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Behind the early warning: Improving impact-based forecasting of riverine floods in Malawi using passive microwave remote sensing

Mokkenstorm, Lone LU (2021) In Student thesis series INES NGEM01 20201
Dept of Physical Geography and Ecosystem Science
Abstract
This thesis investigates whether freely available, coarse-resolution, Passive Microwave Remote Sensing (PMRS) data (37 GHz) can be effectively used for early warning systems for floods in Malawi. The Shire River Basin in Chikwawa and the smaller-scale North Rukuru River Basin in Karonga were studied using two alternative, ratio-based satellite indices that make use of the signal difference between wet and dry pixel cells: The m index and the rcmc index. The m index is directly related to the rcm index, introduced by Brakenridge et al. (2007), and divides the brightness temperature in a relatively stable, dry calibration cell by the brightness temperature measured in the cell with the river. The rcmc index is an adaptation of this, and uses... (More)
This thesis investigates whether freely available, coarse-resolution, Passive Microwave Remote Sensing (PMRS) data (37 GHz) can be effectively used for early warning systems for floods in Malawi. The Shire River Basin in Chikwawa and the smaller-scale North Rukuru River Basin in Karonga were studied using two alternative, ratio-based satellite indices that make use of the signal difference between wet and dry pixel cells: The m index and the rcmc index. The m index is directly related to the rcm index, introduced by Brakenridge et al. (2007), and divides the brightness temperature in a relatively stable, dry calibration cell by the brightness temperature measured in the cell with the river. The rcmc index is an adaptation of this, and uses an additional wet calibration cell. It was investigated whether these indices could aid in the detection and forecasting of flood events and their magnitude. The findings pertaining to detection skill showed that at both study sites, rcmc and m detected a similar seasonality to the observed discharge hydrographs, as long as the downstream virtual gauging station was located at a sufficient distance from a large water body. A regression analysis showed that the indices’ relationship with observed discharge had a moderately strong, positive correlation in Chikwawa, but not in Karonga. Flood occurrence detection skill was assessed using an impact database. A flood threshold corresponding to a return period of 5 years was determined to see if the indices could simulate historical flood events. Both indices did not detect the majority of registered floods, which is likely a consequence of the method used to determine the trigger threshold. There were no upstream virtual gauging stations present that had a sufficient lag time with the downstream satellite signal. A possible forecasting system using merely the downstream satellite signals was shown to have a sufficient accuracy at a lead time of up to nearly 3 days, although in an operational setting, the forecasts would not reach the calculated trigger threshold at this lead time. Overall, the PMRS-model showed a better performance in Chikwawa when compared to the global runoff model GloFAS. As it also does not require extensive input data when used as an Early Warning System (EWS), as many smaller-scale EWS do, we suggest that when perfected, the PMRS-method is implemented in a coupled EWS solution, including a PMRS-model, a global forecasting model and a more detailed national model. This would offer early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of rcmc. (Less)
Popular Abstract
Early warning systems can support humanitarian operations by forecasting hazard impacts and aid to release funds before disasters take place. One challenge humanitarian organizations currently face, however, is a lack of historical and real-time data to set up such a system. Whereas optical satellite remote sensing data has been used in the past to address these issues, cloud cover and infrequent satellite overpasses often yield this data suboptimal for this purpose. This study therefore investigates whether openly available, coarse-resolution, passive microwave satellite data −which is less impeded by cloud-cover and is measured on a daily basis− can be effectively used for early warning systems for floods in the Shire River and the North... (More)
Early warning systems can support humanitarian operations by forecasting hazard impacts and aid to release funds before disasters take place. One challenge humanitarian organizations currently face, however, is a lack of historical and real-time data to set up such a system. Whereas optical satellite remote sensing data has been used in the past to address these issues, cloud cover and infrequent satellite overpasses often yield this data suboptimal for this purpose. This study therefore investigates whether openly available, coarse-resolution, passive microwave satellite data −which is less impeded by cloud-cover and is measured on a daily basis− can be effectively used for early warning systems for floods in the Shire River and the North Rukuru River in Malawi. Two alternative indices were calculated from the raw data and further studied.
