Browsing by Author "Gyawali, Dhiraj Raj"

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Open Access
Development and parameter estimation of conceptual snow-melt models using MODIS snow-cover distribution
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2023) Gyawali, Dhiraj Raj; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Due to a high spatio-temporal variability observed in the inherent snow-related processes in snow-dominated regimes, reliable representation of spatial distribution of seasonal snow has remained a critical challenge for effective monitoring of seasonal evolution of snow and subsequently hydrological estimations, in mountainous regions around the world. This issue, coupled with the crucial relevance to climate change, is further exacerbated by data scarcity in these regions. To address this issue, this thesis presents a novel standalone calibration technique employing the pixel-wise binary (’snow’, ’no snow’) information from MODIS snow-cover images to calibrate independent conceptual snow-melt models, thereby estimating model parameters from individual or sets of MODIS images. This methodology exploits the pertinent information of snow-cover distribution from the freely available remote sensing images, to reliably simulate snow-processes in data scarce regions. Switzerland and Baden-Württemberg were selected as study snow regimes, with the former representing partly longer duration snow and the latter associated with a shorter duration. Different extensions of parsimonious conceptual snow-melt models were developed and used to simulate the snow-cover distribution, with all models showcasing an adept and robust simulation. The selection of binary snow-cover information as calibration variable permits relatively complex snow-melt modules to be calibrated with more robustness because of reduced uncertainty associated with the calibration data. This work further identifies and recommends different simulation thresholds for defining the calibration data (NDSI thresholds), selecting the images for calibration (cloud cover thresholds), and reclassifying the snow water equivalent (SWE) outputs to snow-cover information (SWE thresholds). Furthermore, validation of the MODIS based snow-melt model calibration and the simulated melt outputs was carried out using a modified hydrological model (modified HBV variant) without the snow-routine. This hydrological performance was contrasted with the standard HBV model calibrated solely on discharge. The melt output provided as standalone inputs to the modified HBV was observed to impart an enhanced discharge prediction. As compared with the discharge calibrated standard HBV, a reduction in uncertainty in terms of model performance was observed along with reduced parameter compensation. The increase in model performance is deemed for ‘the right reason’ as the snow processes are adeptly represented by process-informed parameters. The estimation of the parameters solely from MODIS information not only eliminates the reliance on a single calibration variable ’discharge’ which is already an availability constraint in the higher altitudes but also preserves the spatial heterogeneity at a more regional level. This methodology holds a crucial relevance for discharge simulation in areas with episodic days of snow, where the snow processes can be calibrated quickly on images without having to calibrate the entire hydrological model. The study approach shows that the addition of freely available snow-cover information in estimating the parameters of snow-melt models utilizing the snow/no-snow information and a modest and globally available input data demand, facilitates a simple, spatially flexible approach to calibrate snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data, especially in data scarce regions.
Open Access
Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS
(2022) Gyawali, Dhiraj Raj; Bárdossy, András
Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.