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Abstract The Tibetan Plateau is home to numerous glaciers that are important for freshwater supply and climate regulation. These glaciers, which are highly sensitive to climatic variations, serve as vital indicators of climate change. Understanding glacier-fed hydrological systems is essential for predicting water availability and formulating climate adaptation strategies. This study investigated the dynamic fluctuations in the water level of the Blue Moon Lake Valley (BMLV), supplied by meltwater from Baishui River Glacier No. 1 on Yulong Snow Mountain. We focused on the lake pulse phenomenon—subtle yet significant water level fluctuations that have often been overlooked in prior research. By employing fast Fourier transform (FFT), multivariate regression (MVR), and random forest (RF) models, we examined the interactions among glacier melt dynamics, climatic variables, and hydrological responses. Our analysis indicates that the rate of change (ROC) in the water level fluctuates between − 0.006 and 0.01 m/min, with a median ROC of − 7.24E−06 m/min, highlighting the significant variability influenced by glacier melt and precipitation. The maximum cumulative sum (CS) value of 0.09 m suggests a net increase in the water level, predominantly due to increased precipitation, decreased evaporation, and increased glacier melting. We demonstrate that temperature critically influences glacier melt rates and water level variations, alongside solar radiation, rainfall, atmospheric pressure, and wind speed. The ROC of Baishui River Glacier No. 1 melt ranges from − 0.0016 to 0.0015 m/min, reflecting substantial variation with significant downstream implications for water availability during dry seasons. The mean interval between consecutive glacier melt peaks is approximately 2.87 h, with a strong positive linear trend R2 = 0.99, indicating frequent melt events. Conversely, water level peaks occur approximately every 6.5 h, with a strong positive trend R2 = 0.99, indicating a slower recurrence rate. The transit time for meltwater from Baishui River Glacier No. 1 to BMLV is estimated at approximately 4.16 h. Additionally, we quantify the water flux from BMLV across various timescales, highlighting the substantial contribution of glacial meltwater. This novel study systematically examines the hydrological dynamics of BMLV. This study has the potential to reveal broader implications for water resource management, ecosystem dynamics, and climate change adaptation in regions dependent on glacier-fed lakes.

Abstract Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities.

Reference Evapotranspiration (ETo) counted as the main factor for assessing the amount of water, needed for crops as well as for the planning of water resources management. Several techniques, methods, and equations have been used for computing ETo. Thus, required weather data sets are the main challenge for evaluating this factor. FAO Penman-Monteith is the most popular technique to determine the ETo. The FAO 56-PM equation requires accurate weather data like air temperature, humidity, solar radiations, and wind speed. Unfortunately, not all these data are possible to reach easily on the station's side. Therefore, FAO 56 recommended another equation namely Hargreaves-Samani (HS) equation when sufficient weather data may not be available to estimate ETo by FAO56-PM. In the context of this, this study aimed to estimate ETo using the HS equation. For this purpose, historical annual, seasonal, and monthly temperature and wind data were collected from 1981 to 2020 using ‘The Prediction of Worldwide Energy Resources (POWER)’ web portal. It is concluded that the HS method in conjunction with the POWER datasets and spatially mapping with IDW interpolation gave reliable and accurate results of ETo. This technique gives an idea of water losses in a district and demonstrates a trend of historical annual, seasonal, and monthly ETo.

Built-up is one of the most significant type of land use-cover linked with urbanization. Computing, classifying, and mapping the built-up areas by Landsat image, is on priority demand for municipal and policymakers to investigate urban extension. Thus, increasing population and conversion of agricultural land into urban is a major topic understanding the bond between both types of land use. In the context of this, this study investigates the relationship between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) in Khangarh taluka. Therefore, satellite images of Landsat 8 OLI/TIRS 2014, 2016, 2018, and 2021 were downloaded freely from the USGS-GloVIS Earth Explorer website. The images were processed in the ArcGIS 10.3 environment. NDVI was calculated using the Near-Infrared NIR (band 5), Red (band 4) and for NDBI, Middle Infrared Reflectance MIR (band 6) and NIR (band 5) was used following the equation of both indices. The calculated values were then exported in SPSS software for correlation determination and scatter plot development. The results from the case showed that there was a linear and negative correlation between vegetation index and built-up index in all years over the study area. Furthermore, in 2014 the coefficient of correlation explicated R2=0.96; in 2016 R2=0.23, in 2018 R2=0.34, and in 2021 R2=0.22 which indicated that NDBI could be used to illustrate the evaluation of urban construction land. The all-over study recommends that built-up index NDBI not only can be used as a significant indicator for built-up or urban areas estimation but also deliver a consistent source for urban development and planning.

