Peat formation potential of temperate fens increases with hydrological stability.

Peat formation is the key process responsible for carbon sequestration in peatlands. In rich fens, peat is formed by brown mosses and belowground biomass of vascular plants. However, the impact of ecohydrological settings on the contribution of mosses and belowground biomass to peat formation remains an open question. We established seven transects in well-preserved fens in NE Poland along an ecohydrological gradient from mesotrophic sedge-moss communities with stable water levels, to more eutrophic tall sedge communities with higher water level fluctuations. In each transect, we measured the production of brown mosses (using the plug method), aboveground vascular plant biomass (one year after cutting) and belowground biomass (using ingrowth cores). Decomposition rates of all biomass fractions were assessed using litter bags. The first-year surplus of potentially peat-forming fractions, i.e., mosses and belowground biomass, decreased with increasing water level fluctuations and along a vegetation gradient from sedge-moss to tall sedge communities. Moss production was highest in the sedge-moss fen with a stable water level at the ground surface. We did not detect any difference in belowground biomass production across the gradient but found it to be consistently higher in the upper 0-5 cm than in the deeper layers. The decomposition rate also showed no response to the gradient, but differed between biomass types, with aboveground biomass of vascular plants decomposing 2.5 times faster than belowground biomass and mosses. Pattern of peat formation potential along the ecohydrological gradient in rich fen was strongly driven by brown moss production. Sedge-moss fens with a stable water level at the ground surface have the highest peat formation capacity compared to other vegetation types. In the part of the gradient that is poorer in nutrients, vascular plants invest in belowground production, and mosses dominate the aboveground layer.

Agricultural watershed conservation and optimization using a participatory hydrological approach.

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.

Effects of hydrologic regimes on the loading and spatiotemporal variation of mercury in the microtidal river estuary.

The potential effect of hydrological conditions on distribution and loadings of Hg species was investigated in the microtidal Hyeongsan River Estuary (HRE). Dissolved Hg (DHg) and dissolved methylmercury (DMeHg) from the creek receiving industrial wastes were effectively settled to sediment during the post-typhoon period, while persistent input from the Hg-contaminated creek without settling was observed during the dry periods. The event-based mean approach was applied to explore the hydrological effects on the annual flux of Hg. The largest inputs of DHg and particulate Hg (PHg) were found in the Hg-contaminated creek, and DHg input was higher in the dry than wet periods whereas PHg input was higher in the wet than dry periods. In sediment, Hg and MeHg concentrations decreased after the typhoon, attributed to erosion of surface sediments. Overall, the HRE serves as an effective sink of Hg that reduces the degree of Hg contamination in coastal water.

Understanding the global subnational migration patterns driven by hydrological intrusion exposure.

Amid the escalating global climatic challenges, hydrological risks significantly influence human settlement patterns, underscoring the imperative for an in-depth comprehension of hydrological change's ramifications on human migration. However, predominant research has been circumscribed to the national level. The study delves into the nonlinear effects of hydrological risks on migration dynamics in 46,776 global subnational units. Meanwhile, leveraging remote sensing, we procured globally consistent metrics of hydrological intrusion exposure, offering a holistic risk assessment encompassing hazard, exposure, and vulnerability dimensions, thus complementing previous work. Here, we show that exposure is the primary migration driver, surpassing socioeconomic factors. Surrounding disparities further intensified exposure's impact. Vulnerable groups, especially the economically disadvantaged and elderly, tend to remain in high-risk areas, with the former predominantly migrating within proximate vicinities. The nonlinear analysis delineates an S-shaped trajectory for hydrological exposure, transitioning from resistance to migration and culminating in entrapment, revealing dependence on settlement resilience and adaptability.

Methodology for the assessment of poor-data water resources.

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia.

Endorheic lakes, lacking river outflows, are highly sensitive to environmental changes and human interventions. Central Asia (CA) has over 6000 lakes that have experienced substantial water level variability in the past century, yet causes of recent changes in many lakes remain unexplored. Modelling hydrological processes for CA lakes poses challenges in separating climatic change impacts from human management impacts due to limited data and long-term variability in hydrological regimes. This study developed a spatially lumped empirical model to investigate the effects of climate change and human water abstraction, using Shortandy Lake in Burabay National Nature Park (BNNP) as a case study. Modelling results show a significant water volume decline from 231.7x106m3 in 1986 to 172.5x106m3 in 2016, primarily driven by anthropogenic water abstraction, accounting for 92% of the total volume deficit. The highest rates of water abstraction (greater than 25% of annual outflow) occurred from 1989 to 1993, coinciding with the driest period. Since 2013, the water volume has increased due to increased precipitation and, more importantly, reduced water abstraction. Despite limited observational data with which to calibrate the model, it performs well. Our analysis underscores the challenges in modelling lakes in data-sparse regions such as CA, and highlights the importance and benefits of developing lake water balance models for the region.

