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The Impact of Climate Variability on the Hydrological Response of the Lake Urema Wetland, Mozambique

Brodin, Lova LU (2011) VVR820 20101
Division of Water Resources Engineering
Abstract
Lake Urema Wetland - literally and geographically the heart of
Gorongosa National Park in central Mozambique and its main source of
fresh water, is evidently vital for the ecosystem of the park, including
all of its inhabitants. The extension area of Lake Urema Wetland varies
strongly throughout the year, following a natural seasonal cycle. However,
year to year deviations, together with the fact that Mozambique
have been repeatedly hit by severe droughts and floods, emphasize the research objective of investigating the influence of climate variability on the hydrological response of the system. If the variations in the lake's surface area can be linked to global climate phenomena, it makes it possible to model and predict the size... (More)
Lake Urema Wetland - literally and geographically the heart of
Gorongosa National Park in central Mozambique and its main source of
fresh water, is evidently vital for the ecosystem of the park, including
all of its inhabitants. The extension area of Lake Urema Wetland varies
strongly throughout the year, following a natural seasonal cycle. However,
year to year deviations, together with the fact that Mozambique
have been repeatedly hit by severe droughts and floods, emphasize the research objective of investigating the influence of climate variability on the hydrological response of the system. If the variations in the lake's surface area can be linked to global climate phenomena, it makes it possible to model and predict the size of the lake based on climatological data. This would be a large conquest for the understanding and the monitoring of the system, as well as for enabling predictions of future changes in the size of Lake Urema.

The result of this study is a model to estimate the surface water availability at Lake Urema Wetland, based on meteorological and climatological data, spanning the second half of the 20th century. Artificial Neural Networks compose the basis of the model, which further includes data reconstruction, linear regression and fitting to existing measurements from the study area, as well as relating the results to climatological factors. The data, used in the context of this project, was obtained from online databases and through field research in Mozambique. Moreover, the model developed in this study can be used to predict the hydrological response of Lake Urema Wetland based entirely on global climate forecasting. The impact of the climate phenomena on Lake Urema is analysed quantitatively and qualitatively, by means of correlation analyses, and by evaluating the importance of each climate phenomenon on the model targets, namely the surface water availability at Lake Urema Wetland. Using the HIPR approach on the neural network response, the El Niño - Southern Oscillation is found to be the most influential of the reviewed climate phenomena, followed by the Pacific Decadal Oscillation and the Indian Ocean Dipole. The findings are very important for the further hydrological research of Lake Urema Wetland. In addition, the general structure of the model prototype makes it applicable on other systems, given that relevant hydrological data is available. (Less)
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author
Brodin, Lova LU
supervisor
organization
course
VVR820 20101
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Climate Variability, Artificial Neural Networks, ENSO, IOD, PDO, Surface Water Availability, PCA, Mozambique
report number
TVVR - 11/5008
ISSN
1101-9824
language
English
id
1976827
date added to LUP
2011-06-15 13:32:54
date last changed
2011-09-21 08:06:21
@misc{1976827,
  abstract     = {Lake Urema Wetland - literally and geographically the heart of
Gorongosa National Park in central Mozambique and its main source of
fresh water, is evidently vital for the ecosystem of the park, including
all of its inhabitants. The extension area of Lake Urema Wetland varies
strongly throughout the year, following a natural seasonal cycle. However,
year to year deviations, together with the fact that Mozambique
have been repeatedly hit by severe droughts and floods, emphasize the research objective of investigating the influence of climate variability on the hydrological response of the system. If the variations in the lake's surface area can be linked to global climate phenomena, it makes it possible to model and predict the size of the lake based on climatological data. This would be a large conquest for the understanding and the monitoring of the system, as well as for enabling predictions of future changes in the size of Lake Urema.

The result of this study is a model to estimate the surface water availability at Lake Urema Wetland, based on meteorological and climatological data, spanning the second half of the 20th century. Artificial Neural Networks compose the basis of the model, which further includes data reconstruction, linear regression and fitting to existing measurements from the study area, as well as relating the results to climatological factors. The data, used in the context of this project, was obtained from online databases and through field research in Mozambique. Moreover, the model developed in this study can be used to predict the hydrological response of Lake Urema Wetland based entirely on global climate forecasting. The impact of the climate phenomena on Lake Urema is analysed quantitatively and qualitatively, by means of correlation analyses, and by evaluating the importance of each climate phenomenon on the model targets, namely the surface water availability at Lake Urema Wetland. Using the HIPR approach on the neural network response, the El Niño - Southern Oscillation is found to be the most influential of the reviewed climate phenomena, followed by the Pacific Decadal Oscillation and the Indian Ocean Dipole. The findings are very important for the further hydrological research of Lake Urema Wetland. In addition, the general structure of the model prototype makes it applicable on other systems, given that relevant hydrological data is available.},
  author       = {Brodin, Lova},
  issn         = {1101-9824},
  keyword      = {Climate Variability,Artificial Neural Networks,ENSO,IOD,PDO,Surface Water Availability,PCA,Mozambique},
  language     = {eng},
  note         = {Student Paper},
  title        = {The Impact of Climate Variability on the Hydrological Response of the Lake Urema Wetland, Mozambique},
  year         = {2011},
}