Stream Discharge Research Articles

Microplastics removal from a hospital laundry wastewater combining ceramic membranes and a photocatalytic membrane reactor: Fouling mitigation, water reuse, and cost estimation

The release of microplastics (MPs) through industrial laundry wastewater accounts for 35 % of global MPs emissions into the environment and it is a significant environmental problem, especially because MPs can absorb contaminants of emerging concern (CECs) from garments. This study is the first to evaluate and perform a cost estimation of the MP removal from hospital laundry wastewater (HLWW) using a combination of ceramic membranes and a pilot-scale photocatalytic membrane reactor (PMR) as a fouling mitigation strategy. The HLWW, from a hospital in Copenhagen, Denmark, contained a total organic carbon (TOC) of 345 mg L⁻1 and 1.4 × 106 MP L−1, mainly of polyethylene terephthalate (PET) ranging between 100 and 200 μm in size. The pre-treatment with an ultrafiltration (UF) ZrO₂ membrane successfully removed 96 % of MPs and over 98 % of suspended solids and turbidity at an estimated cost of 0.45 US$ per m3 of permeate. In the PMR stage, ultraviolet light emitting diodes (UV LED) irradiation reduced irreversible fouling, improving permeate flow and minimizing the need for chemical cleaning. The Ce–Y–ZrO2/TiO2 photocatalytic membrane achieved over 99 % removal of turbidity, colour, and suspended solids, as well as 99.9 % removal of MPs, allowing the potential effluent reuse within the hospital laundry. Additionally, the retentate from the PMR process had lower TOC, easing the discharge of this concentrated stream. The cost estimation demonstrated that the photocatalytic degradation combined with traditional techniques, i.e. backflush and chemical cleaning, is more economical than using these techniques separately. Therefore, the total treatment cost was 1.09 US$ per m3 of permeate, which is lower than the cost of fresh water in Denmark. In conclusion, this innovative treatment strategy offers a sustainable and cost-effective solution for HLWW management, not only reducing water consumption by enabling water reuse in the hospital laundry but also advances towards achieving net-zero liquid discharge and contributing to the UN Sustainable Development Goals for clean water (Goal 6) and climate action (Goal 13).

Towards a more sustainable hydrogen energy production: evaluating the use of different sources of water for chloralkaline electrolyzers

Water scarcity is becoming a serious threat for the implementation of energy storage devices based on hydrogen in the inland of dry regions, because of the difficulties in finding appropriate sources of water and the important environmental problem associated with the discharge of rejection saline streams produced during the purification of water. This becomes an opportunity for chloralkaline technology in which the quality of water used is lower than the required in other hydrogen production technologies. This work compares the performance of chloralkaline electrolyzers fed with different solutions of NaCl (from brackish water to brines) with that obtained feeding two natural water samples (seawater and porewater) and three processed waters (rejections streams of the electrodialysis, reverse osmosis of the porewater and a real rejection of the reverse osmosis of a vineyard). All tests were made with a lab-scale 3-D printed cell where, in addition, the performance of Ti/RuO2 and Pb/PbO2 anodes were compared, being always better the results obtained in the tests made with the Ti/RuO2 electrode. Results obtained indicate that efficiencies reached using these alternative sources are lower than those which can be reached using synthetic brines that, in turn, were very near to the standards reached in the chloralkaline industry. In the case of hydrogen production, the maximum values were obtained for seawater, reaching efficiencies proximate to 8.0 mg H2 (Wh)-1. Rejection streams of desalination processes were found to be less efficient, attaining values in the range 2 and 4 mg H2 (Wh)-1. Regarding the production of chlorine and caustic soda, better performances were reached again with seawater (161.7 mg Cl2 (Wh)-1 and 0.092 mg OH- (Wh)-1 ) and with the rejection of the electrodialysis, which also reached sound values of 90.4 mg Cl2 (Wh)-1 and 0.107 mg OH- (Wh)-1. Results obtained are promising and indicate a path that must be further worked for the implementation of a greener water management strategy in the hydrogen -based energy storage technology.

Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. Training involves a tradeoff between prediction bias and variance controlled by model complexity. Increased model complexity decreases prediction bias, increases variance, and increases overfitting possibilities. Overfitting is a significantly smaller training prediction error relative to the trained model prediction error for an independent validation set. Uncertain data generate risks for AI–ML because they increase overfitting and limit generalization ability. Specious confidence in predictions from overfit models with limited generalization ability, leading to misguided water resource management, is the uncertainty-related negative consequence. Improved data is the way to improve AI–ML models. With uncertain water resource data sets, like stream discharge, there is no quick way to generate improved data. Data assimilation (DA) provides mitigation for uncertainty risks, describes data- and model-related uncertainty, and propagates uncertainty to results using observation error models. A DA-derived mitigation example is provided using a common-sense baseline, derived from an observation error model, for the confirmation of generalization ability and a threshold identifying overfitting. AI–ML models can also be incorporated into DA to provide additional observations for assimilation or as a forward model for prediction and inverse-style calibration or training. The mitigation of uncertain data risks using DA involves a modified bias–variance tradeoff that focuses on increasing solution variability at the expense of increased model bias. Increased variability portrays data and model uncertainty. Uncertainty propagation produces an ensemble of models and a range of predictions.

Unexpected hydrologic response to ecosystem state change in tallgrass prairie

In grasslands around the world, climate change is occurring in tandem with woody encroachment (the spread of woody vegetation in grass-dominated ecosystems). State transitions from grassland to shrub/woodland have been identified aboveground via changes in species cover and composition, but the hydrological impact of these transitions is not well understood. Shifts from grass- to woody-dominance have the potential to impact evapotranspiration, soil moisture dynamics, and groundwater recharge. Therefore, it is possible that the consequences of aboveground vegetation change may be observable in the hydrological system. We leveraged long-term hydrological records from a tallgrass prairie site in northeastern Kansas, USA, to examine how concurrent changes in climate and land-cover have altered hydrological dynamics over the last century. Stream discharge has declined in recent decades despite > 100 years of climate wetting. The relationship between incoming precipitation and streamflow has weakened over the last 40 years, suggesting that shifts in the physical landscape are altering patterns of hydrological connectivity. Long-term isotope records show a divergence in the isotopic composition of precipitation (no change in δ18O) and stream/groundwater (decreasing δ18O) over the last decade. These results suggest that woody encroachment is accelerating the hydrological cycle, potentially by decreasing groundwater recharge (via increased evapotranspiration) and/or increasing infiltration rates (via creation of macropores). Holistically, these changes illustrate the interdependence of above- and below-ground processes in the local hydrological cycle, and the cascading long-term consequences (decades to centuries) for critical zone function once woody encroachment has occurred.

Potential Amplifiers of Hydrological Drought: A Deep Dive into the Seasonal Variations of New, Young, and Old Water Fractions in the Stream Discharge of a Pre-Alpine Catchment

This study examines runoff generation processes by focusing on new, young, and old water fractions in the stream discharge within the Swiss pre-Alpine Alp catchment and its smaller tributaries, Erlenbach and Vogelbach. Young water represents a contribution from the three most recent rainfall-runoff events. This research utilises a four-year time series of daily stable water isotope data from stream water and precipitation to investigate seasonal variations. An extended two-component hydrograph separation method is applied to analyse the catchment water and tracer mass balance across both the event and pre-event time axes. The calculated new, young and old water components in stream discharge help estimate time-varying backward travel time distributions. Factors such as air temperature, snow depth, and evapotranspiration data are considered to understand isotope fractionation because of phase changes. The results suggest that discharge generated in autumn and winter may have longer backward travel times compared with spring or summer discharge. Additionally, forest cover influences water fractions older than 40 days. The study identifies a nivo-pluvial runoff regime, with new water fractions peaking in August (Erlenbach, Vogelbach) and January (Alp). Young water fractions reach their highest levels at the start of winter and during snowmelt, while old water fractions peak towards the end of snowmelt and in mid-autumn. The Alp catchment and its tributaries exhibit higher new and young water components than old water following a month of low precipitation and high evapotranspiration in July. These findings are significant in light of the projected drier summers in Central Europe.

