Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (18)
En vedette
En vedette (10)
Similaire à Lake Chad Basin Data Analysis Reveals Precipitation and Flow Trends
Similaire à Lake Chad Basin Data Analysis Reveals Precipitation and Flow Trends (20)
Lake Chad Basin Data Analysis Reveals Precipitation and Flow Trends
- 1. Lake Chad Basin Data Research and Analysis Serena Emanuel and Haley Kujawa
- 2. Presentation Overview: 1. Background Research/Literature Review 2. Data Acquisition 3. Data Analysis 4. Watershed Delineation and Mapping 5. Basin Trends and Conclusions
- 3. Directly affected countries: • Chad, Niger, Nigeria, Cameroon Indirectly affected countries: • Central African Republic • Sudan • Algeria Part 1: Background Research and Literature Review
- 4. History of Lake Chad Part 1: Background Research and Literature Review
- 5. Low adaptive capacity of arid regions Part 1: Background Research and Literature Review
- 6. Decreased lake size could be due to decreased inflow Part 1: Background Research and Literature Review
- 7. IPCC GCM report on water and climate change Regions experiencing significant increases or decreases in precipitation – The Lake Chad Basin (LCB) marked by a significant decrease Part 1: Background Research and Literature Review
- 8. On the causes of shrinking Lake Chad Part 1: Background Research and Literature Review
- 9. Model of inflow for Lake Chad to return to pre- 1960s levels Part 1: Background Research and Literature Review
- 10. Impact of droughts, split, and irrigation model Part 1: Background Research and Literature Review
- 11. Historical Lake Levels show cause could be part of a natural cycle Part 1: Background Research and Literature Review
- 12. Part 2: Data Acquisition GRDC: River Discharge Data • Application process • Received daily and monthly data • Daily Data Includes: • Lake Chad Basin • 11 Nigeria Stations • 21 Central African Republic Stations • 12 Chad Stations • Congo River Basin (for future diversion impact study) • 44 stations
- 13. NOAA Precipitation and Temperature Data • Daily and monthly data for: • Cameroon (3 stations) • Nigeria (10 stations) • Central African Republic (17 stations) • Chad (11 stations) Part 2: Data Acquisition
- 14. Part 3: Data Research and Analysis KEY Precipitation Station: Date Range Flow Station: Date Range
- 15. Justifying data selection Criteria for evaluating monotonic trends • Minimum of 5 years monthly data • Data gaps less than 1/3 the total data Climatic analysis of data • Minimum of 50 years preferable • For lack of consistent data, 30 years of data was considered useful in this study Part 3: Data Research and Analysis
- 16. Flow Large Data Ranges Flow Selected Stations: Ndjamena: 1976-1991 Ouli Bangala: 1978-1990 Hadejia: 1963-2006 Wudil: 1963-2005 Bossangoa: 1986-1992 Bangui: 1935-2007 Part 3: Data Research and Analysis
- 17. Precipitation Large Date Ranges Precipitation Selected Stations: Ndjamena: 1950-2016 (67%) Bousso: 1952-1978 (91%) Magaria: 1980-1992 (90%) Moundou: 1950-2016 (62%) Pala: 1952-1978 (92%) Garoua: 1973-2016 (71%) Bour: 1950-1980 (94%) N Guigni: 1926-2016 (79%) Sarh: 1950-2016 (64%) Bouca: 1950-1965 (96%) Bangui: 1950-1980 (93%) Part 3: Data Research and Analysis
- 18. Part 4: Watershed Delineation • Accessed 90 m accuracy data from CGIAR-CSI GeoPortal • Downloaded about 20 DEM files/squares that encompass the Watershed • Use GIS to delineate watershed and sub-basins • Can determine which stations we have data for and where these stations are located in the sub-basins
- 19. Lake Chad Basin • Huge area2,500,000 km^2 at 90 m accuracy • Too large for even ArcGIS to handle • Must decrease accuracy while still maintaining correct watershed Part 4: Watershed Delineation
- 20. Problem Solving: Lake Chad Basin Delineation • Use CIMA watershed delineation to determine correct Lake Chad Basin boundaries • Used AutoCAD to outline basin, and then used previous CIMA file to correctly scale and locate the basin CIMA Watershed Boundaries AutoCAD tracing of watershed Part 4: Watershed Delineation
- 21. Problem Solving: Lake Chad Basin Delineation • Imported AutoCAD basin outline and DEM files into Global Mapper, to chop data to watershed size and reduce cell size, further decreasing the size of the data • Exported the cropped DEM files and imported them into WMS • Used WMS functions in order to delineate watershed and sub-basins Part 4: Watershed Delineation Global Mapper WMS
