TB
T.M. Bakker
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Master thesis
(2020)
-
Tije Bakker, Stefan Aarninkhof, Jeremy Bricker, Stuart Pearson, Alessio Giardino, José Antolínez, Luisa Torres Dueñas
Small Island Developing States, including many low-lying atoll islands, are among the most vulnerable countries to natural hazards and climate change disproportionately amplifies this vulnerability. Hence, there is a strong need for disaster risk reduction and risk management. Further development and implementation of methodologies for flood hazard assessment of atoll islands contributes to this. The methodology proposed in this thesis was applied to Majuro, an atoll island and capital of the Republic of the Marshall Islands. More specifically, the flood hazard related to different flood drivers, and including compound events (i.e. the combination of coastal flooding and precipitation) was assessed for the densely populated Delap, Uliga, and Djarrit region, in the east of Majuro Atoll. Main flood drivers are waves during typhoon events and distantly generated (swell) waves, but precipitation and high water levels (mainly tide) are important as well. To include all possible combinations of these flood drivers, and to include the spatial variation in events, 1000 years of synthetic events was generated based on data for historical events. Accurate simulation of inundation depths was computationally unfeasible for all synthetic events. Hence, a method was developed to reduce the number of model simulations, without losing information on the probability of occurrence of each event. The main steps of this method seem applicable to many other study areas where many scenarios are needed to include all (combinations of) drivers. Main steps are: (1) Selection of representative events – by Maximum Dissimilarity Algorithm, based on parameters that characterize the events. Hereby, the most extreme events are included as well. (2) Simulation of inundation depths for the representative events – by use of Delft3D, SWAN, and XBeach models. The XBeach model included a module for rainfall (first application). (3) Weighted interpolation to obtain the inundation depths for the synthetic events – based on the same parameters as in step 1. Based on the inundation depths for 1000 years of synthetic events, flood maps for different return periods and flood drivers were derived. These provide insight in the flood hazard for the DUD region due to the different flood drivers and for different return periods. The importance of different flood drivers varies significantly per area. Generally speaking, flooding related to (swell) waves in combination with high water levels is more frequent, while infrequent typhoons lead to the most severe flooding. Precipitation is an important flood driver as well, and exclusion would lead to underestimation of the flood hazard. Analysis of inundation depths suggests that for many areas these are limited to a maximum, where after excess water drains to the ocean – mainly into the lagoon. The derived flood maps could be used as a base for assessment of flood risk, climate change impacts, and closely related freshwater availability. In relation to the latter, more in-depth understanding of the contributions of precipitation and coastal flooding to the total flood hazard is needed, as on the long term infiltration of precipitation seems favourable, while that of oceanic water is not.
...
Small Island Developing States, including many low-lying atoll islands, are among the most vulnerable countries to natural hazards and climate change disproportionately amplifies this vulnerability. Hence, there is a strong need for disaster risk reduction and risk management. Further development and implementation of methodologies for flood hazard assessment of atoll islands contributes to this. The methodology proposed in this thesis was applied to Majuro, an atoll island and capital of the Republic of the Marshall Islands. More specifically, the flood hazard related to different flood drivers, and including compound events (i.e. the combination of coastal flooding and precipitation) was assessed for the densely populated Delap, Uliga, and Djarrit region, in the east of Majuro Atoll. Main flood drivers are waves during typhoon events and distantly generated (swell) waves, but precipitation and high water levels (mainly tide) are important as well. To include all possible combinations of these flood drivers, and to include the spatial variation in events, 1000 years of synthetic events was generated based on data for historical events. Accurate simulation of inundation depths was computationally unfeasible for all synthetic events. Hence, a method was developed to reduce the number of model simulations, without losing information on the probability of occurrence of each event. The main steps of this method seem applicable to many other study areas where many scenarios are needed to include all (combinations of) drivers. Main steps are: (1) Selection of representative events – by Maximum Dissimilarity Algorithm, based on parameters that characterize the events. Hereby, the most extreme events are included as well. (2) Simulation of inundation depths for the representative events – by use of Delft3D, SWAN, and XBeach models. The XBeach model included a module for rainfall (first application). (3) Weighted interpolation to obtain the inundation depths for the synthetic events – based on the same parameters as in step 1. Based on the inundation depths for 1000 years of synthetic events, flood maps for different return periods and flood drivers were derived. These provide insight in the flood hazard for the DUD region due to the different flood drivers and for different return periods. The importance of different flood drivers varies significantly per area. Generally speaking, flooding related to (swell) waves in combination with high water levels is more frequent, while infrequent typhoons lead to the most severe flooding. Precipitation is an important flood driver as well, and exclusion would lead to underestimation of the flood hazard. Analysis of inundation depths suggests that for many areas these are limited to a maximum, where after excess water drains to the ocean – mainly into the lagoon. The derived flood maps could be used as a base for assessment of flood risk, climate change impacts, and closely related freshwater availability. In relation to the latter, more in-depth understanding of the contributions of precipitation and coastal flooding to the total flood hazard is needed, as on the long term infiltration of precipitation seems favourable, while that of oceanic water is not.
Dispersion in the Ayeyarwady
A description of the mixing of tracers in the area of the Ayeyarwady River- Chindwin River confluence
The Ayeyarwady River (also called Irrawaddy River) is the most important river of Myanmar and due to the country’s rapid development it is expected to become even more important. The river flows roughly from north to south through Myanmar and is very dynamic and mostly unregulated. With a length of 2170 km and an over the year average (highly seasonally varying) discharge of 13’000 m3/s into the Andaman Sea (Bhardwaj, Owen, & Leinbach, 2012), the Ayeyarwady is one of the bigger rivers in Asia.
