Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Historical Data
2.2.2. GCMs’ Output
2.3. Statistical Analyses
2.3.1. Computation of PET
2.3.2. Characterization of Mean Climatology
2.3.3. Statistical Downscaling
2.3.4. Quantifying Future Changes in Precipitation and PET
3. Results and Discussion
3.1. Historical Precipitation and PET
3.2. Future Precipitation and PET
4. Discussion and Conclusions
- i.
- Observed precipitation and PET over WMZs in Uganda are characterized by distinct variations from one region to another. While the western part of the Victoria WMZ (where Lake Victoria is located) was generally characterized by higher precipitation and lower PET totals than those over land. Moreover, Upper Nile, northern Kyoga, and northern Albert were characterized by high PET but low precipitation totals.
- ii.
- The lowest seasonal precipitation and highest seasonal PET totals exhibited patterns characterized by a north–south gradient for OND and annual scales mostly occurring over the northeastern region while JJAS dominates in the Victoria and southern Albert.
- iii.
- Future changes based on SSPs scenarios depict an increase in annual precipitation across all the WMZs with percentage change ranging from 2.8% to 9.5% for the 2030s. A larger change in precipitation is projected to occur towards the end of 21st century and under SSP3-7.0 and SSP5-8.5 relative to the baseline period. The projected increase for annual precipitation for the 2090s reaches 26.3% under SSP5-8.5.
- iv.
- Seasonal analyses show that the MAM and JJAS are projected to receive an insubstantial amount of precipitation, while JF and OND precipitation will experience an increase in most WMZs in Uganda.
- v.
- Likewise, impacts of climate change on PET demonstrate noticeable changes during the seasons, time periods, and scenarios under consideration. For instance, the largest increases in annual PET during the 2030s, 2050s, 2070s, 2090s (when all the scenarios or SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 are considered) will be in the ranges 1.1–4.0%, 4.8–7.9%, 5.1–11.8%, and 5.3–17.1, respectively. The lower and upper limits of these ranges in all cases were obtained under the SSP1-2.6 and SSP5-8.5, respectively.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Precipitation Changes in the 2050s, 2070s, and 2090s
Appendix B. PET Changes in the 2050s, 2070s, and 2090s
References
- Bates, B.C.; Kundzewicz, Z.W.; Wu, S.; Palutikof, J.P. Climate Change and Water—IPCC. 2008. Available online: https://www.ipcc.ch/publication/climate-change-and-water-2/ (accessed on 10 October 2020).
- United Nation (UN)—Water. United Nations World Water Annual Report 2015. Spotlight on 2015: A New Sustainable Development Agenda and a Global Goal for Water and Sanitation. 2015. Available online: https://www.unwater.org/publications/un-water-annual-report-2015/ (accessed on 12 April 2021).
- IPCC. Global Warming of 1.5° C: An IPCC Special Report on the Impacts of Global Warming of 1.5° C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2018; Available online: https://www.ipcc.ch/sr15/download/ (accessed on 15 May 2021).
- Shongwe, M.E.; Van Oldenborgh, G.J.; van den Hurk, B.; Van Aalst, M. Projected Changes in Mean and Extreme Precipitation in Africa under Global Warming. Part II: East Africa. J. Clim. 2011, 24, 3718–3733. [Google Scholar] [CrossRef] [Green Version]
- United Nation (UN)—Water. United Nations World Water Development Report 2020: Water and Climate Change; UNESCO: Paris, France, 2020; Available online: https://en.unesco.org/themes/water-security/wwap/wwdr/2020 (accessed on 12 February 2021).
