Impact of Climate Change on Environmental Flows at the Basin Scale - Research Paper Example

IMPACT OF CLIMATE CHANGE ON ENVIRONMENTAL FLOWS AT THE BASIN SCALE This was a practical study that assessed the impact of climate change onenvironmental flows at the basin scale. This was done with a focus basin scale of River Okavango whereby the specific impact that climate change has on the river ecosystems was measured using various hydrological projections. Projections were taken from two major dimensions namely under climate change and the magnitude of uncertainty. The multi-complexity of the goals of the study made it relevant to use multivariate approach in the methodology. To this end, the Range of Variability Approach (RVA), which makes use of the Indicators of Hydrological Alteration (IHA) were employed for methodological application. Though the indicators present the opportunity of calculating flows in both daily and monthly scenarios, the monthly flows were used as there was a 30 year marginal calculation involving 1961 to 1990. The indicators were however calculated by making use of extensive data storage systems built on the principles of spatial scales that give credence to multiple sites and scenarios. For the purpose of the present however, the multiple site and scenario factor calculations were used only as comparative analysis to ascertain the acceptable baseline environment flow ranges that could be recorded for the projected hydrological regimes used. Results from the study showed that River Okavango like other rivers across Africa face the risk of negative impact of climate change on river ecosystem. Introduction Climate change has been an important issue of discussion that is not carried only at the academic levels but on a nationalistic and global level. It is against this backdrop that climate simulation is carried out at a global level by making use of General Circulation Models (GCM) (Kapangaziwiri and Hughes, 2008). With such globalised models, globally accepted physical laws regulating not just the atmosphere but also the circulation and behavior of oceans are undertaken by the use of authenticated and empirical mathematical equations. Indeed, as much as knowledge of the causes to some of the problems in climate change are necessary, it is even more necessary to have specific quantification of the risk that faces the basin scale. This is because it is when such authentic quantifications are done that subsequent intervention that address risk management can be undertaken as contingency measures to addressing the trend (Kingston et al, 2009). Coupled with the background that a vivid background quantification of the risk size of climate change on environmental flows at the basin scale is necessary, there is going to be the use of a cosmopolitan assessment approach that makes use of prescriptive, hydrological index (Laize et al., 2010) to illustrate the various environmental flow regime of River Okavango. This will be done the QUEST-GSI basins using atmospheric conditions measured from 1961 to 1990. Based on these outcomes, a projected trend is expected to be established not just for the present era but also into the future. It has been posited that projections that are taken on a meaningful monthly scale over such a long period as 30 years has the potential of aiding in the determination as to whether there could be accurate projected impacts of climate change on river discharge for present scenarios and those of future account (Legates and Willmott, 1990). It would be against this generalized opportunity that a 30 year time series should result in an empirical statement of the current state of impact of climate change on the environmental flows and river ecosystem of River Okavango’s basin scale. 1 To apply a prescriptive, hydrological index method (Laizéet al., 2010) to characterise the environmental flow regime of one of the QUEST-GSI basins under "unmodified" or baseline (1961-1990) conditions; 2 To determine whether the projected impacts of climate change on river discharge (and uncertainty therein) result in unacceptable departures from the environmental flow regime; and 3 To reflect critically on the robustness of the employed methodology to characterize environmental flows and the impacts of climate change on basin ecosystem services. Background Literature River Basin under Study The river basin constitutes with all of highlands, semi-arid and inland alluvial fans as it trans-borders in Angolan highlands, passes through the semi-arid parts of Namibia and finally drains off into the inland alluvial fan in Botswana. From the pictorial description of the river basin in fig. 1, a better understanding can be had on the need to protect the river basin against the harsh effects of climate change as the river basin promises several potentials upon proper protection from natural, but artificial-caused factors like climate change. This is because apart from the socio-economic relevance of the river basin in the form of irrigation and hydro-power purposes, the river basin has also been identified as a potential development agent for its semi-arid downstream sections (Norris and Thoms, 1999). But indeed this cannot happen if scaring figures of the impact of climate change on the river basin continues to rise. Source: Hughes, Kingston and Todd (2011) 2.2 Basin-scale hydrological projections under climate change (QUEST-GSI) As far as risks and impacts of climate change is concerned, projections into the future the use of existing or previous scalar values are the most preferred approach (Olden and Poff, 2003). It is this that has given rise to a number of climate scenarios rather than climate occurring events. One of such climate scenarios is the NERC-funded QUEST-GSI project, which assess the impacts of climate change on river ecology, using stipulated basin scales from as many continents as possible (Poff, et al, 2007). In the latest version, river discharge at basin scales were used from five continents as a way of achieving empirical application of data. Through basin-scale hydrological projections for