Search Results (32)

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting. Full article

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article

As the cornerstone of terrestrial ecosystems, forests have faced mounting challenges due to escalating human activities, jeopardizing their vital ecological functions and even their existence. It has become an important issue to explore how to promote harmonious coexistence of man and nature, or even to improve the forest ecological function (FEF) through human activities. Thus, in this study, we select the Yellow River Basin (YRB) in China as a typical region. Firstly, we assess the FEF at the county level and reveal their spatial distribution and agglomeration characteristics on the basis of the data from the Ninth National Forest Inventory of China. Then, using multiple linear regression (MLR) and geographically weighted regression (GWR) modeling, we further explore the overall impacts of different human activities on FEF and their spatial differences, respectively. Our findings underscored a moderate deficiency in the county-level FEF in the YRB, with pronounced positive spatial agglomerations. The high–high areas are primarily clustered in the southern and central mountainous areas, whereas low–low areas are distributed in the upstream warm temperate steppe and desert-grassland regions. Human activities exert substantial impacts on FEF, with distinct spatial heterogeneity in the coefficient and significance levels. The trend analysis indicates that FEF is more sensitive to the increase in living land, population density and forest protection in the east–west direction. And in the north–south direction, FEF is more easily affected by agricultural development, population growth and urbanization. This study verifies that natural factors dominate FEF in those regions where human activities are quite scarce, and also reveals that due to the inter-constraint or counteract effects among different human activities, FEF may still ultimately depend on the natural endowments in some populated regions. We point out the core human activity factors affecting FEF after excluding the interference from natural conditions. And we recommend that policymakers prioritize sustainable development strategies that mitigate the adverse impacts of human activities on forest ecosystems while promoting conservation efforts tailored to the unique characteristics of each region. Full article

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes. Full article

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes. Full article

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions. Full article

Satellite precipitation data downscaling is gaining importance for climatic and hydrological studies at basin scale, especially in the data-scarce mountainous regions, e.g., the Upper Indus Basin (UIB). The relationship between precipitation and environmental variables is frequently utilized to statistically data and enhance spatial resolution; the non-stationary relationship between precipitation and environmental variables has not yet been completely explored. The present work is designed to downscale TRMM (Tropical Rainfall Measuring Mission) data from 2000 to 2017 in the UIB, using stepwise regression analysis (SRA) to filter environmental variables first and a geographically weighted regression (GWR) model to downscale the data later. As a result, monthly and annual precipitation data with a high spatial resolution (1 km × 1 km) were obtained. The study’s findings showed that elevation, longitude, the Normalized Difference Vegetation Index (NDVI), and latitude, with the highest correlations with precipitation in the UIB, are the most important variables for downscaling. Environmental variable filtration followed by GWR model downscaling performed better than GWR model downscaling directly when compared with observation data. Generally, the SRA and GWR method are suitable for environmental variable filtration and TRMM data downscaling, respectively, over the complex and heterogeneous topography of the UIB. We conclude that the monthly non-stationary relationships between precipitation and variables exist and have the greatest potential to affect downscaling, which requires the most attention. Full article

Long-term and continuous lake sedimentary records offer enormous potential for interpreting paleoenvironmental histories and for understanding how terrestrial environments might respond to current global warming conditions. However, sedimentary records that contain the Late Glacial and Holocene epochs are scarce in deep continental high-mountain regions. A 150 cm sediment core was obtained from Lake Kaskadnoe-1 in the East Sayan Mountains (South Siberia, Russia, 2080 m above sea level), containing a unique record of the last 13,200 calibrated years (cal yr). Chronological control was obtained by AMS 14C dating. Here, we show the first detailed X-ray fluorescence (XRF) geochemical record, with the goal of broadening our knowledge of the paleoenvironmental history of the East Sayan Mountains in the past. The determination of major compounds and trace elements (Sr, Zr) was performed from each centimeter of the Lake Kaskadnoe-1 sediment core. The inorganic geochemistry indicates significant variations in elemental composition between two major lithological units of the sediment core: the Late Glacial dense grey silty clay (150–144 cm), and the upper interval (0–143 cm) mostly consisted of dark biogenic-terrigenous silt, accumulated during the Holocene. The Late Glacial sediments accumulated 13,200–12,800 cal yr BP are characterized by high values of CIA, Mg/Al, K/Al, and Mn/Fe, and are depleted in Si/Al, Fe/Al, and Ca/Al. During the Younger Dryas cold episode, LOI enrichment was probably caused by the presence of less oxic conditions, as seen in lower Mn/Fe values, due to a longer period of lake ice-cover. The Early Holocene (12,000–7500 cal yr BP) is associated with a decreasing trend of mineral matter with fluvial transport to Lake Kaskadnoe-1 (low K/Al, Mg/Al) and stronger chemical weathering in the lake basin. The increase in Ti/Al, K/Al and CIA values over the last 7500 years suggests an increase in the terrigenous input into the lake. Low LOI values can be possibly explained by the presence of less dense vegetation cover in the basin. In summary, our data indicate that the geochemical indices and selected elemental ratios mirror the sedimentation conditions that were triggered by environmental and climate changes during the Late Glacial and Holocene. Full article