- Green, D. The spatial distribution of extreme climate events, another climate inequity for the world’s most vulnerable people. Environ. Res. Lett. 2016, 11, 091002. [Google Scholar] [CrossRef]
- Bathiany, S.; Dakos, V.; Scheffer, M.; Lenton, T.M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 2018, 4, eaar5809. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Onyutha, C.; Tabari, H.; Rutkowska, A.; Nyeko-Ogiramoi, P.; Willems, P. Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5. J. Hydro-Environ. Res. 2016, 12, 31–45. [Google Scholar] [CrossRef]
- Greve, P.; Seneviratne, S.I. Assessment of future changes in water availability and aridity. Geophys. Res. Lett. 2015, 42, 5493–5499. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tabari, H.; Taye, M.T.; Willems, P. Water availability change in central Belgium for the late 21st century. Glob. Planet. Chang. 2015, 131, 115–123. [Google Scholar] [CrossRef]
- Konapala, G.; Mishra, A.K.; Wada, Y.; Mann, M.E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 2020, 11, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Zhou, S.; Williams, A.P.; Lintner, B.R.; Berg, A.M.; Zhang, Y.; Keenan, T.F.; Cook, B.I.; Hagemann, S.; Seneviratne, S.I.; Gentine, P. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Chang. 2021, 11, 38–44. [Google Scholar] [CrossRef]
- Tabari, H.; Aghajanloo, M.-B. Temporal pattern of aridity index in Iran with considering precipitation and evapotranspiration trends. Int. J. Clim. 2013, 33, 396–409. [Google Scholar] [CrossRef]
- Greve, P.; Roderick, M.L.; Ukkola, A.M.; Wada, Y. The aridity index under global warming. Environ. Res. Lett. 2019, 14, 124006. [Google Scholar] [CrossRef] [Green Version]
- Tabari, H. Climate change impact on flood and extreme precipitation increases with water availability. Sci. Rep. 2020, 10, 1–10. [Google Scholar] [CrossRef]
- Ngoma, H.; Wen, W.; Ojara, M.; Ayugi, B. Assessing current and future spatiotemporal precipitation variability and trends over Uganda, East Africa, based on CHIRPS and regional climate model datasets. Theor. Appl. Clim. 2021, 133, 823–843. [Google Scholar] [CrossRef]
- Ongoma, V.; Chen, H.; Gao, C. Projected changes in mean rainfall and temperature over East Africa based on CMIP5 models. Int. J. Clim. 2018, 38, 1375–1392. [Google Scholar] [CrossRef]
- Sridharan, V.; Ramos, E.P.; Zepeda, E.; Boehlert, B.; Shivakumar, A.; Taliotis, C.; Howells, M. The Impact of Climate Change on Crop Production in Uganda—An Integrated Systems Assessment with Water and Energy Implications. Water 2019, 11, 1805. [Google Scholar] [CrossRef] [Green Version]
- Mubialiwo, A.; Onyutha, C.; Abebe, A. Historical Rainfall and Evapotranspiration Changes over Mpologoma Catchment in Uganda. Adv. Meteorol. 2020, 2020, 8870935. [Google Scholar] [CrossRef]
- Onyutha, C. Geospatial Trends and Decadal Anomalies in Extreme Rainfall over Uganda, East Africa. Adv. Meteorol. 2016, 2016, 6935912. [Google Scholar] [CrossRef] [Green Version]
- Mubialiwo, A.; Chelangat, C.; Onyutha, C. Changes in precipitation and evapotranspiration over Lokok and Lokere catchments in Uganda. Bull. Atmos. Sci. Technol. 2021, 2, 2. [Google Scholar] [CrossRef]
- Onyutha, C.; Acayo, G.; Nyende, J. Analyses of Precipitation and Evapotranspiration Changes across the Lake Kyoga Basin in East Africa. Water 2020, 12, 1134. [Google Scholar] [CrossRef]
- Onyutha, C.; Asiimwe, A.; Muhwezi, L.; Mubialiwo, A. Water availability trends across water management zones in Uganda. Atmos. Sci. Lett. 2021, e1059. [Google Scholar] [CrossRef]
- Niang, I.; Ruppel, O.C.; Abdrabo, M.A.; Essel, A.; Lennard, C.; Padgham, J.; Urquhart, P. Africa. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Eds.; Cambridge University Press: Cambridge, UK, 2014; pp. 1199–1265. [Google Scholar]
- Mehdi, B.; Dekens, J.; Herrnegger, M. Climatic impacts on water resources in a tropical catchment in Uganda and adaptation measures proposed by resident stakeholders. Clim. Chang. 2021, 164, 1–22. [Google Scholar] [CrossRef]
- Egeru, A.; Barasa, B.; Nampijja, J.; Siya, A.; Makooma, M.T.; Majaliwa, M.G.J. Past, Present and Future Climate Trends Under Varied Representative Concentration Pathways for a Sub-Humid Region in Uganda. Climate 2019, 7, 35. [Google Scholar] [CrossRef] [Green Version]
- Nsubuga, F.N.W.; Olwoch, J.M.; de Rautenbach, C.J.W.; Botai, O.J. Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda. Theor. Appl. Clim. 2014, 115, 53–71. [Google Scholar] [CrossRef] [Green Version]
- Nyeko-Ogiramoi, P.; Ngirane-Katashaya, G.; Willems, P.; Ntegeka, V. Evaluation and inter-comparison of Global Climate Models’ performance over Katonga and Ruizi catchments in Lake Victoria basin. Phys. Chem. Earth Parts A/B/C 2010, 35, 618–633. [Google Scholar] [CrossRef]
- National Planning Authority. The Uganda Vision 2040; Government of Uganda: Kampala, Uganda, 2013. Available online: http://www.npa.go.ug/uganda-vision-2040/ (accessed on 10 March 2021).
