An approach for decomposing river water-quality trends into different flow classes

Dynamic disparities in inorganic nitrogen and phosphorus fluxes into estuarine systems under different flow regimes and streamflow droughts

Elongated periods of low flow conditions, which can be termed as streamflow droughts, influence the nutrient (e.g., nitrogen and phosphorus) balance in estuarine systems. Analyzing temporal trends of nutrient fluxes into such systems under different streamflow regimes can complement the understanding about the dynamic evolution of streamflow droughts and their impacts on nutrient levels. The objective of this paper was to evaluate how dynamic evolution of streamflow droughts (from low flow conditions) affects the inorganic nutrient flux in a tropical estuarine system. We analyzed a 20-year time series of streamflow data together with the concentrations of two nutrient parameters-dissolved inorganic phosphorus (DIP) and dissolved inorganic nitrogen (DIN)-in the Lower Apalachicola River that drains into Apalachicola Bay in northeastern Gulf of Mexico, Florida. Our findings revealed that droughts affect the seasonal patterns and fluxes of both DIP and DIN. We also observed post-drought flushing patterns in DIP and contrasting changes in DIP and DIN fluxes in the long-term (20 years here) under different streamflow conditions. Dynamically changing correlations between the streamflow and the fluxes were found throughout different phases of droughts. In the long-term (from 2003 to 2021), the DIP flux in high flows increased by 35.3%, while the flux decreased by 15.7% in low flows. Conversely, DIN flux in high flows showed a decrease of <1.2%, but an increase of <23.7% in low flows after droughts end. The insights from this study highlighted the need for effective regulation plans such as proper nutrient management against streamflow droughts to mitigate negative ecological consequences in estuarine systems such as harmful algal blooms.

A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.

Effects of point and nonpoint source controls on total phosphorus load trends across the Chesapeake Bay watershed, USA

Reduction of total phosphorus (TP) loads has long been a management focus of Chesapeake Bay restoration, but riverine monitoring stations have shown mixed temporal trends. To better understand the regional patterns and drivers of TP trends across the Bay watershed, we compiled and analyzed TP load data from 90 non-tidal network stations using clustering and random forest (RF) approaches. These stations were categorized into two distinct clusters of short-term (2013-2020) TP load trends, i.e. monotonic increase (n = 35) and monotonic decline (n = 55). RF models were developed to identify likely regional drivers of TP trend clusters. Reductions in point sources and agricultural nonpoint sources (i.e. fertilizer) both contributed to water-quality improvement in our period of analysis, thereby demonstrating the effectiveness of nutrient management and the importance of continuing such efforts. In addition, declining TP trends have a larger chance to occur in carbonate areas but a smaller chance in Coastal Plain areas, with the latter likely reflecting the effect of legacy P. To provide spatially explicit information, TP trend clusters were predicted for the entire watershed at the scale of river segments, which are more directly relevant to watershed planning. Among the 975 river segments, 544 (56%) and 431 (44%) were classified as 'monotonic increase' and 'monotonic decrease', respectively. Furthermore, these predicted TP trend clusters were paired with our previously published total nitrogen (TN) trend clusters, showing that TP and TN both declined in 185 segments (19%) and neither declined in 337 segments (35%). Broadly speaking, large-scale nutrient reduction efforts are underway in many regions to curb eutrophication. Water-quality responses and drivers may differ among systems, but our work provides important new evidence on the effectiveness of management efforts toward controlling point and nonpoint sources.

Legacy contributions to diffuse water pollution: Data-driven multi-catchment quantification for nutrients and carbon

Legacy pollutants are increasingly proposed as possible reasons for widespread failures to improve water quality, despite the implementation of stricter regulations and mitigation measures. This study investigates this possibility, using multi-catchment data and relatively simple, yet mechanistically-based, source distinction relationships between water discharges and chemical concentrations and loads. The relationships are tested and supported by the available catchment data. They show dominant legacy contributions for total nitrogen (TN), total phosphorus (TP) and total organic carbon (TOC) across catchment locations and scales, from local to country-wide around Sweden. Consistently across the study catchments, close relationships are found between the legacy concentrations of TN and TOC and the land shares of agriculture and of the sum of agriculture and forests, respectively. The legacy distinction and quantification capabilities provided by the data-driven approach of this study could guide more effective pollution mitigation and should be tested in further research for other chemicals and various sites around the world.

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.

Copula-based analysis of socio-economic impact on water quantity and quality: A case study of Yitong River, China

Socio-economic development has a significant impact on both water quantity and quality. However, few studies have considered the complex relationship between water quantity and quality when evaluating such impact. In this study, three indicators based on copula model were proposed, namely, water quantity improvement degree (WQID_w), water quality improvement degree (WQID_q) and water quantity and quality joint improvement degree (WQJID). These indicators were used to assess the impact of social economy on water quantity and quality, and applied to a case study in Yitong River in Northeast China from 2021 to 2025. Four scenarios were set to explore the impact of socio-economic development and water resources protection on WQID_w, WQID_q and WQJID. The maximum WQID_w, WQID_q and WQJID were <1 under the business-as-usual scenario, which showed that the present socio-economic pattern caused great damage to river water quantity and quality. The combined effect of socio-economic development and water resources protection increased the WQJID of COD and NH₃-N by 1.67 and 1.30. This showed that attention should be paid to water resources protection while developing social economy. Compared with comprehensive evaluation, separate evaluation of water quality will underestimate the impact of social economy on rivers, while separate evaluation of water quantity will overestimate the impact. The relationships between WQID_w, WQID_q and WQJID were quantified. Meanwhile, the uncertainty of the evaluation was controlled by the selection of water quality indicators. The WQID_q, WQID_w and WQJID proposed in this study provide a comprehensive assessment tool for guiding water resources management.