The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%). Full article

Glaciers have proven to be a particularly sensitive indicator of climate change, and the impacts of glacier melting on downstream water supplies are becoming increasingly important as the world’s population expands and global warming continues. Data scarcity in mountainous catchments, on the other hand, has been a substantial impediment to hydrological simulation. Therefore, an enhanced glacier hydrological model combined with multi-source remote sensing data was introduced in this study and was performed in the Upper Yarkant River (UYR) Basin. A simple yet efficient degree-day glacier melt algorithm considering solar radiation effects has been introduced for the Soil and Water Assessment Tool Plus model (SWAT+), sensitivity analysis and auto calibration/validation processes were integrated into this enhanced model as well. The results indicate that (i) including glacio-hydrological processes and multi-source remote sensing data considerably improved the simulation precision, with a Nash–Sutcliffe efficiency coefficient (NSE) promotion of 1.9 times and correlated coefficient (R²) of 1.6 times greater than the original model; (ii) it is an efficient and feasible way to simulate glacio-hydrological processes with SWAT+Glacier and calibrate it using observed discharge data in data-scarce and glacier-melt-dominated catchments; and (iii) glacier runoff is intensively distributed throughout the summer season, accounting for about 78.5% of the annual glacier runoff, and glacier meltwater provides approximately 52.5% (4.4 × 10⁹ m³) of total runoff in the study area. This research can serve the runoff simulation in glacierized regions and help in understanding the interactions between streamflow components and climate change on basin scale. Full article

Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six satellite products (GPM-F, CHIRPS, PERSIANN-CCS-CDR, GPM-L, GPM-E and PDIR-Now), with high spatio-temporal resolution, in the sub-Saharan regions of Morocco. Precipitation observation data from 33 rain-gauge stations were collected and used over the period from September 2000 to August 2020. The assessment was performed on three temporal scales (daily, monthly and annually) and two spatial scales (pixel and basin scales), using different quantitative and qualitative statistical indices. The results showed that the GPM-F product performed the best, according to the different evaluation metrics, up to events with 40 mm/day, while the GPM near real-time products (GPM-E and GPM-L) were better at detecting more intense rainfall events. At the daily time scale, GPM-E and GPM-L and, on monthly and annual scales, CHIRPS and PERSIANN-CCS-CDR, provided satisfactory precipitation estimates. Moreover, the altitude-based analysis revealed a bias increasing from low to high altitudes. The continental and mountainous basins showed the lowest performance compared to the other locations closer to the Atlantic Ocean. The evaluation based on the latitudes of rain gauges showed a decrease of bias towards the most arid zones. These results provide valuable information in a scarcely gauged and arid region, showing that GPM-F could be a valuable alternative to rain gauges. Full article

In alpine areas in Northwest China, such as the Tianshan Mountains, the lack of climate data (because of scarce meteorological stations) makes it difficult to assess the impact of climate change on runoff. The main contribution of this study was to develop an integrated method to assess the impact of climate change on runoff in data-scarce high mountains. Based on reanalysis products, this study firstly downscaled climate data using machine learning algorithms, then developed a Batch Gradient Descent Linear Regression to calculate the contributions of temperature and precipitation to runoff. Applying this method to six mountainous basins originating from the Tianshan Mountains, we found that climate changes in high mountains are more significant than in lowlands. In high mountains, the runoff changes are mainly affected by temperature, whereas in lowlands, precipitation contributes more than temperature to runoff. The contributions of precipitation and temperature to runoff changes were 20% and 80%, respectively, in the Kumarik River. The insights gained in this study can guide other studies on climate and hydrology in high mountain basins. Full article