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Training by RSGIS and Partner Builds Capacity for Remote Sensing of Water Quality for the Region

Admin — Thu, 25 Nov 2021 15:33:22 +0000

[23 – 25 November 2021] The Asian Institute of Technology (AIT), in partnership with Asia-Pacific Network for Global Change Research (APN), successfully organized the “Remote Sensing of Water” training event within the frame of the research project “Climate Change Risk Assessment for Southeast Asian Lakes (CCRASEAL)”.

Virtually held on 23 – 25 November 2021 and coordinated by Dr. Salvatore G. P. Virdis, Assistant Professor at RSGIS/ICT/SET and CCRASEAL Project Leader, the course was co-designed and taught along with Dr. Chitrini Mozumder, RSGIS Research Specialist, and Mr. Siwat Kongwarakom, CCRASEAL Project Manager and RSGIS Research Assistant.

The “Remote Sensing of Water” course was attended by senior research and professional staff from the CRASEAL partners’ institutions such as Ministry of Natural Resources and Environment (Thailand), Universiti Kebangsaan Malaysia (Malaysia), Office of the National Economic and Social Development Council (Thailand), National University of Laos (Lao PDR), Ministry of Environment (Cambodia), National Center for Water Resources Planning & Investigation MONRE (Vietnam), Department of Water Resources MONRE (Lao PDR), Department of Climate Change MONRE (Lao PDR).

The three-day virtual training event aimed at providing the participants with basic theoretical concepts of remote sensing of water quality. The attendants gained practical remote sensing processing skills on i) atmospheric correction of satellite imagery and extraction of apparent and inherent optical properties from optically active water constituents; ii) Lake Surface Water Temperature (LSWT) extraction and trend detection and measurement; iii) Multi-temporal flood mapping.

The course organizers extend their sincere thanks to all the distinguished course participants for their active participation and constructive inputs during the whole duration of the event.

The post Training by RSGIS and Partner Builds Capacity for Remote Sensing of Water Quality for the Region appeared first on CCRASEAL.

Impact of Changing Climate on Lake Surface Water Temperature (LSWT) in Mainland Southeast Asia

Admin — Thu, 22 Jul 2021 13:38:50 +0000

[22 July 2021] Ms. Sukanya Phunsen has successfully defended her master thesis titled IMPACT OF CHANGING CLIMATE ON LAKE SURFACE WATER TEMPERATURE (LSWT) IN MAINLAND SOUTHEAST ASIA at AIT. The following is the abstract of the thesis.

Abstract

Lake surface water temperature (LSWT) is an essential component in lake ecosystem. It has been shown that throughout the previous century, LSWT followed an increasing trend with significant repercussions on the regional environment. To regulate and enhance the regional ecological environment, it is necessary to grasp the fundamental explanation for this occurrence. In this study, LSWT estimated across 103 lakes on mainland Southeast Asia using MODIS/Terra in the period of almost 20 years in both
daytime and nighttime. This study has generated the correlation between LSWT and various climatic variables. In addition, the contribution of air temperature, solar radiation, cloud cover, and wind speed on LSWT was used to examine the LSWT. The change of climate in Southeast Asia for 2000-2020 period has been impacting on LSWT particularly on air temperature in both daytime and nighttime. The research has generated the LSWT prediction by using climatic variables from CORDEX historical and RCP 8.5 which has used in the most extreme scenario. The results suggested that the warming trend in both MLR and LSTM can help to reveal the LSWT future trends. However, LSTM performed a strong predictive than MLR. The LSTM results has identified the prediction warming rate of LSWT. In daytime, the mean warming rate was 0.0120 °C/year and the overall mean change rate was 0.010 °C/ year. While, in nighttime, the mean warming rate was 0.009°C/year and the overall mean change rate was 0.007 °C/ year.

The post Impact of Changing Climate on Lake Surface Water Temperature (LSWT) in Mainland Southeast Asia appeared first on CCRASEAL.

Rainfall-Runoff Modelling Using SCS-CN Method at the Multi-Basin Scale of Mainland Southeast Asia

Admin — Thu, 20 May 2021 13:30:15 +0000

[20 May 2021] Mr. Ye Lin Tun has successfully defended his master thesis titled RAINFALL-RUNOFF MODELLING USING SCS-CN METHOD AT THE MULTI-BASIN SCALE OF MAINLAND SOUTHEAST ASIA at AIT. The following is the abstract of the thesis.

Abstract

The relationship of rainfall and runoff for any heavy rainfall relies on the strong interaction of rainfall amount, infiltration of the soil and surface storage. When the rainfall amount is beyond the capacity of infiltration of the soil, runoff can be occurred. In this study, Runoff modelling of SCS-CN is applied to calculate the surface runoff in the Mainland Southeast Asian basins at multi basin scale. The runoff model is an empirical model which is based on the hydrological soil types of the area, land cover/land use, treatment classes and hydrologic conditions of the watershed or drainage basin.
The Global Curve Number is generated using HYSOGs soil dataset and the ESACCI land cover dataset to be consistent with GCN250 for three years of 2000, 2010 and 2018 with extreme weather events. The ESACCI land cover classes are similarity checked with the local governments’ land cover classes before handling. In next step, daily time series data of CHIRPs and ERA5 precipitation are similarity checked with observed rainfall. As CHIRPS is better performed, it is applied to estimate runoff. Regulated and non-regulated basins are identified based on Hydropower and Dam sites are located within basins by visual interpretation with Google Earth. The year and monthly surface runoff and discharge are generated. Monthly large scale spatial variability is calculated and mapping in terms of standard deviation.
The observed discharge and ERA5 surface runoff are used to validate the estimated results in regulated basins and non-regulated basins separately. The validation result with observed data in non-regulated basins better perform R2=0.84, RMSE=34.96 and PBIAS=2.56 while R2=0.69, RMSE=35.33 and PBIAS=6.40 in regulated basins. The model underestimated the observed surface runoff. In the validation with ERA5 surface runoff for all basins, two methods are highly correlated (R2 > 0.7) and errors seems to go with surface runoff. The method generally overestimate the ERA5 (bias) and this overestimation has similar affect for different years. Finally, the mythological and hydrological parts of the model based on estimated results are discussed.

The post Rainfall-Runoff Modelling Using SCS-CN Method at the Multi-Basin Scale of Mainland Southeast Asia appeared first on CCRASEAL.