Abstract: Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.

Abstract: Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.

Abstract: Acid in-situ leaching (ISL) can be used as a mining technique for in situ uranium recover from underground. Acids and oxidants as lixiviants were continuously injected into a sandstone-type uranium deposit in Bayan-Uul (China). It was conducted to facilitate the dissolution of uranium minerals to generate uranyl ions, which could then be extracted for the recovery of uranium resources by the pumping cycle. A reactive transport model based on PHAST was developed to investigate the dynamic reactive migration process of uranium. The simulated results well reproduce the fluid dynamic evolution in the injecting and pumping units, as well as the dynamic release of uranium. The simulated leaching area indicates that the uranium ore leaching area was much larger than the acidification area. In addition, the pollution plume of uranium and acid water was larger than that of the leaching area, which can be used as a reference for uranium mining schemes. Furthermore, the parameter sensitivity analysis indicates the volume fraction of uranium ore and the reaction rate were the main factors affecting uranium leaching efficiency. Without considering the blockage of pores by precipitation, the Fe2+ in the reinjection fluid had a significant negative influence on uranium leaching.

Abstract: Despite numerous studies, there are many knowledge gaps in our understanding of uranium (U) contamination in the alluvial aquifers of Punjab, India. In this study, a large hydrogeochemical dataset was compiled to better understand the major factors controlling the mobility and enrichment of uranium (U) in this groundwater system. The results showed that shallow groundwaters (\textless60 m) are more contaminated with U than from deeper depths (\textgreater60 m). This effect was predominant in the Southwest districts of the Malwa, facing significant risk due to chemical toxicity of U. Groundwaters are mostly oxidizing and alkaline (median pH: 7.25 to 7.33) in nature. Spearman correlation analysis showed that U concentrations are more closely related to total dissolved solids (TDS), salinity, Na, K, HCO3−, NO3− Cl−, and F− in shallow water than deep water, but TDS and salinity remained highly correlated (U-TDS: ρ = 0.5 to 0.6; U-salinity: ρ = 0.5). This correlation suggests that the salt effect due to high competition between ions is the principal cause of U mobilization. This effect is evident when the U level increased with increasing mixed water species (Na-Cl, Mg-Cl, and Na-HCO3). Speciation data showed that the most dominant U species are Ca2UO2(CO3)2− and CaUO2(CO3)3−, which are responsible for the U mobility. Based on the field parameters, TDS along with pH and oxidation-reduction potential (ORP) were better fitted to U concentration above the WHO guideline value (30 μg.L−1), thus this combination could be used as a quick indicator of U contamination. The strong positive correlation of U with F− (ρ = 0.5) in shallow waters indicates that their primary source is geogenic, while anthropogenic factors such as canal irrigation, groundwater table decline, and use of agrochemicals (mainly nitrate fertilizers) as well as climate-related factors i.e., high evaporation under arid/semi-arid climatic conditions, which result in higher redox and TDS/salinity levels, may greatly affect enrichment of U. The geochemical rationale of this study will provide Science-based-policy implications for U health risk assessment in this region and further extrapolate these findings to other arid/semi-arid areas worldwide.