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Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB.

 

Abstract. Offline particle tracking (OPT) is a widely used tool for theanalysis of data in oceanographic research. Given the output of ahydrodynamic model, OPT can provide answers to a wide variety of researchquestions involving fluid kinematics, zooplankton transport, the dispersionof pollutants, and the fate of chemical tracers, among others. In thispaper, we introduce ROMSPath, an OPT model designed to complement theRegional Ocean Modeling System (ROMS). Based on the Lagrangian TRANSport(LTRANS) model (North et al., 2008), ROMSPath is written in Fortran90 and provides advancements in functionality and efficiency compared toLTRANS. First, ROMSPath calculates particle trajectories using the ROMSnative grid, which provides advantages in interpolation, masking, andboundary interaction while improving accuracy. Second, ROMSPath enablessimulated particles to pass between nested ROMS grids, which is anincreasingly popular scheme to simulate the ocean over multiple scales.Third, the ROMSPath vertical turbulence module enables the turbulent(diffusion) time step and advection time step to be specified separately,adding flexibility and improving computational efficiency. Lastly, ROMSPathincludes new infrastructure which enables inputting of auxiliary parameters for addedfunctionality. In particular, Stokes drift can be input and added toparticle advection. Here we describe the details of these updates andperformance improvements.