Groundwater pollution in the Pingshuo mining area is strongly associated with mining activities, with heavy metals (HMs) representing predominant pollutants. To obtain accurate information about the pollution status and health risks of groundwater, 189 groups of samples were collected from four types of groundwater, during three periods of the year, and analyzed for HMs. The results showed that the concentration of HMs in groundwater was higher near the open pit, waste slag pile, riverfront area, and human settlements. Except for Ordovician groundwater, excessive HMs were found in all investigated groundwater of the mining area, as compared with the standard thresholds. Fe exceeded the threshold in 13–75% of the groundwater samples. Three sources of HMs were identified and quantified by Pearson’s correlation analysis and the PMF model, including coal mining activities (68.22%), industrial, agricultural, and residential chemicals residue and leakage (16.91%), and natural sources (14.87%). The Nemerow pollution index revealed that 7.58% and 100% of Quaternary groundwater and mine water samples were polluted. The health risk index for HMs in groundwater showed that the non-carcinogenic health risk ranged from 0.18 to 0.42 for adults, indicating an acceptable level. Additionally, high carcinogenic risks were identified in Quaternary groundwater (95.45%), coal series groundwater (91.67%), and Ordovician groundwater (26.67%). Both carcinogenic and non-carcinogenic risks were greater for children than adults, highlighting their increased vulnerability to HMs in groundwater. This study provides a scientific foundation for managing groundwater quality and ensuring drinking water safety in mining areas.

Appropriate simulation of air flow in porous media needs an accurate estimate of relative air permeability (RAP). Combining 2 traditional and 2 fractal water retention curves (WRCs) respectively with 6 permeability equations, we obtained 12 statistical and 12 fractal RAP models. All these models were subsequently tested with 31 experimental datasets of disturbed soils to examine their predictive ability for RAP. Results showed that irrespective of using traditional or fractal WRCs, the six permeability equations exhibit consistent performances for RAP prediction. The modified Burdine (Yang & Mohanty, 2015, https://doi.org/10.1002/2014wr016190) permeability equation best fits the published soil data. In addition, the selection of a proper permeability equation is critical for accurately predicting the RAP, whereas the choice of WRCs has little effect on RAP prediction. This finding suggests that for disturbed soils, air permeability is primarily dictated by the pore tortuosity‐connectivity but not the pore size distribution inferred from the WRCs.

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation. Graphical Abstract

In the inland closed area of Horqin Sandy Land, China, lakes are the most important source of water, and they maintain the regional hydrological balance and ecosystem health. Clarifying long-term continuous changes of inland lake surface area and water storage in the sandy land is thus of great significance to the management of water resources in arid and semi-arid areas. This study estimated changes in the surface area and water storage of small lakes in the sandy land during 1984–2021 using a multiple index threshold method and an empirical equation based on Shuttle Radar Topography Mission (SRTM) DEM (digital elevation model) data and Landsat 5/7/8 images. Hydro-meteorological variables were also incorporated to explore their potential relationship with changes in the lake elements. The lakes in the sandy land resemble stars or dots, with distinct inhomogeneity. Permanent lakes account for the majority of the total lake area, mostly distributed in the center and east of the study area, whereas most seasonal lakes are small water bodies, with surface areas of 0.1–0.5 km² and irregularly distributed. Lake surface area and water storage underwent frequent changes during the 38 years, and could be divided into three characteristic fluctuation phases. From 1984 to 1999, the lake surface area fluctuated greatly, and the water storage increased by approximately 0.3732 Gt, with an average expansion rate of 0.0572 Gt/y. Specifically, lakes with a surface area >2 km² primarily accounted for the expansion. From 1999 to 2004, the lake area shrank sharply to the lowest point, and the water storage capacity decreased by approximately 0.4003 Gt. From 2004 to 2021, the lake surface area and water storage tended to be stable. Annual fluctuations of lake surface area and water storage were mostly affected by precipitation and evapotranspiration, followed by vapor pressure deficit, wet day frequency, and temperature, which have significant periodicity and hysteresis.

Accurate evapotranspiration (ET) estimation is vital for the ecology and water resource management of arid and semiarid regions. This study focused on the Kolqin Sandy Land Inland Closed Basin. The simulation of energy flux data for the area was conducted by applying Landsat 8 data from 2018 to 2022 during the growing season, the Two-Source Energy Balance (TSEB), and the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) models. The validation of both models was performed via eddy covariance systems and large-aperture scintillometers, and the applicability of both models was compared. At the ecosystem scale, the TSEB model exhibited excellent accuracy of latent heat flux simulation in dune and meadow ecosystems (R²>0.8, RMSE<45.74 W/m2, and MAPE<26%), while the METRIC model excelled in meadow ecosystems (R2 = 0.89, RMSE = 27.79 W/m2, and MAPE = 12%). In contrast, at the regional scale, the TSEB model outperformed METRIC for sensible heat flux simulation (R2: 0.65-0.68, RMSE: 22.83-43.28 W/m2, MAPE: 14%-17%). The TSEB model ranked evapotranspiration in the order of lakes>meadow wetlands>farmland>dunes, peaking in August and following a seasonal pattern of summer>spring>autumn. Additionally, evapotranspiration varied with LAI during the pre-middle growth period and responded significantly to △T in the late vegetation period.

Land evapotranspiration (ET) is essential for the hydrological cycle and surface energy balance. Investigating the spatiotemporal evolution and response to land cover changes is of significance in socioeconomic development and regional water resource management. However, consistent estimation of ET is challenging due to “space-time” conflicts in optical remote sensing data and susceptibility to cloud contamination. This study adopted the GF-SG model for reconstructing high-resolution NDVI time-series data from the Yellow River Basin of Inner Mongolia. These data were then input into the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model for improving the resolution of ET retrieval. ET was estimated at an 8-day 30m resolution from 2000 to 2022, and its spatiotemporal patterns were analyzed. The model sensitivity parameters were optimized and validated based on in-situ observations from the eddy covariance stations. The optimized PT-JPL model demonstrated excellent simulation results in the basin, with calibration period accuracy ranging from the R2 of 0.88 to 0.91, RMSE between 0.57 and 0.60 mm/d, and MAE from 0.38 to 0.46 mm/d. The validation period accuracy ranged from the R2 of 0.85 to 0.87, RMSE between 0.54 and 0.72 mm/d, MAE from 0.36 to 0.47 mm/d. Over the past 23 years, the mean basin-wide ET has been 304.18 mm, with an increasing trend of 2.587 mm/year. Spatially, ET exhibited west-low and east-high distributions. Across the basin, most (82%) of the trends in ET change were not significant, with noticeable increases mainly in the eastern regions, such as the Daheihe and Hunhe River Basins. Future ET trends mainly showed increasing or non-continuing patterns. The sequence of ET increase in various land use transfer areas was transfer to forest land > transfer to cropland > transfer to construction land > transfer to grassland > transfer to unused land.