Understanding the hydrology in the upstream mountainous part of the Karnali basin in Nepal is vital, considering the importance of streamflow for downstream nature conservation and water supply. We use a fully distributed hydrological model to understand the current hydrology, the associated vulnerability of the basin, and the importance of the different hydrological components in regulating flow. Downscaled ERA5 meteorological data is used to force the model for the period 1991–2022 at a high spatial resolution (500 meters). We calibrate our model using observed discharges, and the model performance is considered good with a reported Kling-Gupta efficiency of 0.84 and a bias of −3.33%. Our results show that 40% of the overall discharge generated in the Karnali basin originates from rain runoff, 35% from baseflow, 24% from snowmelt, and a negligible 0.8% from glaciers. The water balance components vary spatially in magnitude, but the overall monthly patterns are comparable. On average, the basin receives 1,485 mm/year of precipitation, peaking in July, and is a pronounced southwest region. The annual average evapotranspiration in the basin is 574 mm/year, and discharge is 914 mm/year. Analysis of anomalies reveals that the discharge has become increasingly more variable over the last decades and, therefore, less predictable. Our results also reveal that the basin is frequently experiencing meteorological droughts, often translating into a hydrological drought with a lag time of a month. The average duration of a hydrological drought period in the basin was about 6 months. Snow storage plays an important role in modulating these droughts, and variability in initial snow storage impacts basin streamflow for up to 6 months. A climate change-induced shift from snow to rain may therefore impact the climate resilience of the Karnali considerably.

An error was made in the definition of the density parameter ρ in Equations 7, 8 and 9 of the original article. It was defined as the bulk density of the aquifer, whereas it should have been the density of pore water. Additionally the density of the aquifer solid matrix ρ_s, used in equation 11 to compute the retardation factor, was not defined in the original article. The misuse of the bulk density instead of water density resulted in incorrect values of the computed thermal distribution coefficient, i.e. the bulk thermal diffusivity, and the retardation factor in Table 5. Some of the units were also incorrect. The corrected table is given here.

As a result, the sub-section ‘Temperature variations in the recovered water in wells’ should be corrected through stating the following: With a corrected retardation factor of 2.85, the average residence time of sources of water contributing to the wells is 74 days, which is sufficiently long to improve the water quality.

Managed aquifer recharge (MAR) is increasingly used to secure drinking water supply worldwide. The city of Amsterdam (The Netherlands) depends largely on the MAR in coastal dunes for water supply. A new MAR scheme is proposed for the production of 10 × 10⁶ m³/year, as required in the next decade. The designed MAR system consists of 10 infiltration ponds in an artificially created sandbank, and 25 recovery wells placed beneath the ponds in a productive aquifer. Several criteria were met for the design, such as a minimum residence time of 60 days and maximum drawdown of 5 cm. Steady-state and transient flow models were calibrated. The flow model computed the infiltration capacity of the ponds and drawdowns caused by the MAR. A hypothetical tracer transport model was used to compute the travel times from the ponds to the wells and recovery efficiency of the wells. The results demonstrated that 98% of the infiltrated water was captured by the recovery wells which accounted for 65.3% of the total abstraction. Other sources include recharge from precipitation (6.7%), leakages from surface water (13.1%), and natural groundwater reserve (14.9%). Sensitivity analysis indicated that the pond conductance and hydraulic conductivity of the sand aquifer in between the ponds and wells are important for the infiltration capacity. The temperature simulation showed that the recovered water in the wells has a stable temperature of 9.8–12.5 °C which is beneficial for post-treatment processes. The numerical modelling approach is useful and helps to gain insights for implementation of the MAR.