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A correlation study between climate indexes and high runoff events in the Lanjiang River basin, China

Krewinkel, Bas Christiaan (2014) A correlation study between climate indexes and high runoff events in the Lanjiang River basin, China.

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Abstract:Flood forecasting is becoming more important for the Lanjiang River Basin according to recent literatures about climate change. A way to forecast more precisely is by looking to climate indexes. In this research finding the relationship between three climate indexes that are thought to be of influence according to the literature, and high runoff events for the Lanjiang River Basin was the main aim. The indexes are the PDO, the SOI and the EASMI. The Lanjiang River basin was split up in two smaller basins, namely the Jinhua and the Quzhou River basin. This choice was made since there were precipitation and runoff data available via the China Meteorological Administration and the Bureau of Hydrology of the Zhejiang Province. Besides that, it covers a great part of the total area of the Lanjiang river basin. To accomplish the aim a number of steps were carried out, including researching the relationship between runoff and precipitation, precipitation and climate indexes and runoff and climate indexes. Using the knowledge of the first two relationships it could be easier to understand the results for the relationship between climate indexes and runoff directly. To perform these correlation studies Pearson and a multiple regression analysis were applied. Besides looking to daily runoff values for high runoffs, also three day average runoff values were looked into, since this may deliver stronger relationships and would tell something about the relation with the volume of the runoff. Firstly the indexes were interpolated to daily values, since high runoffs mainly occur for just a few days. After interpolating the index data, the peak over threshold method was selected instead of annual maximum runoff values since this delivered a larger number of runoff samples. With this method, both for the maximum daily runoff values and three day average runoff values sufficient values were found for the period used for the PDO and SOI. A little less, but still enough samples, were found for the shorter period used for the EASMI. The PDO was investigated for the long term relationship since it is a long term index with an average cycle period of 50 years. The SOI was investigated for the long and short term, since both types of relationships were already found in earlier studies between ENSO and precipitation/runoff in other regions. The EASMI was only investigated for short term relations, since it is a yearly returning event. For both the PDO and SOI there are significant correlations found when looking to the precipitation, runoff and three day average runoff; positive for the PDO, and negative for the SOI. For the SOI Jinhua had a large decline in the correlation value when comparing precipitation with runoff and three day average runoff. This result is according to the results of the runoff - precipitation relationship which is weaker for Jinhua compared to Quzhou. For the short term SOI and EASMI the relationship was also investigated by looking at the different PDO phases, since this could matter for the correlation. Indeed this showed some differences for the SOI index, but they could not be explained. For the EASMI it also showed differences which in contrast were explainable mainly for the Quzhou area. For Quzhou there is a pattern of a strong correlation between precipitation and (three day) runoff with the EASMI during a positive PDO phase, and a weaker correlation during a negative PDO phase, which is understandable. In general the correlation for the EASMI turned out to be the highest (negative direction) of the three indexes, especially for the Quzhou area. Comparing the results between the combinations of runoff – precipitation and precipitation – climate index with the direct runoff – climate index only showed comparable correlations for the SOI index for the Quzhou area. Finally the multiple regression showed that this is a valuable addition, especially in general for the PDO/SOI combination and the EASMI combinations for the Jinhua area since these correlations were higher. Further research is mainly interesting for the EASM and ENSO (SOI index) phenomenon. Especially the EASMI gave a significant correlation in generally. Advisable though is using the whole year instead of the current two months of the data to have even more certainty. Lastly the PDO should always be considered when splitting up the data in different periods, since it seems it has an influence for this area on the other climate phenomena (indexes).
Item Type:Essay (Bachelor)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering BSc (56952)
Link to this item:http://purl.utwente.nl/essays/66602
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