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.

Abstract: Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.