Source availability and hydrological connectivity determined nitrate-discharge relationships during rainfall events in karst catchment as revealed by high-frequency nitrate sensing

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

This study addressed the challenges of cost and portability in synchronous monitoring water quality and greenhouse gas emissions in paddy-dominated regions by developing a novel Internet of Things (IoT)-based monitoring system (WG-IoT-MS). The system, equipped with low-cost sensors and integrated intelligent algorithms, enabled real-time monitoring of dissolved N2O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO2 and N2O emissions from agricultural water bodies while reducing monitoring costs by approximately 60 %. The proposed method was validated in paddy-dominated regions in Danyang, China. Results indicated the excellence of the dissolved N2O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 μg/L. Notably, the model maintained acceptable predictive accuracy (R2 > 0.70) even when some variables were partially absent (with the number of missing variables < 2 and the missing proportion (MP) ≤ 50 %), making up for the data loss caused by sensor malfunctions. Furthermore, the model performed well (R2 > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO2 and N2O emissions were successfully monitored, with the results validated using a floating chamber method (R2 > 0.70). The method provides crucial technical support for quantitative assessment of water quality and greenhouse gas emissions in paddy-dominated regions, laying a foundation for formulating effective emission reduction strategies.

High sensitivity of dissolved organic carbon transport during hydrological events in a small subtropical karst catchment

Dissolved organic carbon (DOC) and discharge are often tightly coupled, though these relationships in karst environments remain poorly constrained. In this study, DOC dynamics over 13 hydrological events, alongside monthly monitoring over an entire hydrological year were monitored in a small karst catchment, SW China. The concurrent analyses of power-law model and hysteresis patterns reveal that DOC behavior is generally transport-limited due to flushing effects of increased discharge but highly variable at both intra- and inter-event scales. The initial discharge at event onset and discharge-weighted mean concentration of DOC ([DOC]DW) of individual events can explain 37.7 % and 19.9 % of the variance of DOC behavior among events, respectively. The sustained dry-cold antecedent conditions make DOC hysteresis behavior during the earliest event complex and different from subsequent events. At event scale, the variability in DOC export is primarily controlled by [DOC]DW (explaining 64.3 %) and the yield of total dissolved solutes (YTDS, explaining 30.4 %), reflecting the impacts of variable hydrological connectivity and intense soil-water-rock interactions in this karst catchment. On an annual scale, DOC yield (YDOC, 222.86 kg C km-2) was mostly derived during the wet season (98.19 %) under the hydrological driving force. The difference in annual YDOC between this karst catchment and other regions can be well explained by annual water yield (Ywater, explaining 24.2 %) and [DOC] (explaining 35.4 %), whereas the variance in DOC export efficiency among catchments is almost exclusively controlled by [DOC] alone, independent of drainage area and annual Ywater. This study highlights the necessity of high-frequency sampling for modeling carbon biogeochemical processes and the particularity of the earliest hydrological events occurred after a long cold-dry period in karst catchments. Under the changing climate, whether DOC dynamics in karst catchments will present source-limited patterns during more extreme hydrological events merits further study.

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning

Nitrate dynamics within a catchment are critical to the earth's system process, yet the intricate details of its transport and transformation at high resolutions remain elusive. Hydrological effects on nitrate dynamics in particular have not been thoroughly assessed previously and this knowledge gap hampers our understanding and effective management of nitrogen cycling in watersheds. Here, machine learning (ML) models were employed to reconstruct the annual variation trend in nitrate dynamics and isotopes within a typical karst catchment. Random forest model demonstrates promising potential in predicting nitrate concentration and its isotopes, surpassing other ML models (including Long Short-term Memory, Convolutional Neural Network, and Support Vector Machine) in performance. The ML-modeled NO3--N concentrations, δ15N-NO3-, and δ18O-NO3- values were in close agreement with field data (NSE values of 0.95, 0.80, and 0.53, respectively), which are notably challenging to achieve for process models. During the transition from dry to wet period, approximately 23.0 % of the annual precipitation (∼269.1 mm) was identified as the threshold for triggering a rapid response in the wet period. The modeled nitrate isotope values were significantly supported by the field data, suggesting seasonal variations of nitrogen sources, with precipitation as the primary driving force for fertilizer sources. Mixing of multiple sources appeared to be the main control of the transport and transformation of nitrate during the rising limb in the wet period, whereas process control (denitrification) took precedence during the falling limb, and the fate of nitrate was controlled by biogeochemical processes during the dry period.

