Dynamics of land cover changes and carbon emissions driven by large dams in China

The recent surge in dam construction has sparked debates regarding their contribution to carbon neutrality and food security, focusing on trade-offs between production benefits and ecological drawbacks. However, how dams affect carbon emissions and land cover changes, including their spatial differentiations, remains unclear. We quantified spatiotemporal variations in carbon emissions and storage of 137 large dams in China from 1992 to 2020, resulting from land cover change in potentially affected areas. We observed a lesser increase in carbon emissions and a more pronounced increase in carbon storage driven by forest conservation and regeneration within dam-affected areas compared to unaffected areas. Additionally, we noticed an increased grain yield in nearby areas potentially due to increased water availability. Our findings highlight the importance of considering land cover change when assessing carbon neutrality or grain yield at regional and national scales. This study provides useful insights into optimizing dam locations to mitigate future carbon emissions effectively.

Antimicrobial resistance in rural rivers: Comparative study of the Coquet (Northumberland) and Eden (Cumbria) River catchments

Many studies have characterised resistomes in river microbial communities. However, few have compared resistomes in parallel rural catchments that have few point-source inputs of antimicrobial genes (ARGs) and organisms (i.e., AMR) - catchments where one can contrast more nebulous drivers of AMR in rural rivers. Here, we used quantitative microbial profiling (QMP) to compare resistomes and microbiomes in two rural river catchments in Northern England, the Coquet and Eden in Northumberland and Cumbria, respectively, with different hydrological and geographical conditions. The Eden has higher flow rates, higher annual surface runoff, and longer periods of soil saturation, whereas the Coquet is drier and has lower flowrates. QMP analysis showed the Eden contained significantly more abundant microbes associated with soil sources, animal faeces, and wastewater than the Coquet, which had microbiomes like less polluted rivers (Wilcoxon test, p < 0.01). The Eden also had greater ARG abundances and resistome diversity (Kruskal Wallis, p < 0.05), and higher levels of potentially clinically relevant ARGs. The Eden catchment had greater and flashier runoff and more extensive agricultural land use in its middle reach, which explains higher levels of AMR in the river. Hydrological and geographic factors drive AMR in rural rivers, which must be considered in environmental monitoring programmes.

Degradation of ice-wedge polygons leads to increased fluxes of water and DOC

Ice-wedge polygon landscapes make up a substantial part of high-latitude permafrost landscapes. The hydrological conditions shape how these landscapes store and release organic carbon. However, their coupled water‑carbon dynamics are poorly understood as field measurements are sparse in smaller catchments and coupled hydrology-dissolved organic carbon (DOC) models are not tailored for these landscapes. Here we present a model that simulates the hydrology and associated DOC export of high-centered and low-centered ice-wedge polygons and apply the model to a small catchment with abundant polygon coverage along the Yukon Coast, Canada. The modeled seasonal pattern of water and carbon fluxes aligns with sparse field data. These modeled seasonal patterns indicate that early-season runoff is mostly surficial and generated by low-centered polygons and snow trapped in troughs of high-centered polygons. High-centered polygons show potential for deeper subsurface flow under future climate conditions. This suggests that high-centered polygons will be responsible for an increasing proportion of annual DOC export compared to low-centered polygons. Warming likely shifts low-centered polygons to high-centered polygons, and our model shows that this shift will cause a deepening of the active layer and a lengthening of the thawing season. This, in turn, intensifies seasonal runoff and DOC flux, mainly through its duration. Our model provides a physical hypothesis that can be used to further quantify and refine our understanding of hydrology and DOC export of arctic ice-wedge polygon terrain.

Plastic does not simply flow into the sea: River transport dynamics affected by tides and floating plants

Plastic pollution is ubiquitous in aquatic environments worldwide. Rivers connect terrestrial and marine ecosystems, playing a key role in the transport of land-based plastic waste towards the sea. Emerging research suggests that in estuaries and tidal rivers, tidal dynamics play a significant role in plastic transport and retention dynamics. To date, observations in these systems have been limited, and plastic transport dynamics during single tidal cycles remain poorly understood. Here, we investigated plastic transport, trapping, and re-mobilization of macroplastics (> 0.5 cm) in the Saigon River, focusing on short-term dynamics of individual tidal cycles. We used GPS trackers, released at different stages of the tidal cycle (ebb, flood, neap, spring). Plastic items demonstrated dynamic and intermittent transport behavior. Items spent almost half of the time (49%) temporarily stopped, mainly due to their entrapment in vegetation, infrastructure, or deposition on riverbanks. Items were almost always re-mobilized within 10 h (85%), leading to successive phases of stopping and transport. Tidal dynamics also resulted in bidirectional transport of plastic items, with median daily total transport distance within the 40 km study reach (8.9 km day-1) over four times larger than the median daily net distance (2.0 km day-1). The median retention time of plastic items within the reach was 21 days (mean = 202 days). In total, 81% of the retrieved items were trapped within water hyacinths, emphasizing the important role of floating vegetation on river plastic transport dynamics. With this paper, we aim to provide data-driven insights into macroplastic transport and retention dynamics in a tropical tidal river. These are crucial in the design of effective intervention and monitoring strategies, and estimating net plastic emission from rivers into the sea.

