Distributed and semi-distributed hydrological modeling approaches commonly involve the discretization of a catchment into several modeling elements. Although some modeling studies were conducted using triangulated irregular networks (TINs) previously, little attention has been given to assess the impact of TINs as compared to the standard catchment discretization techniques. Here, we examine how different catchment discretization approaches and radiation forcings influence hydrological simulation results. Three catchment discretization methods, i.e., elevation zones (Hypsograph) (HYP), regular square grid (SqGrid), and TIN, were evaluated in a highly steep and glacierized Marsyangdi-2 river catchment, central Himalaya, Nepal. To evaluate the impact of radiation on model response, shortwave radiation was converted using two approaches: one with the measured solar radiation assuming a horizontal surface and another with a translation to slopes. The results indicate that the catchment discretization has a great impact on simulation results. Evaluation of the simulated streamflow value using Nash–Sutcliffe efficiency (NSE) and log-transformed Nash–Sutcliffe efficiency (LnNSE) shows that highest model performance was obtained when using TIN followed by HYP (during the high flow condition) and SqGrid (during the low flow condition). Similar order of precedence in relative model performance was obtained both during the calibration and validation periods. Snow simulated from the TIN-based discretized models was validated with Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. Critical Success Indexes (CSI) between TIN-based discretized model snow simulation and MODIS snow were found satisfactory. Bias in catchment average snow cover area from the models with and without using imputed radiation is less than two percent, but implementation of imputed radiation into the Statkraft Hydrological Forecasting Toolbox (Shyft) gives better CSI with MODIS snow.

Abstract. This paper presents Shyft, a novel hydrologic modelling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables the rapid development and implementation in operational settings, the capability to perform distributed hydrologic modelling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well known routines or through the creation of new routines, each defined for processes such as: evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an Application Programming Interface (api) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The api allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide an enterprise level software, strong focus is given to computational efficiency, code quality, documentation and test coverage. Shyft is released Open Source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os, facilitating effective cooperation between core developers, industry, and research institutions.

<div> <div> <div> <p>Triangular Irregular Network (TIN) is known to be an efficient way to represent surface topography (Marsh et al. 2018). However, little attention has been given to assess direct benefits of the TIN-based terrain representation in operational hydrology. We connect Shyft-hydrology, a part of Shyft open-source project dedicated to distributed hydrologic modelling in operational environments, with Rasputin software intended for conversion of digital elevation models into simplified triangular meshes. Shyft is known for its high flexibility: the framework lets researcher test different functioning hypothesis with very little programming effort. We implemented new routine in Shyft-hydrology, which allows translation of solar radiation onto inclined surfaces based on (Allen et al. 2006). Thus, Shyft and Rasputin is a unique toolchain to study impact of hillslope variations in solar radiation onto snowmelt, evapotranspiration and discharge simulation.</p> <p>We conducted several experiments on subcatchments of Narayani river located in Central Nepal. This area is known to be very steep, with meteorological stations, located mainly in the low-land. The re-analysis data for the area is coarse and prone to different kind of issues (Bhattarai et al 2020). The outcomes are promising: tin-based solution outperfoms regular grid, when running with Shyft-hydrology model most used in the operations. The new model with translated radiation also works well, giving us no decrease in performance of discharge simulations, but some more insights in snow modelling. We clearly see, what we expect from observations: sunny slopes melt earlier while shady ones keep snow for longer periods.</p> <div> <div> <div> <p>Acknowledgments. This project contributes to LATICE (Land Atmosphere Interaction in Cold Environments) initiative at the University of Oslo.</p> <p>References</p> <p>Marsh, C. B., Spiteri, R. J., Pomeroy, J. W., and Wheater, H. S.: Multi-objective unstructured triangular mesh generation for use in hydro- logical and land surface models, Computers and Geo- sciences, 119, 4967, 2018.</p> <p>Richard G. Allen, Ricardo Trezza, and Masahiro Tasumi. Analytical integrated functions for daily solar radiation on slopes. Agricultural and Forest Meteorology, 139:5573, 2006.</p> <p>Bhattarai, B. C., Burkhart, J. F., Tallaksen, L. M., Xu, C.-Y., and Matt, F. N.: Evaluation of forcing datasets for hydropower inflow simulation in Nepal, Accepted for publication. Hydrology research, 2020</p> </div> </div> </div> </div> </div> </div>

Terrain topography controls insolation variations at catchment scale. This effects are known to be important in cold and mountainous regions due to high diurnal and seasonal variability in incoming radiation. However, meteorological data in such areas lacks accuracy due to sparse station network and coarse re-analysis grids. Simulation tools that model hydrologic processes at local scales require ways to overcome the lack of accuracy in the observational data, particular at high elevations, so downscaling to cell level is done carefully. With an introduction of irregular triangular networks into distributed hydrologic modelling framework Shyft, steps are taken to account for hillslope-scale terrain structures within scope of operational hydrology. This new functionality allows translation of radiation measurements or re-analysis data onto inclined surfaces improving the predictive power of the model. Based on the Shyft and Rasputin toolbox we show importance of topographic details such as slope and aspect on predicting snowmelt and rainfall-runoff simulations in snow-covered mountainous region of Himalaya. We conduct the series of experi- ments for catchments in Narayani area of central Nepal in two steps. First, we demonstrate sensitivity of streamflow simulation to mesh size and shape. The results show that there is an upper limit after which further mesh refinement is not useful for simulations. Second, we incorporate "on the fly" correction of incoming solar radiation depending on surface inclination. The experiments with both coarse and fine tin meshes demonstrate that snow-water equivalent and potential evapotranspiration are directly affected by variations in insolation, with less snow and more evaporation on south-facing slopes. Finally, we perform 10-years of hydrological simulation of Budhi-Gandaki catchment with several model configuration incorporating both pro- posed features (tins and radiation correction), where we reveal better correspondence of simulated and observed discharge forgrid-based solution, which is contradictory to our previous study at Marsyangdi-2 catchment.This is an unfinished study: the Budhi-Gandaki catchment hydrology has to be analyzed carefully. One of the possible reasons of the unexpected results is a complex precipitation pattern in the catchment, which was not caught by the modelproperly.

Abstract. This paper presents Shyft, a novel hydrologic modeling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables rapid development and implementation in operational settings and the capability to perform distributed hydrologic modeling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well-known routines or through the creation of new routines, each defined for processes such as evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an application programming interface (API) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The API allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide enterprise-level software, strong focus is given to computational efficiency, code quality, documentation, and test coverage. Shyft is released open-source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os (last access: 22 November 2020), facilitating effective cooperation between core developers, industry, and research institutions.