Abstract. For analysing karst hydrogeological systems, observations of karst springs and cave drips are considered indispensable. In addition to hydrometric observations, knowing the oxygen and hydrogen stable isotope ratios has improved the understanding of vadose zone and aquifer dynamics, likewise supporting system characterisation and modelling. However, limited accessibility and high costs of the analysis of stable isotopes in karst aquifers have hindered progress in karst research and impeded the accurate understanding of karst processes especially when it comes to comparative or large-scale studies. In this study, we present our workflow to compile the WoKaS-Iso database, the first extensive collection of time series data for Oxygen-18 and Deuterium isotopes in karst springs and cave drip water from diverse sources, encompassing publications, theses, reports, online archives, and collaborative initiatives worldwide. The database incorporates data sourced from 236 springs and 74 caves, comprising in total 997 time series (379 time series for the springs and 618 time series for the cave drip water). These datasets provide coverage across significant karst regions globally, spanning China, the USA, Europe, the Middle East, and Australia. Within datasets, 79% for springs and 68% for cave drip water exhibit resolutions finer than monthly intervals. In addition, by integrating isotopic records with ancillary environmental variables including spring discharge, cave drip rate, precipitation, and rainwater isotopes, the database offers a more comprehensive perspective on hydrological behaviours in karst aquifers, hence advancing hydrogeological characterisation and modelling. The WoKaS-Iso database not only deepens the understanding of the complex systems but also promotes sustainable water resource management as well as the potential to foster collaborative research. The database can be accessed at: https://doi.org/10.25532/OPARA-909.

Reservoir sedimentation poses a critical threat to water storage capacity globally, particularly in the U.S. Great Plains where storage loss has continually declined over the past several decades. While regional management often prioritizes streambank stabilization, effectively targeting mitigation requires distinguishing between channel-derived and upland sediment sources across varying flow regimes. This study employed sediment fingerprinting to apportion streambank, cropland and grassland contributions to the Cottonwood River and the downstream John Redmond Reservoir, utilizing a multi-method sampling approach to capture different temporal scales of sediment transport. For flows contained within the river channel, time-integrated traps identified streambanks as the dominant sediment source (similar to 50%), whereas discrete storm samples underestimated bank contributions (similar to 35%). Statistical analysis of flow regimes and trap positioning indicates this discrepancy is not driven by vertical stratification of the sediment load or particle size differences but rather by the ability of time-integrated samplers to capture pulses of sediment transport that discrete sampling misses. In contrast, lakebed deposits in John Redmond Reservoir, which represent a longer-term record of sediment transported by large-magnitude floods, were dominated by cropland sources (similar to 40%). The event-driven shifts in sediment provenance suggest a hydro-geomorphic threshold: during extreme, out-of-bank flows, hydrologic connectivity expands beyond the channel to the cultivated floodplain, mobilizing vast quantities of surficial cropland soil in exceedance of the river's bank-dominated internal load. Consequently, watershed management faces a dual challenge: streambank stabilization is essential to reduce the chronic sediment pulses delivered during routine within-bank flows, but soil conservation on croplands is equally critical to mitigate the large-scale sediment delivery associated with extreme out-of-bank floods.

The isotopic ratios of nitrate (δ15NNO3 and δ18ONO3) are common tracers of nitrate source and transformation in soil and aquatic systems. However, investigation of their seasonality is limited owing to seldom reported multi-year datasets. This study collects groundwater and springhead samples, measures and analyzes a six-year dataset from the inner bluegrass karst region of central Kentucky, USA to analyze seasonal patterns of nitrate isotopic ratios and investigate their controls. We observe distinct isotopic measurements in groundwater and at the springhead during the wetter winter-spring season (δ15NNO3 = 6.0 ± 1.6‰ and δ18ONO3 = 1.6 ± 2.3‰) compared to the drier fall season (δ15NNO3 = 9.0 ± 1.1‰ and δ18ONO3 = 3.4 ± 1.7‰). Results of time series analyses and empirical mode decomposition suggest the seasonality of the isotopic ratios in the aquifer are controlled by intra-annual variability of soil nitrate isotopic composition, with leaching of nitrate mineralized from soil nitrogen dominating the winter signal and partially denitrified nitrate dominating the fall signal. Results show seasonality of the denitrification line and seasonality of the Rayleigh diagram attributed to the dominant soil nitrogen source and the denitrification controls. Meta-analysis of data from 28 journal papers reporting δ15NNO3 and δ18ONO3 measurements of groundwater and streamwater show a continuum of denitrification-controlled to end-member-controlled results. Groundwater and surface water studies with a dominant end-member of nitrate show potential estimating denitrification rates with assistance from δ15NNO3 and δ18ONO3 as tracers. Studies with water exhibiting relatively fast transport from multiple nitrate sources show potential for δ15NNO3 and δ18ONO3 as tracers in end-member mixing. Researchers should exercise caution when multiple sources exhibit some denitrification influence, as equifinality will be problematic in numerical modelling.

Abstract Extreme floods and historical public policies both leave lasting impacts. We analyzed flood exposure across the neighborhoods of 202 US cities historically assessed for lending risk by the Home Owners’ Loan Corporation on a scale from ‘best (A) to ‘hazardous’ (D). Using high-resolution flood modeling and dasymetric population mapping, we found that D-graded neighborhoods bear a disproportionate flood burden in roughly three-quarters of cities. This inequality follows a systematic gradient: A- and B-graded neighborhoods are underrepresented in the flooded population, C-graded neighborhoods are proportionately represented, and D-graded neighborhoods are overrepresented. A greater fraction of D-graded residents are exposed to the 100-yr flood (6.4%) than A-graded residents to the 1000-yr flood (5.9%) with the gap widening at higher-magnitude floods. Flood disparities are most pronounced in the Midwest and Mid-Atlantic regions, aligning with the historical concentration of industrial labor and redlined neighborhoods along waterways. However, we find that proximity to waterways alone does not explain the observed inequality: flood disparities persist even among neighborhoods far from major rivers and coasts, implicating infrastructure deficits as a compounding factor. Neighborhoods marked as ‘hazardous’ nearly a century ago remain disproportionately vulnerable today, underscoring the need for equitable urban planning.

Excess riverine nitrate causes downstream eutrophication, notably in the Gulf of Mexico where hypoxia is linked to nutrient-rich discharge from the Mississippi River Basin (MRB). We developed a long short-term memory (LSTM) model using high-frequency sensor data from across the conterminous US to predict daily nitrate concentrations, achieving strong temporal validation performance (median KGE = 0.60). Spatial validation-or prediction in unmonitored basins-yielded lower performance for nitrate concentration (median KGE = 0.18). Nonetheless, spatial validation was crucial in quantifying the impact of current data gaps and guiding the model's targeted application to the MRB where spatial validation performance was stronger (median KGE = 0.34). Modeling results for the MRB from 1980 to 2022 showed relatively low riverine nitrate export (19 ± 4% of surplus), indicating large-scale retention of surplus nitrate within the MRB. Interannual nitrate yields varied significantly, especially in Midwestern states like Iowa, where wet-year export fractions (42 ± 24%) far exceeded dry year export (6 ± 6%), suggesting increased hydrologic connectivity and remobilization of legacy nitrogen. Further evidence of legacy nitrate remobilization was noted in a subset of Midwestern basins where, on occasion, annual surplus export fractions exceeded 100%. Interpretable Shapley values identified key spatial drivers influencing mean nitrate concentrations-tile drainage, roadway density, wetland cover-and quantitative, non-linear thresholds in their influence, offering management targets. This study leverages machine learning and aquatic sensing to provide improved spatiotemporal predictions and insights into nitrate drivers, thresholds, and legacy impacts, offering valuable information for targeted nutrient management strategies in the MRB.