Groundwater level forecasting with machine learning models: A review

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Improving water efficiency is more effective in mitigating water stress than water transfer in Chinese cities

The interactions between human and natural systems and their effects have unforeseen results, particularly in the management of water resources. Using water stress mitigation as an example, a water resources management effect index (WRMEI) was created to quantitatively evaluate the trends of water management effects. This revealed that the WRMEI was decreasing due to the impact of the water resources management process. The findings demonstrate that water resources management has unintended effects: there was a gap between the expectation of water stress to be mitigated and the actual results of water stress increasing. That is caused by human activities in water utilization: (1) increasing available water resources from water transfer was not utilized sparingly in the receiving cities-increased water transfers from external sources increase domestic water consumption per capita; (2) improving water efficiency has a positive effect on mitigating water stress, but the population growth decreased the efficiency. It was concluded that much greater attention needs to be paid to water conservation in residential and living use to counter these unintended water management effects.

Planning hydrological restoration of coastal wetlands: Key model considerations and solutions

The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.

Coupling machine learning and physical modelling for predicting runoff at catchment scale

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.

Influence of vegetation type on infiltration rate and capacity at Ie jue geothermal manifestation, Mount Seulawah Agam, Indonesia

Ie Jue is one of the geothermal manifestation of hot water and fumaroles in the northern zone of Mount Seulawah Agam. Because hot water in the Ie Jue manifestation is derived from meteoric water, the presence of Ie Jue hot springs is determined by rain intensity and surface infiltration rates in the vicinity of the manifestation. The purpose of this research is to determine the rate of precipitation infiltration and its link to the type of flora that grows around the manifestation. Ie Jue. The 100 m line transect approach was used to determine sampling locations. Transects were placed in stratified sampling based on the four cardinal directions from the manifestation's center, namely east, south, west, and north. In each transect, three sampling stations were placed at intervals of 0 m, 50 m, and 100 m from the manifestation's midpoint. In each transect, three sampling stations were placed at intervals of 0 m, 50 m, and 100 m from the manifestation's midpoint. At each sampling point, the vegetation type was identified, and soil temperature, moisture, pH, texture, density and organic C were measured. A single ring infiltrometer was used to quantify infiltration rate at each sampling location, and the Horton equation was used to compute infiltration capacity. The results showed that the type of tree vegetation found in the south had the maximum infiltration rate of 54 cm/h - 28.8 cm/h with a constant interval of 40 min, followed by an infiltration rate of 44.4 cm/h - 8.4 cm/h for 45 min in the north with pole vegetation type. In comparison to other regions, the south and north have exceptionally quick infiltration criteria (36.87 cm/h and 29.88 cm/h, respectively). When compared to poles, shrubs, and herbs, tree-type vegetation had the highest infiltration rate. The results showed that vegetation type, soil moisture, bulk density, and soil organic C are the most important elements influencing infiltration in the Ie jue hot spring area.

Prioritizing water availability study settings to address geogenic contaminants and related societal factors

Water availability for human and ecological uses depends on both water quantity and water quality. The U.S. Geological Survey (USGS) is developing strategies for prioritizing regional-scale and watershed basin-scale studies of water availability across the nation. Previous USGS ranking processes for basin-scale studies incorporated primarily water quantity factors but are now considering additional water quality factors. This study presents a ranking based on the potential impacts of geogenic constituents on water quality and consideration of societal factors related to water quality. High-concentration geogenic constituents, including trace elements and radionuclides, are among the most prevalent contaminants limiting water availability in the USA and globally. Geogenic constituents commonly occur in groundwater because of subsurface water-rock interactions, and their distributions are controlled by complex geochemical processes. Geogenic constituent mobility can also be affected by human activities (e.g., mining, energy production, irrigation, and pumping). Societal factors and relations to drinking water sources and water quality information are often overlooked when evaluating research priorities. Sociodemographic characteristics, data gaps resulting from historical data-collection disparities, and infrastructure condition/age are examples of factors to consider regarding environmental justice. This paper presents approaches for ranking and prioritizing potential basin-scale study areas across the contiguous USA by considering a suite of conventional physical and geochemical variables related to geogenic constituents, with and without considering variables related to societal factors. Simultaneous consideration of societal and conventional factors could provide decision makers with more diverse, interdisciplinary tools to increase equity and reduce bias in prioritizing focused research areas and future water availability studies.

Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States

This research explores the geospatial patterns of historical runoff for the period 1958-2021 in the Mid-Atlantic region and uses these time-series data plus nine external climatic and hydrologic variables to predict future runoff for the period 2022-2031. Gridded, average monthly climatic water balance data were obtained from the TerraClimate dataset. A cluster analysis of the long term (1958-2021) historical runoff found 13 significant temporal trends, which tend to form large contiguous regions associated with climate gradients and topographic patterns. The runoff time-series clusters, and the associated time-series of 9 TerraClimate variables, were used to generate random forest based forecast models to predict future (2022-2031) runoff. The random forest-based forecast with the greatest accuracy included inputs of actual evapotranspiration, climate water deficit, minimum, average, and maximum temperature, and vapor pressure deficit. The final model predicted significantly increasing runoff in nine of the 13 clusters.

Rainfall-driven resuspension of pathogenic Leptospira in a leptospirosis hotspot

Leptospirosis is a zoonosis caused by Leptospira bacteria present in the urine of mammals. Leptospira is able to survive in soils and can be resuspended during rain events. Here, we analyzed the pathogenic Leptospira concentration as a function of hydrological variables in a leptospirosis hot spot. A total of 226 samples were collected at the outlet of a 3 km2 watershed degraded by ungulate mammals (deer and feral pigs) and rats which are reservoirs for leptospirosis. Water samples collected at the beginning of a rain event following a dry period contained high concentrations of pathogenic Leptospira. The concentration was generally correlated with the water level and the suspended matter concentration (SMC) during the main flood event. A secondary peak of pathogenic Leptospira was sometimes detected after the main flood and in slightly turbid waters. Lastly, the pathogenic Leptospira concentration was extremely high at the end of a wet season. The pathogenic Leptospira concentrations could not be explained by a linear combination of hydrological variables (e.g. the rainfall, water level, SMC and soil moisture). However, nonlinear machine learning models of rainfall data only provided a fair fit to the observations and explained 75 % of the variance in the log10-transformed pathogenic Leptospira concentration. A comparison of identical machine learning models for the water level, SMC and pathogenic Leptospira concentration showed that the residual error in the Leptospira concentration was due to not only the small dataset but also the intrinsic characteristics of the signal. Our results support the hypothesis whereby pathogenic Leptospira survive at different depths in soils and superficial river sediments (depending on their water saturation) and are transferred to surface water during erosion. These results might help to refine leptospirosis warnings given to the local population. Future research should be focused on larger watersheds in more densely populated areas.

Prioritizing river basins for nutrient studies

Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The "impact" approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The "variability" approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.

Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition

In stream systems, disentangling relationships between biology and flow and subsequent prediction of these relationships to unsampled streams is a common objective of large-scale ecological modeling. Often, streamflow metrics are derived from aggregating continuous streamflow records available at a subset of stream gages into long-term flow regime descriptors. Despite demonstrated value, shortcomings of these long-term approaches include spatial restriction to locations with long-term continuous flow records (commonly, biased toward larger systems) and omission of potentially ecologically important short-term (i.e., ≤1 year) antecedent streamflow information. We used long-term flow regime and short-term antecedent streamflow alteration information to evaluate relative performance in modeling stream fish biological condition. We compared results to understand whether short-term antecedent streamflow information improved models of fish biological condition. Results indicated that models incorporating short-term antecedent data performed better than those relying solely on long-term flow regime data (kappa statistic = 0.29 and 0.23, respectively) and improved prediction accuracy among stream sizes and in six of nine ecoregions. Additionally, models relying solely on short-term streamflow information performed similarly to those with only long-term streamflow information (kappa = 0.23). Incorporating short-term antecedent streamflow metrics may provide added ecological information not fully captured by long-term flow regime summaries in macroscale modeling efforts or perform similarly to long-term streamflow data when long-term data are not available.

