Climate change has been a strong driving force impacting the distribution of global water resources over the past few decades, especially in cold regions at high latitudes. Hydrological models are essential to analyse complex changing cold region's processes, such as permafrost, seasonally frozen soil, and snow cover, which are prevalent across much of Canada and the pan-Arctic basins. Here, we utilize the Hydrological Predictions for the Environment (HYPE) model with seven discretized vertical soil layers to assess climate change response to different water balance portioning components and permafrost extent. The study also explores seasonal and interannual shifts, examining the implications of model uncertainty associated with streamflow generation for the Nelson Churchill River Basin (NCRB). The calibrated HYPE model is run with a suite of fourteen GCMs and two RCPs (RCP 4.5 and RCP 8.5) scenarios representing 87% of the variability of 154 climate scenarios to discern the relationship between climate projections and water balance components. Increasing precipitation and temperature are anticipated in the future, but reduced, or balanced runoff is projected due to the dominant impact of rising temperature on evapotranspiration from thawing soil layers. Under an extreme scenario (RCP 8.5) 82% reduction in permafrost degradation is projected by the mid-future period (2050s). In this study, the future projections of streamflow, soil moisture, permafrost projection, and interrelationships of water balance processes at a continental scale are presented to aid in large-scale planning and implementation of sustainable development principles and guidelines for decision-making in the NCRB. Le changement climatique a & eacute;t & eacute; une force motrice majeure influen & ccedil;ant la r & eacute;partition des ressources en eau & agrave; l'& eacute;chelle mondiale au cours des derni & egrave;res d & eacute;cennies, en particulier dans les r & eacute;gions froides des hautes latitudes. Les mod & egrave;les hydrologiques sont essentiels pour analyser les processus complexes en & eacute;volution dans les r & eacute;gions froides, tels que le perg & eacute;lisol, les sols gel & eacute;s de mani & egrave;re saisonni & egrave;re et le couvert neigeux, qui sont r & eacute;pandus dans une grande partie du Canada et des bassins pan-arctiques. Dans cette & eacute;tude, nous utilisons le mod & egrave;le Hydrological Predictions for the Environment (HYPE), qui comprend sept couches de sol verticales discr & eacute;tis & eacute;es, pour & eacute;valuer la r & eacute;ponse au changement climatique des composantes du bilan hydrique et de l'& eacute;tendue du perg & eacute;lisol. L'& eacute;tude explore & eacute;galement les variations saisonni & egrave;res et interannuelles, en examinant les implications de l'incertitude du mod & egrave;le associ & eacute;e & agrave; la g & eacute;n & eacute;ration des d & eacute;bits fluviaux dans le bassin de la rivi & egrave;re Nelson Churchill (NCRB). Le mod & egrave;le HYPE calibr & eacute; est ex & eacute;cut & eacute; avec une s & eacute;rie de quatorze mod & egrave;les climatiques globaux (GCM) et deux sc & eacute;narios RCP (RCP 4.5 et RCP 8.5), repr & eacute;sentant 87 % de la variabilit & eacute; de 154 sc & eacute;narios climatiques, afin d'analyser la relation entre les projections climatiques et les composantes du bilan hydrique. Une augmentation des pr & eacute;cipitations et des temp & eacute;ratures est anticip & eacute;e dans le futur, mais un ruissellement r & eacute;duit ou & eacute;quilibr & eacute; est projet & eacute; en raison de l'impact dominant de la hausse des temp & eacute;ratures sur l'& eacute;vapotranspiration provenant des couches de sol en d & eacute;gel. Dans un sc & eacute;nario extr & ecirc;me (RCP 8.5), une r & eacute;duction de 82 % de la d & eacute;gradation du perg & eacute;lisol est projet & eacute;e d'ici la p & eacute;riode du milieu du si & egrave;cle (ann & eacute;es 2050). Cette & eacute;tude pr & eacute;sente des projections futures du d & eacute;bit fluvial, de l'humidit & eacute; du sol, de la d & eacute;gradation du perg & eacute;lisol et des interrelations des processus du bilan hydrique & agrave; l'& eacute;chelle continentale afin de soutenir la planification & agrave; grande & eacute;chelle et la mise en oeuvre de principes de d & eacute;veloppement durable pour & eacute;clairer la prise de d & eacute;cision dans le NCRB.

In mid-July 2021, a quasi-stationary extratropical cyclone over parts of western Germany and eastern Belgium led to unprecedented sustained widespread precipitation, nearly doubling climatological monthly rainfall amounts in less than 72 h. This resulted in extreme flooding in many of the Eifel-Ardennes low mountain range river catchments with loss of lives, and substantial damage and destruction. Despite many reconstructions of the event, open issues on the underlying physical mechanisms remain. In a numerical laboratory approach based on a 52-member spatially and temporally consistent high-resolution hindcast reconstruction of the event with the integrated hydrological surface-subsurface model ParFlow, this study shows the prognostic capabilities of ParFlow and further explores the physical mechanisms of the event. Within the range of the ensemble, ParFlow simulations can reproduce the timing and the order of magnitude of the flood event without additional calibration or tuning. What stands out is the large and effective buffer capacity of the soil. In the simulations, the upper soil in the highly affected Ahr, Erft, and Kyll river catchments are able to buffer between about one third to half of the precipitation that does not contribute immediately to the streamflow response and leading eventually to widespread, very high soil moisture saturation levels. In case of the Vesdre river catchment, due to its initially higher soil water saturation levels, the buffering capacity is lower; hence more precipitation is transferred into discharge.

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R-2 of 0.75). The simulation was satisfactory (NSE >0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.

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.

Rainfall infiltration plays a crucial role in the near-surface response of soils, influencing other hydrological processes (such as surface and subsurface runoff, groundwater dynamics), and thus determining hydro-geomorphological risk assessment and the water resources management policies. In this study, we investigate the infiltration processes in pyroclastic soils of the Campania region, Southern Italy, by combining measured in situ data, physical laboratory model observations and a 3D physically based hydrological model. First, we validate the numerical model against the soil pore water pressure and soil moisture measured at several points in a small-scale flume of a layered pyroclastic deposit during an infiltration test. The objective is to (i) understand and reproduce the physical processes involved in infiltration in layered volcanoclastic slope and (ii) evaluate the ability of the model to reproduce the measured data and the observed subsurface flow patterns and saturation mechanism. Second, we setup the model on the real site where soil samples were collected and simulate the 3D hydrological response of the hillslope. The aim is to understand and model the dynamics of hydrological processes captured by the field observations and explain the redistribution of water in different layers during 2 years of precipitation. For both applications, a Monte Carlo analysis has been performed to account for the hydrological parameter uncertainty. Results show the capability of the model to reproduce the observations in both applications, with mean KGE of 0.84 and 0.68 for pressure and soil moisture data in the laboratory, and 0.83 and 0.55 in the real site. Our results are significant not only because they provide insight into understanding and simulating infiltration processes in layered pyroclastic slopes but also because they may provide the basis for improving geohazard assessment systems, which are expected to increase, especially in the context of a warming climate. Combining physical model and in situ measurements of soil water content and soil water pressure together with a 3D hydrological models, we detailed and disentangled the infiltrations processes trough layered pyroclastic soils. The finding will be relevant for accurate geo-hydro risk management in a changing climate. image

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.

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC-HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: 'low risk', 'moderate risk', 'high risk', and 'very high risk'. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5-11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

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.