Enhancing hydrological insight: isotopic methods revealing groundwater-surface water interactions in the Lower Quang Tri River Group, Vietnam.

The Lower Quang Tri River Group, situated in central Vietnam, faces a myriad of challenges, notably the decline in groundwater levels and the salinisation of both groundwater and surface water, significantly impacting water availability for domestic, agricultural, and industrial purposes. To address these pressing concerns, this study adopts a comprehensive methodology integrating hydrogeological measurements, isotopic techniques, and chemical analyses of various water sources, including local precipitation, surface water bodies, reservoirs, and groundwater samples. Utilising the deuterium and oxygen-18 signatures (δ2H and δ18O) in water molecules as environmental tracers for the assessment of base flow and water sources enables a nuanced understanding of the intricate interaction between surface water and groundwater. Research findings elucidate that during the dry season, groundwater recharge primarily stems from water in the reservoirs over approximately seven months. Base flow contributes between 80 and 85 % of streamflow during the rainy season, escalating to 100 % during the dry season. The mean travelling time of the base flow is estimated at 120 ± 10 days using the sine curve model developed by Rodgers et al. The insights gleaned from this study are poised to play a pivotal role in guiding the local water resources managers in licensing for the exploitation of a right quantities of groundwater as sustainable management strategies in the region.

An ecohydrological approach to assess water provisioning and supporting ecosystem services in the Budhabalanga River Basin, India.

Rivers are vital and complex natural systems that provide a wide range of ecosystem services. This study presents a methodology for assessing the riverine provisioning and supporting ecosystem services, whose applicability has been demonstrated over the Budhabalanga River Basin of India. The Soil and Water Assessment Tool (SWAT) is used to generate streamflow time series at various ungauged sites, and then the streamflow is characterized for the evaluation of provisioning services. Further, the diversity and abundance of macroinvertebrates, along with the Lotic-invertebrate Index for Flow Evaluation (LIFE), is used to study the riverine supporting ecosystem services. The streams show intermittent behavior and strong seasonality for low flows, which limits the water availability, particularly during pre-monsoon season. The Baseflow Index (BFI) is greater than 0.6, indicating that groundwater contributes more than 60% of the total streamflow. Interestingly, despite the high BFI, the streams did not conform to the prevailing opinion that a greater baseflow contribution results in a later commencement of the low-flow period in the hydrological year. Furthermore, the study depicts significant variations in the diversity and abundance of the macroinvertebrates across the various sampling sites. However, the LIFE score across the sites remained consistent within a narrow range, i.e., 8 to 9, suggesting a steady supply of supporting ecosystem services. The results of the study can help the policymakers towards an informed decision making and the simplistic methodology proposed in this study can be replicated in other river basins for identifying vulnerable watersheds and prioritizing management actions.

A comparative assessment of five precipitation products in the Saharan desert of Morocco: Sakia El Hamra basin case study.

This study evaluates the performance of five satellite precipitation products (GPM IMERG, TRMM 3B42, ERA5, PERSIANN, and CHIRPS) compared to monthly observations from two weather stations (Laayoune and Essmara) over 2001-2017 using statistical metrics including correlation coefficient (CC) and mean square error (MSE). The results reveal notable differences between products. On a monthly timescale, GPM IMERG shows the best overall accuracy with a MSE of 16.8 mm/month. However, TRMM 3B42 exhibits higher temporal correlations with a CC around 0.83. The analysis provides insights into product capabilities and limitations for hydrological monitoring in data-sparse regions. Key findings include the superior performance of latest generation datasets like GPM alongside biases requiring localized calibration. The study delivers an assessment framework to guide integration of multiple satellite estimates for enhanced precipitation quantification and hydroclimatic modeling in water-stressed environments.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 event as 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

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.

Enhancing the SWAT model for creating efficient rainwater harvesting and reuse strategies to improve water resources management.