Seasonal controls on stream metal(loid) signatures in mountainous discontinuous permafrost

We assess physical and chemical processes driving seasonal fluctuations in dissolved (<0.45 μm) trace metal(loid) concentrations in subarctic streams in discontinuous permafrost. Our analysis integrates multiple years of stream hydrometric and geochemical data with geochemical analyses of bedrock, permafrost, and active-layer samples. Three principal flow regimes govern stream hydrology: winter baseflow, spring freshet, and summer high flows. Metal(oid) concentrations in streams respond seasonally to these flow regimes. Baseflows are dominated by discharge of circumneutral-pH groundwater draining fractured bedrock. This discharge acts as a source of metals found as oxyanions or neutrally charged complexes, such as uranium and molybdenum. High stream flows are associated with peak concentrations of aluminium, cobalt, copper, iron, nickel, titanium, and vanadium. Concentrations of the metal cations aluminium, cobalt, copper, nickel, and titanium peak during freshet, when infiltration of snowmelt through organic-rich and moderately acidic soils favors their complexation with dissolved organic carbon. Concentrations of vanadium peak during summer high flows, likely reflecting flow through mineral soils in the active layer and involving reductive dissolution of iron(III)-(oxyhydr)oxides. The seasonal variation of arsenic concentrations is complex; at the majority of catchments it is sourced from shallow flowpaths in the active layer, but it can also be locally associated with discharge of deeper bedrock groundwater, which is spatially constrained by the presence of permafrost. Based on our analysis, we present a conceptual model that describes the flowpaths and processes governing metal(loid) release to streams in discontinuous permafrost. This model provides a framework upon which we consider changes in metal(loid) export into water resources in the context of thawing permafrost.

Application of a GIS-Based Hydrological Model to Predict Surface Wetness of Blanket Bogs

Understanding hydrological processes operating on relatively intact blanket bogs provides a scientific basis for establishing achievable restoration targets for damaged sites. A GIS-based hydrological model, developed to assess restoration potential of Irish raised bogs, was adapted and applied to four relatively intact blanket bogs in Ireland. The Modified Flow Accumulation Capacity (MFAC) model utilised high-resolution topographic data to predict surface wetness, based on climatic conditions, contributing catchment and local surface slope. Modifications to MFAC parameters aimed to account for differences in hydrological processes between raised bogs and blanket bogs. Application of a climatic correction factor accounted for variations in effective rainfall between the four study sites, while monitoring of water table levels indicated a log-linear relationship between MFAC values and summer water table levels and range of water table fluctuations. Deviations from the observed relationship between MFAC and water table levels were associated with hydrological pressures, such as artificial drainage or the occurrence of subsurface macropores (peat pipes), which further lowered summer water tables. Despite being effective as a predictor of relative surface wetness, the relationship between MFAC and ecological variables such as Sphagnum spp. cover proved poor, pointing to the impact of past activities and damage caused by anthropogenic pressures. Findings demonstrated MFAC as an effective tool in predicting surface wetness within blanket bog-covered landscapes, thus proving useful to peatland practitioners in planning and prioritising areas for restoration.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13157-023-01765-5.

Catchment-scale rapid transfer of livestock pharmaceuticals under Mediterranean climate

Various pharmaceuticals are essential for livestock farming, but some are highly toxic to aquatic life if they reach surface water bodies. Mediterranean Climate is characterized by dry summers followed by intense autumn storms. We studied the effect of these climatic conditions on the risk of pharmaceutical residues transfer to streams at the catchment-scale. Pharmaceutical products routinely used in the study area, as well as their application frequency and season, were identified through interviews with farmers. As a proof a concept, three veterinary pharmaceuticals (Fenbendazole (FBZ), Mebendazole (MBZ) and Ivermectin (IVM)) were chosen as model chemicals based on their relatively high usage, their specificity to represent different types of livestock (swine, sheep and cattle), and their ability to be analyzed using the same analytical method. Stream water was analyzed during low flow periods and at high frequency (up to 2 h-1) during flood events. The selected veterinary pharmaceuticals were not detected during low flow, but FBZ and MBZ reached high concentrations for short periods during floods. Due to the event-driven nature of their transfer, a significant load of veterinary pharmaceuticals can reach the river and cause temporary but significant degradation of water quality (e.g. for FBZ, the water concentration reached up to 355 times the predicted no effect concentration (PNEC)). This indicates that special care should be taken to avoid keeping freshly treated livestock on pastures that may become hydrologically connected under wet conditions. In addition, it suggests that low-frequency monitoring is not sufficient to detect those high concentration levels that exist during very short periods.