To more than before take into account the interests of different stakeholders, as well as ecological aspects, sustainable management of the river is needed. Understanding the key aspects of the river flow can be a first step to sustainable river management (Richter et al., 2003). Pollution due to a large variety of activities of different nature make that water quality monitoring is of high importance (Thanda Thatoe Nwe Win, Bogaard, & Van de Giesen, 2015).
For monitoring and modelling the water quality, information about the mixing of tracers trough the river is needed, which can be quantified with the use of dispersion coefficients. Little research has been done about the Ayeyarwady River in general considered its size and importance. Very limited data about the mixing of tracers and the parameters needed to estimate the mixing of tracers was available.
This research focuses on the situation around the Ayeyarwady-Chindwin confluence in the first week of February 2017 (dry season). Hence, there is a very different situation during for example wet season. For the water quality, mainly the mixing in the longitudinal direction (direction of the main river flow) is of interest, which can be quantified by a longitudinal dispersion coefficient (Kx).
First relevant parameters for estimating Kx were identified based on the theory. This appeared to be the discharge, roughness and bathymetry. Besides, Kx has to be calibrated by floater experiments. To get better insight into the magnitude of these parameters, flow velocity and depth measurements (needed for estimating the discharge, roughness and bathymetry) and floater experiments have been done during a week of fieldwork in the area. Due to loss, theft and destruction of floaters, less data was collected than planed. To get further insight in the mixing of tracers, a numerical model was made in the software Delft3D based on data collected during the fieldwork.
Based on the combined results of the theory, measurements done during the fieldwork and the Delft3D model, it is expected that the magnitude of Kx in the Ayeyarwady River is somewhere in between 50-500 m2/s (best estimate: Kx~300 m2/s), although this has to be confirmed by further research. When the found value is compared with values found for other bigger rivers this value for Kx appears to be somewhat on the low side.
From the Delft3D model runs follows that the longitudinal dispersion coefficient in the Chindwin River is higher than in the Ayeyarwady, possibly even a factor 10. Besides, insight in the effect of the different parameters on the dispersion was obtained, contributing to a better understanding of processes causing the mixing of tracers in the Ayeyarwady and Chindwin rivers.
Estimating the highly sensitive longitudinal dispersion coefficient (Kx) appeared to be challenging, mostly due to the remote and highly dynamic character of the area. To make a better estimate of Kx, the uncertainty in the parameters needed (discharge, roughness, bathymetry and spreading of floaters for calibration) has to be reduced. Although some modelling options in Delft3D could be tried to narrow the range of these parameters, the best option to reduce this uncertainty is collecting more (high quality) data in the field.
...
The Ayeyarwady River (also called Irrawaddy River) is the most important river of Myanmar and due to the country’s rapid development it is expected to become even more important. The river flows roughly from north to south through Myanmar and is very dynamic and mostly unregulated. With a length of 2170 km and an over the year average (highly seasonally varying) discharge of 13’000 m3/s into the Andaman Sea (Bhardwaj, Owen, & Leinbach, 2012), the Ayeyarwady is one of the bigger rivers in Asia.
To more than before take into account the interests of different stakeholders, as well as ecological aspects, sustainable management of the river is needed. Understanding the key aspects of the river flow can be a first step to sustainable river management (Richter et al., 2003). Pollution due to a large variety of activities of different nature make that water quality monitoring is of high importance (Thanda Thatoe Nwe Win, Bogaard, & Van de Giesen, 2015).
For monitoring and modelling the water quality, information about the mixing of tracers trough the river is needed, which can be quantified with the use of dispersion coefficients. Little research has been done about the Ayeyarwady River in general considered its size and importance. Very limited data about the mixing of tracers and the parameters needed to estimate the mixing of tracers was available.
This research focuses on the situation around the Ayeyarwady-Chindwin confluence in the first week of February 2017 (dry season). Hence, there is a very different situation during for example wet season. For the water quality, mainly the mixing in the longitudinal direction (direction of the main river flow) is of interest, which can be quantified by a longitudinal dispersion coefficient (Kx).
First relevant parameters for estimating Kx were identified based on the theory. This appeared to be the discharge, roughness and bathymetry. Besides, Kx has to be calibrated by floater experiments. To get better insight into the magnitude of these parameters, flow velocity and depth measurements (needed for estimating the discharge, roughness and bathymetry) and floater experiments have been done during a week of fieldwork in the area. Due to loss, theft and destruction of floaters, less data was collected than planed. To get further insight in the mixing of tracers, a numerical model was made in the software Delft3D based on data collected during the fieldwork.
Based on the combined results of the theory, measurements done during the fieldwork and the Delft3D model, it is expected that the magnitude of Kx in the Ayeyarwady River is somewhere in between 50-500 m2/s (best estimate: Kx~300 m2/s), although this has to be confirmed by further research. When the found value is compared with values found for other bigger rivers this value for Kx appears to be somewhat on the low side.
From the Delft3D model runs follows that the longitudinal dispersion coefficient in the Chindwin River is higher than in the Ayeyarwady, possibly even a factor 10. Besides, insight in the effect of the different parameters on the dispersion was obtained, contributing to a better understanding of processes causing the mixing of tracers in the Ayeyarwady and Chindwin rivers.
Estimating the highly sensitive longitudinal dispersion coefficient (Kx) appeared to be challenging, mostly due to the remote and highly dynamic character of the area. To make a better estimate of Kx, the uncertainty in the parameters needed (discharge, roughness, bathymetry and spreading of floaters for calibration) has to be reduced. Although some modelling options in Delft3D could be tried to narrow the range of these parameters, the best option to reduce this uncertainty is collecting more (high quality) data in the field.