Trends in Streamflow and Water Level in Xiang River Basin in China

Transcription

1 DRH-CASiFiCA Collaboration Meeting, Feb,, 8, Beijing Trends in Streamflow and Water Level in Xiang River Basin in China Bo CHEN Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education Academy of Disaster Reduction and Emergency Management,Ministry of Civil Affairs & Ministry of Education

2 Outlines Introduction Data Method Results Conclusions and Discussion

3 Introduction Hunan. Study Area: Xiang River Basin Sketch map of Xiangjiang River Basin of Hunan Province,China Hunan,Province N Xiang River Basin Location: middle and lower reach of Yangtze River Basin (9.% area within Hunan Province, China) Area: 9,66 km Length: 86 km Investigate Points Rivers Km Disaster Investigate Route affected Area Boundary of Xiangjiang River Basin

4 . Flood Disaster in Study Area Flood Disaster Loss in the Past Six Decade Time Deaths Disaster Affected Population( 6 ) Direct Economic Loss ( 8 RMB) Data source:dehua Mao,Jingbao Li et al. Study on the flood-waterlogging Disaster in Hunan Province [M]. Hunan Normal University Press,.

5 Flood disaster is getting worse, why? Climate Change vs. Human Dimension Hydrologic variables tends to the combination of climate and human activities, so it is popular to explore the role of climate change and human dimension played in the process of the intensification of flood disaster. What s the situation in Xiang River Basin?

6 . Flood Disaster in Study Area Loss in Years With Serious Flood Disaster Time Economic Loss ( 8 RMB) Disaster Affected Population( 6 ) Areas Covered by Flood ( 9 m ) Area Affected by Flood ( 9 m ) Data source:dehua Mao,Jingbao Li et al. Study on the flood-waterlogging Disaster in Hunan Province [M]. Hunan Normal University Press,.

7 Data and Method. Data Selection of Hydrologic Variables (8 variables) Extreme Events (Flood Disaster Hazard) Annual Maximum Daily Streamflow (AMDS) Monthly Maximum Daily Streamflow (MMDS) Annual Maximum Daily Water Level (AMDWL) Monthly Maximum Daily Water Level (MMDWL) Temporal Distribution of Streamflow) Monthly Streamflow (MS) Seasonal Streamflow (SS) Annual Streamflow (AS) Monthly Mean Water Level (MMWL) Selection of Stations Data Availability Spatial Distribution (spatial-correlation, representative samples) Record Length (representative samples)

8 Data and Method Streamflow and Water Level data set are daily data stations for streamflow Period: 96- stations with a record length of 8 years, stations with a record length of years and station years 9 stations for water level Period: 96-8 stations with a record length of 8 years, stations years, stations years, stations years, and station years Most stations with a record length larger than years, the data set is representative

9 Method Drawbacks of Classical Parametric Methods Assumptions: normality, linearity, independence Sometimes the analysis maybe influenced by monotonic transformation of the original data Mostly not satisfied by hydrologic variables Advantages of the Mann-Kendall non-parametric test No assumption of normality is required Dealing with outliers. If a monotonic transformation such as the ladder of powers is all that is required to produce constant variance, the test statistic will be identical to that for the original units.

10 Mann-Kendall Trend Test

11 Results. Mann-Kendall test results Trends in Monthly Streamflow (MS) and Mothly Maximum Daily Streaflow (MMDS) Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Number of stations with trend at the significance level of.,.,. respectively ( totally there are stations selected in Xiang River Basin) MS 6. MMS MS. MMS MS. MMS MS (+) (-) 9(9+) 9(9+) 8(8+) (+). MMS (+) 6 9(+)

12 Summary: In recent decades, at the significance level of %: Monthly Streamflow and monthly maximum daily streamflow show significant trends generally. The numbers of stations with upward trends in Monthly Streamflow in January, July, August, September and December are and and the percent are % 6% 6%, % and % respectively. The Percent of stations with a significant downward trend in monthly streamfow in May is 9%. Upward trends in monthly maximum daily streamflow in January and December are detected for 6% and % of the stations.

13 6 8 9 MMWL Trends in Monthly Mean Water Level (MMWL) and Mothly Maximum Daily Water Level (MMDWL) Number of stations with trend at the significance level of.,.,. respectively ( totally there are 9 stations selected in Xiang River Basin) MMDWL MMWL MMDWL 6 6 MMWL. MMDWL MMWL (+) (9-) (9-) (-) (-) (+) (6+) 6(+) (8-) (+) (9-) (+). MMDWL 9(+) (-) 9 (9-) 6 (+) 6(+)

14 Summary: In recent decades, at the significance level of %: Monthly mean water level and monthly maximum daily water level show significant trends generally. Monthly mean water level from February to May are detected to be with Downward trend and for other months with upward trend for % of the stations. % of the stations show significant trend in monthly maximum daily water level in 8 months, and the main direction of trend is downward except in January.

15 Spring Summer Autumn Winter Trends in Seasonal Streamflow (SS) Number of stations with trend at the significance level of.,.,. respectively ( totally there are stations selected in Xiang River Basin) (8+) (+) (+) Trends in Annual Streamflow (AS) Annual Maximum Daily Stramflow (AMDS) and Annual Maximum Daily Water Level (AMDWL) Number of stations with trend at the significance level of.,.,. respectively ( totally there are stations selected in Xiang River Basin).... AS 6 6(6+) AMDS 9(9+) AMDWL 6 (+)

16 Conclusions and Discussion. Trend analysis summary In recent decades, at the significance level of %: significant trends are detected in seasonal streamflow in summer and autumn, annual streamflow, annual maximum daily streamflow and annual maximum daily water level. And for spring and winter, no significant trend. Upward trends in seasonal streamflow in summer and autumn are detected for 6% and 8% of the stations. The numbers of stations with upward trends in annual streamflow, annual maximum daily streamflow and annual maximum daily water level are 6 9 and and the percent are % 6% 6%, % and % respectively.

17 . Spatial Distribution of Trend Results

18 Spatial Distribution of Trends in Streamflows c) Monthly Flow in July b) Monthly Flow in May f) Monthly Flow in Dec. e) Monthly Flow in Sep. a) Monthly Flow in Jan. d) Monthly Flow in Aug.

19 Spatial Distribution of Trends in Streamflows i) Seasonal Flow in Winter h) Seasonal Flow in Autumn l) Annual Maximum Flow k) Monthly Maximum Flow in Jan g) Seasonal Flow in Summer j) Annual Flow

20 Spatial Distribution of Trends in Monthly Mean Water Level a) January b) February c) March d) April e) May f) June

21 Spatial Distribution of Trends in Monthly Mean Water Level g) July i) August j) September k) October l) November m) December

22 Spatial Distribution of Trends in Monthly Maximum Daily Water Level and Annual Maximum Daily Water Level a) January b) February c) March d) April e) October f) November g) December h) Annual

23 Conclusions and Discussion. Trend analysis summary Trend in Extreme events: monthly maximum daily streamflow, monthly maximum daily water level, annual maximum daily streamflow and annual maximum daily water level show significant upward trends generally. Monthly mean water level, seasonal streamflow in summer and autumn, annual streamflow and Monthly Streamflow show significant upward trends generally. Flood Hazard tends to be more intensive. From the upward trends in monthly mean water level, conclusion may be draw as: human impact also plays an important role in the intensification process of flood disaster in Xiang River Basin.

24 . Improvement needed auto-correlation Spatial correlation Other variables How do human activities amplify flood disaster in Xiang River Basin?

25 DRH-CASiFiCA Collaboration Meeting, Feb,, 8, Beijing Thank You for Your Attention!