Understanding global groundwater-climate interactions

Global warming is emerging as an important predictor of water availability and future water supplies across the world through inducing the frequency and severity in hydrological extremes. These extremes (e.g., drought) have potential impacts on groundwater, environmental flows, as well as increase social inequalities (limited access to water by the poor), among a range of other issues. Understanding the influence of global climate on groundwater systems is thus critical to help reshape global water markets through policies underpinned by the knowledge of climatic processes driving the water cycle and freshwater supply. The main aim of this study is to improve understanding of the influence of climate variability on global groundwater using statistical methods (e.g., multi-linear regression and wavelet analyses). The response of groundwater to climate variability are assessed and the feasibility of identifying climatic hotspots of groundwater-climate interactions are explored (2003-2017). Generally, climate variability plays a major role in the distribution of groundwater recharge, evidenced in the groundwater-rainfall relationship (r ranging from 0.6 to 0.8 with lags of 1-5 months) in several regions (Amazon and Congo basins, West Africa, and south Asia). Some of the areas where no relationship exists coincide with major regional aquifer systems (e.g., Nubian sand stone in north Africa) in arid domains with fossil groundwater. Our results also show that groundwater fluxes across the world are driven by global climate teleconnections. Notable among these climate teleconnections are PDO, ENSO, CAR, and Nino 4 with PDO showing the strongest relationship (r= 0.80) with groundwater in some hotspots (e.g. in South America). The explicit role of the Pacific ocean in regulating groundwater fluxes provides an opportunity to improve the prediction of climate change impact on global freshwater systems. As opposed to remarkably large productive hydrological systems (Amazon and Congo basins), in typically arid domains, groundwater could be restricted during prolonged drought, constraining the persistence of surface water in the maintenance of a healthy surface-groundwater interactions.

Variations in copepod composition and diversity in relation to eutrophication and hydrology in İzmir Bay, Aegean Sea

Hyrographic and trophic gradient along Izmir Bay used to assess their effects on the copepod composition and diversity. Trophic state of the bay varied from oligotrophic in the outer region to hypertrophic condition in the inner region as stated by trophic index (TRIX). Aegean Sea water entrance and trophic conditions of the bay affected species assemblages. Oithona davisae, Oithona nana and Acartia clausi were associated with hyper-eutrophic conditions whereas the typical Aegean Sea species, such as Oncaea media group, Oithona similis, Paracalanus parvus, A. clausi, Farranula rostrata and Calocalanus styliremis were related to oligotrophic condition. Copepod diversity indices decreased linearly along the trophic gradient, from oligotrophic to hyper-eutrophic conditions, highlighting the substantial impact of eutrophication on the copepod community structure in the inner region. These findings emphasize TRIX's value in assessing eutrophication in anthropized systems, and immense potential for using copepod composition and diversity in monitoring programs for ecological assessments studies.

High spatiotemporal resolution analysis on suspended sediment and microplastic transport of a lowland river

The suspended sediment (SS) and microplastic (MP) transport in rivers is quite a complex process, influenced by several spatially and temporally changing factors (e.g., hydrology, sediment availability, human impact). Researchers usually investigate these factors individually and based on limited repetition in space and time. Therefore, this study aims to compare the driving factors of SS and MP transport by applying dense temporal (72 measurements) and spatial monitoring (at 26 sites). This study was performed on the medium-sized Tisza River, Central Europe. The suspended sediment concentration (SSC) was measured by water sampling and estimated based on Sentinel-2 images, while MP concentration was measured by pumping of water (1 m3). The SSC of the Tisza varied between 12.6 and 322.5 g/m3, whereas the MP concentration ranged 0-129 item/m3. Most of the transported particles were fibers (81-98 %), thus, it was assumed that MPs originated from wastewater. The results reflect that the hydrological conditions basically influence the SS and MP concentrations, as a strong positive correlation was found (ρSSC-MP = 0.6) between them during a year; however, the correlation during floods (minor floods: ρ = 0.63; medium floods: ρ = 0.41) was higher than at low stages (ρ = 0.1). It was assumed that run-off and mobilization of channel materials both contribute to increased SS and MP transport during floods. In contrary, the importance of mobilization of channel materials and wastewater input increase during low stages. The repeated measurements revealed that slope and velocity conditions, proximity of sources, tributaries, and dams influence the longitudinal changes in SS and MP concentrations. However, the effects of tributaries and dams are ambiguous (especially for MP) and require further research. The longitudinal measurements were conducted at low stages; hence, moderate negative correlations (ρ2021 = -0.35; ρ2022 = -0.41) were found between the SS and MP concentrations. Therefore, additional monitoring during (overbank) floods and denser spatial sampling are required to precisely reveal the spatiotemporal changes of SS and MP concentrations in rivers.

Environmental factors influencing DDT-DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques

The presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT-DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topography and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT-DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT-DDE concentration levels based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/validation partitions in order to extract the most informative parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand texture fraction were the most informative environmental variables for predicting DDT-DDE water concentration clusters.

