SDGs and COVID-19

This challenge invites you to analyze the impact of COVID-19 on the United Nations (UN) Sustainable Development Goals (SDGs) by looking at the current and ongoing change in the monitoring indicators of the UN SDGs using Earth observation/remote sensing and global Earth system model-derived analysis products.

Flood Management and Forecasting using Satellite Data during the Pandemic COVID-19.

Summary

Floods are the most dangerous natural disasters. They may cause huge losses and casualties every year, whence comes the importance of rainfall forecasting and managment. Recently, with The global health crisis related to the COVID-19, has been an opportunity to regulate and improve the air quality of our planet, this is manifested positively on climate and rainfall especially in aride regions, and was proclaimed by heavy rainfall after a long period of drought.

How I Addressed This Challenge

Our challenge is to create a flood forecasting application in real time based on satellite data, in order to protect the most vulnerable populations, and try to manage flash floods after heavy rainfall.

Indeed, this pandemic has improved the quality of the Earth's climate, and has resulted in unexpected rainfall in a year of drought especially in arid regions.

However, during the COVID-19 period, the change in behavior and habits, generate a positive effects of purification on the quality of the climate, the environment, and the air. This is why it's important to continue to protect our environment, even after the COVID-19 crisis.

How I Developed This Project

In arid and semi-arid zones, Precipitation plays an important role in hydrological and meteorological applications; extreme precipitation should receive much attention because of its implications for hazard assessment and risk management. However, it is still difficult to obtain accurate information on extreme precipitation from rain gauges in mountainous areas, where there are limited, sparse, uneven, and sometimes not available stations.

Satellite precipitation products with high spatial resolution are a new way of supplementing data from gauge observations. In order to remedy the problem of the lack of measuring stations, and to avoid the numerous human and material damages caused by floods, the inspiration and willingness to participate in this challenge is deeply manifested, and it is also due to the fact that this project is the focus of my research work that I intend to develop. It's also a problem from which the population of my country suffers enormously, and that is why I have decided to go deeper into this approach and put my knowledge in the service of humanity.

The approach adopted aims to develop a real-time flood forecasting application based mainly on satellite data from a different space agency.

The various sources of satellite precipitation data used in this project came mainly from the following satellite products: The Tropical Rainfall Measuring Mission TRMM 3B42V7, Global Precipitation Measurement GPM IMERG V05B, ERA-Interim and ERA5 reanalysis by the European Center for Medium-Range Weather Forecast (ECMWF).

Various statistical evaluation techniques and extreme precipitation indices are used to assess and compare the performance of the selected satellite products.

The tools and software used for the application of this approach are: The R programs, and Matlab; ARC-GIS, and HEC-HMS software.

Project Demo

Various statistical evaluation techniques and extreme precipitation indices are used to evaluate and compare the performance of the selected products. The results show good correspondence and are adopted to arid climate. With regard to the spatial distribution of precipitation, the datasets performed better over plains and were disappointing over mountainous areas. This study provides a basis for selecting alternative precipitation data for climate transition basins.

Data & Resources

http://trmm.gsfc.nasa.gov/

https://gpm.nasa.gov/


Global Judging
This project was submitted for consideration during the Space Apps Global Judging process.