Controls for multi-temporal patterns of riverine nitrogen and phosphorus export to lake: Implications for catchment management by high-frequency observations

Intensifying human activity coupled with climate change increase the transport of excess riverine nitrogen (N) and phosphorus (P) loading from catchment to lake, leading to eutrophication and harmful algal blooms worldwide. To improve understanding of multi-temporal patterns of riverine N and P export and their hydro-biogeochemical controls over both episodic events and long-term trend, we analyzed and interpreted high-frequency data of total nitrogen (TN), ammonia-nitrogen (NH₄-N), and total phosphorus (TP) provided by an automatic water quality monitoring station in a typical agricultural catchment draining to Lake Chaohu, China. Mann-Kendall test revealed a significant decreasing trend of riverine N and P concentration most of the time during 2018-2020. At the sub-daily scale, intraday TN concentrations varied by more than 1 mg/L in 31.8% of the period. Monthly TN and TP concentrations were particularly high in December 2019, indicating combined effect of hydrologic (long dry antecedent period and subsequent intensive rainfall events) and anthropogenic controls (fertilization and agricultural drainage). Significantly higher TN concentrations in winter and TP concentrations in summer reflected coupled dominances of precipitation and temperature on hydrologic and biogeochemical processes. Rainfall events with very heavy intensity drove disproportionate N and P loads (more than 20% of the total export) in only 3.2% of the period. Moderate and very heavy events registered the highest TN and TP concentrations, respectively. Our results highlighted the importance of automatic water quality monitoring station to reveal dynamics of riverine N and P export, which may imply future nutrient loading abatement plans for lake-connected catchment.

Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

Copula-based framework for integrated evaluation of water quality and quantity: A case study of Yihe River, China

Water quantity and quality are two key factors affecting the performance of integrated watershed management. Conventional water resources assessment of rivers often deals with water quantity and quality separately. However, how to make an objective and impartial assessment of water resources by incorporating both water quantity and quality remains unclear, especially in watersheds with significant human activity impacts and high spatiotemporal variations in flows. In such areas, the nonmonotonic relationship between the water quality and discharge rate of a river, in contrast to near-natural conditions, is often ignored. To resolve this problem, this paper develops a new framework for the integrated evaluation of water quantity and quality by incorporating a new index, namely, the water quality improvement degree (WQID). The WQID is proposed to quantify the disturbance degree of human activities to the near-natural relationship between the water quality and discharge rate of a river. The Yihe River in Northern China is selected as a case study to apply the proposed framework. The results show that the observed flow discharge rates of some abnormal months after a specific time of change-point are greater than the estimated discharges under the river's near-natural condition. The WQID values in these abnormal months are less than 1, resulting in a decrease in the modified water resources surplus (WRS*) or an increase in the modified water resources deficit (WRD*). This indicates that the WQID can take into account the near-natural law between water quantity and quality to make a more objective evaluation of integrated water resources management for the months of interest. The proposed framework can serve as a useful and reliable tool for a comprehensive assessment of the watershed management performance of a river system.

Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources

Human agriculture, wastewater, and use of fossil fuels have saturated ecosystems with nitrogen and phosphorus, threatening biodiversity and human water security at a global scale. Despite efforts to reduce nutrient pollution, carbon and nutrient concentrations have increased or remained high in many regions. Here, we applied a new ecohydrological framework to ~12,000 water samples collected by the U.S. Environmental Protection Agency from streams and lakes across the contiguous U.S. to identify spatial and temporal patterns in nutrient concentrations and leverage (an indicator of flux). For the contiguous U.S. and within ecoregions, we quantified trends for sites sampled repeatedly from 2000 to 2019, the persistence of spatial patterns over that period, and the patch size of nutrient sources and sinks. While we observed various temporal trends across ecoregions, the spatial patterns of nutrient and carbon concentrations in streams were persistent across and within ecoregions, potentially because of historical nutrient legacies, consistent nutrient sources, and inherent differences in nutrient removal capacity for various ecosystems. Watersheds showed strong critical source area dynamics in that 2-8% of the land area accounted for 75% of the estimated flux. Variability in nutrient contribution was greatest in catchments smaller than 250 km2 for most parameters. An ensemble of four machine learning models confirmed previously observed relationships between nutrient concentrations and a combination of land use and land cover, demonstrating how human activity and inherent nutrient removal capacity interactively determine nutrient balance. These findings suggest that targeted nutrient interventions in a small portion of the landscape could substantially improve water quality at continental scales. We recommend a dual approach of first prioritizing the reduction of nutrient inputs in catchments that exert disproportionate influence on downstream water chemistry, and second, enhancing nutrient removal capacity by restoring hydrological connectivity both laterally and vertically in stream networks.