Prediction of total phosphorus in reservoir cascade systems

Reservoir cascade systems have attracted the attention of scientists worldwide. The present study investigates the cascade of five reservoirs (R1, R2, R3, R4, and R5) along a 192-km water channel system located in the state of Ceará, in the Brazilian semiarid region. This cascade system was implemented in 2012 to promote water availability and security to the capital of Ceará and the strategic industry and port complex of the region. However, these reservoirs have faced a progressive degradation of water quality, which has resulted in intense eutrophication and high-water treatment costs. The study evaluates the dynamics of water quality from 2013 to 2021 along this reservoir cascade (from R1 to R5). The results revealed that water quality did not improve along the cascade system, differently from previous studies on reservoirs interconnected by natural rivers. This was attributed to the low water residence time and low capacity of pollutant removal along the man-made water channel system, as well as to the high internal phosphorus loads of the reservoirs. Multiple regression models involving the explanatory variables of total phosphorus, total nitrogen, chlorophyll-a, cyanobacteria, transparency, rainfall, and volume from upstream reservoirs were obtained to determine total phosphorus concentration in downstream reservoirs, considering different combinations of reservoir pairs in the cascade and different time delays. A clear trend of R2 decline with the distance between the upstream and downstream reservoirs was observed. For example, the R2 values for the correlations adjusted between R1 and R2 (48 km), R1 and R3 (172 km), R1 and R4 (178 km), and R1 and R5 (192 km) were 0.66, 0.32, 0.22, and 0.12, respectively. On the other hand, the adoption of time delays of the order of the cumulative residence times of the reservoirs promoted a significant improvement in the R2 values. For instance, the best correlation adjusted between R1 and R5 improved from R2 = 0.12 to 0.69 by considering a time delay of 21 months. This suggests that previous data from upstream reservoirs can be used to predict current and future total phosphorus concentration in downstream reservoirs. The results from this study are important to better understand the spatiotemporal dynamics of water quality in reservoir cascade systems and thus improve water resources management, especially in drylands.

Practical Guide to Measuring Wetland Carbon Pools and Fluxes

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

Macroplastic concentrations in the water column of the river Rhine increase with higher discharge

Riverine macroplastic pollution (>0.5 cm) negatively impacts ecosystems and human livelihoods. Monitoring data are crucial for understanding this issue and for the design of effective interventions strategies. Macroplastic pollution floating on the river surface and plastic deposited on riverbanks are studied relatively often. Data on riverine plastics in the water column remain scarce. In this study, we utilized trawl nets at different depths to sample plastic pollution in the water column at the entry point of the river Rhine to the Netherlands. We show that plastic concentrations in the water column increased during higher discharge. Moreover, the results indicate that the vertical distribution of macroplastic pollution changes during different flow conditions. Significantly higher concentrations of macroplastic can be seen near the riverbed during low discharge conditions, while no significant differences in concentration are observed between the bottom, middle, and surface samples during high discharge conditions. Taking into account the recurrence time of low discharge conditions the transport of plastic during low discharge conditions is substantial. These findings provide first insights into the key role of hydrology in explaining macroplastic transport in the water column. These insights can be used to improve future monitoring and intervention strategies.

Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake's ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake's basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts' general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1-6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake's ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Short-term responses of the soil microbiome and its environment indicate an uncertain future of restored peatland forests

Restoring peatland ecosystems involves significant uncertainty due to complex ecological and socio-economic feedbacks as well as alternative stable ecological states. The primary aim of this study was to investigate to what extent the natural functioning of drainage-affected peat soils can be restored, and to examine role of soil microbiota in this recovery process. To address these questions, a large-scale before-after-control-impact (BACI) experiment was conducted in drained peatland forests in Estonia. The restoration treatments included ditch closure and partial tree cutting to raise the water table and restore stand structure. Soil samples and environmental data were collected before and 3-4 years after the treatments; the samples were subjected to metabarcoding to assess fungal and bacterial communities and analysed for their chemical properties. The study revealed some indicators of a shift toward the reference state (natural mixotrophic bog-forests): the spatial heterogeneity in soil fungi and bacteria increased, as well as the relative abundance of saprotrophic fungi; while nitrogen content in the soil decreased significantly. However, a general stability of other physico-chemical properties (including pH remaining elevated by ca. one unit) and annual fluctuations in the microbiome suggested that soil recovery will remain incomplete and patchy for decades. The main implication is the necessity to manage hydrologically restored peatland forests while explicitly considering an uncertain future and diverse outcomes. This includes their continuous monitoring and the adoption of a precautionary approach to prevent further damage both to these ecosystems and to surrounding intact peatlands.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

Forecasting the flooding dynamics of flatwoods salamander breeding wetlands under future climate change scenarios