- Ministry of Water and Environment. Uganda Catchment Management Planning Guidelines; Republic of Uganda, Ministry of Water and Environment, Directorate of Water Resources Management: Kampala, Uganda, 2014.
- Republic of Uganda. Second National Development Plan (NDPII) 2015/16–2019/20; Government of Uganda: Kampala, Uganda, 2015. Available online: http://npa.go.ug/wp-content/uploads/NDPII-Final.pdf (accessed on 10 March 2021).
- Food and Agriculture Organization. Country Files, Uganda. 2020. Available online: http://www.fao.org/3/t0473e/T0473E11.htm (accessed on 10 November 2020).
- Yang, W.; Seager, R.; Cane, M.A.; Lyon, B. The Annual Cycle of East African Precipitation. J. Clim. 2015, 28, 2385–2404. [Google Scholar] [CrossRef] [Green Version]
- United States Geological Survey. USGS EROS Archive—Products Overview. 2020. Available online: https://lta.cr.usgs.gov (accessed on 7 January 2020).
- Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
- Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model. Dev. 2016, 9, 1937–1958. [Google Scholar] [CrossRef] [Green Version]
- Hargreaves, G.H.; Samni, Z.A. Estimation of potential evapotranspiration. Proc. Am. Soc. Civ. Eng. 1982, 108, 223–230. [Google Scholar]
- Hargreaves, G.H.; Samni, Z.A. Reference crop evapotranspiration from temperature. Trans. Am. Soc. Agric. Eng. 1985, 1, 96–99. [Google Scholar] [CrossRef]
- Tabari, H.; Paz, S.M.; Buekenhout, D.; Willems, P. Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought. Hydrol. Earth Syst. Sci. 2021, 25, 3493–3517. [Google Scholar] [CrossRef]
- Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang. 2017, 42, 153–168. [Google Scholar] [CrossRef] [Green Version]
- Blau, M.T.; Ha, K. The Indian Ocean Dipole and its Impact on East African Short Rains in Two CMIP5 Historical Scenarios with and without anthropogenic influence. J. Geophys. Res. Atmos. 2020, 125. [Google Scholar] [CrossRef]
- Chu, J.-E.; Ha, K.-J.; Lee, J.-Y.; Wang, B.; Kim, B.-H.; Chung, C.E. Future change of the Indian Ocean basin-wide and dipole modes in the CMIP5. Clim. Dyn. 2013, 43, 535–551. [Google Scholar] [CrossRef] [Green Version]
- Liebmann, B.; Hoerling, M.P.; Funk, C.; Bladé, I.; Dole, R.M.; Allured, D.; Quan, X.; Pegion, P.; Eischeid, J.K. Understanding Recent Eastern Horn of Africa Rainfall Variability and Change. J. Clim. 2014, 27, 8630–8645. [Google Scholar] [CrossRef]
- Lyon, B.; De Witt, D.G. A recent and abrupt decline in the East African long rains. Geophys. Res. Lett. 2012, 39, L02702. [Google Scholar] [CrossRef] [Green Version]
- Williams, A.P.; Funk, C. A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa. Clim. Dyn. 2011, 37, 2417–2435. [Google Scholar] [CrossRef] [Green Version]
- Engelbrecht, F.; Adegoke, J.; Bopape, M.-J.; Naidoo, M.; Garland, R.; Thatcher, M.; McGregor, J.; Katzfey, J.; Werner, M.; Ichoku, C.; et al. Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 2015, 10, 085004. [Google Scholar] [CrossRef]
