Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Abstract

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.

Shyft and Rasputin: a toolbox for hydrologic simulations on triangular irregular networks

“…The efficiency parameters for this subcatchment are less satisfying than for 8 and 13, notifying us that there is actually no reason for more cells, as source data might be a greater issue. However, for CID-10 we found that grid-based solution perform better than TIN-based, which seems to be contradictory to our study (Bhattarai et al, 2020b) with CID-10, so additional investigations needed to understand such difference.…”

“…This study is our first step in assessing benefits for the practitioners in the power production industry, and the results are promising. Further study by Bhattarai et al (2020b) gives more light on the usage of TIN-mesh for hydrological simulation.…”

“…Our results in the section 3.7 showed that we can't just propose tin-based configuration as a suitable (what are the metrics?) choice for practical problems, as we did in Bhattarai et al (2020b), but need to force the users to analyse particular catchment in details, which is not always possible. Thus, as an outlook for this study we have a purpose to come with a recommendations for most efficient usage of Shyft in other regions with more variety of catchment types and climates.…”

“…In our case study Bhattarai et al (2020b) we analyzed impact of catchment discretization and imputed radiation on model response for CID-8 (Marsyangdi-2) catchment. The study showed that for particular catchment the selection of discretization played an important role in performance of the model.…”

“…For further details on calibration values and their meaning, please refer toBurkhart et al (2021) and https://gitlab.com/shyft-os/shyft-doc. The description of PTGSK-stack is also provided inBhattarai et al (2020a) andBhattarai et al (2020b). The catchment delineation is presented on the Figure5.…”