Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Abstract Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations

3-D hydrogeological model of limestone aquifer for managed aquifer recharge in Raipur of central India

Raipur of Chhattisgarh state, central India, is facing water problems due to rapid urbanization and altered hydrological processes despite adequate rainfall. Deteriorated surface water, diminished recharge and elevated pumping have reduced water availability in the region. Therefore, proper measures like managed aquifer recharge (MAR) need to be successfully implemented to conserve rain water for long-term sustainability. Hydrogeological data, combined with geophysical investigations lead to the construction of a conceptual model of the Chandi limestone aquifer for this purpose. The results indicate that the compact and massive limestone has negligible primary porosity, but solution weathering (karstification) has developed secondary pores at variable depths that favour recharge, groundwater storage and movement. The study helped to locate potential recharge sites that have significance in optimizing MAR structures such as check dams and injection wells. The hydrogeological model has a great significance in managing these complex and heterogeneous aquifers for better future water supply in the region