Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower.

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1-11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021-2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061-2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.

Assessment of the impacts of constructing artificial structures on the water quality and hydrological environment of a meandering river.

When an artificial structure is built in a river, the river changes significantly in water quality and hydraulic properties. In this study, the effects of the weirs constructed in the middle section of a river as a four major rivers restoration project in Korea on water quality and hydrological characteristics were analyzed. For multi-dimensional data analysis, a self-organizing map was applied, and statistical techniques including analysis of variation were used. As a result of analysis, the cross-sectional area of the river increased significantly after the construction of the weir compared to before the construction of the weir, and the flow velocity decreased at a statistically significant level. In the case of water quality, nitrogen, phosphorus, and suspended solids tended to improve after weir construction, and chlorophyll-a and bacteria tended to deteriorate. Some water quality parameters such as chlorophyll-a were also affected by seasonal influences. In order to improve the water quality deteriorated by the construction of the weir, it is necessary to consider how to improve the flow velocity of the river through partial opening or operation of the weir. In addition, in order to determine the effect of sedimentation of particulate matter due to the decrease in flow rate, it is necessary to conduct investigations on sediments around weirs in the future. PRACTITIONER POINTS: Compared to before the construction of the weir, there was no significant change in the flow rate of the river after the construction of the weir. In the case of chlorophyll-a and bacteria, the water quality was deteriorated after weir construction. To improve the deteriorated water quality, it is required to consider the fundamental management of each pollutant source and the flexible operation of both weirs. For some improved water quality parameters, further research is needed to determine whether these improvements are directly attributable to the construction of a weir.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management.

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km-2 to 5.5 km km-2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model.

Accurate and reliable hydrological forecasts play a pivotal role in ensuring water security, facilitating flood preparedness, and supporting agriculture activities. This study investigates the potential of hydrological forecasting in South Korea, focusing on medium-range lead times ranging from 1 to 10 days. The methodology involves leveraging a Transformer neural network, a model entirely based on attention mechanisms. Specifically, our study introduces the Dualformer, a dual-encoder-based transformer model capable of accommodating two distinct datasets: historical and forecast meteorological data. The performance of this proposed model, along with its variants designed to test specific structural aspects, is evaluated in predicting daily streamflow across 473 grid cells covering extensive regions within the study area. Furthermore, the proposed model is assessed against the performance of a recently developed approach aiming for the same objective. These models are trained using historical meteorological variables and geographic characteristics, alongside the Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts, in addition to historical runoff. The results indicate that our proposed model performs competitively, especially for relatively short lead times while effectively managing information from two distinct data sources. For instance, the mean Nash-Sutcliffe efficiency for 473 grids is 0.664 for the first one-day lead when using the Dualformer, whereas the benchmark model achieves a score of 0.535. Additionally, we observe an additional enhancement in Dualformer's performance when utilizing a larger dataset. Finally, we conclude this paper with a discussion regarding potential improvements to the forecast model through the incorporation of additional input and modeling structures.

Constructing wetland ecological corridor system based on hydrological connectivity with the goal of improving regional biodiversity.

Hydrological connectivity is crucial for the healthy operation of wetland ecosystems. However, the current design of ecological corridors in wetland biodiversity networks is mostly based on species migration resistance, neglecting the important role of hydrological connectivity. How to incorporate hydrological connectivity into the wetland ecological corridor system (ECS) is still unclear. To answer the question, we proposed a framework for constructing a wetland ECS with the goal of improving conservation value of previously identified wetland biodiversity hotspots based on hydrological connectivity. In the proposed framework, we clarified the function-level-dimension of each corridor based on the dynamics of conservation value of biodiversity hotspots, the hierarchical classification of rivers and the dimension of hydrological connectivity. Then we determined the spatial distribution and functional zoning of the corridors by least cost model (LCM) using indicators that reflect wetland hydrological connectivity resistance, including water coverage, water use efficiency of vegetation, and land use suitability. The results are as follows: (1) to improve the overall hydrological connectivity and conservation value of biodiversity hotspots, 25 corridors should be constructed for vertical hydrological connectivity (with 3 for maintaining the status quo, 6 for improving and 16 for restoring connectivity) and 3 corridors should be constructed for lateral hydrological connectivity; (2) total area of all corridors are 11 km2, accounting for 6.79% of the study area (2.47% of core zone and 4.32% of buffer zone); (3) low suitability areas of hydrological vegetation gradient (HVG) are the most extensive, followed by low suitability areas of land use/cover change (LUCC) and the average fraction coverage of water surface (AFCW), accounting for 65.08%, 47.87% and 6.76% of the corridor coverage, respectively. The proposed framework of constructing wetland ECS in this study has the potential to provide the post-2020 global biodiversity framework and sustainable development goals with specific technical support and more targeted-control strategies for building a hydrological connected wetland biodiversity network.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards.

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989-2018) and projected future periods (2041-2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Seeking a pathway towards a more sustainable human-water relationship by coupled model - From a perspective of socio-hydrology.

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010-2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Identifying the optimal type and locations of natural water retention measures using spatial modeling and cost-benefit analysis.

Water management has shifted from solely technical and engineering approaches towards nature-based solutions (NBS), like natural water retention measures (NWRM), offering benefits beyond hydrology, such as improved well-being and biodiversity conservation. Determining the best type and location of these measures is challenging due to diverse options with varying benefits and effects depending on measure type and location characteristics. While most studies regarding the optimal allocation and implementation of NBS focus on the urban environment, this study presents a methodology for decision-makers focusing on inter-urban regions with limited data on NWRM implementation. Through hydrological modeling and cost-benefit analysis (CBA), we identify Pareto optimal NWRM sites and types, considering water quantity and quality alongside economic, environmental, and social objectives. We defined optimal locations that seek the most significant reduction of runoff, sediment, and pollutants, whilst optimal NWRM types are defined to seek the most cost-effective measures based on hydrological, ecological, and social criteria. Using the Open Non-point Source Pollution and Erosion Comparison Tool (OpenNSPECT), we simulated increased infiltration in different inter-urban areas and identified the optimal placement. The criteria for selecting suitable NWRM types for the identified areas are derived from the EU Directorate General for the Environment (DG-ENV) NWRM database. The results show different effective areas for reducing runoff, sediment, and pollutants. While one NWRM (natural bank stabilization) was identified as most beneficial for reducing sediment, several measures were selected for runoff reduction. Interestingly, measures with high potential for pollutant reduction seem to offer limited social and biodiversity benefits, suggesting conflicting objectives and highlighting the importance of accounting for multiple criteria. By employing simplified models and qualitative benefit assessments, this paper presents a practical decision-making approach to facilitate NWRM implementation in data-scarce areas.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources.

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

Call for caution regarding the efficacy of large-scale afforestation and its hydrological effects.

Large-scale afforestation programmes are generally presented as effective ways of increasing the terrestrial carbon sink while preserving water availability and biodiversity. Yet, a meta-analysis of both numerical and observational studies suggests that further research is needed to support this view. The use of inappropriate concepts (e.g., the biotic pump theory), the poor simulation of key processes (e.g., tree mortality, water use efficiency), and the limited model ability to capture recent observed trends (e.g., increasing water vapour deficit, terrestrial carbon uptake) should all draw our attention to the limitations of available theories and Earth System Models. Observations, either based on remote sensing or on early afforestation initiatives, also suggest potential trade-offs between terrestrial carbon uptake and water availability. There is thus a need to better monitor and physically understand the observed fluctuations of the terrestrial water and carbon cycles to promote suitable nature-based mitigation pathways depending on pre-existing vegetation, scale, as well as baseline and future climates.

Geospatial AI solution to monitor and mitigate increasing adverse ecological and hydrological impacts of climate change in Uttarakhand Himalaya (India).

