A machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties.

As a basic infrastructure, sewers play an important role in the innards of every city and town to remove unsanitary water from all kinds of livable and functional spaces. Sewer pipe failures (SPFs) are unwanted and unsafe in many ways, as the disturbance that they cause is undeniable. Sewer pipes meet manholes frequently, unlike water distribution systems, as in sewers, water movement is due to gravity and manholes are needed in every intersection as well as through pipe length. Many studies have been focused on sewer pipe failures and so on, but few investigations have been done to show the effect of manhole proximity on pipe failure. Predicting and localizing the sewer pipe failures is affected by different parameters of sewer pipe properties, such as material, age, slope, and depth of the sewer pipes. This study investigates the applicability of a support vector machine (SVM), a supervised machine learning (ML) algorithm, for the development of a prediction model to predict sewer pipe failures and the effects of manhole proximity. The results show that SVM with an accuracy of 84% can properly approximate the manhole effects on sewer pipe failures.

Rainfall-runoff partitioning in small watersheds of different vegetation types in the loess area based on hydrogen and oxygen isotope tracing.

Recognizing watershed runoff process and its component sources is a prerequisite for the rational use of water resources. To elucidate the effects and quantitative contributions of various vegetation types on the components of watershed runoff, we centered on the Caijiachuan main channel watershed in Jixian, Shanxi and five sub-watersheds with distinct vegetation types. By tracking the hydrological responses to two representative rainfall events and assessing the spatiotemporal variations in hydrogen and oxygen isotope signatures, we aimed to discern disparities in the runoff processes across these sub-watersheds and pinpoint their constituent origins. The results showed that under medium rainfall condition, the contribution rates of event water to the river flow of each watershed were in an order of protected forest (94.3%) > Caijiachuan main channel (83.1%) > agro-pastoral composite (64.3%) > plantation-secondary forest (52.4%) > cropland (0.3%) > secondary forest (0.0%); under light rainfall condition, plantation-secondary forest (52.4%) > protected forest (58.5%) > cropland (40.6%) > secondary forest (15.8%) > agro-pastoral composite (12.5%) > Caijiachuan main channel (9.3%). The event water contribution rate of secondary forest and protected forest watersheds to runoff was higher than that of plantation watersheds. The secondary forests watersheds had a stronger runoff storage capacity. The event water contribution rate of protected forest and agro-pastoral composite watersheds under medium rainfall intensity condition was greater than that under light rainfall intensity condition, while the event water contribution rate of cropland, plantation-secondary forest, and secondary forest watersheds was in adverse. The event water contribution to the runoff of forested watersheds was greater than that of cropland watersheds, which may be related to the presence of silt dams at the mouth of agricultural watershed channels. This study can provide a scientific basis for the analysis of water conservation and runoff change attribution in the loess area of west Shanxi.

Analyzing river disruption factors and ecological flow in China's Liu River Basin amid environmental changes.

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194 days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments.

Different intensities and directions of hyporheic water exchange in habitats of aquatic Ranunculus species in rivers-a case study in Poland.

Hyporheic water exchange driven by groundwater-surface water interactions constitutes habitat conditions for aquatic biota. In our study, we conducted a field-research-based analysis of hyporheic water exchange to reveal whether the hyporheic water exchange differentiates particular Ranunculus sp. habitats. We measured the density of the stream of upwelling and hydraulic gradients of water residing in the hyporheic zone in 19 Polish rivers. We revealed that R. peltatus and R. penicillatus persist in habitats of considerably higher hyporheic water exchange upwelling flux (respectively 0.0852 m3∙d-1∙m-2 and 0.0952 m3∙d-1∙m-2) than R. circinatus, R. fluitans, and a hybrid of R. circinatus × R. fluitans (respectively m3∙d-1∙m-2; 0.0222 m3∙d-1∙m-2 and 0.0717 m3∙d-1∙m-2). The presented results can be used to indicate aquatic habitat suitability in the case of protection and management of ecosystems settled by Ranunculus sp.

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model.

The water body's suspended concentration reflects many coastal environmental indicators, which is important for predicting ecological hazards. The modeling of any concentration in water requires solving the settling-diffusion equation (SDE), and the values of several key input parameters therein (settling velocity ws, eddy diffusivity Ds, and erosion rates p(t)) directly determine the prediction performance. The time-consuming large-scale simulations would benefit if the parameter values could be estimated through available observations in the target sea area. The present work proposes a new optimization method for synchronously estimating the three parameters from limited concentration observations. First, an analytical solution to the one-dimensional vertical (1DV) SDE for suspended concentrations in an unsteady scenario is derived. Second, the near bottom suspended sediment concentration (SSC) profiles are measured with high-resolution observation. Third, the key parameters are optimized through the best fit of the measured SSC profiles and those modeled with the unsteady solution. Nonlinear least square fitting (NLSF) is introduced to judge the best fits automatically. The high-resolution concentration measurements in a specially-designed cylindrical tank experiment using the Yellow River Delta sediments test the proposed method. The method performs well in the initial period of turbulence generation when sediment resuspension is significant. It optimizes p(t), ws, and Ds with reasonable values and uniqueness of their combination. The proposed theory is a practical tool for quickly estimating key substance transport parameters from limited observations; it also has the potential to construct local parametric models to benefit the 3D modeling of coastal substance transport. Although the present work takes SSC as an example, it can be extended to any suspended particulate concentration in the water.