Gully regulates snowmelt runoff, sediment and nutrient loss processes in Mollisols region of Northeast China

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully's role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.

Transport Pathways of Nitrate in Stormwater Runoff Inferred from High-Frequency Sampling and Stable Water Isotopes

Storm events can mobilize nitrogen species from landscapes into streams, exacerbating eutrophication and threatening aquatic ecosystems as well as human health. However, the transport pathways and storm responses of different nitrogen forms remain elusive. We used high-frequency chemical and isotopic sampling to partition sources of stormwater runoff and determine transport pathways of multiple nitrogen forms in an agricultural catchment. Bayesian mixing modeling reveals shallow subsurface water as the dominant source of stormwater runoff, contributing 74% of the water flux and 72, 71, and 79% of total nitrogen (TN), total dissolved nitrogen (TDN), and nitrate (NO3-N), respectively. Groundwater, by contrast, contributed 11% of stormwater runoff and 21, 22, and 17% of TN, TDN, and NO3-N, respectively. The remaining 14% of stormwater runoff can be attributed to rainwater, which contains much less TN, TDN, and NO3-N. Surprisingly, during storm events, the dominant nitrogen form was NO3-N rather than dissolved organic nitrogen. Antecedent conditions and runoff characteristics have an important influence on nitrogen loads during storm events. Our results provide insight into hydrological mechanisms driving nitrogen transport during storm events and may help in developing catchment management practices for reducing nitrogen pollution in aquatic ecosystems.

High-frequency monitoring during rainstorm events reveals nitrogen sources and transport in a rural catchment

Rural areas lacking essential sewage treatment facilities and collection systems often experience eutrophication due to elevated nutrient loads. Understanding nitrogen (N) sources and transport mechanisms in rural catchments is crucial for improving water quality and mitigating downstream export loads, particularly during storm events. To further elucidate the sources, pathways, and transport mechanisms of N from a rural catchment with intensive agricultural activities during storm events, we conducted an analysis of 21 events through continuous sampling over two rainy seasons in a small rural catchment from the lower reaches of the Yangtze River. The results revealed that ammonia-N (NH4+-N) and nitrate-N (NO3--N) exhibited distinct behaviors during rainstorm events, with NO3--N accounting for the primary nitrogen loss, its load being approximately forty times greater than that of NH4+-N. Through examinations of the concentration-discharge (c-Q) relationships, the findings revealed that, particularly in prolonged rainstorms, NH4+-N exhibited source limited pattern (b = -0.13, P < 0.01), while NO3--N displayed transport limited pattern (b = -0.21, P < 0.01). The figure-eight hysteresis pattern was prevalent for both NH4+-N and NO3--N (38.1% and 52.0%, respectively), arising from intricate interactions among diverse sources and pathways. For NO3--N, the hysteresis pattern shifted from clockwise under short-duration rainstorms to counter-clockwise under long-duration rainstorms, whereas hysteresis remained consistently clockwise for NH4+-N. The hysteresis analysis further suggests that the duration of rainstorms modifies hydrological connectivity, thereby influencing the transport processes of N. These insights provide valuable information for the development of targeted management strategies to reduce storm nutrient export in rural catchments.

