Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

The paper presents the National Hydrological Modeling System (NHMS) developed as an element of the adaptive hydrological monitoring system within the first stage of the Key Innovative Project of National Importance (KIPNI) The Unified National System for Monitoring Climate-Active Substances in 2022-2024. The structure, tools for informational and technological support of the NHMS, and the results of testing the system using observational data in the basins of large Russian rivers are described. The paper describes algorithms for deriving the NHMS-based gridded hydrological reanalysis, which includes characteristics of the water regime of rivers, snow cover, and soil moisture based on hybridization of Roshydromet observational data with the NHMS simulations for pilot river basins of the Russian Federation. The possibility of using the NHMS and data of the hydrological reanalysis to implement the second stage of the KIPNI in 2025-2030 and to provide informational support for decision-making on adapting the water management sector of the Russian economy to climate change is under discussion.

The impact of climate change on vegetation ecosystems is a prominent focus in global climate change research. The climate change affects vegetation growth and ecosystem stability in the upper reaches of the Yellow River (UYR). However, the spatiotemporal patterns and driving mechanisms of vegetation growth status (VGS) in the region remain poorly understood. Based on the hydrological model PLS, an innovative WEP-CHC model was developed by integrating regional environmental and vegetation growth characteristics. Furthermore, combined with the PLS-SEM model and other methods, this study systematically investigated the spatiotemporal patterns and driving mechanisms of VGS in the UYR. The results indicated that: (1) VGS exhibited significant spatiotemporal variation trends within the study area. In the study period of 1970-2020, the GPP onset time was significantly advanced (p < 0.05) while the GPP peak value was significantly increased. Spatial analysis revealed significant spatial complexity in the GPP onset time and peak values across the region. (2) Soil freeze-thaw conditions significantly influenced VGS (p < 0.05). The complete thawing time of permafrost was closely coincided with the GPP onset time, with a correlation coefficient exceeding 0.84. After controlling soil freeze-thaw effects using partial correlation analysis, it was found that better initial soil hydrothermal conditions would lead to better VGS; (3) The model constructed with annual hydrothermal conditions (AHC), soil freeze-thaw period (SFTP), vegetation growth season (VGS), initial soil hydrothermal conditions (ISHC), and annual solar radiation conditions (ASRC), demonstrated good explanatory power for vegetation growth. The R 2 values of PLS-SEM were above 0.76 in all five subregions. However, their effects on VGS varied significantly across subregions. Overall, AHC and SFTP were the dominant factors in all subregions. Furthermore, the impacts of ISHC and VGC were statistically insignificant, whereas the effects of ASRC exhibited high complexity. This study not only provides new insights into the current state of hydrological-ecological coupling in the UYR but also offers a new tool for ecological conservation and sustainable water management in other cold regions and similar watersheds worldwide.

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.