Solutions towards hydrological challenges in Africa in support of hydropower developments Ms. Catherine Blersch, Civil Engineer, Aurecon, South Africa Dr Verno Jonker, Civil Engineer, Aurecon, South Africa
Outline of Presentation 1. Background and Context 2. Data Collection and Review 3. Rainfall-Runoff Modelling 4. System Analysis 5. Design Flood Analysis 6. Lessons Learnt
1 Background and Context Background Huge potential for hydropower development in Africa Growing need for clean and sustainable power Numerous investigations into both large- and smallscale hydropower projects across the African continent
1 Background and Context Problem Statement: Hydrology is an essential component of any hydropower project Long term flow sequences required to determine viability of hydropower project (generation capacity) Flow sequences required for design and operation Flood hydrographs and levels required for design of hydropower dams and associated infrastructure
1 Background and Context Conventional Approach Quality assured data available from relevant local/national department Published local/national manuals, guidelines and standards for design Localised information available regarding design rainfall, runoff coefficients, catchment response characteristics Established techniques that have been tried and tested in the area can be readily applied
1 Background and Context Challenges in Africa Difficult to source data Often have to pay large sums for poor quality data No quality control data is often unverified Only average daily flow values no instantaneous daily peaks Very rare to find concurrent rainfall and flow data within a catchment often calculations from adjacent catchments must be applied In many countries flow data stops in the 70 s / 80 s due to political instability or war Rating curves are not updated regularly Conflicting data from different sources
2 Data Collection and Review Topographical Data 90m x 90m NASA Shuttle Radar Topographic Mission (SRTM) DEM ArcHydro used to delineate sub-catchments Perry Castaneda Library (Topographical maps etc.) Land Cover / Vegetation Freely available images and maps online Satellite imagery Soil Types Soil maps available from EU Soils
2 Data Collection and Review
2 Data Collection and Review Monthly and Daily Rainfall Data FAOClim 2.0 Software CD database available for all countries in Africa National Oceanographic and Atmospheric Administration (NOAA) National meteorological / hydrological institutes Data available in previous reports and publications Colonial databases and yearbooks Evaporation Data Monthly averages available in FAOClim 2.0 Very important to take note of the type of evaporation data (A-Pan, S-Pan, Pennman Monteith etc.)
2 Data Collection and Review Monthly and Daily Flow Data: Global Runoff Data Centre Recent flows often recorded by developers, new mines etc. National hydrological institutes Data available in previous reports and publications Substantiate with anecdotal evidence from locals (e.g. recollections of large floods) and spot checks
2 Data Collection and Review Data Review and Quality Control: Single mass plot shows whether significant portions of data are missing and confirms stationarity Missing values are flagged particular attention paid to missing values in wet months for flood analysis For catchment modelling missing rainfall and flow values can be patched using adjacent stations and advanced regression techniques Potential outliers are flagged often better to assess the sensitivity if they are included or excluded rather than excluding them outright
2 Data Collection and Review Typical Single Mass Plot:
3 Rainfall-Runoff Modelling A flow duration curve is required to determine the design flows for hydropower generation Often empirical approaches are applied very simplified and not based on long term sequences Observed records are often very short therefore only using observed records can be careless Long term flow sequences are required to realistically determine the long term assurance of supply
3 Rainfall-Runoff Modelling Various models can be applied Water Resource Simulation Model (WRSM2000) WRSM2000 employs the lumped conceptual Pitman rainfall-runoff model Operates on the network principle which allows water to be transferred between modules, depending on a user-specified configuration for the system South African model but has been applied successfully in a number of African countries including Angola, Nigeria, DRC, Cameroon, Namibia, Botswana, Zimbabwe and Mozambique
3 Rainfall-Runoff Modelling Catchment Parameters: Name Zmin Zmax POW TL ST FT Description Minimum absorption rate (mm/month) Maximum absorption rate (mm/month) Power of the runoff vs. soil moisture capacity Time lag of Runoff (months) Maximum soil moisture capacity (mm) Runoff rate from soil when soil moisture is at full capacity (mm/month) R GW GL SL PI Controls rate at which evaporation reduces as soil moisture is depleted (Coefficient in the evaporation soil moisture equation) Maximum groundwater runoff (mm/month) Lag of subsurface flow in the lower zone (months) Soil moisture state below which no runoff occurs (mm) Interception storage (mm)
3 Rainfall-Runoff Modelling Typical Calibration Results:
3 Rainfall-Runoff Modelling Results: Long term sequence used to produce a flow duration curve: 80% of the time the flow exceeds 78 m 3 /s
4 System Analysis For a run-of-river project long term sequences alone are sufficient For a hydropower dam, system modelling often required to determine assurance of supply System modelling allows for consideration of dam operating rules and additional water uses Simple systems can be modelled in excel Water Resource Yield Model has been successfully applied on a number of projects across Africa
4 System Analysis Water Resource Yield Model (WRYM): Monthly time-step model that simulates the behaviour of reservoirs or water supply systems Includes inflows, storage, demands and operating rules Outputs the number of times that the dam cannot meet the target demand over the analysis period Used to determine the assurance of supply and optimise the target drafts from the dam Uses sophisticated network solver Historical and stochastic models are available
4 System Analysis Typical Network Diagram: 1 - [1] SPILL 2000 192.000 ZONE1 20 186.000 ZONE2 20 185.530 DSL 3000 165.010 3 - [2] 0.694 to 0.694 0 0 to 0.694 100 3 - domestic 3 5 5 - irrigation 100% x [1] Kash 1 Kashimbila Dam 1 1-7 7 - seepage 1 - [1] 0 to inf 0 7 - [2] 0.024 to 0.024 0 0 to 0.024 100 5 - [2] 0.000 to 0.000 0 0 to 0.000 100 2 9 - [1] 0 to inf 0 9 13 - [3] 260.002 to 260.002 0 0 to 260.002 50 13 - YIELD 13 11-9 - Spill 14 - [3] 999.000 to 999.000 0 0 to 999.000 50 11 11 - [3] 0.000 to 0.000 0 0 to 0.000 50 14 14-3