- 22. Lake Chad Basin Delineation Part 4: Watershed Delineation Comparison between WMS Delineation (left) and CIMA delineation (right)
- 23. Lake Chad Basin Delineation: Adding the Lake Part 4: Watershed Delineation • Used GIS raster calculator to add 290 m elevation lake (historic size) and 280 m elevation lake (2008 size) • Compared with Google Earth to check accuracy of raster calculator/elevation method
- 24. Lake Chad Basin Delineation using WMS Sub-Basin 1Sub-Basin 2Sub-Basin 3Sub-Basin 4Sub-Basin 5Overall Basin Part 4: Watershed Delineation
- 25. Part 5: Basin Trends and Conclusions Key: Precipitation Flow
- 26. Mann-Kendall analysis Part 5: Basin Trends and Conclusion Sen slope; estimate of rate of change in the trend Test statistic; large absolute value indicates a trend β0 is the null hypothesis (no trend); when non-zero, the null hypothesis is rejected n = number of data points Tau statistic; similar to correlation coefficient Linear regression of the data Significance Equations
- 27. Relative Factor • Used to fill missing data • Stations with spatial relations could be plotted on x (station A) and y- axis (station B) to determine a linear fit • If R>0.8, the linear relationship between the data can be used to transform data from station A to station B Part 5: Basin Trends and Conclusion
- 28. Part 5: Basin Trends and Conclusions Ndjamena Moundou Bangui Bossangoa Wudil Hadejia Station Precipitation Date Range Flow Date Range Ndjamena 1950-1978 1953-2009 Bangui -------- 1935-2007 Bossangoa -------- 1951-1972 Hadejia -------- 1963-2005 Wudil -------- 1963-1991 Moundou 1950-1978 ---------- Significant: CI>90% Seasonal Trend Analysis: Rainy season
- 29. Conclusion of Data Analysis and Results • Map of trends concludes decrease in flow and precipitation could be a significant cause of the shrinking of the Lake Chad • Conclusion coincides with background research Part 5: Basin Trends and Conclusion
- 30. Data Discussion • Historically, countries around Lake Chad haven’t focused on investments in data collection • Data is sporadic and infrequent, many sets are missing too large of gaps to run Mann-Kendall analysis • Relative analysis was able to fill some of the data gaps • Using only one month out of the year may produce a more continuous series of data to run in the Mann-Kendall (i.e. August or September, during the rainy season) Part 5: Basin Trends and Conclusions
- 31. Future Work • Further analysis and organization of the data must be done • Collection of more data sets to verify and fill gaps in the data • More research on and a better understanding of the Mann-Kendall analysis • Finding more data for stations in upper sub-basins (Sahel region) • Using historical data to project future trends • Analyzing impact of the basin transfer project on the the Lake Chad Basin and Congo River Basin Part 5: Basin Trends and Conclusions
- 32. Thank you! Any questions? Special thanks to: Dr. Guo, Bibi, David, Mr. Song, Yang, and all others who have graciously helped with our project and our internship
Notes de l'éditeur
- Discuss goals/purpose
- Economy depends on agriculture and fishing which depends on lake Water use for drinking and daily activities Around 30 million people depend on Lake Chad
- 25000 to 15000 km^2 This is the historywhat is the cause? Huadong’s major goal before going forth with the water diversion project Anthropogenic such as dams or irrigation or natural climate variation Brittanica: Travelers reported high water levels and overflow into the El-Ghazal during the 13th and 19th centuries. In 1870, for example, Lake Chad covered some 10,800 square miles (28,000 square km). At the turn of the 20th century the lake began to diminish in size, but by the 1920s it had recovered, and in 1956 it again overflowed into the El-Ghazal. During the 1970s and ’80s the amplitude of the lake’s annual variability was the highest recorded in the 20th century, with average levels falling below long-term norms; the surface area was reduced to less than 1,500 square miles (3,900 square km) for a time in the mid-1980s and again in the early 21st century. The corresponding variability in rainfall appears to have been related to the effects of environmental degradation.