Rain barrels/cisterns are a type of green infrastructure (GI) practice that can help restore urban hydrology. Roof runoff captured and stored by rain barrels/cisterns can serve as a valuable resource for landscape irrigation, which would reduce municipal water usage and decrease runoff that other stormwater infrastructures need to treat. The expected benefits of rainwater harvesting and reuse with rain barrels/cisterns are comprehensive but neither systematically investigated nor well documented. A comprehensive tool is needed to help stakeholders develop efficient strategies to harvest rainwater for landscape irrigation with rain barrels/cisterns. This study further improved the Soil and Water Assessment Tool (SWAT) in simulating urban drainage networks by coupling the Storm Water Management Model (SWMM)'s closed pipe drainage network (CPDN) simulation methods with the SWAT model that was previously improved for simulating the impacts of rainwater harvesting for landscape irrigation with rain barrels/cisterns. The newly improved SWAT or SWAT-CPDN was applied to simulate the urban hydrology of the Brentwood watershed (Austin, TX) and evaluate the long-term effects of rainwater harvesting for landscape irrigation with rain barrels/cisterns at the field and watershed scales. The results indicated that the SWAT-CPDN could improve the prediction accuracy of urban hydrology with good performance in simulating discharges (15 min, daily, and monthly), evapotranspiration (monthly), and leaf area index (monthly). The impacts of different scenarios of rainwater harvesting and reuse strategies (rain barrel/cistern sizes, percentages of suitable areas with rain barrels/cisterns implemented, auto landscape irrigation rates, and landscape irrigation starting times) on each indicator (runoff depth, discharge volume, peak runoff, peak discharge, combined sewer overflow-CSO, freshwater demand, and plant growth) at the field or watershed scale varied, providing insights for the long-term multi-functional impacts (stormwater management and rainwater harvesting/reuse) of rainwater harvesting for landscape irrigation with rain barrels/cisterns. The varied rankings of scenarios found for achieving each goal at the field or watershed scale indicated that tradeoffs in rainwater harvesting and reuse strategies exist for various goals, and the strategies should be evaluated individually for different goals to optimize the strategies. Efficient rainwater harvesting and reuse strategies at the field or watershed scale can be created by stakeholders with the assist of the SWAT-CPDN to reduce runoff depth, discharge volume, peak runoff, peak discharge, CSO, and freshwater demand, as well as improve plant growth.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method.

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Investigation of canopy interception characteristics in slope protection grasses: A laboratory experiment.

Canopy interception significantly affects hydrological processes such as infiltration, runoff and evapotranspiration. Research on grass canopy interception remains limited, and the experimental methods employed differ substantially. To thoroughly investigate the canopy interception characteristics of grass and clarify the methodological differences, five commonly utilized slope protection grass species in temperate regions were cultivated in a laboratory setting, and their canopy interception characteristics were experimentally investigated using the water-balance method (WBM), the water-wiping method (WWM) and the water-immersion method (WIM), respectively. The results showed that the WBM is more accurate for measuring canopy interception in grass, whereas both the WWM and the WIM underestimate grass canopy interception capacity. The canopy interception capacity measured by the WBM was 1.61-2.09 times higher than that of the WWM and 1.93-3.47 times higher than that of the WIM. Grey correlation analysis of the eight evaluated factors indicated that leaf area is the most influential factor affecting canopy interception in grass, followed by rainfall amount, dry mass, rainfall intensity, canopy projection area, leaf contact angle, fresh weight, and average height. There is a negative power function relationship between the interception ratio and the rainfall amount. With increasing rainfall intensity, the canopy interception capacity initially increases and then decreases, peaking at rainfall intensities of 15 to 20 mm/h. Leaf contact angle is a key quantifiable parameter that explains the differences in canopy interception among different grass species, and the canopy interception per unit leaf area decreases as the leaf contact angle increases. This study demonstrates that the WBM provides the most accurate measurements of grass canopy interception compared to the WWM and WIM, and highlights the leaf contact angle as a key factor in explaining interspecies differences. These findings could enhance the understanding of grass canopy interception and guide the selection of experimental methods.

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.

Response of dissolved inorganic carbon dynamics to simulated tidal hydrological processes in coastal wetlands.

