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Welcome To This Presentation. Downscaling and Modeling the Climate of Blue Nile River Basin-Ethiopia. By: Netsanet Zelalem Supervisors : Prof. Dr. rer.nat.Manfred Koch, Kassel University Dr. Solomon Seyoum , IWMI, Ethiopia Nov9/2012

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  1. Welcome To This Presentation

  2. Downscaling and Modeling the Climate of Blue Nile River Basin-Ethiopia By: NetsanetZelalem Supervisors: Prof. Dr. rer.nat.Manfred Koch, Kassel University Dr. SolomonSeyoum, IWMI, Ethiopia Nov9/2012 Kassel University, Germany

  3. Statement of the Problem • High population pressure, poor water and land management and climate change are inducing declining agricultural productivity and vulnerability to climate impact [Haileslassieet al., 2008]. • In order to alleviate poverty and food insecurity, it is widely recognized to utilize water resources such as Blue Nile. • So, assessment of the impact of climate change on future water resource may provide substantial information to the area where more than 85% of the basin depends entirely on rain-fed agriculture.

  4. Objective • Evaluate the possible relationships between large scale variables with local meteorological variables. • Evaluate the most common statistical downscaling methods, SDSM and LARSWG, for the assessment of the hydrological conditions of the basin. • Generate climate change scenarios for the basin using different emission scenarios and AOGCMs (Atm.and Ocean). • Investigate the possiblity of climate change on hydrology in UBRB based on the downscaled meteorological scenario data. • Provide streamflow predictions of the basin for current and downscaled future climate conditions.

  5. Contents • Background on Climate System • Study Area • Data collection, analysis and results • Climate Modeling • Results of Climate Modeling • Conclusions

  6. Background (Climate system) • Climate is a statistical description of weather including averages and variability. • The earth climate system is an interaction of various components of climate system: • Ocean • Land surface • Atmosphere • Cryospher • Biosphere • Anthropogenic

  7. ---Background (Climate system) • Climate Change: refers to a statistical significant variations that persist for an extended period, typically decades or longer. • The mea annual global temperature has increased by about 0.3-0.60C since the late 19 century.

  8. ---Background (Climate change Impact ) • Today, the impact of climate change become the biggest concern of mankind.

  9. ---Background (Climate Change Impact) • This will impact the hydrology of the watershed systems and hence it exhibits long-term changes.

  10. ---Background (Climate Change Impact) • This impact needs integrated modeling to evaluate alternate future watershed scenarios. • IPCC findings indicate that developing countries, such as Ethiopia, will be more vulnerable to climate change Higher Relative Risks Lower Relative Risks

  11. ---Background (Climate Model) • Climate Models try to simulate the likely responses of climate system to a change in any of the parameter interactions between them mathematically. • Generally refers as GCMs (Global Circulation Models) • The 3-D model formulation is based on the fundamental laws of physics: • Conservation of energy • Conservation of momentum • Conservation of mass and • The “Ideal Gas Law”

  12. ---Background (Emission Scenarios) • Emission scenarios are important components and tools for the modeling of climate change (Werner and Gerstengarbe, 1997)

  13. ---Background (Downscaling GCM) • In climate change impact studies, hydrological modeling: • Are usually required to simulate sub-grid scale phenomenon. • Require input data (such as pcp, temp) at similar sub-grid scale. • Downscaling is a means of relating the large scale atmospheric predictor variables to local scale so as to use for hydrological model inputs.

  14. ---Background (Downscaling Methods) 1. Dynamic downscaling • Extract local-scale information by developing and using regional climate models (RCMs) with the coarse GCM data used as boundary conditions. 2. Statistical downscaling • Drive the local scale information from the larger scale through inference from the cross-scale relationship. It Can be categorized in to three types • Regression downscaling • Stochastic weather generators • Weather typing schemes

  15. ---Background (Statistical downscaling) 1. Regression downscaling techniques: Predicted=f(Predictors). The function f could be. Linear or non-linear regression. 2.Stochastic weather generators: The relationships between daily weather generator parameters and climatic average can be used to characterize the nature of future daily statistics (wilby, 1999).

  16. ---Background (Statistical downscaling) 3. Weather typing schemes • Involve grouping local, meteorological variables in relation to different classes of atmospheric circulation. • Future regional climate scenarios are constructed by: Resembling from observed variable distribution • Climate change is then estimated by determining the change of the frequency of weather classes.