Though climate change and its adverse ecological and geohydrological impacts are being experienced across the world in all types of ecosystems but as far as the Himalaya mountain ecosystem is concerned, the rate of climate change and subsequent impacts have reached an alarming stage due to anthropogenic and technogenic intervention on natural process and now need most effective and less time taking management strategy. Addressing this burning environmental problem, a geospatial artificial intelligence (GeoAI) technique-based case study is presented here from one of the most densely populated and urbanized regions of Himalaya mountain, viz Uttarakhand Himalaya, which is also called central Himalaya. The results of the study suggest that due to quite a high rate of climate change, the climatic zones shifting towards higher altitudes at the average rate of 5.6 2 m/year, causing several adverse ecological impacts in terms of decreasing quality dense temperate forest cover (0.05%/year), snow cover (0.02%/year), water bodies (0.01%/year), agricultural land (0.31%/year), and horticultural land (0.01%/year). Conversion of these eco-friendly land use land cover into barren land, fallow land, and built-up land causes geohydrological consequences of climate change in terms of decreasing rainy days (1%/year), drying perennial springs (0.20%/year), perennial streams (0.11%/year), decreasing spring and stream discharge during non-monsoon season, increased extreme rainfall events (6-8%/year), and subsequent surface runoff during monsoon season. Further, the study advocates that the degraded geohydrological process has resulted in an increased frequency of disaster events (floods, cloudbursts, landslides. etc.) with a 3% (12 events) annual rate, causing great loss of environment, infrastructure, lives, and economy each year. Therefore, it has been very urgent to mitigate climate change and increase geohydrological disaster events through an integrated approach. Keep in view this, the present study proposed an integrated watershed management plan which is equally useful to be implemented across the Himalaya region and other similar ecosystems across the world.

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm.

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance.

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity Ci, and the HCCI of the whole river network is defined as the mean of the Ci for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that Ci showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of Ci in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management.

Parameter Estimation Using the Inverse Problem Method for Simulating Lateral Inflow and Runoff Depth in a small catchment of Amazon.

The inverse problem method can be applied to determine the properties of hydrological phenomena and estimate the parameters, which cannot be measured directly. This type of inverse focus can facilitate the implementation of the kinematic wave model (direct model-DM), to fill gaps for lateral inflow rate and runoff depth in watersheds. Thus, the goal of the study was the application of the inverse problem method (IP). The lateral inflow rate was generally obtained as a Fourier transform to represent any watersheds. The study was developed using a small catchment in the Amazon where intense rainfall events occur, producing runoff and sediments, which affect rural populations. Lateral inflow rate and runoff depth were derived using precipitation data and parameters estimated through the KINEROS2 (K2)/direct model (DM) model and the ensuing solution methods with MCMC (Markov chains Monte Carlo)/Fourier transform. The developed method was applied to four rainfall-runoff events, leading to a good fit between the observed and predicted data (Nash-Sutcliffe coefficients between 0.76 and 0.85 and RMSE values between 1.80 mm and 6.72 mm).

Deforestation-induced runoff changes dominated by forest-climate feedbacks.

Large-scale deforestation alters water availability through its direct effect on runoff generation and indirect effect through forest-climate feedbacks. However, these direct and indirect effects and their spatial variations are difficult to separate and poorly understood. Here, we develop an attribution framework that combines the Budyko theory and deforestation experiments with climate models, showing that widespread runoff reductions caused by the indirect effect of forest-climate feedbacks can largely offset the direct effect of reduced forest cover on runoff increases. The indirect effect dominates the hydrological responses to deforestation over 63% of deforested areas worldwide. This indirect effect arises from deforestation-induced reductions in precipitation and potential evapotranspiration, which decrease and increase runoff, respectively, leading to complex patterns of runoff responses. Our findings underscore the importance of forest-climate feedbacks for improved understanding and prediction of climate and hydrological changes caused by deforestation, with profound implications for sustainable management of forests and water resources.

Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments.

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult to be modelled mathematically owing to the existence of short wet episodes and long dry periods. In this paper, the suitability of an open-source SWAT method was checked to estimate the comportment of a karstic catchment in a Mediterranean semiarid domain (southeast of Spain), which wet and dry periods were evaluated using box-whisker plots and self-developed wavelet test. A novel expression of the Nash-Sutcliffe index for arid areas (ANSE) was considered through the calibration and validation of SWAT. Both steps were completed with 20- and 10-year discharge records of stream (1996-2015 to calibrate the model as this period depicts minimum gaps and 1985-1995 to validate it). Further, SWAT assessments were made with records of groundwater discharge and relating SWAT outputs with the SIMPA method, the Spain's national hydrological tool. These methods, along with recurrent neural network algorithms, were utilised to examine current and predicted water resources available to supply urban demands considering also groundwater abstractions from aquifers and the related exploitation index. According to the results, SWAT achieved a "very good" statistical performance (with ANSE of 0.96 and 0.78 in calibration and validation). Spatial distributions of the main hydrological processes, as surface runoff, evapotranspiration and aquifer recharge, were studied with SWAT and SIMPA obtaining similar results over the period with registers (1980-2016). During this period, the decreasing trend of rainfalls, characterised by short wet periods and long dry periods, has generated a progressive reduction of groundwater recharge. According to algorithms prediction (until 2050), this declining trend will continue reducing groundwater available to meet urban demands and increasing the exploitation index of aquifers. These results offer valuable information to authorities for assessing water accessibility and to provide water demands in karstic areas.

