The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Aug 2019)

AN OPEN DROUGHT MONITORING SYSTEM FOR THE DEDURU OYA BASIN IN SRI LANKA IN THE CONTEXT OF THE 4ONSE PROJECT

  • D. Strigaro,
  • M. Cannata,
  • E. Warusavitharana,
  • B. H. Sudantha,
  • R. Ratnayake

DOI
https://doi.org/10.5194/isprs-archives-XLII-4-W14-227-2019
Journal volume & issue
Vol. XLII-4-W14
pp. 227 – 231

Abstract

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During the last decade, many climatic parameters are more and more deviating from the mean values calculated over historical climatic time-series. The World Meteorological Organization (WMO) stated that years 2015, 2016 and 2017 were the warmest since 1850. According to the preliminary data analysis of the last year, this trend is going to continue in 2018 as well. These climatic changes have accelerated the occurrence of droughts in many parts of the world including Europe and Asia. The North Western region is one such region in Sri Lanka which always affected by droughts due to its inherent dry weather condition. Although drought is considered as a normal part of the climate, its increasing negative impacts on human activities and the environment urges the application of novel technologies in drought monitoring. The 4onse project (analysis of Open, Non-conventional, Sustainable and Effective monitoring systems), funded by the Swiss National Science Foundation (SNSF), is a project began in this context to address the aforementioned mentioned issues by deploying 27 open technologies based Environmental Monitoring Systems (EMS) in Deduru Oya basin of Sri Lanka. These systems measure weather parameters such as temperature, humidity, pressure, rainfall, wind speed and wind direction. Thanks to the collected data, it was possible to calculate some drought indexes to evaluate the intensity of the dry periods. The Standard Precipitation Index (SPI) has been selected as the principal indicator to evaluate droughts by integrating the 4onse data together with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset.