Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze-thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS-IC, ESA CCI, AMSR2 LPRM, SMAP-L3, SMAP-L4, ERA5-Land, GLDAS-Noah) and three root-zone soil moisture (RZSM) products (SMAP-L4, ERA5-Land, GLDAS-Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July-October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP-L3 and SMAP-L4 exhibit the highest SSM accuracy (R = 0.24 and 0.23, respectively) with low ubRMSE (0.037-0.038), while ERA5-Land shows the best RZSM correlation (R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

Study region: Gandaki River basin in the central Himalayan region. Study focus: Spatiotemporal investigation of meteorological, agricultural, and hydrological droughts over historical (1986-2014) and future periods (2024-2100). New hydrological insights for the region: Historical analysis reveals that meteorological, agricultural, and hydrological droughts exhibit an insignificant increase in severity and duration. Agricultural and hydrological droughts are characterized by higher severity and longer duration compared to meteorological droughts. Regarding the impact of precipitation and temperature on agricultural drought severity, precipitation replenishes soil moisture in various ways across different elevation zones, thereby alleviating agricultural drought. Conversely, temperature primarily intensifies agricultural drought severity by reducing soil moisture through evaporation and transpiration. Glaciers play an important role in hydrological drought, with both precipitation and temperature helping to alleviate drought severity in subbasins containing glaciers. This phenomenon is particularly pronounced for subbasins with a glacier area ratio exceeding 10.5 %, showcasing a significant negative correlation between temperature and drought severity. Future projections show that meteorological and agricultural droughts, particularly in elevation zones below 3000 m, which cover 79.4 % of agricultural land, will become more severe and prolonged, threatening agricultural productivity. Climate change and glacier retreat are expected to increase hydrological droughts' severity and duration. These findings enhance understanding of drought evolution and highlight the urgent need for drought planning and management to protect socioeconomic development in the Central Himalaya.

Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.

Climate change has led to increased frequency, duration, and severity of meteorological drought (MD) events worldwide, causing significant and irreversible damage to terrestrial ecosystems. Understanding the impact of MD on diverse vegetation types is essential for ecological security and restoration. This study investigated vegetation responses to MD through a drought propagation framework, focusing on the Yangtze River Basin in China, which has been stricken by drought frequently in recent decades. By analyzing propagation characteristics, we assessed the sensitivity and vulnerability of different vegetation types to drought. Using Copula modeling, the occurrence probability of vegetation loss (VL) under varying MD conditions was estimated. Key findings include: (1) The majority of the Yangtze River Basin showed a high rate of MD to VL propagation. (2) Different vegetation types exhibited varied responses: woodlands had relatively low sensitivity and vulnerability, grasslands showed medium sensitivity with high vulnerability, while croplands demonstrated high sensitivity and moderate vulnerability. (3) The risk of extreme VL increased sharply with rising MD intensity. This framework and its findings could provide valuable insights for understanding vegetation responses to drought and inform strategies for managing vegetation loss.

Landslides are recognized as major natural geological hazards in the mountainous region, and they are accountable for enormous human causalities, damage to properties, and environmental issues in the Teesta River basin, Sikkim, India. GIS approaches are widely used in landslide susceptibility mapping (LSM) that can help relevant authorities to mitigate landslide risk. The binary logistic regression is applied to estimate the landslide susceptibility zonation (LSZ) in the upper Teesta River basin areas. The landslide inventory data are subdivided into training data sets (70%) for applying algorithms in models and testing data sets (30%) for testing model accuracy. The LSZ mapping is designed after analyzing multicollinearity test of 14 landslide CFs and the result shows that the VIF value is less than 10, and TOL is greater than 0.1, respectively. There is no multicollinearity for the 14 conditioning landslides factors. The upper Teesta River basin is categorized into five groups: very low-to-very high landslide susceptibility zones. The results highlighted that most of the middle and southern parts of the study region are highly prone to landslides compared to the other parts. The susceptibility of landslide in the upper Teesta River basin areas validated by performing the Receiver Operating Characteristics (ROC) curve, which showed an 83% confidence level. The present research demonstrated landslide vulnerability circumstances for the Teesta River basin, Sikkim, an area prone to landslides, emphasizing the need for an effective mitigation and management roadmap.