Researchers use sediment hysteresis in watershed sedimentation studies, however underlying processes controlling sediment hysteresis observations remain an open topic of investigation. We investigate the hypothesis that baseflow water and sediment can control sediment hysteresis in some cases by: (i) modelling water- -sediment mixing permutations that considers baseflow and runoff with their own sediment concentration distributions; (ii) analyzing sediment hysteresis for a karst basin with high baseflow contributions in Kentucky, USA by decoupling baseflow and runoff hysteresis via sensor data, a mixing model, and sediment transport modelling; and (iii) analyzing the alternative hypothesis of sediment origin controlling hysteresis for this system using modelling and tracing of sediment origin with stable isotopes. Results from mixing model permutations show that changes to the timing and magnitude of baseflow water and its sediment concentration can shift hysteresis looping from clockwise (HI > 0.1) to counterclockwise (HI <-0.1). Varying the baseflow contribution can reproduce most of the sediment hysteresis results reported in the literature, such as single loops, double loops, figure eights and complex loops. Results from the Kentucky basin where baseflow contributions are high show the dominance of a new taxonomy of sediment hysteresis loops called a 'J-loop'. 71 % of the loops observed were J-loops while the remaining 29 % were complex loops. The J-loop occurs when baseflow dominates over runoff for a hydrologic event and the baseflow to runoff volume ratio falls between 1.5 and 3. Analyses of the alternative hypothesis show that looping patterns do not depend on sediment origin for the events studied. Sediment origin varied by dominance of the distal sediment source (26 % of events), proximal source (55 %), and nearly equal mixture of the two sources (18 %). J-loops and complex loop occurrence was not consistent with any sediment origin dominance. We analyzed 43 sediment hysteresis studies reported in hydrology journals and found many studies show hysteresis that resembles J-loops. These occur during events with low antecedent moisture, low-intensity rain, and low amounts of runoff-all of which point towards baseflow dominance. The results herein suggest the importance of the J-loops in systems with high baseflow contributions as well as the overall influence of baseflow to impact loop interpretations. This result is relevant because recent findings show that the majority of hydrologic events in many regions are dominated by baseflow. In such systems, the baseflow contribution and it's control on sediment hysteresis looping challenges the common interpretation that hysteresis loops reflect proximal and distal sediment sources.

Watershed water quality models are mathematical tools used to simulate processes related to water, sediment, and nutrients. These models provide a framework that can be used to inform decision-making and the allocation of resources for watershed management. Therefore, it is critical to answer the question “when is a model good enough?” Established performance evaluation criteria, or thresholds for what is considered a ‘good’ model, provide common benchmarks against which model performance can be compared. Since the publication of prior meta-analyses on this topic, developments in the last decade necessitate further investigation, such as the advancement in high performance computing, the proliferation of aquatic sensors, and the development of machine learning algorithms. We surveyed the literature for quantitative model performance measures, including the Nash-Sutcliffe efficiency (NSE), with a particular focus on process-based models operating at fine temporal scales as their performance evaluation criteria are presently underdeveloped. The synthesis dataset was used to assess the influence of temporal resolution (sub-daily, daily, and monthly), calibration duration (< 3 years, 3 to 8 years, and > 8 years), and constituent target units (concentration, load, and yield) on model performance. The synthesis dataset includes 229 model applications, from which we use bootstrapping and personal modeling experience to establish sub-daily and daily performance evaluation criteria for flow, sediment, total nutrient, and dissolved nutrient models. For daily model evaluation, the NSE for sediment, total nutrient, and dissolved nutrient models should exceed 0.45, 0.30, and 0.35, respectively, for ‘satisfactory’ performance. Model performance generally improved when transitioning from short (< 3 years) to medium (3 to 8 years) calibration durations, but no additional gain was observed with longer (> 8 years) calibration. Dissolved nutrient models calibrated to load (e.g., kg/s) out-performed those calibrated to concentration (e.g., mg/L), whereas selection of target units was not significant for sediment and total nutrient models. We recommend the use of concentration rather than load as a water quality modeling target, as load may be biased by strong flow model performance whereas concentration provides a flow-independent measure of performance. Although the performance criteria developed herein are based on process-based models, they may be useful in assessing machine learning model performance. We demonstrate one such assessment on a recent deep learning model of daily nitrate prediction across the United States. The guidance presented here is intended to be used alongside, rather than to replace, the experience and modeling judgement of engineers and scientist who work to maintain our collective water resources.

Large-scale hydrologic models are increasingly being developed for operational use in the forecasting and planning of water resources. However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr−1, road density greater than 5 km km−2, and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models.

ABSTRACTWhile tracing the sources of fluvial sediment using carbon and nitrogen stable isotopic ratios (δ13C and δ15N) has progressed significantly over the last two decades, the conservativeness of these tracers remains questionable. Recent work indicates that δ13C and δ15N alterations in streambed deposition zones likely represent the largest source of uncertainty impacting usefulness of the isotopic ratios as tracers. Here we report a 14‐year dataset of δ13C and δ15N of fluvial sediment from a streambed‐dominated basin in Kentucky, USA, and employ empirical model decomposition (EMD) to identify dominant temporal trends that may impact conservativeness. Results from EMD show significant seasonality of δ13C and δ15N for sediment as well as underlying multi‐year variation. The seasonal and multi‐year variance account for 72% and 50% of the total data variation for δ13C and δ15N, respectively. The prominent seasonality for δ13C and δ15N show a mean intra‐annual change of 0.6‰ and 1.1‰, respectively, and the seasonal change is attributed to algal accrual and organic matter turnover in the streambed sediment deposits. Mixing model simulations show that the mean streambed isotopic ratios should be separated from other sediment sources by 3.0‰ and 3.6‰ for δ13C and δ15N, respectively, to achieve 90% accuracy in source apportionment when the isotopic ratios are used independently; and the mean streambed value of both isotopic ratios should be separated from other sediment sources by 3.0‰ when δ13C and δ15N are used in combination. Our results lead to the recommendation that isotope ratios of sources be separated by at least 3‰ when the streambed is expected to be a prominent sediment source, which far exceeds the prior recommendation of 1‰ mean separation of sources.

Eogenetic karst aquifers that maintain high carbonate bedrock permeability can have distinctive aquifer hydrodynamics that would be captured in hysteresis behaviour. Analysing the hysteresis behaviour of these systems can be a concise and efficient method to provide insight into the dominant recharge mechanisms, porosity integration and contributing land uses to spring discharge. The availability of deployable water quality sensors that can monitor spring water chemistry at high temporal resolution across multiple events allows us to capture delayed or attenuated signals that may occur in eogenetic karst. Additionally, analysing hysteresis across multiple events can identify patterns in the responses that occur. The Upper Floridan Aquifer (UFA) is an example of an eogenetic karst aquifer that has extensive phreatic conduits embedded in a high permeability carbonate matrix. We collected high temporal resolution (15 min) discharge, specific conductance and nitrate. We analysed storm-induced changes to nitrate and specific conductance and quantified hysteresis by calculating the hysteresis index (HI) and complementary flushing index (FI) at two major springs that drain agriculturally dense regions of the UFA. The combined analysis of HI and FI showed that 95% of all events had delayed connectivity of discrete feature recharge emerging at each spring. Mobilisation (FI > 0) of specific conductance occurred 95% of the time at both springs, but nitrate was diluted (FI < 0) 85% of the time at one spring and mobilised 95% of the time at the other. Changes to both specific conductance and nitrate were less than 15% of the pre-storm values, which illustrates how discrete recharged water mixes with substantial volumes of older, stored water in the aquifer. The hysteresis behaviour in the UFA was in contrast with a telogenetic setting, where higher variability in nitrate responses occurred between events, dilution was mostly observed for specific conductance and nitrate, and responses varied between delayed and rapid connectivity. Our work quantifies the storm-induced hydrodynamics of an eogenetic karst aquifer, which can help guide local water resource management decisions and advance our knowledge of karst aquifer hydrodynamics across a wide range of diagenetic and karstification stages.