Precipitation Patterns, Mountaintop Removal Mining, and the July 2022 North Fork Kentucky River Flood

ABSTRACT Heavy rain over eastern Kentucky during late July 2022 caused catastrophic flooding along the North Fork Kentucky River. A disproportionate number of the 45 deaths attributed to the flood occurred along or near Troublesome Creek, a tributary that has had 25 percent of its watershed affected by mountaintop removal coal mining over the last 40 years. Flood recurrence intervals at gages along the North Fork ranged from 94 years at Jackson to 2 years upstream at Hazard to 850–1,000 years farthest upstream at Whitesburg. The recurrence interval variability is consistent with the spatial distribution of rainfall during the event, which varied by a factor of 3 over the watershed upstream from Jackson. A topographically driven cumulative flux model based upon a cumulative precipitation map, a lidar digital elevation model, and absorption coefficients calibrated to streamflow and precipitation data show that Troublesome Creek contributed 35 percent of the cumulative discharge of the North Fork at the confluence from 28 percent of the upstream watershed area. Comparison of the calibrated model to a hypothetical model that assumes no mining-related reduction in absorption suggests the maximum increase in cumulative stream discharge during the flood potentially attributable to mountaintop removal mining is 22 percent along the North Fork at Whitesburg, 28 percent along the North Fork at Jackson, 41 percent along Troublesome Creek at its confluence with the North Fork, greater than 50 percent upstream along reaches adjacent to mined areas, and 50 to 150 percent in small tributaries emanating from mined areas.

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

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

Drought Characterization With GPS: Insights Into Groundwater and Surface‐Reservoir Storage in California

AbstractDrought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle.

Assessing climate-driven impacts on Karakoram glacier surge: A geospatial analysis highlighting Shishper Glacial Lake outburst flood events (2019–2022) as prime example

The Northern Areas of Pakistan encompass the Hindukush, Karakoram, and Himalayan mountain ranges witnessing glacier surging, exacerbated by climate warming. As glaciers rapidly melt, ravines experience heightened blockage and migration, obstructing stream discharges and forming expansive ice-dammed lakes. The rupture of these natural dams triggers Glacial Lake Outburst Floods downstream in the primary glacier's ravine. The catastrophic Glacial Lake Outburst Floods in 2022 across the Karakoram ranges in Northern Pakistan prompted this study. It focuses on Shishper Glacier Lake. The aim is to provide complete flood observations and their devastating effects on downstream communities. Analysis of Landsat 08 Imagery reveals the evolution of Shishper Glacier Lake from its initiation in November 2018 to the catastrophic GLOF in May 2022. The lake reached a maximum area of 0.32 km2 in 2019 and its successive breaches on June 22, 2019, and May 29, 2020, reduced it to 0.018 km2. Draining continued until July 2021, shrinking the lake area to 0.009 km2. A noteworthy 2.73 °C temperature increase in 2022 correlated with an expansion of the lake area to 0.33 km2, culminating in the GLOF on May 7th, 2022. The study emphasizes the critical need for mapping, assessing, and monitoring surging glaciers and glacier-formed lakes in the Karakoram ranges to safeguard downstream communities from potential hazards.

Calibration and Validation of Semi-distributed Model of Monthly Stream Discharge Using SUFI-2 Algorithm for Shimsha Catchment, Karnataka, India

Calibration and Validation of Semi-distributed Model of Monthly Stream Discharge Using SUFI-2 Algorithm for Shimsha Catchment, Karnataka, India

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 eventas 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.