climate change, the most suitable and implicit strategy for meeting up projected values has been identified to mitigation and adaptation to climate change at the basin scale. This is indeed a justification of the level of accuracy associated with the projected values, for which reason no further risks can be taken upon coming to terms with the projected threats. This is however not without a say that there are further approaches that could be used in ensuring that there exists better accuracy and perfection with projections. One of such has to do with the use of EFAs, which is a form of basin-scale analyses aimed at providing basis for indicator metric of adaption, risk and vulnerability with high resolution projections based GCM pattern-scaling values of 0.5º x 0.5º (Nilsson et al, 2005). Modeling the hydrology of the Okavango basin Indeed the impact of climate change on environmental flows at basin scale will be best understood with the model of hydrology of any basin scale understudy is well expatiated. This is because most basin scales are affected by different hydrological factors and so having a non-divisible discussion of the hydrology may not be a true reflection of the actual situation. Once this happens also, the validity and reliability of data collected could be affected since they will not be the exact representation of the basin scale. With reference to the Okavango basin, there are four distinctively identified components namely the Cubango River, Cuito River, Omatako River and the lower parts of the major Okavango River. Each of these components have different hydrological constituents that make the discussion of impact of climate change on the basin scale quite technical. For example Cubango River has a drainage area of approximately 99, 800km2 accounting for one of the largest flow areas where any major river ecosystem dispositions may come with adverse effect. Cuito River, which is the second largest with an approximate size of 61, 600km2 , is said to have a far less stream flow (Olden and Poff, 2003). As for the Omatako River, it is said to have no data records for low contributions while the lower part of the main Okavango River is made up of only 8600km2 lying directly downstream the confluence of the other three tributaries discussed above. Methods and Study Area Instrumentation The methodology is made up of the empirical collection of quantitative collection of secondary data from an identified primary study area, which in this case was the Okavango River basin. To understand the hydrological behavior of the basin, the river discharge was first identified. This was done by using a modeled river flows measured in (m3s-1) for the site. To ensure that the data collection was accurate and reliable, a well researched model in the shape of the WaterGAP model was used. WaterGAP denotes Water Global Analysis and Prognosis and has been one of the most credible global models for calculating flows and storage of fresh water in almost all continents of the world (Andersson et al, 2006). The credibility of the model is actually the rationale behind its selection and also for the fact that it is favorable for freshwater, of which the study area basin is. The WaterGAP functions by considering not only the natural hydrological activities of the basin but also the human influence of the water system that results from abstractions and dams built from the basin (Norris and Thoms, 1999). Noting that prominent global organizations including the United Nations Water Development Reports, the United Nations Global Environmental Outlooks and the Intergovernmental Panel on Climate Change have all used the model in their international assessment of impact of climate change on various river basins in all continents with the exception of the Antarctica (Olden and Poff, 2003), it would be right to say that the selection of the researcher’s model was highly appropriate. Results of the river flows gathered with the instrument have been presented under the results section of the dissertation. Data Collection Procedure The procedure for collecting data was based on the functionality associated with the WaterGAP model. As part of the model, it was mandatory to collect data over a longer time frame from the past, based on which future projections would be done. Data were therefore collected from 1961 to 1990, based on which future projections were done from 2040 to 2069 with the first year of data collection, corresponding with the first year of projection, second year, corresponding with the second year, and so on. Another characteristic of the data collection process was that data were collected from several sites within the basin, after which a computation was done to get the average values of river discharge in cumecs. Of the basin area of 95 642 km2, there were a total of 664 sites generated using the WaterGAP model. The locations include but not limited to 140 gauging stations, whereby sites were sites were evenly spaced across the river basin. It would be noted however for the sake of proximity, tributaries were not included in the sites and thus the collection process. Given the basin area and the number of sites, the average stretch of river for which a site was found could be calculated as follows 95, 642km2 makes one basin therefore 664 sites = 95,642 664 = 144km stretch of river. Finally, it would be noted that the modeling of the flows were generated on a monthly mean for seven different model runs that catered for separate climate models comprising 2.0ºC rise in global mean air temperature and different rises in global mean air temperature. There were also other human interface activities including socio-economic scenarios such as displayed in the results. Results Examination of baseline and projected river discharge for basin The river discharge for basin was collected over a period of 30 years spanning from 1961 to 1900. A ten year period presentation of the data collected is graphically displayed in the two time series plotting below for the yearly baseline intervals of 161 to 1975 and 1975 to 1990 on one hand and