Exploring the adoption of water quality trading as an alternative stormwater regulatory compliance strategy for land development projects: A case study for Roanoke, Virginia.

Urban stormwater runoff is a significant source of nutrient pollution that is very costly to treat. Water quality trading (WQT) is a market-based strategy that can be used to lower the costs associated with meeting stormwater quality regulations. While many WQT programs have experienced low participation, Virginia's program has seen high participation due to the inclusion of land developers and other regulated stormwater dischargers. However, the extent to which WQT is used as a compliance option by regulated stormwater dischargers is not well understood, particularly when compared with the adoption of traditional compliance options. To address this knowledge gap, we collated a novel dataset comprising site characteristics and stormwater compliance methods for all development projects in the City of Roanoke, Virginia from December 2015 to March 2022. We analyzed this dataset to characterize the adoption of nutrient offset credits and other compliance methods being used, including best management practices (BMPs) and improved land covers associated with reduced nutrient export. Results show that credits are the preferred compliance option in Roanoke and were used as the only treatment compliance method for 59% of projects with treatment requirements. Projects using credits corresponded with a lower median disturbed area (1.36 acres) and lower median nutrient load reduction requirement (0.69 pounds of total phosphorus per year) compared with other compliance methods. Furthermore, we found that 58% of the projects that used credits achieved stormwater quantity compliance using methods other than implementing stormwater control devices. By mapping buyers and sellers of credits, we found that all credit sellers are downstream of the development projects. We discuss how this downstream trading could be a cause for concern, as part of a larger discussion of the advantages of tracking stormwater compliance methods, drawing on Roanoke as a case study.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces.

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban-rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8-18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6-38.0%) was shown to carry a higher proportion of the load (70.0-93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6-49.0%, load: 37-90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.

Temporal variations in micropollutant inlet concentrations matter when planning the design and compliance assessment of stormwater control measures.

Stormwater Control Measures (SCMs) contribute to reducing micropollutant emissions from separate sewer systems. SCM planning and design are often performed by looking at the hydrological performance. Assessment of pollutant removal and the ability to comply with discharge concentration limits is often simplified due to a lack of data and limited monitoring resources. This study analyses the impact of using different time resolutions of input stormwater concentrations when assessing the compliance of SCMs against water quality standards. The behaviour of three indicator micropollutants (MP - Copper, Diuron, Benzo[a]pyrene) was assessed in four SCM archetypes, which were defined to represent typical SCM removal processes. High resolution MP data were extrapolated by using high resolution (2 min) measurements of TSS over a long period (343 events). The compliance assessment showed that high resolution input concentrations can result in a different level of compliance with water quality standards, especially when discharged concentrations are close to the limit values. This study underlines the importance of considering the high temporal variability of stormwater micropollutants when planning and designing SCMs to identify the most effective solutions for stormwater pollution management and to ensure a thorough consideration of all the environmental implications.

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach.

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

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

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

Effects of storm runoff on the spatial-temporal variation and stratified water quality in Biliuhe Reservoir, a drinking water reservoir.