Firstly, the potential for detecting both river discharge and individual flood events was assessed. At both study sites, the indices detected a seasonality similar to that of the observed discharge, as long as the satellite data pixel studied was sufficiently far from a large water body. The indices were moderately correlated to discharge in the Shire River, but not in the North Rukuru River. A comparison of the satellite data to an impact database with flood events showed that the indices did not detect the majority of registered floods, although this is likely a consequence of the method used to determine the threshold of what ‘counts’ as a flood.
Satellite data from the upstream part of the river catchment could unfortunately not be used to forecast the satellite signal and hence the flood events downstream, as the correlation between the two was strongest without a time shift. If it would have been stronger with a time shift of at least one day, this would add time to the window available to give out early warnings. Therefore, a forecasting model was set up using just the satellite data from the downstream point of interest. Statistically, this model showed to have sufficient accuracy with a lead time up to nearly three days, but a test conducted with a historical flood event showed that this model would, in practice, not have triggered an early warning before the flood event happened. Overall, the passive microwave system presented in this thesis had a stronger relationship with discharge in the study area than the existing global runoff model GloFAS does. As it also does not require extensive input data when used as an early warning system, we suggest that when perfected, the PMRS-method is implemented in a coupled solution, including a remote sensing-model, a global forecasting model and a more detailed national model. The use of these systems could offer early warnings in data-scarce regions and at a variety of lead times, which has the potential to make humanitarian aid in response to floods faster and more efficient. (Less)
Please use this url to cite or link to this publication:
author
Mokkenstorm, Lone LU
supervisor
organization
alternative title
The potential of passive microwave satellite data for flood early warning systems in Malawi
course
NGEM01 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Physical Geography, Ecosystem Analysis, Early Warning, Riverine Floods, Humanitarian Work, Impact-based Forecasting, Malawi
publication/series
Student thesis series INES
report number
528
language
English
additional info
External supervisor: Marc van den Homberg, 510, an initiative of the Netherlands Red Cross
id
9031490
date added to LUP
2020-10-30 10:37:17
date last changed
2020-10-30 10:37:17
@misc{9031490,
  abstract     = {This thesis investigates whether freely available, coarse-resolution, Passive Microwave Remote Sensing (PMRS) data (37 GHz) can be effectively used for early warning systems for floods in Malawi. The Shire River Basin in Chikwawa and the smaller-scale North Rukuru River Basin in Karonga were studied using two alternative, ratio-based satellite indices that make use of the signal difference between wet and dry pixel cells: The m index and the rcmc index. The m index is directly related to the rcm index, introduced by Brakenridge et al. (2007), and divides the brightness temperature in a relatively stable, dry calibration cell by the brightness temperature measured in the cell with the river. The rcmc index is an adaptation of this, and uses an additional wet calibration cell. It was investigated whether these indices could aid in the detection and forecasting of flood events and their magnitude. The findings pertaining to detection skill showed that at both study sites, rcmc and m detected a similar seasonality to the observed discharge hydrographs, as long as the downstream virtual gauging station was located at a sufficient distance from a large water body. A regression analysis showed that the indices’ relationship with observed discharge had a moderately strong, positive correlation in Chikwawa, but not in Karonga. Flood occurrence detection skill was assessed using an impact database. A flood threshold corresponding to a return period of 5 years was determined to see if the indices could simulate historical flood events. Both indices did not detect the majority of registered floods, which is likely a consequence of the method used to determine the trigger threshold. There were no upstream virtual gauging stations present that had a sufficient lag time with the downstream satellite signal. A possible forecasting system using merely the downstream satellite signals was shown to have a sufficient accuracy at a lead time of up to nearly 3 days, although in an operational setting, the forecasts would not reach the calculated trigger threshold at this lead time. Overall, the PMRS-model showed a better performance in Chikwawa when compared to the global runoff model GloFAS. As it also does not require extensive input data when used as an Early Warning System (EWS), as many smaller-scale EWS do, we suggest that when perfected, the PMRS-method is implemented in a coupled EWS solution, including a PMRS-model, a global forecasting model and a more detailed national model. This would offer early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of rcmc.},
  author       = {Mokkenstorm, Lone},
  keyword      = {Physical Geography,Ecosystem Analysis,Early Warning,Riverine Floods,Humanitarian Work,Impact-based Forecasting,Malawi},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Behind the early warning: Improving impact-based forecasting of riverine floods in Malawi using passive microwave remote sensing},
  year         = {2021},
}