Accurately quantifying the impact of permafrost degradation and soil freeze-thaw cycles on hydrological processes while minimizing the reliance on observational data are challenging issues in hydrological modeling in cold regions. In this study, we developed a modular distributed hydro-thermal coupled hydrological model for cold regions (DHTC) that features a flexible structure. The DHTC model couples heat-water transport processes by employing the conduction-advection heat transport equation and Richard equation considering ice-water phase change. Additionally, the DHTC model integrates the influence of organic matter into the hydrothermal parameterization scheme and includes a subpermafrost module based on the flow duration curve analysis to estimate cold-season streamflow sustained by subpermafrost groundwater. Moreover, we incorporated energy consumption due to ice phase changes to the available energy, enhancing the accuracy of evaporation estimation in cold regions. A comprehensive evaluation of the DHTC model was conducted. At the point scale, the DHTC model accurately replicates daily soil temperature and moisture dynamics at various depths, achieving average R-2 of 0.98 and 0.87, and average RMSE of 0.61degree celsius and 0.03 m(3)m(-3), respectively. At the basin scale, DHTC outperformed (Daily: R-2 = 0.66, RMSE = 0.75 mm; Monthly: R-2 = 0.90, RMSE = 15.7 mm) the GLDAS/FLDAS Noah, GLDAS/VIC, and PML-V2 models in evapotranspiration simulation. The DHTC model also demonstrated reasonable performance in simulating daily (NSE = 0.70, KGE = 0.84), monthly (NSE = 0.86, KGE = 0.90), and multi-year monthly (NSE = 0.97, KGE = 0.93) streamflow in the Source Regions of Yangtze River. DHTC also successfully reproduced the snow depth in basin-averaged time series and spatial distributions (RMSE = 0.86 cm). The DHTC model provides a robust tool for exploring the interactions between permafrost and hydrological processes, and their responses to climate change.

This study uses a new dataset on gauge locations and catchments to assess the impact of 21st-century climate change on the hydrology of 221 high-mountain catchments in Central Asia. A steady-state stochastic soil moisture water balance model was employed to project changes in runoff and evaporation for 2011-2040, 2041-2070, and 2071-2100, compared to the baseline period of 1979-2011. Baseline climate data were sourced from CHELSA V21 climatology, providing daily temperature and precipitation for each subcatchment. Future projections used bias-corrected outputs from four General Circulation Models under four pathways/scenarios (SSP1 RCP 2.6, SSP2 RCP 4.5, SSP3 RCP 7.0, SSP5 RCP 8.5). Global datasets informed soil parameter distribution, and glacier ablation data were integrated to refine discharge modeling and validated against long-term catchment discharge data. The atmospheric models predict an increase in median precipitation between 5.5% to 10.1% and a rise in median temperatures by 1.9 degrees C to 5.6 degrees C by the end of the 21st century, depending on the scenario and relative to the baseline. Hydrological model projections for this period indicate increases in actual evaporation between 7.3% to 17.4% and changes in discharge between + 1.1% to -2.7% for the SSP1 RCP 2.6 and SSP5 RCP 8.5 scenarios, respectively. Under the most extreme climate scenario (SSP5-8.5), discharge increases of 3.8% and 5.0% are anticipated during the first and second future periods, followed by a decrease of -2.7% in the third period. Significant glacier wastage is expected in lower-lying runoff zones, with overall discharge reductions in parts of the Tien Shan, including the Naryn catchment. Conversely, high-elevation areas in the Gissar-Alay and Pamir mountains are projected to experience discharge increases, driven by enhanced glacier ablation and delayed peak water, among other things. Shifts in precipitation patterns suggest more extreme but less frequent events, potentially altering the hydroclimate risk landscape in the region. Our findings highlight varied hydrological responses to climate change throughout high-mountain Central Asia. These insights inform strategies for effective and sustainable water management at the national and transboundary levels and help guide local stakeholders.

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.

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 degrees C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy.

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980-2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

Study region: Nelson Churchill River Basin (NCRB), Canada, and USA.Study Focus: Soil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model's soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale.New hydrological insights: Our study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21-0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis.

This study diagnoses the impact of projected changes in climate and glacier cover on the hydrology of several natural flowing Bow River headwater basins in the Canadian Rockies: the Bow River at Lake Louise (-420.7 km2), the Pipestone River near Lake Louise (-304.2 km2), the Bow River at Banff (-2192.2 km2) all of which drain the high elevation, snowy, partially glaciated Central Range, and the Elbow River at Calgary (-1191.9 km2), which drains the drier Front Ranges and foothills, using models created using the modular, flexible, physically based Cold Regions Hydrological Modelling platform (CRHM). Hydrological models were constructed and parameterised in CRHM from local research results to include relevant streamflow generation processes for Canadian Rockies headwater basins, such as blowing snow, avalanching, snow interception and sublimation, energy budget snow and glacier melt, infiltration to frozen and unfrozen soils, hillslope sub-surface water redistribution, wetlands, lakes, evapotranspiration, groundwater flow, surface runoff and open channel flow. Surface layer outputs from Weather Research and Forecasting (WRF) model simulations for the current climate and for the late 21st century climate under a business-as-usual scenario, Representative Concentration Pathway 8.5 (RCP8.5) at 4-km resolution, were used to force model simulations to examine the climate change impact. A projected glacier cover under a business-as-usual scenario (RCP8.5) was incorporated to assess the impact of concomitant glacier cover decline. Uncalibrated model simulations for the current climate and glacier coverage showed useful predictions of snow accumulation, snowmelt, and streamflow when compared to surface obser-vations from 2000 to 2015. Under the RCP8.5 climate change scenario, the basins of the Bow River at Banff and Elbow River at Calgary will warm up by 4.7 and 4.5 degrees C respectively and receive 12% to 15% more precipitation annually, with both basins experiencing a greater proportion of precipitation as rainfall. Peak snow accumulation in Bow River Basin will slightly rise by 3 mm, whilst it will drop by 20 mm in Elbow River Basin, and annual snowmelt volume will increase by 43 mm in Bow River Basin but decrease by 55 mm in Elbow River Basin. Snowcovered periods will decline by 37 and 46 days in Bow and Elbow river basins respectively due to sup-pressed snow redistribution by wind and gravity and earlier melt. The shorter snowcovered period and warmer, wetter climate will increase evapotranspiration and glacier melt, if the glaciers were held constant, and decrease sublimation, lake levels, soil moisture and groundwater levels. The hydrological responses of the basins will differ despite similar climate changes because of differing biophysical characteristics, climates and hydrological processes generating runoff. Climate change with concomitant glacier decline is predicted to increase the peak discharge and mean annual water yield by 12.23 m3 s-1 (+11%) and 11% in the higher elevation basins of the Bow River but will decrease the mean annual peak discharge by 3.58 m3 s-1 (-9%) and increase the mean annual water yield by 18% in the lower elevation basin of the Elbow River. This shows complex and compensatory hydrological process responses to climate change with the reduced glacier contribution reducing the impact of higher precipitation in high elevation headwaters and drier soil conditions and lower spring snowpacks reducing peak discharges despite increased precipitation during spring runoff in the Front Range and foothills headwaters under a warmer climate.

In cold and high-elevation mountainous catchments, climate and landscape vary with elevation, which leads to elevational variability in runoff. The short-term variation and long-term change of climate would temporally and permanently alter the conditions of frozen ground and runoff characteristics at different elevation zones. In this study, a conceptually hydrological model is developed to investigate the responses of soil freeze-thaw and runoff processes to climate change from 1979 to 2013 in a glacierized catchment in the Tibetan Plateau (TP). Results show that our model can accurately reproduce the observed daily streamflow. In addition to rainfall (63.8%), meltwater from glacier (22.2%) and snowpack (14.0%) are also key contributors to streamflow, especially at high elevations. As temperature declines with rising of elevation, the elevation-runoff relationship depicts a convex formation with a runoff peak at the elevation of -5800 m. Below 5800 m, surface flow increases towards high elevations accompanied by the increase of glacier coverage, while groundwater flow reduces because the enlarged frozen ground areas inhibit the percolation of the infiltrated water. Above 5800 m, the runoff declines sharply as the ground changes to a perennially freezing condition. The long-term climate warming during 1979 - 2013 significantly increases annual runoff with a rate of 12.2 mm/10a. The increment in streamflow is primarily attributed to an increase in surface flow in the summer season when glacier meltwater increases at high elevations. Whereas the permafrost degradation enhances infiltrated water percolation and hence, groundwater flow in the low elevations and the low flow periods. Although climate warming benefits the local water resources availability during the historical periods, streamflow could be substantially decreased if the glacier vanished, which threatens the sustainability of the water tower over TP.

Study RegionThe Naryn River Basin, KyrgyzstanStudy FocusWe investigate the impacts of climate change in the basin based on two families of General Circulation Models (GCMs) using the hydrological model SWAT. The forcing datasets are the widely used ISIMIP2 (I2) and the newly derived ISIMIP3 (I3) data which refer to the 5th and 6th stage of the Coupled Model Intercomparison Project (CMIP). Due to notable differences in the forcing we evaluate their impacts on various hydrological components of the basin, such as discharge, evapotranspiration (ETA) and soil moisture (SM). Besides, a partial correlation (PC) analysis is used to assess the meteorological controls of the basin with special emphasize on the SM-ETA coupling. New Hydrological Insights for the RegionAgreement in the basin's projections is found, such as discharge shifts towards an earlier peak flow of one month, significant SM reductions and ETA increases. I3 temperature projections exceed their previous estimates and show an increase in precipitation, which differs from I2. However, the mitigating effects do not lead to an improvement in the region's susceptibility to soil moisture deficits. The PC study reveals enhanced water-limited conditions expressed as positive SM-ETA feedback under I2 and I3, albeit slightly weaker under I3.

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (1000 km(2)). The analysis included a search for the relation between the use of these datasets to different regions and the geographical scale at which they are most widely used. Additionally, co-authorship analysis was performed on the articles to identify the collaboration patterns among researchers. The study further categorized the analysis based on the type of datasets, including rainfall, digital elevation model, land use, soil distribution, leaf area index, snow-covered area, evapotranspiration, soil moisture and temperature. The research concluded by identifying knowledge gaps in the use of each data type at different scales and highlighted the varying performance of datasets across different locations. The findings underscore the importance of selecting the right datasets, which has a significant impact on the accuracy of hydrological models. This study provides valuable insights into the use of remote sensed and/or global datasets in hydrological modelling, and the identified knowledge gaps can inform future research directions.