Subsurface hydrological connectivity controls nitrate export flux in a hilly catchment

Subsurface runoff represents the main pathway of nitrate transport in hilly catchments. The magnitude of nitrate export from a source area is closely related to subsurface hydrological connectivity, which refers to the linkage of separate regions of a catchment via subsurface runoff. However, understanding of how subsurface hydrological connectivity regulates catchment nitrate export remains insufficient. This study conducted high-frequency monitoring of shallow groundwater in a hilly catchment over 17 months. Subsurface hydrological connectivity of the catchment over 38 rainfall events was analyzed by combining topography-based upscaling of shallow groundwater and graph theory. Moreover, cross-correlation analysis was used to evaluate the time-series similarity between subsurface hydrological connectivity and nitrate flux during rainfall events. The results showed that the maximum subsurface hydrological connectivity during 32 out of 38 rainfall events was below 0.5. Although subsurface flow paths (i.e., the pathways of lateral subsurface runoff) exhibited clear dynamic extension and contraction during rainfall events, most areas in the catchment did not establish subsurface hydrological connectivity with the stream. The primary pattern of nitrate export was flushing (44.7%), followed by dilution (34.2%), and chemostatic behavior (21.1%). A threshold relationship between subsurface hydrological connectivity and nitrate flux was identified, with nitrate flux rapidly increasing after the subsurface connectivity strength exceeded 0.121. Moreover, the median value of cross-correlation coefficients reached 0.67, which indicated subsurface hydrological connectivity exerts a strong control on nitrate flux. However, this control effect is not constant and it increases with rainfall amount and intensity as a power function. The results of this study provide comprehensive insights into the subsurface hydrological control of catchment nitrate export.

Storm-induced nitrogen transport via surface runoff, interflow and groundwater in a pomelo agricultural watershed, southeast China

The storm-induced export of nitrogen (N) from agricultural watersheds significantly impacts aquatic ecosystems, yet the mechanisms of source supply and transport behind N species remain unclear. Here, we investigated the hydrological factors influencing the timing and magnitude of river N species export in a Chinese pomelo agricultural watershed. We conducted continuous observations of watershed hydrology, N species, and their isotopic ratios along a soil-groundwater-river continuum during two storm events in 2018-2019. We found the export flux of river NO3-N covers ∼80% of the total N flux during storms, and the rest for other N species. Our results further revealed distinct pathways and timing of N transport among different N species, especially between ammonium N (NH4-N) and nitrate N (NO3-N). NH4-N in stormflow predominantly originates from sewage and soil leachate, rapidly transported via surface runoff and interflow. Orchard fertilization (contributed 41-56% based on SIAR analysis) was the major source of river NO3-N, which underwent initial dilution via surface runoff and subsequently became enriched through delayed discharge of soil leachate and groundwater. The variations in timing and magnitude of N transport between storms can be explained by antecedent conditions such as precipitation, soil N pools, and storm size. These findings emphasize the hydrological controls on N export from agricultural watersheds, and highlight the variations in source supply and transport pathways among different N species. The insights gained from this study hold significance for managing agricultural pollution and restoring impaired aquatic systems.

Prioritizing ecological restoration in hydrologically sensitive areas to improve groundwater quality

Greening is the optimal way to mitigate climate change and water quality degradation caused by agricultural expansion and rapid urbanization. However, the ideal sites to plant trees or grass to achieve a win-win solution between the environment and the economy remain unknown. Here, we performed a nationwide survey on groundwater nutrients (nitrate nitrogen, ammonia nitrogen, dissolved reactive phosphorus) and heavy metals (vanadium, chromium, manganese, iron, cobalt, nickel, copper, arsenic, strontium, molybdenum, cadmium, and lead) in China, and combined it with the global/national soil property database and machine learning (random forest) methods to explore the linkages between land use within hydrologically sensitive areas (HSAs) and groundwater quality from the perspective of hydrological connectivity. We found that HSAs occupy approximately 20 % of the total land area and are hotspots for transferring nutrients and heavy metals from the land surface to the saturated zone. In particular, the proportion of natural lands within HSAs significantly contributes 8.0 % of the variability in groundwater nutrients and heavy metals in China (p < 0.01), which is equivalent to their contribution (8.8 %) at the regional scale (radius = 4 km, area = 50 km2). Increasing the proportion of natural lands within HSAs improves groundwater quality, as indicated by the significant reduction in the concentrations of nitrate nitrogen, manganese, arsenic, strontium, and molybdenum (p < 0.05). These new findings suggest that prioritizing ecological restoration in HSAs is conducive to achieving the harmony between the environment (improving groundwater quality) and economy (reducing investment in area management).

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.