Investigating the impact of irrigation practices on hydrologic fluxes in a highly managed river basin

Irrigation practices and sources can have significant impacts on water resources and the hydrologic fluxes that control these resources. To better manage water resources and future water supply, the influence of irrigation practices and management on these hydrologic fluxes should be quantified in time and space at varying scales, under potential irrigation management practices. To fulfill this objective, a surface-subsurface modeling approach was applied to simulate watershed-scale hydrologic processes in the Cache la Poudre River Basin, Colorado, USA (4824 km2), in which both surface water irrigation and groundwater irrigation are prevalent. The model chosen for this study is the watershed model SWAT+, using the spatially distributed, physically based groundwater module gwflow, in which unconfined groundwater storage, flows, and interaction with land surface features are simulated using a collection of grid cells that represent control volumes of the aquifer. Major groundwater inflows and outflows include pumping, recharge, groundwater-channel exchange, groundwater-lake exchange, and tile drainage outflow. To investigate the impact of irrigation practices, detailed surface and groundwater irrigation routines and canal-aquifer interactions were added to the SWAT+ source code, requiring information of irrigation sources and irrigation canal locations throughout the river basin. Model calibration and testing was performed using monthly stream discharge and groundwater head. The calibrated model is used to quantify the impact of surface water and groundwater irrigation scenarios on water availability and hydrologic fluxes within the river basin. A total of 22 scenarios were conducted and grouped into five main groups: irrigation source, irrigation amount, irrigation type, canal bed thickness, and partial or full sealing of earthen irrigation canals. Using groundwater as the only irrigation source decreases groundwater discharge to streams (by 14 %) due to lowering groundwater levels; converting flood irrigation to sprinkler irrigation throughout the basin decreases surface runoff by 22 %; and sealing earthen canals leads to a lowering of groundwater levels, which decreases groundwater discharge to streams by 9 %, leading to an overall decrease in streamflow in the Cache la Poudre River and changes to temporal patterns in streamflow. Overall, irrigation amount and type and canal sealing have a small impact on total groundwater storage, compared to changes in the percent of fields irrigated by groundwater pumping. Results are helpful for informed decision-making in agriculture water management and can lead to sustainable, efficient, and equitable use of water resources, helping to address the challenges posed by water scarcity and environmental conservation.

On the influence of rock glacier dynamics on the runoff in basin of the Ulken Almaty (Bolshaya Almatinka) River, Northern Tien Shan

The purpose of the work was to study the dynamics of mountain stone glaciers and streams flowing down from them for an understanding their hydrological significance. Two glaciers: Morenny and Gorodetsky, were analyzed, both located in the river Ulken Almaty (Big Almatinka) basin, Northern Tien Shan. On the average, the rate of surface displacement of the Morennoye glacier is higher than that of the Gorodetsky, which is associated with increased ice content of the last one. The increase in rates of surface displacement of both stone glaciers revealed in 2021–2022 when compared to previous years turned out to be consistent with the widely discussed idea of intensification of such glaciers dynamics under the present-day climate changes and glacier retreats. It was found that the stone glaciers have a pronounced influence on the water regime, temperature and chemical composition of watercourses formed by water running out from them. They are distinguished by the almost complete absence of intraday fluctuations in the level, temperature, and specific electrical conductivity of water, which are more pronounced in rivers and streams with glacial alimentation during the summer time. Some waterflows from the Gorodetsky glacier do not freeze in winter, which is indicative of significant groundwater reserves in its body. The waters of each of the above stone glaciers differ in their characteristic mineralization, temperature, and the ratio of the major ions. The stable water flow from such glaciers can play an important role in maintaining a river runoff during periods of the low stream discharge.