To clarify the impacts of tidal hydrological process shifts caused by sea level rise on the blue carbon cycle, a typical coastal wetland in Jiaozhou Bay was selected for this study. The soils of Suaeda salsa (SS) and Phragmites australis (PA) wetlands were collected to simulate the effects of three types of tidal hydrological processes (Neap tide group, NT; Middle tide group, MT; Spring tide group, ST) on the soil-water dissolved inorganic carbon (DIC) dynamic. The results showed that the concentration of water dissolved inorganic carbon (WDIC) increased rapidly (115% higher) at early stage (days 0-4) under the influence of the tidal hydrological processes. Significant differences were found in WDIC concentration during different tidal hydrological processes (P < 0.05), which were expressed as MT (52.7 ± 13.3 mg L-1) > ST (52.5 ± 12.9 mg L-1) > NT (48.4 ± 10.1 mg L-1). After experiencing the tidal hydrological processes, the soil DIC content showed a net accumulation (55.1 ± 1.29 mg L-1vs. 46.7 ± 1.76 mg L-1, P < 0.001), whereas the soil inorganic carbon (SIC) decreased (2.73 ± 1.64 mg L-1vs. 4.61 ± 1.71 mg L-1), which may be attributed to the dissolution of SIC caused by the uptake of CO2 to form DIC. The accumulation of soil DIC was directly related to the SIC (λ = 1.03, P < 0.01), and indirectly related to soil nutrients (SOC substrate, λ = -0.003) and microbes (microbial biomass, λ = -0.10), and was mainly dominated by abiotic processes (abiotic: 58.1 ± 1.8% to 82.7 ± 2.46% vs. biotic: 17.4 ± 2.46% to 41.9 ± 1.76%). The increase of tidal frequency generally inhibited the accumulation of soil DIC content and promoted the output of WDIC. However, the response of soil DIC in different wetland types to tidal frequency was divergent, which was mainly regulated by the trade-off between soil nutrients and SIC content. Taken together, tidal hydrological processes and their frequency changes reshaped DIC dynamics, promoted the dissolution of SIC and the potential uptake of CO2. These findings enhance the comprehension of the inorganic carbon cycle within coastal wetlands, particularly amidst the backdrop of climate change and the rising sea levels.

Temporal variations of spring hydrochemistry as clues to the karst system behaviour: an example of Louros Catchment.

Effectively managing water resources in karst systems requires a thorough understanding of their general conduit network along with their seasonal dynamics. Their investigation has involved well construction or several advanced natural tracer data, most of which are not always available. Hence, this work showcases a pragmatic approach that makes use of basic hydrochemical variables of springs with coarse temporal resolution in characterising a karst system. In this study's example, physicochemical variables like major ion concentrations/ratios, Electrical Conductivity (EC), pH and water temperature (Tw) were measured on 20-day basis for a hydrological year at the Louros Catchment, Greece. We further performed the frequency distribution and variation analysis of EC and Tw, principal component analysis (PCA), scatter plots of carbonate ions vs sulphate and hydrochemographs to determine relevant hydrochemical processes and hydrogeological features. PCA and the scatter plots showed that the simple-type upper karst level is entirely dominated by carbonate dissolution, whereas the complex-type middle and lower levels also involve gypsum and dolomite dissolution. Presence of mixing between karst units was also detected. EC and Tw analyses revealed the degree of karstification of different units and relative depths of flow systems. Hydrochemographs reflected the seasonality of limestone and gypsum dissolution's contributions linked to the dominant flow type (conduit vs diffuse). This study thus was able to demonstrate the usefulness of such holistic hydrochemical analyses to better understand karst systems. Given their cost-effectiveness, they can be easily applied to any understudied karst system worldwide.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

Modeling multi-source plastic pollution yield and transport driven by catchment hydrometeorological processes.

Plastic pollution has emerged as a global environmental concern, impacting both terrestrial and marine ecosystems. However, understanding of plastic sources and transport mechanism at the catchment scale remains limited. This study introduces a multi-source plastic yield and transport model, which integrates catchment economic activities, climate data, and hydrological processes. Model parameters were calibrated using a combination of field observations, existing literature, and statistical random sampling techniques. The model demonstrated robust performance in simulating both plastic yield and transport from 2010 to 2020 in the upper and middle Mulan River Catchment, located in southeast China. The annual average yield coefficients were found to closely align with existing estimations, and the riverine outflow exhibited a high correlation coefficient of 0.97, with biases ranging from -63.0 % to -21.4 % across all monitoring stations. The analysis reveals that, on average, 12.5 ± 2.5 % of the total plastic yield is transported to rivers annually, with solid waste identified as the primary source, accounting for 37.8 ± 20.7 % of the total load to rivers, followed by agricultural film (26.4 ± 9.8 %), impermeable surfaces (21.5 ± 10.3 %), urban and rural sewage (10.4 ± 5.0 % and 3.0 ± 1.5 %, respectively), and industrial wastewater (0.9 ± 0.7 %). The annual average outflow was estimated to between 9.3 and 43.0 ton/year (median: 23.1) at a 95 % confidence level. This study not only provides insights into the primary sources and transport pathways of plastic pollution at the catchment scale, but also offers a valuable tool for informing effective plastic pollution mitigation strategies.