Ephemeral wetlands are globally important systems that are regulated by regular cycles of wetting and drying, which are primarily controlled by responses to relatively short-term weather events (e.g., precipitation and evapotranspiration). Climate change is predicted to have significant effects on many ephemeral wetland systems and the organisms that depend on them through altered filling or drying dates that impact hydroperiod. To examine the potential effects of climate change on pine flatwoods wetlands in the southeastern United States, we created statistical models describing wetland hydrologic regime using an approximately 8-year history of water level monitoring and a variety of climate data inputs. We then assessed how hydrology may change in the future by projecting models forward (2025-2100) under six future climate scenarios (three climate models each with two emission scenarios). We used the model results to assess future breeding conditions for the imperiled Reticulated Flatwoods Salamander (Ambystoma bishopi), which breeds in many of the study wetlands. We found that models generally fit the data well and had good predictability across both training and testing data. Across all models and climate scenarios, there was substantial variation in the predicted suitability for flatwoods salamander reproduction. However, wetlands with longer hydroperiods tended to have fewer model iterations that predicted at least five consecutive years of reproductive failure (an important metric for population persistence). Understanding potential future risk to flatwoods salamander populations can be used to guide conservation and management actions for this imperiled species.

Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory

The study of non-perennial streams requires extensive experimental data on the temporal evolution of surface flow presence across different nodes of channel networks. However, the consistency and homogeneity of available datasets is threatened by the empirical burden required to map stream network expansions and contractions. Here, we developed a data-driven, graph-theory framework aimed at representing the hierarchical structuring of channel network dynamics (i.e., the order of node activation/deactivation during network expansion/retraction) through a directed acyclic graph. The method enables the estimation of the configuration of the active portion of the network based on a limited number of observed nodes, and can be utilized to combine datasets with different temporal resolutions and spatial coverage. A proof-of-concept application to a seasonally-dry catchment in central Italy demonstrated the ability of the approach to reduce the empirical effort required for monitoring network dynamics and efficiently extrapolate experimental observations in space and time.

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality

The study goal was to determine spatiotemporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R2 = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light deficiency due to increased turbidity. In 49% of the models, precipitation intensified Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

Effects of stand age, tree species, and climate on water table fluctuations and estimated evapotranspiration in managed peatland forests

Lowland conifer forests dominated by black spruce (Picea mariana) and tamarack (Larix laricina) typically occur in peatlands in the boreal North American forest with near-surface water tables throughout the year. These forests are ecologically and economically important resources that may be impacted by climate change. However, information characterizing effects of forest disturbance, such as even-aged harvest on water table dynamics is needed to evaluate which forest tree species cover types are most hydrologically susceptible to even-aged harvest and changes in precipitation. We used a chronosequence approach to evaluate water table fluctuations and evapotranspiration across four stand age classes (<10, 15-30, 40-80, and >100-years old) and three distinct forest cover types (productive black spruce, stagnant black spruce, and tamarack) for a period of three years in Minnesota, USA. In general, there is limited evidence for elevated water tables in the younger age classes; the <10-year age class had no significant difference in mean weekly water table depth compared to the older age classes across all cover types. Estimated actual daily evapotranspiration (ET) generally agreed with the water table observations, with the exception of the tamarack cover type where ET was significantly lower in the <10-year age class. Productive black spruce sites that are 40-80-years old had higher evapotranspiration, and lower water table, possibly reflecting increased transpiration associated with the stem exclusion stage of stand development. Tamarack in the 40-80-year age class had higher water tables but no difference in ET compared to all other age classes, indicating that other external factors are driving higher water tables in that age class. To evaluate susceptibility to changing climate, we also assessed the sensitivity and response of water table dynamics to pronounced differences in growing season precipitation that occurred across study years. In general, tamarack forests are more sensitive to changes in precipitation compared to the two black spruce forest cover types. These findings can inform expected responses of site hydrology for a range of precipitation scenarios that may occur under future climate and be used by forest managers to evaluate hydrologic impacts of forest management activities across lowland conifer forest cover types.

Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress

Tracing and quantifying water fluxes in the hydrological cycle is crucial for understanding the current state of ecohydrological systems and their vulnerability to environmental change. Especially the interface between ecosystems and the atmosphere that is strongly mediated by plants is important to meaningfully describe ecohydrological system functioning. Many of the dynamic interactions generated by water fluxes between soil, plant and the atmosphere are not well understood, which is partly due to a lack of interdisciplinary research. This opinion paper reflects the outcome of a discussion among hydrologists, plant ecophysiologists and soil scientists on open questions and new opportunities for collaborative research on the topic "water fluxes in the soil-plant-atmosphere continuum" especially focusing on environmental and artificial tracers. We emphasize the need for a multi-scale experimental approach, where a hypothesis is tested at multiple spatial scales and under diverse environmental conditions to better describe the small-scale processes (i.e., causes) that lead to large-scale patterns of ecosystem functioning (i.e., consequences). Novel in-situ, high-frequency measurement techniques offer the opportunity to sample data at a high spatial and temporal resolution needed to understand the underlying processes. We advocate for a combination of long-term natural abundance measurements and event-based approaches. Multiple environmental and artificial tracers, such as stable isotopes, and a suite of experimental and analytical approaches should be combined to complement information gained by different methods. Virtual experiments using process-based models should be used to inform sampling campaigns and field experiments, e.g., to improve experimental designs and to simulate experimental outcomes. On the other hand, experimental data are a pre-requisite to improve our currently incomplete models. Interdisciplinary collaboration will help to overcome research gaps that overlap across different earth system science fields and help to generate a more holistic view of water fluxes between soil, plant and atmosphere in diverse ecosystems.

Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation

Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Here, we use a long-term (14-year) comparative study of 152 depressional wetlands in west-central Florida to identify sources of variation in wetland inundation during two key time periods, 2005-2009 and 2010-2018. These time periods are separated by the enactment of water conservation policies in 2009, which included regional reductions in groundwater extraction. We investigated the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Results show that water levels were lower and hydroperiods were shorter in wetlands of all vegetation classes during the first (2005-2009) time period, which corresponded with low rainfall conditions and high rates of groundwater extraction. Under water conservation policies enacted in the second (2010-2018) time period, median wetland water depths increased 1.35 m and median hydroperiods increased from 46 % to 83 %. Water-level variation was additionally less sensitive to groundwater extraction. The increase in inundation differed among vegetation classes with some wetlands not displaying signs of hydrological recovery. After accounting for effects of several explanatory factors, inundation still varied considerably among wetlands, suggesting a diversity of hydrological regimes, and thus ecological function, among individual wetlands across the landscape. Policies seeking to balance human water demand with the preservation of depressional wetlands would benefit by recognizing the heightened sensitivity of wetland inundation to groundwater extraction during periods of low precipitation.

Per- and polyfluoroalkyl substances (PFAS) contamination in a microtidal urban estuary: Sources and sinks

This study evaluates PFAS contamination and determines the major drainage sources to a temperate microtidal estuary, the Swan Canning Estuary, in Perth Western Australia. We describe how variability in these sources influences PFAS concentrations within this urban estuary. Surface water samples were collected from 20 estuary sites and 32 catchment sites in June and December from 2016 to 2018. Modelled catchment discharge was used to estimate PFAS load over the study period. Three major catchment sources of elevated PFAS were identified with contamination likely resulting from historical AFFF use on a commercial airport and defence base. Estuary PFAS concentration and composition varied significantly with season and spatially with the two different estuary arms responding differently to winter and summer conditions. This study has found that the influence of multiple PFAS sources on an estuary depend on the historical usage timeframe, groundwater interactions and surface water discharge.

High-temporal-resolution of lithium isotopes in Yangtze River headwater: Hydrological control on weathering in high-relief catchments

How climate change regulates silicate weathering in tectonically active areas remains clear. To evaluate the roles of temperature and hydrology in continental-scale silicate weathering in high-relief catchments, we applied a high temporal resolution of lithium isotopes in the Yalong River, which drains the high-relief borders of the eastern Tibetan Plateau. The dissolved δ⁷Li values range from +12.2‰ to +13.7‰ in the non-monsoon season and are higher and significantly vary from +13.5‰ to +19.4‰ in the monsoon season. The negative correlation between dissolved δ⁷Li and the Li/Na ratio is attributed to the formation of various proportions of δ⁷Li-low secondary minerals during weathering. From non-monsoon to monsoon season, the weathering intensity decreases with increasing secondary minerals formation and the weathering transforms from a supply limited to a kinetically limited weathering regime, indicated by a negative correlation between dissolved δ⁷Li value and SWR/D ratio (SWR = silicate weathering rate and D = total denudation rate). No correlations between temperature and dissolved δ⁷Li values were observed, and SWR suggested that temperature is not the direct control factor of silicate weathering in high-relief areas. The dissolved δ⁷Li values display positive correlations with discharge, physical erosion rates (PERs), and SWR. This positive correlations was attributed to an increase in the PER which caused the formation of more secondary minerals with increasing discharge. These results indicate the rapid temporal variability of riverine Li isotopes and chemical weathering process in response to changes in hydrology rather than temperature. Combined with the compiled PER, SWR, and Li isotopes at various altitudes, we further suggest that weathering in high-altitude catchments is more sensitive to hydrological changes than weathering in low-altitude catchments. These results highlight the key role of the hydrologic cycle (runoff and discharge) and the geomorphic regime in controlling global silicate weathering.