- Recent studies have confirmed that particularly the arid and semi-arid regions of Africa are the most vulnerable areas to climate variability and change because of multiple stresses and low adaptive capacity (Osman- Elasha, 2007; IPCC WGII, 2007) The changes in the lake chad may be due to natural climate variability and droughts and natural erosion Could also be due to human activity such as unsustainable farming practices that use irrigation or grazing that removes plants important to the hydrology of the lake
- Map of gauging station on the Chari-Logone R., where Lake Chad receives an estimated 80% of its inflow Study on discharge measurements Has daily measurements, but not throughout the course of a month at the same data station Used velocity measurements from ADCP (acoustic principle) to get a 3-dimensional view of the velocity. The map of the velocity distribution in the cross sectional area is included in the report. Concludes rating curves should be used with caution during flood/ estimating peak flow. During floods water burst into banks of the river system and caused lower flow than expected in the river, sometimes by 50%
- https://www.ipcc.ch/pdf/technical-papers/climate-change-water-en.pdf The intergovernmental panel on climate change technical report on climate change and water The lake chad region is one of significant decrease
- Historical split in the northern and southern basin caused seasonal changes in hydrology Northern basin separated by great barrier and almost always dry now Of interest because this is a new occurance in the basin; in models, this split makes the lake chad more difficult to return to pre-1960s lake chad, could be a turning point in hydrologic history
- This graph shows what inflows would be needed over the course of years to return Lake Chad to pre-1960s levels. These flow rates are highly unlikely to happen in nature. From On the causes of the shrinking of Lake Chad
- Climate in 1990s was not conducive to a full recovery from previous droughts These results clearly show that even without irrigation withdrawals, the 1952–2006 mean climate state does not favor a single lake. We also tested the impact of temperature change by increasing the annual average air temperature by 2K (approximately the observed temperature increase from 1952– 2006), and found that modeled inflow decreased by about 10% relative to climatology due to increased evapotranspiration Figure 5 shows that with a recurring net annual inflow of 50 km3 yr−1, the two lakes would have merged into one in about four years, and a total of 10 years would be required for the lake to resume its 1963 size(9.25 m annual minimumlake level). For an annual inflow of 60 km3 it would take five years to recover to the 1963 size. A larger annual inflow not only implies a shorter time but also a smaller total volume needed for recovery to a given size. However, because 1963 was one of the wettest years in the study period (and had low irrigation withdrawals), it is unlikely that an equivalent/largerinflow(after irrigationusage) will occur for even one year, let alone multiple consecutive years, under foreseeable climate conditions.
- GRDC Lat/Long of each station Catchment area Altitude of Station Mean Daily Discharge Mean monthly Discharge
- NOAA Lat/Long of each station Elevation of station Daily precipitation total Max daily temperature Min daily temperature
- National nonpoint source monitoring program by EPA
- GRDC Selected Stations: Ndjamena: 1976-1991 Ouli Bangala: 1978-1990 Hadejia: 1963-2006 Wudil: 1963-2005 Bossangoa: 1986-1992 Bangui: 1935-2007
- NOAA Selected Stations: Ndjamena: 1950-2016 (67%) Bousso: 1952-1978 (91%) Magaria: 1980-1992 (90%) Pala: 1952-1978 (92%) Garoua: 1973-2016 (71%) Bour: 1950-1980 (94%) N Guigni: 1926-2016 (79%) Sarh: 1950-2016 (64%) Bouca: 1950-1965 (96%) Bangui: 1950-1980 (93%)
- Huge area2,500,000 km^2 at 90 m accuracy Too large for even ArcGIS to handle. Must decrease accuracy while still maintaining correct watershed
- -“Mann-Kendall trend test is a nonparametric test used to identify a trend in a series, even if there is a seasonal component in the series.” Null hypothesis states that β0 = 0 and no trend is present S indicates an upward trend (large positive number) or downward trend (large negative number) τ, the test statistic, is the equivalent of a correlation coefficient (-1 to 1) When S and τ are significantly different from zero, null hypothesis is rejected and a trend is indicated Sen slope estimator used to find rate of change (β1)
- 100% of precipitation from May- October Over 50% of the data is between aug-oct Used months of September for flow data and September or August for rainfall NDJA 1953-2009 GRDC, 1950-1978 NOAA BANGUI 1935-2007 GRDC BOSSANGOA 1951-1972 GRDC HADEJIA 1963-2005 GRDC WUDIL 1963-1991 GRDC MOUNDO 1950-1978 GRDC
- Flow is: Precipitation is: Relate to previous research
- Relative analysis = Bossangoa and Moissala, Often times the relative analysis did match, but the data in both sequences was missing too much and didn’t create a full enough data set CIMA said they had data in their files, but it was not found in the files given (i.e. for the Ouli Bangala station)
- Further analysis Outside influences need to be eliminated for “pristine” flow data to detect changes due to climate