Stream chemical response is mediated by hydrologic connectivity and fire severity in a Pacific Northwest forest

AbstractLarge‐scale wildfires are becoming increasingly common in the wet forests of the Pacific Northwest (USA), with predicted increases in fire prevalence under future climate scenarios. Wildfires can alter streamflow response to precipitation and mobilize water quality constituents, which pose a risk to aquatic ecosystems and downstream drinking water treatment. Research often focuses on the impacts of high‐severity wildfires, with stream biogeochemical responses to low‐ and mixed‐severity fires often understudied, particularly during seasonal shifts in hydrologic connectivity between hillslopes and streams. We studied the impacts of the 2020 Holiday Farm Fire at the HJ Andrews Experimental Forest where rare pre‐fire stream discharge and chemistry data allowed us to evaluate the influence of mixed‐severity fire on stream water quantity and quality. Our research design focused on two well‐studied watersheds with low and low‐moderate burn severity where we examined long‐term data (pre‐ and post‐fire), and instantaneous grab samples collected during four rain events occurring immediately following wildfire and a prolonged dry summer. We analysed the impact of these rain events, which represent the transition from low‐to‐high hydrologic connectivity of the subsurface to the stream, on stream discharge and chemistry behaviour. Long‐term data revealed total annual flows and mean flows remained fairly consistent post‐fire, while small increases in baseflow were observed in the low‐moderately burned watershed. Stream water concentrations of nitrate, phosphate and sulfate significantly increased following fire, with variance in concentration increasing with fire severity. Our end member mixing models suggested that during rain events, the watershed with low‐moderate severity fire had greater streamflow inputs from soil water and groundwater during times of low connectivity compared to the watershed with low severity fire. Finally, differences in fire severity impacts on concentration‐discharge relationships of biogenic solutes were most expressed under low catchment connectivity conditions. Our study provides insights into post‐wildfire impacts to stream water quality, with the goal of informing future research on stream chemistry responses to low, moderate and mixed severity wildfire.

Field measurement of entrapped pore‐air pressure and the effect of rising groundwater level in the soil layer

AbstractUnsaturated soil contains pore air, some of which becomes entrapped in the soil layer during the infiltration process. Such entrapped pore air in the soil layer inhibits infiltration, decreases hydraulic conductivity, and causes erroneous estimates of groundwater response. Experimental studies suggest an effect of compressed entrapped pore air on stream discharge. However, few studies have investigated the behaviour of pore air in the field, and no method for measurement of pore‐air behaviour has been established. We measured pore‐air pressure (Pair) with a simple handmade probe, combined with atmospheric pressure (Patm) and groundwater level. The entrapment and compression of pore air were detected based on the pressure difference (ΔP = Pair – Patm). Observations were conducted for approximately 1 year in two small headwater catchments (TC and HA, which have differences in soil depth and groundwater level dynamics) in Ibaraki Prefecture, Japan. Positive responses of ΔP during some rainfall events were confirmed at both TC and HA. Pore‐air entrapment occurred even during weak rainfall events with rainfall intensity of <10 mm/h. During a typhoon event in October 2019, a maximum ΔP of 3.9 kPa was observed at HA. The pore pressure increase recorded by the tensiometer was mostly explained by the increase in ΔP, which was supported by the soil moisture data. Therefore, we concluded that our handmade measurement system could successfully measure Pair and detect pore‐air entrapment in the field. This showed that Pair is not negligible. Total event rainfall was related to the maximum ΔP. It also showed that the rise in the groundwater level plays a significant role in pressurization of the entrapped pore air. We present the first field data demonstrating the effect of groundwater level rise on the pressurization of entrapped Pair.

