Computational environment HYDRO-PATH as a flexible tool for the operational rainfall-runoff model design

Abstract

the overall objective of the ongoing work is to develop the computational environment hyDrO-Path as a flexible tool for forecasting runoff from catchment areas for various hydrometeorological conditions while taking into account the information available on a real-time basis. ensuring the model's operational reliability and reducing the uncertainty of generated forecasts is accomplished through the adjustment of both the internal structure of the model and the spatial representation of the computational grid to the physiographical, hydrological and climatological characteristics of a given basin. The research focused on the development of methods for selecting the optimal model structure and parameters by analysing the results obtained for different model structures. This is achieved through the computational environment, in which it is possible to implement different types of hydrological rainfall-runoff models. these models have a unified system of data input, parameter optimisation rules, and procedures for result generation. The developed elements of the computational environment correspond to generation potential of models with a given structure and complexity. Furthermore, within the framework of hyDrO-Path the following components were developed: an application programming interface (aPi), a data assimilation module, a module for computational representation of a real object, and a module for the estimation and optimisation of model parameters. the developed computational environment was applied to prepare a version of tOPO-Flex and perform hydrological validation of the model's results. the hydrological validation was performed for selected flood events in the Bystrzyca Dusznicka subbasin of the nysa kłodzka river.

River Flow Prediction for Future Climate Using Long Series of Multi-Site Synthetic Data and MIKE SHE Model

“…The daily flow simulation, particularly seasonal extremes for future climate conditions given by different scenarios are important for the Polish rivers for several reasons [15,17,27]. The paper presents an example of new simulation of seasonal flow for the relative small catchment of Kaczawa River a left side tributary of the Odra River (Southwest region of Poland) for selected climate change scenarios is presented.…”

Evaluation of multisite synthetic data generated by spatial weather generator and long climate data series

“…Above applications indicate that high-quality spatial weather generator is required. A high-quality spatial weather generator means that the generated data are same distributed as the climate data [10,[19][20]. This means that basic statistical measures such as averages, variances and others are very close.…”

“…Particularly, the forthcoming hydrology in the river catchments, runoff response, and predictions are strongly impacted by future climate and possible change [1][2][3][4][5][6][7][8][9][10][11][12][13]. The daily flow simulation, particularly seasonal extremes for future climate conditions given by different scenarios are important for water management and hydrology for several reasons [10][11][14][15][16][17][18]. Above applications indicate that high-quality spatial weather generator is required.…”

Precipitation estimation and nowcasting at IMGW-PIB (SEiNO system)

“…The SEiNO is able: (i) to provide high quality input data required for hydrological rainfall-runoff models (Tokarczyk et al 2016), (ii) to track rapidly changing meteorological phenomena at a small spatial scale (as the forecasting cycle is 10 min and the spatial resolution is 1 km), and (iii) to predict their evolution for longer time periods (the lead time is up to 4 hours). In figure 1 a block diagram of the SEiNO system is depicted.…”