  17. Study area

  18. ---Study Area • Features of Upper Blue Nile watershed • The total area=176,000 km2 • Latitude: 7° 45’ and 12° 45’N and longitude: 34° 05’ and 39° 45’E • Altitude: Min. 485m to Max. 4,257m asl • UBNB has 14 sub-basins • It contributes 40% of Ethiopia surface water resources [World Bank 2006] • 87% of the Nile flow at Aswan dam is from Ethiopia from this UBNB contributes 60% and the Atbara (13%) and the Sobat (14%)

  19. Data sources

  20. Data Collection and Quality Checking • After collection of precipitation data from 53 stations and temperature from 33 stations  for 1970-2000 period at daily time scale, data quality( Such as, filling missing data and consistency check) control has been conducted. • Areal precipitation and temperature based on Thiessen Polygon method: Stn. Results: 

  21. Sub-Basin Results of Observed Data

  22. Large Scale Data • Criterion to chose GCMs • Based on outputs of MAGICC-SCENGEN 2. Based on data availability 3. Based on their participation IPCC-AR4 4. Allowable number of GCMs ECHAM-5, GFDLCM21 and SCIRO-MK3

  23. Data of selected GCMs • A1b and A2 emission scenarios are considered to account the worst (A2) and the middle(A1B). • Re-griding has been done using Xconv package.

  24. Large-scale Predictor Variables

  25. Large Scale Data • Re-analysis grid lines covering the study area

  26. Statistical Downscaling Tools • Two statistical downscaling tools: • *SDSM: A regression based statistical downscaling model (wilby, et al., 2002) • *LARS-WG: Long Ashton Research Station Stochastic Weather Generators (Semenov et al, 1998).

  27. SDSM: A regression based Statistical Downscaling models • Identify predictand relationships using multiple linear regression techniques. • The predictor variables provide daily information concerning the large-scale state of the atmosphere, • The predictand describes condition at the site scale.

  28. LARS-WG • Generate precipitation, min and max temperature. • Semi-empirical distributions are used to state a day as wet/dry series. • Semi-empirical distributions are used for precipitation amounts, dry/wet series. • Semi-empirical distributions are used for Temperature. It is conditioned on wet/dry status of a day.

  29. Cases considered • Three cases are employed in climate modeling • All the cases are applied for each of 14 sub-basins in UBNB.

  30. Climate modeling-Case1 • SDSM reduces the task into a number of discrete processes as follows: • 1. Quality control of data and transformation. • 2. Selection of appropriate predictor variables for model calibration. • 3. Calibrate Model. • 4. Generate the daily data. • 5. Analyze the outputs. • 6. Scenario generation: Then analysis of climate change scenarios

  31. Selecting predictor variables • Predictor is selected based on correlation analysis off-line of SDSM and using SDSM screening methods in the software.

  32. SDSM Calibration Approach • Model calibration is performed in two approaches: • Unconditional: It assumes a direct link between the regional-scale predictors and the local predictand. • Maximum and minimum temperature • Conditional: depend on an intermediate variable such as the probability of wet-day occurrence, intensity, amount etc. • Precipitation The performance of calibration result for each sub basin

  33. Results-Case1

  34. Results-case1

  35. Climate Modeling –Case2 • The weather generator consists of three main sections: • Model calibration Analysis ofobserved station data in order to calculate the weather generators. • Model validation Qtest is used for determining how well the model is simulating observed conditions. The statistical characteristics of the observed data are compared with those of the synthetic data. • Model use Generating the synthetic weather based on the available data parameter generated during model calibration or by combining scenario file with the generated parameter to account climate change.

  36. Incorporating Climate Scenario • Climate changes derived from GCMs can be incorporated in stochastic weather generator by applying climate change scenarios expressed on a monthly basis in the relevant climate variable.

  37. Results-Case2

  38. Climate Modeling: Case-3 • The methodology is same as case-2. • The climate change scenario is constructed from 3GCMs. 

  39. Comparison of Mono-Modal and Multi-Modal Approaches • Multi-modal approach under estimated pcp prediction and this is more apparent in 2050s than 2090s. • Annual relative % change in pcp increases due to relatively high increase in dry periods. • Tmx and Tmn change has no significant difference between two approaches in 2050s. • Multi-modal approach underestimates both Tmx and Tmn during 2090s • Summer season in the case of mono-modal is warmer while spring season is warmer in multimodal approach.

  40. Comparison of Mono-Modal and Multi-Modal Approaches

  41. Mono/Multi-modal Comparisons

  42. Comparisons of SDSM and LARS-WG outputs • Generally, downscaled precipitation results from SDSM and LARS-WG show marked difference. • Both downscaling tools illustrate an increase in maximum and minimum temperature in both 2050s and 2090s time compare with the base line period.

  43. SDSM and LARS-WG Comparison

  44. SDSM and LARS-WG Comparison

  45. Conclusions • LARS-WG performs better in precipitation prediction than SDSM. • simulation of future precipitation using SDM has significant spatial variation than LARS-WG. • LARS-WG illustrate similar trend across each sub-basins in the simulation of precipitation, maximum and minimum temperature. • LARS-WG shows better performance over the study area than SDSM.

  46. THANK YOU.

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