Hydrological response and crack resistance of polypropylene-fiber reinforced compacted steel slag-bentonite mixtures under wetting-drying cycles.

Desiccation-induced cracks in a compacted clay liner significantly deteriorate the hydraulic barrier performance of landfill covers. The present study explores the effects of polypropylene (PP) fiber reinforcement on the hydrological response and crack resistance of compacted steel slag (SS; 90 wt%) - bentonite (10 wt%) mixtures under drying and wetting cycles. Comprehensive tests were conducted to explore the impact of different fiber lengths (6-12 mm) and contents (0-0.4 % wt.%), including hydraulic conductivity tests for measuring the saturated hydraulic conductivity (ks), unconfined-penetration tests for measuring the tensile strength, small-sized plate tests for quantifying crack development, and large-sized bucket tests for studying the hydrological response and crack characteristics. Higher fiber contents and longer fiber lengths increased the ks-value of the specimens. For a 0.3 % fiber content, the tensile strength peaked for the 9-mm fiber. Consistently, the specimen reinforced with the 9-mm fibers exhibited significantly fewer cracks than those reinforced with the 6-mm and 12-mm fibers. It was because the 6-mm fibers had a shorter anchorage length, while the 12-mm fibers tended to agglomerate. The large-sized bucket tests showed that fiber reinforcement limited crack development significantly under wetting and drying cycles, reducing the rainfall infiltration by 40 % and enhancing the soil water retention capacity. Finally, a 0.3 wt% of 9-mm PP was recommended to reinforce the compacted SS-bentonite mixtures.

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing the intricate interplay between hydrological factors, their "black box" nature makes it challenging to identify the dynamic drivers of runoff. To overcome this challenge, we employed an interpretable machine learning method to inversely deduce the dynamic determinants within hydrological processes. In this study, we analyzed land use changes in the Ningxia section of the middle Yellow River across four periods, laying the foundation for revealing how these changes affect runoff. The sub-watershed attributes and meteorological characteristics generated by the Soil and Water Assessment Tool (SWAT) model were used as input variables of the Extreme Gradient Boosting (XGBoost) model to simulate substantial sub-watershed rainfall runoff in the region. The XGBoost was interpreted using the SHapley Additive exPlanations (SHAP) to identify the dynamic responses of runoff to the land use changes over different periods. The results revealed increasingly frequent interchanges between the land use types in the study area. The XGBoost effectively captured the characteristics of the hydrological processes in the SWAT-derived sub-watersheds. The SHAP analysis results demonstrated that the promoting effect of agricultural land (AGRL) on runoff gradually weakens, while forests (FRST) continuously strengthen their restraining effect on runoff. Relevant land use policies provide empirical support for these findings. Furthermore, the interaction between meteorological variables and land use impacts the runoff generation mechanism and exhibits a threshold effect, with the thresholds for relative humidity (RH), maximum temperature (MaxT), and minimum temperature (MinT) determined to be 0.8, 25 °C, and 15 °C, respectively. This reverse deduction method can reveal hydrological patterns and the mechanisms of interaction between variables, helping to effectively addressing constantly changing human activities and meteorological conditions.

Application of RiTiCE in understanding hydro-meteorological controls on ice break-up patterns in River Tornionjoki.

The Arctic region experiences significant annual hydrologic events, with the spring flood and ice break-up being the most prominent. River ice break-up, in particular, poses high socioeconomic and ecological expenses, including morphological changes and damage to riverine structures. This study aims to investigate the spatiotemporal patterns of river ice in the River Tornionjoki, including the timing of ice break-up at different latitudes. We utilized observation data and remote sensing techniques to track changes in ice patterns overtime on the River Tornionjoki. The study indicates that the ice break-up in the River Tornionjoki basin typically occurs during Apr-Jun based on the reach location in different latitudes; therefore, different stations behave according to their latitudinal location. We observed significant spatial variations in ice break-up timing across the basin, with an earlier break-up in the lower latitudes compared to the upper latitudes. The average ice break-up day in lower latitude stations ranges between 200-205, while in higher latitude stations the average ice break-up day ranges between 215-228.