Streams draining karst areas with rapid groundwater transit times may respond relatively quickly to nitrogen reduction strategies, but the complex hydrologic network of interconnected sinkholes and springs is challenging for determining the placement and effectiveness of management practices. This study aims to inform nitrogen reduction strategies in a representative agricultural karst setting of the Chesapeake Bay watershed (Fishing Creek watershed, Pennsylvania) with known elevated nitrate contamination and a previous documented groundwater residence time of less than a decade. During baseflow conditions, streamflow did not increase with drainage area. Headwaters and the main stem lost substantial flow to sinkholes until eventually discharging along large springs downstream. Seasonal hydrologic conditions shift the flow and nitrogen load spatially among losing and gaining stream sections. A compilation of nitrogen source inputs with the geochemistry and the pattern of enrichment of δ15N and δ18O suggest that the nitrogen in streams and springs during baseflow represents a mixture of manure, fertilizer, and wastewater sources with low potential for denitrification. The pH and calcite saturation index increased along generalized flow paths from headwaters to springs and indicate shorter groundwater residence times in baseflow during the spring versus summer. Given the substantial investment in management practices, fixed monitoring sites could incorporate synoptic water sampling to properly monitor long-term progress and help inform management actions in karst watersheds. Although karst watersheds have the potential to respond to nitrogen reduction strategies due to shorter groundwater residence times, high nitrogen inputs, effectiveness of conservation practices, and release of legacy nutrients within the karst cavities could confound progress of water quality goals.

Freshwater benthic algae form complex mat matrices that can confer ecosystem benefits but also produce harmful cyanotoxins and nuisance taste-and-odor (T&O) compounds. Despite intensive study of the response of pelagic systems to anthropogenic change, the environmental factors controlling toxin presence in benthic mats remain uncertain. Here, we present a unique dataset from a rapidly urbanizing community (Kansas City, USA) that spans environmental, toxicological, taxonomic, and genomic indicators to identify the prevalence of three cyanotoxins (microcystin, anatoxin-a, and saxitoxin) and two T&O compounds (geosmin and 2-methylisoborneol). Thereafter, we construct a random forest model informed by game theory to assess underlying drivers. Microcystin (11.9 ± 11.6 µg/m2), a liver toxin linked to animal fatalities, and geosmin (0.67 ± 0.67 µg/m2), a costly-to-treat malodorous compound, were the most abundant compounds and were present in 100 % of samples, irrespective of land use or environmental conditions. Anatoxin-a (8.1 ± 11.6 µg/m2) and saxitoxin (0.18 ± 0.39 µg/m2), while not always detected, showed a systematic tradeoff in their relative importance with season, an observation not previously reported in the literature. Our model indicates that microcystin concentrations were greatest where microcystin-producing genes were present, whereas geosmin concentrations were high in the absence of geosmin-producing genes. Together, these results suggest that benthic mats produce microcystin in situ but that geosmin production may occur ex situ with its presence in mats attributable to adsorption by organic matter. Our study broadens the awareness of benthic cyanobacteria as a source of harmful and nuisance metabolites and highlights the importance of benthic monitoring for sustaining water quality standards in rivers.