It is important to comprehend the evolution of drought characteristics and the relationships between different kinds of droughts for effective drought mitigation and early warnings. The study area was the Pearl River Basin, where spatiotemporal changes in the multiscale water balance and soil moisture at various depths were analyzed. The meteorological data used in this study were derived from the China Meteorological Forcing Dataset, while the soil moisture data were obtained from the ECMWF ERA5-Land reanalysis dataset. The Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI) were applied to represent meteorological and agricultural droughts, respectively. By using the run theory for drought event identification, the characteristic values of drought events were analyzed. The correlation between the multiscale SPEI and SSI was examined to represent the propagation time from meteorological drought to agricultural drought. This study indicated that while the western part of the Pearl River Basin experienced a worsening atmospheric moisture deficit and the southern part had intensifying dry conditions for soil moisture, the rest of the basin remained relatively moist and stable. Soil conditions were moister in the deeper soil layers. The durations of agricultural droughts have generally been shorter than those of meteorological droughts over the past 40 years. Within the top three soil layers, the severity, duration, and frequency of drought events progressively increased, increased, and decreased, respectively, as soil depth increased. The propagation time scale from a meteorological drought to a four-layer agricultural drought was typically within 1-5 months. This study advanced existing research by systematically analyzing drought propagation times across soil depths and seasons in the Pearl River Basin. The methodology in this study is applicable to other basins to analyze drought complexities under climate change, contributing to global drought resilience strategies. Understanding the spatiotemporal characteristics of meteorological and agricultural droughts and the propagation time between them can help farmers and agricultural departments predict droughts and take appropriate drought-resistant measures to alleviate the damage of droughts on agricultural production.

In the mountainous headwaters of the Colorado River episodic dust deposition from adjacent arid and disturbed landscapes darkens snow and accelerates snowmelt, impacting basin hydrology. Patterns and impacts across the heterogenous landscape cannot be inferred from current in situ observations. To fill this gap daily remotely sensed retrievals of radiative forcing and contribution to melt were analyzed over the MODIS period of record (2001-2023) to quantify spatiotemporal impacts of snow darkening. Each season radiative forcing magnitudes were lowest in early spring and intensified as snowmelt progressed, with interannual variability in timing and magnitude of peak impact. Over the full record, radiative forcing was elevated in the first decade relative to the last decade. Snowmelt was accelerated in all years and impacts were most intense in the central to southern headwaters. The spatiotemporal patterns motivate further study to understand controls on variability and related perturbations to snow water resources.

Rapid socio-economic development has precipitated substantial transformations in land use and land cover (LUCC) within the Yanhe River basin, significantly impacting production dynamics, confluence mechanisms, and the basin's runoff response processes. To elucidate the runoff response patterns under varying land use/land cover change conditions, this study analyzed the land use change characteristics from 1980 to 2020. Employed the SWAT (Soil and Water Assessment Tool) model, and simulated the precipitation-runoff dynamics under five distinct land use scenarios to scrutinize the basin's runoff response to varying land use conditions. The results demonstrated the applicability of the SWAT model to the Yanhe River basin, with R-2 and Ens values for monthly runoff at two hydrological stations exceeding 0.6 during both calibration and validation periods. Between 1980 and 2020, the area of farmland decreased by 27.96%, whereas the areas of woodland and grassland by 36.59% and 16.2%, respectively. Scenario analysis revealed that the primary contributors to the increased runoff in the study area, in descending order, were grassland, farmland, and woodland. The results indicated that converting farmland to woodland would reduce the runoff depth by 0.26 mm, while converting farmland to grassland would increase the runoff depth by 0.39 mm in the watershed. The conversions exhibited pronounced seasonal effects, with varying degrees of runoff depth changes observed across different seasons. The contribution order of different hydrological years to runoff depth change rates was median flow year > low flow year > high flow year. Land use conversion, particularly among farmland, grassland, and woodland, exerts diversified impacts on runoff depth across different water periods.

Covered sinkhole, due to its hidden, uncertain, and sudden characteristics, often becomes a key and difficult issue in the prevention and control of karst geological disasters. This paper takes the sinkhole in Yaoshan Huamu Farm, Guilin City as an engineering case, and uses field investigation, indoor and outdoor experiments, and theoretical analysis to systematically analyze the main patterns, influencing factors, and evolution laws of sinkhole. The results show that: (1) High-density resistivity tests show that there are many significant low-resistance anomalies at different locations and depths in the study area, indicating that karst fissures are developed in the study area. This is the basic condition for the occurrence of sinkhole. (2) Drilling results show that the groundwater level in the study area is shallow and groundwater is abundant. Groundwater changes the state and strength of the soil, or dissolves the mineral components of the soil layer and dissolves and transports the soil particle aggregates through subsurface erosion and seepage. Therefore, groundwater destroys the soil structure, resulting in the formation of soil caves or sinkholes. (3) Rainfall monitoring shows that the rainy season from May to July each year provides abundant groundwater for the karst area and changes the physical and mechanical properties of the rock and soil mass; while the small rainfall peak around November may trigger the occurrence of sinkhole through mechanisms such as groundwater level fluctuations and enhanced seepage. (4) The vibrations caused by long-term pumping irrigation, surface water leakage, and planting activities in the study area provide important external dynamic conditions for sinkhole. This study can provide theoretical basis and technical support for the prevention and control of collapse disasters in karst areas.