- Dike, V.N.; Shimizu, M.H.; Diallo, M.; Lin, Z.; Nwofor, O.K.; Chineke, T.C. Modelling present and future African climate using CMIP5 scenarios in HadGEM2-ES. Int. J. Clim. 2014, 35, 1784–1799. [Google Scholar] [CrossRef]
- Onyutha, C. Trends and variability of temperature and evaporation over the African continent: Relationships with precipitation. Atmósfera 2021, 34, 267–287. [Google Scholar] [CrossRef]
- Scheff, J.; Frierson, D. Scaling Potential Evapotranspiration with Greenhouse Warming. J. Clim. 2014, 27, 1539–1558. [Google Scholar] [CrossRef] [Green Version]
- Emori, S.; Brown, S.J. Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett. 2005, 32. [Google Scholar] [CrossRef]
- Ayugi, B.; Tan, G.; Gnitou, G.T.; Ojara, M.A.; Ongoma, V. Historical evaluations and simulations of precipitation over East Africa from Rossby centre regional climate model. Atmos. Res. 2020, 232, 104705. [Google Scholar] [CrossRef]
- Kisembe, J.; Favre, A.; Dosio, A.; Lennard, C.; Sabiiti, G.; Nimusiima, A. Evaluation of rainfall simulations over Uganda in CORDEX regional climate models. Theor. Appl. Climatol. 2018, 137, 1117–1134. [Google Scholar] [CrossRef] [Green Version]
- Ngoma, H.; Wen, W.; Ayugi, B.; Babaousmail, H.; Karim, R.; Ongoma, V. Evaluation of precipitation simulations in CMIP6 models over Uganda. Int. J. Clim. 2021. [Google Scholar] [CrossRef]
- Nikulin, G.; Jones, C.; Giorgi, F.; Asrar, G.; Büchner, M.; Cerezo-Mota, R.; Christensen, O.B.; Déqué, M.; Fernandez, J.; Hänsler, A.; et al. Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate Simulations. J. Clim. 2012, 25, 6057–6078. [Google Scholar] [CrossRef] [Green Version]
- Ayugi, B.; Tan, G.; Ruoyun, N.; Babaousmail, H.; Ojara, M.; Wido, H.; Mumo, L.; Ngoma, N.H.; Nooni, I.K.; Ongoma, V. Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa. Water 2020, 12, 801. [Google Scholar] [CrossRef] [Green Version]
- Onyutha, C. Analyses of rainfall extremes in East Africa based on observations from rain gauges and climate change simulations by CORDEX RCMs. Clim. Dyn. 2020, 54, 4841–4864. [Google Scholar] [CrossRef]
- Füssel, H.M.; Klein, R.J. Climate change vulnerability assessments: An evolution of vulnerability matter in climate change discourses. Clim. Policy 2006, 7, 73–88. [Google Scholar]
- Brunner, L.; Lorenz, R.; Zumwald, M.; Knutti, R. Quantifying uncertainty in European climate projections using combined performance-independence weighting. Environ. Res. Lett. 2019, 14, 124010. [Google Scholar] [CrossRef]
- Hawkins, E.; Sutton, R. The Potential to Narrow Uncertainty in Regional Climate Predictions. Bull. Am. Meteorol. Soc. 2009, 90, 1095–1108. [Google Scholar] [CrossRef] [Green Version]
- Akinsanola, A.A.; Ongoma, V.; Kooperman, G.J. Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa. Atmos. Res. 2021, 254, 105509. [Google Scholar] [CrossRef]
- Tabari, H.; Hosseinzadehtalaei, P.; AghaKouchak, A.; Willems, P. Latitudinal heterogeneity and hotspots of uncertainty in projected extreme precipitation. Environ. Res. Lett. 2019, 14, 124032. [Google Scholar] [CrossRef] [Green Version]