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Nitrate pollution of water bodies is a critical issue in many parts of the world because of its negative effects on aquatic ecosystem and human health. Effective management of pollution, such as the continuous or instantaneous release from point-sources, requires an understanding – with high spatial and temporal resolution – of how nitrate is dispersed and cycled within rivers. Nitrate sensing data show promise for this purpose, but their integration into numerical models is scarce; thus, questions remain regarding the necessary spatial grid size and temporal resolution required to resolve sensor readings. In this study, we developed an unsteady two-dimensional model to simulate nitrate transport, dispersal, and cycling along a 33-km stretch of the Kansas River (USA), following a strategic release of nitrogen from a decommissioned fertilizer plant. To validate modeled estimates of dispersion and uptake, we integrated 15-minute nitrate and temperature data from two aquatic sensors, one located proximal to the fertilizer release point and a second further downstream after complete lateral mixing. Model results at the site near to the contamination (0.4 km) were highly sensitive to river grid size and turbulent mixing, but insensitive to uptake. Results at the site far downstream of the contamination (31 km) were unaffected by grid size or mixing parameterization but were very sensitive to selection of uptake rate. High-frequency sensors allowed us to resolve diel variability in nitrate signals, which we incorporated into the model to improve performance and model realism. The 33-km study reach assimilated 14% of the total nitrate load in the river, or approximately half of what was contributed by the fertilizer release, during the two-month study period. Regarding nitrate cycling, modeled Cdiel/Cmax ranged from 0.04 to 0.11 whereas sensor observations showed much higher Cdiel/Cmax values of 0.11 to 0.25. Disagreements between data observations and model simulations in cycling are hypothesized to exist due to potential breakdown of the first-order rate kinetics. Together, our study shows the potential of combining numerical models and high-frequency data for a better understanding of the physical and biogeochemical processes that control nitrate dynamics in aquatic environments.

Land use change threatens aquatic ecosystems through freshwater salinization and sediment pollution. Effective river management requires an understanding of the dominant hydrologic pathways of sediment and solute delivery. To address this, we applied hysteresis analysis, hydrograph separation, and linear regression to hundreds of events across a decade of specific conductance and turbidity data from three streams along a rural-to-urban gradient. Thereafter, we developed an index (βrunoff') to quantify the relative influence of surface runoff to event-scale suspended sediment generation, where a value of '1' indicates complete alignment of suspended sediment generation with the temporal structure of runoff whereas '0' indicates total alignment with baseflow. Solute hysteresis results showed a predominance of dilution for the rural and mixed-use streams irrespective of road salt presence. On the other hand, urban stream behavior shifted from dilution to flushing following salt application, which was largely driven by greater runoff coefficients and the connectivity of distal solutes to the stream corridor. The newly developed index (βrunoff') indicated that suspended sediment dynamics were more aligned with runoff in all three streams: rural stream (βrunoff' = 0.70), mixed stream (βrunoff' = 0.57), and urban stream (βrunoff' = 0.64). The relative importance of baseflow to sediment generation grows slightly in urbanizing streams, as impervious surfaces disconnect upland sediment, which would otherwise transport with runoff, while piston-flow baseflow erodes exposed streambanks. Our findings emphasize the need to consider the impact of human modification of the landscape on solute and sediment transport in freshwater systems for effective water quality management. Further, our βrunoff' index provides a useful tool for assessing the relative influence of surface runoff on event-scale solute or sediment generation in streams, supporting river management and conservation efforts.

Understanding the physics of nitrate contamination in surface and subsurface water is vital for mitigating downstream water quality impairment. Though high frequency sensor data have become readily available and computational models more accessible, the integration of these two methods for improved prediction is underdeveloped. The objective of this study was to utilize high‐frequency data to advance our understanding and model representation of nitrate transport for an agricultural karst spring in Kentucky, USA. We collected 2‐years of 15‐min nitrate and specific conductance data and analyzed source‐timing dynamics across dozens of events to develop a conceptual model for nitrate hysteresis in karst. Thereafter, we used the sensing data, specifically discharge‐concentration indices, to constrain modeled nitrate prediction bounds as well as the uncertainty of hydrologic and nitrogen processes, such as soil percolation and biogeochemical transformation. Observed nitrate hysteresis behavior at the spring was complex and included clockwise ( n = 11), counterclockwise ( n = 13), and figure‐eight ( n = 10) shapes, which contrasts with surface systems that are often dominated by a single hysteresis shape. Sensing results highlight the importance of antecedent connectivity to nitrate‐rich storages in determining the timing of nitrate delivery to the spring. After integrating hysteresis analysis into our numerical model evaluation, simulated nitrate prediction bounds were reduced by 43 ± 12% and parameter uncertainty by 36 ± 20%. Taken together, this study suggests that discharge‐concentration indices derived from high‐frequency sensor data can be successfully integrated into numerical models to improve process representation and reduce modeled uncertainty.

