The hydrogeochemical characteristics of groundwater are an important element in the study of the spatial and temporal variation of groundwater resources, which is crucial to water resources utilization, ecological environmental protection, and human development. Water samples were collected at eight observation of Nandong Karst Water System (NKWS) sites in each month of 2019, and the main ions and isotopes of the water samples were examined. The hydrogeochemistry characteristics of groundwater and its differences with surface water were explored by using the methods of multivariate statistics, Gibbs model. Results showed that the water chemistry types of groundwater were mainly HCO₃–Ca and HCO₃–Ca·Mg. The analysis of hydrogen and oxygen isotope showed that the initial recharge source of surface water and groundwater were atmospheric precipitation, and the measured hydrogen and oxygen isotopes of surface water were heavier due to the strong evaporation effect. The natural and anthropogenic processes contributed to the chemical composition of surface water and groundwater in the study area. However, the main factor affecting the quality of surface water and groundwater was the input of anthropogenic contaminants. In terms of natural factors, the main chemical ions of surface water and groundwater were mainly controlled by water-rock action originating from weathering and hydrolysis of rocks and soils. Ca²⁺, Mg²⁺, and ${\mathrm{H}\mathrm{C}\mathrm{O}}_3^{-}$ mainly originated from natural dissolution of carbonate rocks. K⁺, Na⁺, ${\mathrm{S}\mathrm{O}}_4^{2-}$, and Cl⁻ were partly from atmospheric precipitation. For human activities, Na⁺ and Cl⁻ were partly from domestic water for local residents. ${\mathrm{S}\mathrm{O}}_4^{2-}$ in surface water mainly came from mining. ${\mathrm{N}\mathrm{O}}_3^{-}$ in groundwater mainly came from chemical fertilizers, and ${\mathrm{N}\mathrm{O}}_3^{-}$ in surface water were mainly from human waste and domestic sewage.

Due to frequent drought events, increased water demand for agricultural production and limited, accurate estimation of reference evapotranspiration (ETo) is necessary for developing crop irrigation schemes and rational allocation of regional water resources. The extreme learning machine (ELM) was optimized using four biological heuristic algorithms, namely, Grey Wolf Optimizer (GWO-ELM), Moth-Flame Optimization (MFO-ELM), Particle Swarm Optimization (PSO-ELM), Whale Optimization Algorithm (WOA-ELM), and besides three types of empirical models (temperature-, radiation-, and mass transfer-based), and Penman model (P-M) were also applied to estimate the daily ETo in the Hetao irrigation district (HID). The results demonstrated that GWO-ELM obtained the highest estimation accuracy (R²= 0.945–0.955; RRMSE = 14.52–15.29%; MAE = 0.124–0.141 mm d⁻¹, and NSE = 0.942–0.952) at all stations when using mass transfer combination (Tmax, Tmin, RH, u₂) as models input, and the GWO-ELM hybrid model outperformed other models. Herein, the biogenic heuristic algorithm can effectively enhance the ELM performance in ETo estimation, it was strongly recommended for estimating daily ETo in the HID using the hybrid GWO-ELM model and mass transfer combination as input. The optimized hybrid algorithms, especially GWO-ELM, can accurately estimate daily ETo with limited meteorological data, which can provide scientific guidance for the development of precision agriculture in the HID. Abbreviations: ANN: artificial neural network; BP: back propagation neural network; CMA: China Meteorological Administration; D-T: Dalton; EL: elevation; ELM: extreme learning machine; ETo: reference evapotranspiration; GEP: gene expression programming; GP: genetic programming; GWO: gray wolf optimization; H-S: Hargreaves-Samani; MFO: moth flame optimization; ML: machine learning; P-M model: FAO-56 Penman-Monteith (P-M) model; P-T: Priestley-Taylor; PSO: particle swarm optimization; RF: random forest; R-O: Rohwer; SVM: support vector machine; SVR: support vector regression; WOA: whale optimization algorithm

This study aimed to evaluate the feasibility of using Mg/Al-based layered double hydroxides (Mg/Al-LDHs) treatment to prevent phosphorus release from sediments of agricultural drainage ditches. A high-resolution diffusive gradient film technique and a high-resolution peeper technique were used to measure the phosphorus and iron concentrations in the overlying water and sediment profiles at sub-millimeter vertical resolution. Results demonstrated that Mg/Al-LDHs effectively reduced the concentrations of soluble reactive P (SRP) (about 69%) in the overlying water and the concentrations of SRP (about 37.42%) and labile P (about 36.72%) in the pore water. The highly positive correlation (p < 0.01) between SRP and soluble Fe, labile P and labile Fe in the sediment profiles provided high-resolution evidence for the simultaneous release of iron and phosphorus in sediments. Furthermore, Mg/Al-LDHs inactivated mobile P (NH₄Cl-P and BD-P) in the uppermost sediment (0-50 mm) and then transformed the mobile P to more stable P (NaOH-rP, HCl-P, and Res-P) (about 81% of total extractable P). An inactivation layer with low phosphorus concentrations was observed in the upper sediment. In brief, the addition of Mg/Al-LDHs to the sediment surface of agricultural drainage ditches was effective in reducing SRP concentrations in the overlying water while effectively hindering the release of sediment internal phosphorus from the pore water to the overlying water.