projected river discharge from 2040 to 2054 and 2055 to 2069 on the other hand. For the baseline readings, because data were collected on a monthly basis, the results presented have been taken as the annual average by dividing the sum of all river discharge in the year by 12. That is ∑n 12 For the projected river discharge however, the computations were done using the monthly average reading of all six (6) variables namely CCCma, CSIRO, HadCM3, IPSL, MPI, NCAR and HadGEM1. Fig. 1 Annual baseline and projected river discharge for basin from 161 to 1975 and 1940 to 1955 respectively. Fig. 2 Annual baseline and projected river discharge for basin from 1976 to 1990 and 2055 to2069 respectively. Environmental Flow Regime using the Indicators of Hydrological Alteration (IHA) The researcher used the indicators of hydrological alteration (IHA) as small spatial and temporal scales at a specified catchment area of the river reach. Through this, all forms of sub-daily readings were ignored as daily and monthly data alone were used. But there was later a conversion of all daily readings into a monthly flow because of proximity, cost involved and the large nature of spatial scale under consideration. This means that there were 12 indicators of hydrological alterations parameters that were supposed to be exempted from the IHA statistics given that they headed for negligible results. Examples of those exempted were the 1-day minimum and maximum flows, which were noted to be highly insignificant at the monthly scale. The method used for a correlation analysis for finding both qualitative and quantitative outcomes of environmental flow indicators were similar to those used for finding the annual baseline and projected river discharge of which results have been produced in the table below. Based on Hughes et al (2011) It must be noted that the computations done were based on the following indicators of hydrological alteration in relation to the regime characteristics of basin. Discussion Impacts of climate change on river discharge There is a marginal change in the magnitude of river flow at the basin, which is related to the 2°C global warming. Another indication of the results that raises concern on river discharge is the revelation that there were as many as 5 out of 7 GCMs, which forms a percentage score of 71.43% that recorded changes in excess of their means at 10% annual river flow. Robustness of the employed methodology to characterize environmental flows In the diagram below, there is an adjustment in the methodology whereby there has been a re-calibration on the Pitman model using similar gridded rainfall data collected for future climate scenarios (Hughes et al, 2011). This re-calibration was necessitated to ensure the robustness of the methodology as previous calibrations have been found not be to compatible with historical guage data and that the hydrological model parameter, which was embodied in the IHA is said not to be independent of the rainfall inputs (Oudin et al., 2006). The recalibration thus produced the results below for selected years of 1961 to 1972. From the figure, it can be seen that there were excessive flows in 1967 as well as deficient flows in many different years when the calibration was undertaken. Conclusion As part of the methods, regression and time-series analysis was done on the levels of flows at an identified basin scale. This was necessitated to understand what the long term impact of current climate situations would be on the basin. It was found however that there is currently a wide spread of change in the mean of river flow, which heads towards a downward direction. There were also high levels of uncertainty recorded where comparison with previous studies such as Anderson et al (2006) shows that in terms of both magnitude and range of projected changes, there are higher values (Hughes et al, 2011). Consequentially, the impact of climate change on basin scale can be concluded to be devastating and thus needs urgent interventions that would ensure that the ecosystem does not continue to be affected as the water body serves as an import natural resource for the regions in which it is situated. REFERENCE LIST Andersson, L., Wilk, J., Todd, M. C., Hughes, D. A., Earle, A., Kniveton, D., Layberry, R., and Savenije, H. H. G.: Impact of climate change and development scenarios on flow patterns in the Okavango River, J. Hydrol., 331, 43–57, 2006. Hughes, D. A, Kingston, D. G. and Todd M. C. Uncertainty in water resources availability in the Okavango River basin as a result of climate change. Hydrology and Earth system Science. 15, 931–941, 2011 Kapangaziwiri, E. and Hughes, D. A.: Revised physically-based parameter estimation methods for the Pitman monthly rainfallrunoff model, Water SA 32, 183–191, 2008. Kingston, D., Todd, M. C., Taylor, R., and Thompson, R. T.: Uncertainty in future freshwater availability associated with the estimation of potential evapotranspiration, Geopyhs. Res. Lett., 36, L20403, doi:10.1029/2009GL040267, 2009. Laizé et al., (2010). Monthly hydrological indicators to assess impact of change on river ecosystems at the pan-European scale: preliminary results. British Hydrological Society. 320, 62–83 Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127, 1990. Nilsson C., Reidy C., Dynesius and M., Revenga C. (2005). Fragmentation and flow regulation of the world’s large river systems. Science, 308, 405–408. Norris, R.H. and Thoms, M.C. (1999). What is river health? Freshwater Biol., 41, 197–209. Olden, J.D. and Poff, N.L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res. Appl., 19, 101–121. Oudin, L., Perrin, C., Mathevet, T., Andr´eassian, V., and Michel, C.: Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models, J. Hydrol., 320, 62–83, 2006 Poff, N.L., Allan, J.D., Bain, M.B., Karr, J.R., Prestegaard, K.L., Richter, B.D., Sparks, R.E. and Stomberg, J.C. (1997). The natural flow regime. Bioscience, 4, 769–784. Richter, B.D., Baumgartner, J.V., Wigington, R. and Braun, D.P. 1997. How much water does a river need? 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