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment

AbstractIntact peatlands provide hydrological ecosystem services, such as regulating water regimes and immobilizing pollutants within catchments. Climate change impacts including drought and wildfire may impair their functioning, potentially impacting ecosystem service delivery. Here we investigate stream water quality changes following the combined impacts of a summer drought and wildfire in a peat‐dominated catchment in the UK during 2018. The study catchment stores legacy pollutants (i.e., metals) due to past industrial activity, thus making it particularly susceptible to pollutant release during wildfires. We quantified changes in water chemistry during five storm events over a 9‐month period following the wildfire. Concentration‐discharge (C‐Q) relationships for nine solutes were analyzed to explore changes in activation and connectivity of solute source zones. Hysteresis and flushing indices of C‐Q responses further described solute dynamics during storm events. We found that most nutrient and base cation concentrations in the stream discharge were highest in the immediate post‐fire storm events and decreased during subsequent autumn and spring storms. Metal concentrations increased during autumn and spring storms, indicating delayed mobilization from within‐peat or distal headwater sources. Our findings suggest that seasonal re‐wetting and hydrologic connectivity following disturbance influenced solute source zone activation and transport in the study catchment. Water quality responses associated with wildfire and drought were primarily observed in the months following the wildfire, suggesting mobilization of pollutants peaks shortly after fire. Our results contribute to a critical understanding of the future of water quality risks in temperate peatland catchments subject to disturbances exacerbated by climate change.

Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins

The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m3/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity ck=2.619meterspersecond,m/s±0.556percent and dynamic celerity cd=3.533m/s±0.181percentandtheiruncertainties were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.

Analysis of streamflow and rainfall trends and variability over the Lake Kariba catchment, Upper Zambezi Basin

ABSTRACT Hydro-meteorological trend analysis is critical for assessing climate change and variability at basin and regional levels. This study examined the long- and short-term trends from stream discharge and rainfall data in the Lake Kariba catchment. A trend and change point analysis was carried out on the mean, minimum and maximum monthly average time series for 14 gauging stations that are located within the Kariba catchment. The Mann–Kendall and the Pettitt tests were used to determine the trend and any changes in the long-term average of the time series. The magnitude of the trend was determined by Sen's slope method. The results indicate that generally there has been a decreasing trend in river and rainfall long-term mean values across the catchment. A statistically significant trend (p ≤ 0.05) was observed at Zambezi River at Lukulu, Senanga and Victoria Falls, with a positive correlation in Pearsons's coefficient of water levels and rainfall at Lukulu (0.312) and Senanga (0.365). The decrease in the time series trend and the change point observed have been attributed to anthropogenic activities, climate change and variability impact on the catchment. The findings are critical for climate risk management and reduction decisions for near- and long-term timescales.

Combined effects of geological heterogeneity and discharge events on groundwater and surface water mixing

Stream water (SW) infiltration to the subsurface and subsequent mixing with groundwater (GW) is crucial for controlling water quality in river-corridors. Mixing of solutes from SW and GW triggers biogeochemical reactions, which can attenuate contaminant influx from upstream SW or from GW that eventually enters the river in downstream regions. It is known that geological heterogeneity affects SW-GW exchange fluxes (EF), however, the combined effects of both hydrological and geological aspects (e.g., sand fraction present in the aquifer material) on EF and on SW-GW mixing in strong contrasting bimodal aquifer units remain unclear. Here, we examined this gap by combining geostatistical realizations, fully-integrated numerical flow simulations, and a mixing-cell routine to assess the major controls on EF and to evaluate how mixing develops and is affected by stream discharge events with different magnitudes and durations. Results show that subsurface heterogeneity at the river-reach scale mainly affects EF magnitudes rather than EF patterns. Yet, both EF magnitudes and SW-GW mixing increased with the introduction of subsurface heterogeneity and with the increase of average hydraulic conductivity (K) values and sand fraction in the models. The simulations further indicated a larger potential for mixing under more frequent, short events regardless of the aquifer material, however, mixing values were generally higher for sandier and heterogeneous models. Lastly, for a high K contrast between subsurface units, these effects were more pronounced. This characterization is critical for river restoration strategies and for downstream management of dam-regulated rivers. Our study elucidates the interplay between hydrological and geological controls on the development of SW-GW mixing at intermediate scales and highlights the importance of considering aquifer characteristics in future studies.