- Knutti, R.; Sedláček, J.; Sanderson, B.M.; Lorenz, R.; Fischer, E.M.; Eyring, V. A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett. 2017, 44, 1909–1918. [Google Scholar] [CrossRef] [Green Version]
- Giorgi, F.; Mearns, L.O. Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method. J. Clim. 2002, 15, 1141–1158. [Google Scholar] [CrossRef]
- Asiimwe, A. Impacts of Climate Change on Precipitation and Potential Evapotranspiration Across Water Management Zones in Uganda. Master’s Thesis, Kyambogo University, Kyambogo, Uganda, 2021. [Google Scholar]
WMZs | Areas Calculated in This Research (km2) | MWE (2014) Area (km2) | Area Difference (km2) |
---|---|---|---|
Albert | 56,702 | 45,000 | 11,702 |
Kyoga | 57,258 | 58,000 | −742 |
Upper Nile | 59,908 | 50,000 | 9908 |
Victoria | 61,665 | 78,100 | −16,435 |
TOTAL | 235,533 | 231,100 | 4433 |
No. | Model | Institution | Original Spatial Resolution |
---|---|---|---|
1 | BCC-CSM2-MR | Beijing Climate Center, China Meteorological Administration, China. | 1.1 × 1.1° |
2 | CNRM-CM6-1 | National Center for Meteorological Research, Météo-France and CNRS laboratory, France | 1.4 × 1.4° |
3 | CNRM-ESM2-1 | National Center for Meteorological Research, Météo-France and CNRS laboratory, France | 1.4 × 1.4° |
4 | CanESM5 | Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8W 2P2, Canada | 2.8 × 2.8° |
5 | IPSL-CM6A-LR | Institute Pierre-Simon Laplace, France | 2.5 × 1.3° |
6 | MIROC-ES2L | Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan. | 2.8 × 2.8° |
7 | MIROC6 | Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan. | 1.4 × 1.4° |
8 | MRI-ESM2-0 | Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan | 1.1 × 1.1° |
WMZ | Time Scale | ||||
---|---|---|---|---|---|
JF | MAM | JJAS | OND | Annual | |
Precipitation (mm) | |||||
Albert | 93.5 | 369.3 | 362.0 | 336.5 | 1161.3 |
Kyoga | 72.3 | 399.3 | 435.6 | 240.8 | 1148.1 |
Victoria | 158.0 | 508.6 | 254.4 | 358.1 | 1279.2 |
Upper Nile | 39.7 | 340.2 | 552.0 | 234.1 | 1166.2 |
PET (mm) | |||||
Albert | 329.4 | 455.0 | 572.2 | 469.4 | 1826.1 |
Kyoga | 417.9 | 542.2 | 646.8 | 570.7 | 2177.6 |
Victoria | 296.3 | 418.1 | 550.0 | 428.7 | 1693.0 |
Upper Nile | 460.3 | 584.9 | 667.4 | 616.0 | 2328.5 |
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Onyutha, C.; Asiimwe, A.; Ayugi, B.; Ngoma, H.; Ongoma, V.; Tabari, H. Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections. Atmosphere 2021, 12, 887. https://doi.org/10.3390/atmos12070887
Onyutha C, Asiimwe A, Ayugi B, Ngoma H, Ongoma V, Tabari H. Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections. Atmosphere. 2021; 12(7):887. https://doi.org/10.3390/atmos12070887
Chicago/Turabian StyleOnyutha, Charles, Arnold Asiimwe, Brian Ayugi, Hamida Ngoma, Victor Ongoma, and Hossein Tabari. 2021. "Observed and Future Precipitation and Evapotranspiration in Water Management Zones of Uganda: CMIP6 Projections" Atmosphere 12, no. 7: 887. https://doi.org/10.3390/atmos12070887