The Ohio River Basin (ORB) is responsible for 35% of total nitrate loading to the Gulf of Mexico yet controls on nitrate timing require investigation. We used a set of submersible ultraviolet nitrate analyzers located at 13 stations across the ORB to examine nitrate loading and seasonality. Observed nitrate concentrations ranged from 0.3 to 2.8 mg L−1 N in the Ohio River's mainstem. The Ohio River experiences a greater than fivefold increase in annual nitrate load from the upper basin to the river's junction with the Mississippi River (74–415 Gg year−1). The nitrate load increase corresponds with the greater drainage area, a 50% increase in average annual nitrate concentration, and a shift in land cover across the drainage area from 5% cropland in the upper basin to 19% cropland at the Ohio River's junction with the Mississippi River. Time‐series decomposition of nitrate concentration and nitrate load showed peaks centered in January and June for 85% of subbasin‐year combinations and nitrate lows in summer and fall. Seasonal patterns of the terrestrial system, including winter dormancy, spring planting, and summer and fall growing‐harvest seasons, are suggested to control nitrate timing in the Ohio River as opposed to controls by river discharge and internal cycling. The dormant season from December to March carries 51% of the ORB's nitrate load, and nitrate delivery is high across all subbasins analyzed, regardless of land cover. This season is characterized by soil nitrate leaching likely from mineralization of soil organic matter and release of legacy nitrogen. Nitrate experiences fast transit to the river owing to the ORB's mature karst geology in the south and tile drainage in the northwest. The planting season from April to June carries 26% of the ORB's nitrate and is a period of fertilizer delivery from upland corn and soybean agriculture to streams. The harvest season from July to November carries 22% of the ORB's nitrate and is a time of nitrate retention on the landscape. We discuss nutrient management in the ORB including fertilizer efficiency, cover crops, and nitrate retention using constructed measures.

&lt;p&gt;Karst characterizes almost 15% of the worlds terrain, however the mechanics of sediment transport and its prediction in karst river and cave systems remains underdeveloped. Hysteresis analysis has recently been used more to investigate the behaviors of sediments during storm events in surface systems and to some extent in karst systems. Historically, clockwise and counter-clockwise hysteresis typically refer to proximal and distal sourcing for streams. For karst systems, clockwise and counter-clockwise hysteresis has been identified to refer to an saturated and unsaturated aquifer prior to the event.&lt;/p&gt; &lt;p&gt;However, most interpretation of hysteresis assumes a single dominant water source, for example runoff, and assumes that baseflow is not contributing to the sediment load. One aspect of sediment hysteresis and its interpretation that has received less attention is the occurrence of several, significant water sources, eroding and delivering sediment to the watershed outlet. It is common for both surface stream systems &amp;#160;and karst subsurface systems to have multiple water sources contributing to the total sediment load. Each of the sources carries their own sediment time distribution, and often lead to complex hysteresis looping behavior after mixing. The primary goals of this work are to (1) study how the complex source water-sediment mixing processes impact hysteresis results and (2) to carry out solutions to the water-sediment mixing processes for karst streams, caves, and springs and show the utility and uncertainty of the method.&lt;/p&gt; &lt;p&gt;Several high-resolution sensors have collected data at a karst spring in central Kentucky, USA, for a 2.5 year period. Water unmixing was performed using electrical conductivity as a tracer to separate the groundwater from the surface water and infer sediment sources. Theoretical analyses have shown that not only timing and magnitude of sedigraphs influence the result of the hysteresis loops, but also timing and magnitude of each of the multiple water sources have a strong effect on the resulting hysteresis loop. The groundwater flow shows to have dominant counter-clockwise hysteresis loop, surface water shows to have clockwise loops dominating. Depending on the timing and magnitude of the water sources, the hysteresis loop at the karst spring varies from strictly counterclockwise, to a figure-8 loop, to a complex pattern.&lt;/p&gt;

Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth's surface. The strength of a landscape's connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world's most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.