Stormflow runoff is an important non-point source of pollution in drinking water reservoirs. Storm runoff is usually very turbid and contains a high concentration of organic matter, therefore affecting water quality when it enters reservoirs. In order to investigate the impact of storm runoff on spatial-temporal variation and stratification of water quality during this rainstorm event, the inflow process of the storm runoff was studied through a combination of field investigation and simulation using the Delft3D-Flow model. Water samples were collected from Biliuhe Reservoir at four different periods: before storm runoff, storm runoff flood peak period, 1 week after storm runoff, and 5 weeks after storm runoff. The results showed that the input of storm runoff resulted in a significant increase in the nitrogen (N) and phosphorus (P) in the reservoir water, especially in the reservoir entrance. The concentrations of total nitrogen (TN) and total phosphorus (TP) gradually decreased after the flood peak period; however, the average concentrations of TN and TP in the entire reservoir remained higher than those before the storm runoff levels for an extended duration. The storm runoff will greatly contribute to the contamination of water quality in a reservoir, and the water quality cannot be quickly restored by self-purification in the short term. During the flood peak period, under the influence of density current, the electrical conductivity (EC) and turbidity increased significantly in the water depth of 10-15 m, so that the reservoir water had obvious stratification between 10 and 15 m. The form of pollutants in storm runoff was mostly in particle phosphorus. Total particulate phosphorus (TPP) concentration was 0.015 ± 0.011 mg/L, accounting for 44.12% of total phosphorus (TP) concentration in storm runoff flood peak period. The process of a rainstorm caused runoff, which carried high levels of turbidity, particulate phosphorus, and organic matter. The storm runoff disrupts the stratification of the reservoir water. In terms of vertical distribution, the turbidity in the reservoir area increased to 73.75 NTU. Therefore, the occurrence of significant turbidity density flow in the reservoir is frequently accompanied by intense rainfall events. Gaining insights into the impact of storm runoff on the vertical distribution of reservoir turbidity can help managers in selecting an appropriate inlet height to mitigate high turbidity outflow.

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.

New phosphorus losses via tile drainage depend on fertilizer form, placement, and timing.

Agricultural phosphorus (P) losses are harmful to water quality, but knowledge gaps about the importance of fertilizer management practices on new (recently applied) sources of P may limit P loss mitigation efforts. Weighted regression models applied to subsurface tile drainage water quality data enabled estimating the new P losses associated with 155 P applications in Ohio and Indiana, USA. Daily discharge and dissolved reactive P (DRP) and total P (TP) loads were used to detect increases in P loss following each application which was considered new P. The magnitude of new P losses was small relative to fertilizer application rates, averaging 79.3 g DRP ha-1 and 96.1 g TP ha-1 , or <3% of P applied. The eight largest new P losses surpassed 330 g DRP ha-1 or 575 g TP ha-1 . New P loss mitigation strategies should focus on broadcast liquid manure applications; on average, manure applications caused greater new P losses than inorganic fertilizers, and surface broadcast applications were associated with greater new P losses than injected or incorporated applications. Late fall applications risked having large new P losses applications. On an annual basis, new P contributed an average of 14% of DRP and 5% of TP losses from tile drains, which is much less than previous studies that included surface runoff, suggesting that tile drainage is relatively buffered with regard to new P losses. Therefore old (preexisting soil P) P sources dominated tile drain P losses, and P loss reduction efforts will need to address this source.

Macrolitter budget and spatial distribution in a groyne field along the Waal river.

Current research on riverine macrolitter does not yet provide a theoretic framework on the dynamics behind its accumulation and distribution along riverbanks. In an attempt to better understand these dynamics a detailed field survey of three months was conducted in which location of macrolitter items within a single groyne field along the Waal riverbanks was tracked. The data provided insight into the daily changing patterns of spatial item distribution with respect to the waterline. Furthermore, the rates of item uptake and deposition were monitored and related to hydrologic fluctuations. Uptake was initiated by rising water levels and was generally higher when the water level increased faster. Deposition occurred continuously, despite hydrologic fluctuations. This caused the riverbank macrolitter budget to be positive during stable or dropping water levels and negative during rising water levels. Although the results show clear patterns an extended monitoring duration is required to fully understand the fate of plastic objects.

A comparative analysis of methods and tools for low impact development (LID) site selection.

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry.

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes.

Tracing lacustrine groundwater discharge (LGD) is essential for understanding the hydrological cycle and water chemistry behaviour of lakes. LGD usually exhibits large spatial variability, but there are few reports on quantitatively revealing the spatial patterns of LGD at the whole lake scale. This study investigated the spatial patterns of LGD in Daihai Lake, a typical closed inland lake in northern China, based on the stable isotopes (δ2H and δ18O) of groundwater, surface water, and sediment pore water (SPW). The results showed that there were significant differences between the δ2H and δ18O values of different water bodies in the Daihai Lake Basin: groundwater < SPW < lake water. The LGD through SPW was found to be an important recharge pathway for the lake. Accordingly, stable isotopes of SPW showed that LGD in the northeastern and northwestern of Daihai Lake was significantly greater both horizontally and vertically than that in the other regions, and the proportions of groundwater in SPW in these two regions were 55.53% and 29.84%, respectively. Additionally, the proportion of groundwater in SPW showed a significant increase with profile depth, and the proportion reached 100% at 50 cm below the sediment surface in the northeastern of the lake where the LGD intensity was strongest. The total LGD to Daihai Lake was 1.47 × 107 m3/a, while the LGD in the northeastern and northwestern of the lake exceeded 1.9 × 106 m3/a. This study provides new insights into assessing the spatial patterns of LGD and water resource management in lakes.