Developing a gauge-discharge relationship in rivers, canals, and minor flow is vital for controlling floods, managing water resources, Spatio-temporal analysis, socio-economic development, and sustaining the ecosystem. Accurate and consistent data of irrigation networks are perilous to scheduling and managing for accurate application of irrigation water. Most of the hydrologic engineering activities like hydraulics structure, designs, flood monitoring, surplus water, reservoir, canal, and minor’s operation depend on flowing water derived from Rating Curve (RC). The effective management of irrigation water is necessary for crop water requirements and seepage losses estimation. In this context, the present study showed the actual field level work tested at two minors of the Ghotki feeder canal namely Pyaro minor and Dilwaro minor. The main object of the study is to develop gauge-discharge relation and development of RC and Rating Table (RT). The current meter was used for taking discharge measurements with the area velocity technique in both minors. Moreover, stage-discharge RC and RT were developed for different flows of water for both minors in Origin Software. From the calculated results, Power equations were developed for both minors for the actual requirement of crop water in the command area. The results of the study calculated in RT of Piyaro minor between 0.5-5ft stage gave discharge 0.053 cusecs to 90.616 cusecs. While the RT of Dilwaro minor showed the range between 0.5ft-4ft stage gave 26.575cusec to 168.888 cusecs. Hence, the present study suggested that for both minors, automatic gauging stations should be established for the actual demand of irrigation water in the command area and di-siltation should be done on both minors to make availability of water at the tail section.

Predicting water levels in glacier-fed lakes is vital for water resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by the Baishui River glacier on Yulong Snow Mountain. The study introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), gradient boosting machine (GBM), and decision tree (DT) – through meta-learning to improve the prediction accuracy for water levels under complex environmental conditions. High-frequency water depth data, recorded every five minutes using an RBR logger, alongside climate variables such as temperature, wind speed, humidity, evaporation, solar radiation, and rainfall, were analyzed. Temperature was identified as the most significant factor influencing water levels, with an importance score of 15.69, followed by atmospheric pressure (14.08) and solar radiation (12.89), which impacted surface conditions and evaporation. Relative humidity (10.24) and wind speed (8.71) influenced lake stability and mixing. The QM model outperformed individual models, and achieved RMSE values of 0.003 m (climate data) and 0.001 m (water depth data), with R2 values of 0.994 and 0.999, respectively. In comparison, XGB and GBM exhibited higher RMSE and lower R2 scores. RF struggled with an RMSE of 0.008 and R2 of 0.962, while DT performed better (RMSE: 0.006 m for climate data) but remained inferior to the proposed model. These findings demonstrate the robustness of the QM ensemble approach in handling complex environmental data, particularly where individual models fall short. This study highlights the potential of ensemble learning for enhanced prediction accuracy in glacier-fed lake systems, recommending future research directions that incorporate the QM model with deep learning and long-term forecasting to expand predictive capabilities on a global scale.

Temperature and precipitation variations have huge environmental, socio-economic impacts. This study aims to detect the trend of temperature, precipitation, and discharge from 2000-2020 in the district Ghotki. Mann Kendal test and Sen’s slope were applied by using XLSTAT in MS Excel to investigate the significance of all trends. The results showed that the annual rainfall trend was increased with the highest intensity noted in 2003; 275mm and 2010; 271 mm. The trend in the monsoon season was increased with the highest slope 0.863 by comparing with non-monsoon which showed the slope was 0.642. The annual temperature was increased an average temperature recorded in 2016; 28.5 & 2018; 28 °C. Further, the summer-autumn season’s trend has sharply increased. While the trend of Ghotki feeder discharge was slightly increased in January due to the continuous flow of water and less demand for water during the Rabi season. But in July, the highest discharge was recorded in 2010 due to heavy rainfall and flood situations over the study area. The trend in Kharif was continuously declined due to farmers started sowing sugarcane crops instead of rice and cotton which need less irrigation water. It is concluded that the performance of MK and SS tests was consistent at the verified significance level.