CHARACTERIZING MOUNTAIN SYSTEM RECHARGE PROCESSES AND VARIABILITY - A RECENT SYNTHESIS (Invited Presentation)

Mountain System Recharge (MSR) is one of the main components of recharge in many arid and semi-arid aquifers, and includes recharge from streamflow infiltration at the mountain fronts (mountain front recharge, MFR) and subsurface lateral flow from the mountain block to the adjacent alluvial aquifer (Mountain block recharge, MBR). Despite the importance of MSR, not enough is known about MSR rates and how they might be altered by climate variability. The high complexity of recharge processes in the mountain catchments and lack of groundwater observations contribute to this problem. Here, we present a range of methods applied in multiple mountain catchments to estimate MSR rates. These methods range from streamflow recession analysis, empirical models constrained by stable water isotopes to semi-distributed and process-based hydrologic models. Results illustrate that catchment scale storage-discharge functions are able to estimate MBR at seasonal scale, but their application is limited to perennial streamflow conditions. Stable water isotopes provide information about overall average MSR seasonality, and can validate seasonal MSR partitioning from numerical model simulations and climate based indices such as the normalized seasonal wetness index. Such a simple index based on seasonal precipitation and actual or potential evapotranspiration is useful to assess climate change impacts on seasonal MSR in regions with limited data. While application of process-based integrated surface water-groundwater models can provide valuable information regarding MSR rates, accuracy of model simulations depends on model forcing, characterizing subsurface heterogeneity, groundwater flow circulation depth as well as residence time in mountain bedrock. Results of this synthesis highlight the role of integrating multiple approaches based on hydrometric, geochemical and remotely sensed based observations in addition to numerical modeling to improve MSR predictions.