4 System Analysis Typical reservoir simulation and results: Month Flow rate Assurance of (m 3 /s) Supply (%) Oct 215 77% Nov 200 75% Dec 107 75% Jan 80 75% Feb 70 75% Mar 59 75% Apr 82 77% May 117 75% Jun 115 75% Jul 79 74% Aug 70 74% Sep 135 74%
5 Design Flood Analysis Statistical Methods Deterministic Methods Empirical Methods
5 Design Flood Analysis Typical approaches to design flood in Africa: Empirical approaches to unit hydrographs are often applied which are not applicable in Africa Single site statistical analysis is often favoured with no attention to record length or the type of flow data Extrapolating statistical results from short records to large recurrence intervals (e.g. 1 in 10000 year check flood) is very dangerous Deterministic methods often not used or the simpler methods are used where not applicable (e.g. on very large catchments)
5 Design Flood Analysis Statistical Methods: If daily flow data is available, the annual maxima can be extracted and a statistical distribution fit to determine floods for various recurrence intervals Statistical methods are often favoured by hydrologists because they extrapolate from actual observed values Typically Log Pearson Type III, Long Normal and General Extreme Value are applied and best fit determined by visual inspection Floods can then be scaled to the hydropower site using the ratio of catchment areas
5 Design Flood Analysis Statistical Methods: Daily flow records are average or once off daily values and not daily peaks therefore often need to scale values up before performing an analysis Often the available records are too short for a realistic statistical analysis If a number of records are available in the vicinity they can be combined to perform a regional analysis Regional analysis provides a longer record and helps to reduce the impact of potential outliers
5 Design Flood Analysis Typical plot: Log Normal not realistic in this case Observed values
5 Design Flood Analysis Deterministic Methods Design Rainfall: All deterministic methods require design storm rainfall as an input Point rainfall values are typically derived by performing a single site or regional probabilistic analysis using the available daily records Storm duration is selected based on the catchment characteristics (time of concentration / lag time) typically a range of durations is considered 1 day rainfall must be converted to 24 hour rainfall
5 Design Flood Analysis Deterministic Methods Design Rainfall: Aerial reduction factors are applied to convert point rainfall to aerial rainfall using country or region specific curves or published guidelines Storm loss factors are applied to derive the storm rainfall typically South African values are applied but adjusted based on site specific information If concurrent rainfall and storm events can be identified, these can be used to calculated the storm losses to be applied to the design rainfall Probably Maximum Precipitation (PMP) is sometimes required to determined the PMF Herschfield Method and WMO Methods can be applied
5 Design Flood Analysis Deterministic Methods Rational and SCS Methods: Can only be applied to smaller catchments Runoff coefficients and adjustment parameters are available for Southern Africa questionable whether these can be applied to wetter, tropical countries in Africa
5 Design Flood Analysis Deterministic Method - Unit Hydrograph: Unit hydrograph: catchment s signature response to 1 mm of excess rainfall of a particular duration Conventional approach requires the identification of a large number of particular rainfall events that resulted in particular flood events High density rainfall and flow data is required at subdaily time intervals very rarely available for hydropower studies in Africa
5 Design Flood Analysis Deterministic Method - Pseudo Unit Hydrograph: Pseudo Unit Hydrograph: Derived from hydrographs only, not from the causative rainfall events Flood hydrographs extracted from daily flow records The base flow is subtracted to give the flood flows The volume of the hydrograph is determined and divided by the catchment area to give the representative runoff depth Each ordinate is divided by the runoff depth to give a unit hydrograph in m 3 /s/mm.
5 Design Flood Analysis Deterministic Method - Pseudo Unit Hydrograph: Typical pseudo-unit hydrographs for various storms:
5 Design Flood Analysis Deterministic Method - Pseudo Unit Hydrograph: Selected unit hydrographs:
5 Design Flood Analysis Deterministic Method - Pseudo Unit Hydrograph: The unit hydrograph is then multiplied by the design rainfall and the base flows added back to give the flood hydrograph Advantages of this method: Can be applied even when flow records are short Based on actual floods A flood hydrograph is the output which is essential for dam design
5 Design Flood Analysis Empirical Methods: Empirical methods are applied where a quick answer is required or for comparison with other methods Francou-Rodier most commonly applied: Magnitude of the flood is largely by the catchment area. Kovaćs (1988) conducted an extensive regional maximum observed flood study for southern Africa and developed the Regional Maximum Flood (RMF) approach Identified envelopes of flood peaks defined by a regional Kvalue that relates catchment area to expected maximum flood discharge (range K=0 to K=6.5) K-values of around 4.0 are typically applied for central and West Africa K values can also be calculated from envelopes of global extreme flood events
5 Design Flood Analysis Sense Checking: In general a number of methods are applied and compared Any available flood information from previous reports or studies is also extracted for comparison Empirical methods are generally considered an upper limit for design floods Statistical methods will often be lower due to: Lack of instantaneous flow data and daily peaks Gauges are often inaccurate during extreme flooding conditions
5 Design Flood Analysis
6 Lessons Learnt Data is available if you know where to look Sense checking on all data is vital Empirical methods are often over-simplified and are not based on actual data Due to the data limitations, non-conventional methods are often required Validating answers with previous studies or research helps to confirm results
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