Research on the purification efficiency and mechanism for road runoff pollutants in pervious concrete with recycled aggregates.

The use of recycled aggregate (RA) in pervious concrete (PC) is a green approach that can effectively mitigate urban waterlogging, excessive RA, and runoff pollution, thereby enhancing the urban ecological environment. This article focuses on the long-term purification efficiency of runoff pollutants by PC at different porosities and RA dosages. Moreover, the purification mechanism of pollutants by recycled aggregate pervious concrete (RAPC) was revealed utilizing particle size analysis, microstructure, and elemental analysis. Finally, the recovery effects of different maintenance approaches on the purification capacity of RAPC were explored. The results indicate that an increase in the RA dosage reduced the effective porosity of PC, thereby decreasing the permeability of RAPC. In addition, PC with a lower porosity demonstrated a slightly greater purification effectiveness for pollutants. However, the utilization of RA significantly enhanced the purification capacity of PC for various pollutants, primarily by leveraging advantages in terms of pore structure, micromorphology, and surface chemical composition. Additionally, RAPC exhibited nearly 100 % retention effectiveness for particles larger than 68.95 µm but relatively lower purification efficiency for particles ranging from 1.541 to 17.11 µm. In particular, it displayed the poorest purification performance for particles with a diameter of 6.396 µm. The surface of RAPC's pore channels exhibited a loose state with high porosity and appeared rough and uneven with numerous pits and grooves. RAPC had a larger surface area and contained more components, such as SiO2, CaCO3, and Al2O3, than regular PC. Therefore, RAPC possessed a higher purification capacity. High-pressure flushing (HPF) and sodium citrate flushing (SCF) under different maintenance frequencies significantly contributed to the recovery of the purification efficiency of RAPC. However, overall, a lower maintenance frequency led to a less favorable recovery effect. Furthermore, SCF had a better recovery effect than HPF.

The storm runoff management strategy based on agricultural ditch nutrient loss characteristics in Erhai Lake, China.

Initial flush management is an effective measure to control non-point source pollution (NPSP) in storm runoff. However, determining the parameter of the initial flush in different areas may pose challenges in storm runoff management strategies. To address this issue, Erhai Lake in China, Yunnan-Guizhou Plateau, was selected as an example for the study. Erhai Lake is a typical mesotrophic lake with the profound influence of NPSP. The NPSP control strategy in this area will provide a valuable reference for other lakes. In 2021, 289 storm events and 190 ditchwater samples were detected around Erhai Lake. The average flow in the ditches ranged from 0.004 to 0.147 m3/s, the instant total nitrogen (TN) concentration ranged from 0.28 to 91.43 mg/L, and the instant total phosphorus (TP) concentration ranged from 0.26 to 7.35 mg/L in the storm events. It was found that the concentration of pollutants was lower than expected in the initial flush period. Instead, the event mean concentrations of TN and TP were 9.3 and 2.1 times higher than in the wet seasons, showing high nutrient concentration levels throughout the entire rainfall period. To manage storm runoff effectively, a flow-processes-division method was proposed to analyze the inflow condition and pollutant removal rate in different runoff periods. The peak flow interception strategy was recommended as the optimal stormwater management plan, as it showed the highest inflow conditions and 50% pollutant removal rate. Considering the need to reduce the constant flush of stormwater runoff, it is essential to establish a healthy water cycle system to alleviate NPSP and raise the Erhai water level. The storm runoff management method can serve as a practical tool for lake areas that do not exhibit initial flush characteristics.

Experimental study on damage of slope under drying and wetting cycle based on DC electric method.

In order to investigate the seepage law and crack development characteristics of dump slopes, as well as the impact on slope stability during drying and wetting cycles, a simulation test slope system was constructed in a rainfall environment, specifically designed to mimic the engineering conditions of dump slope. The apparent resistivity response formula for the seepage and crack development processes was derived based on the three-phase medium theory of rock-soil bodies and Maxwell's conductivity formula. The geoelectric field characteristics pertaining to slope damage and the corresponding patterns of alteration were comprehensively investigated. The results demonstrate a negative correlation between resistivity and slope water content, with resistivity increasing as cracks develop and decreasing with water infiltration. The progression of crack formation in a rainfall environment on a dump slope can be categorized into three stages: The initial phase involves the saturation of the slope as water content increases. Subsequently, the second phase entails the initiation and expansion of capillary zones, along with the formation of dominant waterways. Lastly, the third phase encompasses the formation and expansion of cracks within the dumping site. The occurrence of sudden changes and abnormal fluctuations in apparent resistivity within a saturated slope signifies the presence of cracks and weak surfaces, leading to gradual and irreversible damage. This phenomenon serves as an indicator of slope damage and can be utilized for the early prediction of slope instability.