Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC_1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m⁻¹). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC_1:5 = 3.0 dS·m⁻¹. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC_1:5 = 3.0 dS·m⁻¹ as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC_1:5 = 1.5 dS·m⁻¹. All tested sensors performed reasonably well at EC_1:5 ≤ 1.0 dS·m⁻¹. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm³·cm⁻³. At EC_1:5 ≤ 1.0 dS·m⁻¹, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC_1:5 > 1.0 dS·m⁻¹. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

Groundwater contaminant source identification is an endeavor task in highly developed areas that have been impacted by diverse natural processes and anthropogenic activities. In this study, groundwater samples from 84 wells in the pilot promoter region of the Yangtze River Delta integration demonstration zone in eastern China were collected and then analyzed for 17 groundwater quality parameters. The principal component analysis (PCA) method was utilized to recognize the natural and anthropogenic aspects impacting the groundwater quality; furthermore, the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to quantify the contribution of potential sources to each groundwater quality parameter. The results demonstrated that natural hydro-chemical evolution, agricultural activities, domestic sewage, textile industrial effluent and other industrial activities were responsible for the status of groundwater quality in the study area. Meanwhile, the contribution of these five sources obtained by the APCS-MLR model were ranked as natural hydro-chemical evolution $(18.89\%)$ > textile industrial effluent $(18.18\%)$ > non-point source pollution from agricultural activities $(17.08\%)$ > other industrial activities $(15.09\%)$ > domestic sewage $(4.19\%)$. It is believed that this contaminant source apportionment result could provide a reliable basis to the local authorities for groundwater pollution management.

Since the mainstream of the Yangtze River lower reach is an important drinking water source for residents alongside it, it is essential to investigate the concentration, distribution characteristics and health risks of heavy metals in the water. In this study, a total of 110 water samples were collected on both the left and right banks from the upstream to the downstream. Principal component analysis (PCA) was used to determine the sources of heavy metals. Their non-carcinogenic and carcinogenic risks were studied with health risk assessment models, and uncertainties were determined through Monte Carlo simulation. Results showed that concentrations of all heavy metals were significantly lower than the relevant authoritative standards in the studied area. From the upstream to the downstream, Ni, Cu and Cr had similar concentration distribution rules and mainly originated from human industrial activities. Pb, Cd and Zn had a fluctuating but increasing trend, which was mainly due to the primary geochemistry, traffic pollution and agricultural activities. The maximum As concentration appeared in the upstream mainly because of the carbonatite weathering or mine tail water discharge. Concentrations of Zn, As, Cd and Pb on the left bank were higher than those on the right bank, while concentrations of Cu, Ni and Cr on the right bank were higher than those on the left bank. The non-carcinogenic risk index (HI) was less than 1 (except of L11), and HI on the left bank was higher than that on the right bank. The carcinogenic risk (CR) was generally larger than 1.0 × 10⁻⁴, CR on the right bank overall was higher than that on the left bank, and the health risk of kids was greater than that of adults. Furthermore, Monte Carlo simulation results and the actual calculated values were basically the same.

Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on the rhythmic bed surface morphology and have extracted crest and trough, while flat bed and scour pit have been ignored. In this study, to extend the feature description of riverbeds, geomorphic elements mapping was used by employing three geomorphic element classification methods: Wood’s criteria, a self-organization map (SOM) technique, and geomorphons. The results showed that geomorphic element mapping can be controlled by adjusting the slope tolerance and curvature tolerance of Wood’s criteria, using the map unit number and combination of the SOM technique and the flatness of geomorphons. Relatively flat bed can be presented using “plane”, “flat planar”, and “flat” elements, while scour pit can be presented using a “pit” element. A comparison of the difference between parameter settings for landforms and bedforms showed that SOM using 8 or 10 map units is applicable for land and underwater surface and is thus preferentially recommended for use. Furthermore, the use of geomorphons is recommended as the optimal method for characterizing bedform features because it provides a simple element map in the absence of area loss.

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I ($28\%$), $N{H}_4\text{-}N$ ($18\%$), and Mn ($14\%$); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry ($10.63\%$) > urban domestic sewage discharge ($8.69\%$) > agricultural non-point source pollution ($6.26\%$).

Plain farmland areas without significant topographic slope exhibit microtopographic features of different scales. Quantitative assessment of the effects of microtopography at different scales on runoff generation in typical farmland areas is of great significance for regional water resources management and flood disaster forecasting. The main objective of the study was to develop an event-based rainfall–runoff model based on the layered Green–Ampt model (LGAM) with the consideration of plot-scale microtopographic features in plain farmland areas. Our experimental field, located in Taihu Lake Basin, was classified into three types of topographic subunits (i.e., main field, rill, and ditch) according to the average elevation. To simplify the concentration process for three topographic subunits, the average concentration time method was employed. Here, various experimental scenarios were simulated, including two classical unsteady rainfall events in homogeneous soil, one ponding infiltration experiment, and two typical rainfall–runoff events in the experimental field. We found that the multilayered setting showed higher accuracy than the homogeneous setting for simulating infiltration in the ponding infiltration experiment in the field. The RMSE of simulated ponding water depth reduced from 0.166 cm to 0.035 cm and NSE rose from 0.988 to 0.999. The simulated hydrograph considering microtopography effects proved higher accuracy than that under unified topography assumption. After classifying topography, the RMSE and NSE of simulated hydrographs decreased and increased, respectively. The lower the topographic subunit, the earlier the outflow occurred. At the early stage, the runoff mostly originated from the relatively low topographic subunits. Infiltration-excess regime under saturated condition may initially dominate in the low-lying ditch under intense rainfall, with extremely high runoff coefficient. Concentration process in the plain farmland area was affected by both rainfall intensity and microtopography. The greater the rainfall intensity, the shorter the average concentration time. The concentration velocity under heavy rainfall was four times faster than that under light rainfall. The lower topographic subunit was characterized by shorter concentration pathway and average concentration time. Ditches reduced the peak flow and advanced the time to peak. This quantitative study provides new insights into effects of microtopography on runoff generation in plain farmland area as well as an effective alternative for plot-scale rainfall–runoff modeling.