<p>Snow cover is an essential climate variable directly affecting the Earth&#8217;s energy balance, therefore estimating the snow parameters play an important role in hydrological, land surface, meteorological and climate models. Remote sensing provides a good understanding of snow cover monitoring thus several satellite snow products have been developed and disseminated so far. &#160;In this study, Jupyter Notebook as an open source interactive satellite snow products retrieval, visualization and analysis tool has been developed by using Python language. Jupyter Notebook allows easy and straightforward data analysis with the possibility of live interaction and requires little programming knowledge.</p><p>The developed tool provides the capabilities of downloading the satellite snow products, georeferencing them and performing spatial analysis like zonal statistics. In this study EUMETSAT HSAF snow products, namely H10 (Snow detection), H13 (Snow Water Equivalent) and H34 (Snow cover) are used. The tool allows user to upload their own region in ESRI shapefile format for spatial and temporal analysis and the uploaded region can be visualized on interactive map via custom interactive widget like ipyleaflet. The cloud percentage for the snow cover product can be selected and daily snow covered area or snow water equivalent change for the uploaded region can be calculated for the selected period. With this tool, it is aimed to retrieve the satellite snow products easily and perform spatial and temporal analysis of snow cover for the area of interest without getting lost in data formats. Therefore, users with little or no knowledge about programming can interact easily with EUMETSAT HSAF snow products. Furthermore, with the high extensibility of Jupyter Notebook, it can also be improved or modified in accordance with the need of the end users.</p>
<p>Bucket-type conceptual hydrological models are widely popular, because of their relatively low data and computational demands. With the improved computational techniques and advances in computer sciences, web based hydrological modelling tools are becoming available too. Conceptual rainfall-runoff (CRR) models are designed to approximate the general physical mechanisms which govern the hydrologic cycle and found practical by many hydrologists and engineers. In this context, a web based, open-source, platform independent, easily accessible hydrological modelling tool <strong>Hidro-Odtu</strong> has been designed. Aiming at providing fast and accurate results, <strong>Hidro-Odtu</strong> utilize lumped and semi-distributed hydrological modelling capabilities. The design of the <strong>Hidro-Odtu</strong> contains pre-processing using the tools to automatically delineate the river network and basin boundaries, input the forcing data, lumped hydrological modelling with parameter calibration capability, hydrological overland flow routing and dynamic result visualization. Moreover, web-based technologies allow remotely prepare model input files, run model calculation and display model results for rainfall-runoff calculations. Bucket storage lumped, conceptual rainfall-runoff model is selected as core feature for hydrological model and it is enhanced to a semi-distributed model by including the Muskingum-Cunge flow routing method to simulate overland flow. Model results are evaluated by several performance indices such that Nash&#8211;Sutcliffe Efficiency Index (NSE), Sum of Square of Error (SSE) or Kling-Gupta Efficiency (KGE).</p><p>Hydrological modelling, calibration and routing algorithms have been implemented by using Python programming language for the back-end calculations and Node.js framework, html, JavaScript have been utilized for front-end side to handle data preparation and results visualization.</p><p>Hidro-Odtu have been evaluated with numerous data sets with different study areas and found successful to delineate sub basins and river network, to define rainfall-runoff relationship on the basis of the sub-basins. With this tool, it is aimed to obtain practical hydrological modelling results using web technologies.</p>
<p>The fractional snow cover (FSC) product H35 is a daily operational product based on multi-channel analysis of AVHRR onboard to NOAA and MetOp satellites. H35 is supplied by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF). The &#8220;traditional&#8221; H35 FSC product is generated at pixel resolution by exploiting the brightness intensity, which is the convolution of the snow signal and the fraction of snow within the pixel and the sampling is carried out at 1-km intervals. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. This presentation aims to represent the latest findings of our efforts in developing an &#8220;alternative&#8221; H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs). In total, 332 Sentinel 2 images over Alps, Tatra Mountains and Turkey acquired between November 2018 and April 2019 are used in order to generate the necessary reference FSC maps for the training of the MARS and RF models. AVHRR bands 1-5, NDSI and NDVI are used as predictor variables. Binary classified Sentinel 2 snow maps, ERA5 snow depth and MODIS MOD10A1 NDSI data are employed in the validation of the models. The results show that both MARS- and RF-based H35 product are i) in good agreement with reference FSC maps (as indicated by low RMSE and relatively high R values) and ii) able to capture the spatial variability of the snow extend. However, MARS-based H35 is preferred for an operational FSC product generation due to the high computational cost required in RF model.</p>
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