental variables was constructed. The results indicate that (1) the RF-based SOM inversion model exhibits moderate predictive accuracy acceptable for regional-scale SOM mapping, with a coefficient of determination (R²) of 0.55 and a root-mean-square error (RMSE) of 3.3 g/kg, effectively enabling the quantitative estimation of SOM at a regional scale. (2) The model’s inversion results reflect the spatial distribution of SOM in cultivated land in Anhui Province for the years 2019, 2022 and 2024. The provincial average SOM value shows an upward trend, with SOM content exhibiting a pattern of higher levels in the south and lower levels in the north, higher levels in the west and lower levels in the east, as well as a tendency to cluster. (3) Analysis using GeoDetector indicates that topography and precipitation are the primary drivers influencing SOM distribution, and the interaction between these two factors provides significantly greater explanatory power for SOM distribution than either factor alone. Through the integration of multi-source remote sensing data and model optimization, this study has validated the feasibility of multi-scale remote sensing-based SOM inversion, revealed the spatial differentiation characteristics and driving mechanisms of SOM in Anhui Province’s cultivated land, and provided a scientific basis for improving cultivated land quality and soil carbon sink management. Full article

Raindrop size distribution (DSD) is a crucial parameter for microphysics parameterizations and radar quantitative precipitation estimation (QPE). Using disdrometer and ERA5 reanalysis data collected during the rainy season (July–September 2021) in the Liupan Mountains (LP), this study investigated how the two dominant airflow transport pathway types—the deep warm-moist monsoon (C1) and deep dry-cold continental (C2) types—modulated DSDs in the LP. The results showed that C1 had maritime characteristics, with higher number concentrations and a smaller mass-weighted mean diameter (D_m). C2 showed continental characteristics: low-level evaporation preferentially depleted small drops and increased the contribution of large drops (>2.38 mm), resulting in a larger D_m. Under both types, convective precipitation had broader DSDs than stratiform precipitation. Triggered by orographic lifting, C2 convective precipitation enhanced large-drop growth, making its D_m much larger than that of C1. The Z–R relationships were highly sensitive to airflow transport pathways. Dominated by small drops, C1 yielded a smaller Z–R coefficient A than C2, whereas reflectivity in C2 was more sensitive to the enhanced large-drop tail. These findings provide an observational basis for improving regional radar QPE accuracy, hydrometeorological forecasting, and water-resource assessment over complex terrain. Full article

Due to complex terrain, Earth surface curvature, and limited distribution of radars, there are often serious data gaps in base radar data or in 3D radar reflectivity mosaics of a radar network. These gaps greatly limit the application of radar data in short-term severe convection forecasting and quantitative precipitation estimation for flood events. This paper develops a generative adversarial network (GAN)-based radar data gap-filling model, named RadGF-GAN, for completing gaps in 3D radar reflectivity mosaic data. The 2020–2025 high-resolution (at 1 km grid spacing) outputs of a Weather Research and Forecasting and four-dimensional data assimilation model (WRF-FDDA) in an eastern China region are used to generate the data to train and test RadGF-GAN. Observations of the geostationary satellite FY-4A 15-channel AGRI (Advanced Geostationary Radiation Imager) are simulated with the radiative transfer for TOVS (RTTOV), and the radar reflectivity data are simulated with an empirical diagnostic model. By testing on 1705 test samples for satellite-only, radar-only, and radar–satellite fused inputs, it is demonstrated that the proposed RadGF-GAN gap-filling model significantly outperforms the existing interpolation methods in restoring the spatial distribution and structural textures of the radar reflectivity in the 3D gaps. Furthermore, satellite imager measurements play a great role in reconstructing the overall rainband structures in large 3D gaps, and by jointly inputting radar and satellite data, RadGF-GAN greatly outperforms the model with either radar data or satellite data alone. Full article

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions. Full article

The Tianshan–Pamir mountain region, serving as the core “water tower” for countries in Central Asia east of the Aral Sea, is a critical bulwark for sustaining downstream socioeconomic systems. However, constrained by complex topography and harsh climatic conditions, this region suffers from a severe scarcity of long-term, continuous hydrological observation data. This study focuses on a typical data-scarce mountainous area, coupling UAV and satellite imagery-based (e.g., Landsat/Sentinel) flow inversion with a hybrid spatial regionalization method—integrating spatial proximity, basin similarity, and regression-based hydrograph reconstruction—to quantitatively estimate long-term discharge time series. The results indicate that, for the validation of instantaneous discharge inversion, the Nash–Sutcliffe efficiency coefficient (NSE) at 29 river cross-sections was consistently greater than 0.80, with the coefficient of determination (R²) reached 0.94 (p < 0.01). Subsequently, for the long-term discharge series reconstructed using the regionalization method, the NSE values at three representative verification sites—each corresponding to a distinct basin type—were 0.88, 0.84, and 0.86, respectively. These findings exhibit higher precision compared to direct temporal upscaling, confirming the reliability of the regionalization method across varying temporal scales. An analysis of monthly discharge trends from 1989 to 2020 revealed a decreasing trend in the discharge of glacier-dominated rivers, with an average rate of change of −2.89 ± 2.54% (p < 0.05); the Pamir Plateau experienced the largest decline (−4.89 ± 6.58%), which is closely linked to large-scale glacial retreat within the basins. Conversely, the discharge of non-glacier-dominated rivers showed an increasing trend, with a multi-year average rate of change of +0.32 ± 8.43% (n.s.), primarily driven by shifts in precipitation and vegetation cover. This research introduces a new approach for hydrological monitoring in data-scarce regions and provides essential data and methodological support for water resource management decisions in arid zones. Full article