Abstract:Snow and glacial melt processes are an important part of the Himalayan water balance. Correct quantification of melt runoff processes is necessary to understand the region's vulnerability to climate change. This paper describes in detail an application of conceptual GR4J hydrological model in the Tamor catchment in Eastern Nepal using typical elevation band and degree-day factor approaches to model Himalayan snow and glacial melt processes. The model aims to provide a simple model that meets most water planning applications. The paper contributes a model conceptualization (GR4JSG) that enables coarse evaluation of modelled snow extents against remotely sensed Moderate Resolution Imaging Spectroradiometer snow extent. Novel aspects include the glacial store in GR4JSG and examination of how the parameters controlling snow and glacial stores correlate with existing parameters of GR4J. The model is calibrated using a Bayesian Monte Carlo Markov Chain method against observed streamflow for one glaciated catchment with reliable data. Evaluation of the modelled streamflow with observed streamflow gave Nash Sutcliffe Efficiency of 0.88 and Percent Bias of <4%. Comparison of the modelled snow extents with Moderate Resolution Imaging Spectroradiometer gave R 2 of 0.46, with calibration against streamflow only. The contribution of melt runoff to total discharge from the catchment is 14-16% across different experiments. The model is highly sensitive to rainfall and temperature data, which suffer from known problems and biases, for example because of stations being located predominantly in valleys and at lower elevations. Testing of the model in other Himalayan catchments may reveal additional limitations.

Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search%23/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability.

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There is a growing appreciation of the uncertainties in the estimation of snow-melt and glacier-melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046-2075 compared to 1976-2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world.We suggest that modellers of glaciated catchments should present results from at least the book-ends: models with low sensitivity to ice-melt and models with high sensitivity to ice-melt. This would reduce confusion among decision makers when they are faced with similar contrasting results.

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