Due to climate change the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher of Himachal Pradesh. This study used different climatic factors along with long-term rainfall data to understand the decreasing trend in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a general as well as partial correlation plot. Based on 40 years (1981-2021) of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model's effectiveness was effectively proved by the validation results (NSE = 0.79, R2 = 0.944, RMSE = 0.23, PBIAS = 32%). Model calibration and simulation revealed that both observed and simulated spring-water runoff decreased by almost 29%, within the past 40 years. Consequently, reduced spring-water discharge is made sensitive to the hydrological (groundwater stress, base flow, and stream water flow) and environmental entities (drinking water, evaporation, soil moisture, and evapotranspiration). This study will help researchers and policymakers to think and work on the spring disappearance and water security issues in the Himalayan region.

Landslide volume plays a pivotal role in controlling landslide movement and potential damage. Although rainfall is widely recognized as one of the most important factors underlying landslide occurrence worldwide, its impact on landslide volume has been investigated only for individual landslide types. In this study, we show that rainfall characteristics and magnitude control the volume produced by both shallow and deep-seated landslides. A total of ten shallow and deep-seated landslides in Japan were compiled with volume, occurrence time, and rainfall data. Rainfall characteristics that triggered landslides were identified using the Soil Water Index and the threelayer tank model, which is a simple runoff model, and magnitude was quantified based on lag time. A strong positive correlation was found between lag time and landslide volume, indicating that landslide volume increases with increasing magnitude of rainfall to induce landslides. This study is the first attempt to suggest a relationship between rainfall magnitude and the volume produced by shallow and deep-seated landslides systematically and will promote the development of landslide risk management strategies.

In the context of global research in snow-affected regions, research in the Australian Alps has been steadily catching up to the more established research environments in other countries. One area that holds immense potential for growth is hydrological modelling. Future hydrological modelling could be used to support a range of management and planning issues, such as to better characterise the contribution of the Australian Alps to flows in the agriculturally important Murray-Darling Basin despite its seemingly small footprint. The lack of recent hydrological modelling work in the Australian Alps has catalysed this review, with the aim to summarise the current state and to provide future directions for hydrological modelling, based on advances in knowledge of the Australian Alps from adjacent disciplines and global developments in the field of hydrologic modelling. Future directions proffered here include moving beyond the previously applied conceptual models to more physically based models, supported by an increase in data collection in the region, and modelling efforts that consider non-stationarity of hydrological response, especially that resulting from climate change.

The extreme precipitation resulting from climate change has been causing increasingly serious damage in populated areas over the past 10-15 years. The torrents of flash floods cause significant financial damage to both the natural environment and man-made structures (such as roads and bridges). The determination of the physical geographic parameters of this phenomenon (e.g. the amount of runoff water) is significantly affected by technical uncertainties, usually due to the lack of monitoring systems. However, the application of modern geospatial tools can improve the quality of input data needed for runoff modelling. In the present study, an existing runoff model (the Stowe model) developed by ESRI was further enhanced with field measurements, soil parameters, GIS, and remote sensing data, resulting in the creation of the model named ME-Hydrograph. Finally, the two models were compared, and we examined the capacity of an urban stormwater drainage system through surface runoff modelling. The aim of the research was to create a runoff model that can be easily and quickly used. The application of this geospatial model presented in the study can be useful not only in the examination of urban stormwater drainage but also in contributing to the understanding and management of flash floods that occur in Hungary. Additionally, it can aid in the development of risk mapping related to flash floods in the country.

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 (R-2) 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 x 10(9) m(3)) 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.

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17 7, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction. (C) 2008 Elsevier B.V. All rights reserved.

A spatially distributed, physically based, hydrologic modeling system (MIKE SHE) was applied to quantify intra- and inter-annual discharge from the snow and glacierized Zackenberg River drainage basin (512 km 2; 20% glacier cover) in northeast Greenland. Evolution of snow accumulation, distribution by wind-blown snow, blowing-snow sublimation, and snow and ice surface melt were simulated by a spatially distributed, physically based, snow-evolution modelling system (SnowModel) and used as input to MIKE SHE. Discharge simulations were performed for three periods 1997-2001 (calibration period), 2001-2005 (validation period), and 2071-2100 (scenario period). The combination of SnowModel and MIKE SHE shows promising results; the timing and magnitude of simulated discharge were generally in accordance with observations (R-2 = 0.58); however, discrepancies between simulated and observed discharge hydrographs do occur (maximum daily difference up to 44.6 m(3) s(-1) and up to 9% difference between observed and simulated cumulative discharge). The model does not perform well when a sudden outburst of glacial dammed water occurs, like the 2005 extreme flood event. The modelling study showed that soil processes related to yearly change in active layer depth and glacial processes (such as changes in yearly glacier area, seasonal changes in the internal glacier drainage system, and the sudden release of glacial bulk water storage) need to be determined, for example, from field studies and incorporated in the models before basin runoff can be quantified more precisely. The SnowModel and MIKE SHE model only include first-order effects of climate change. For the period 2071-2100, future IPCC A2 and B2 climate scenarios based on the HIRHAM regional climate model and HadCM3 atmosphere-ocean general circulation model simulations indicated a mean annual Zackenberg runoff about 1.5 orders of magnitude greater (around 650 mmWE year(-1)) than from today 1997-2005 (around 430 mmWE year(-1)), mainly based on changes in negative glacier net mass balance. Copyright (c) 2007 John Wiley & Sons, Ltd.