Patterns in Climate-Related Parameters as Proxy for Rainfall Deficiency and Aridity: Application to Burkina Faso
Language
English
Obiettivo Specifico
7SR AMBIENTE – Servizi e ricerca per la società
Status
Published
JCR Journal
N/A or not JCR
Peer review journal
Yes
Issue/vol(year)
1/3 (2017)
Pages (printed)
A4016001
Date Issued
2017
Abstract
This work is aimed to propose a methodology for the identification of areas for which extreme climatological conditions may intensify aridity processes and rainfall deficiency. The proposed procedure, which is based on the analysis of climate projections derived from high-resolution regional simulations, is composed of three main elements. First, extreme temperature, extreme precipitation, and extreme dry periods (in terms of consecutive dry days) are modeled using extreme value theory. Second, an aridity index is used as a proxy of long-term
processes leading to aridity. Third, clustering techniques are used to group zones with similar climatic parameters. In this way, areas with the more extreme climate conditions are identified. Possible effects due to selected climate-change scenarios are considered by analyzing possible nonstationary conditions in extreme events and by performing calculations in both a historical period and a projection period (where different
scenarios are considered). An application of the proposed procedure is implemented in an area around Ouagadougou, Burkina Faso. From the analyses, it emerged that the eastern part of the case study area will experience both large rainfall deficit and the highest extreme temperatures. Those two aspects, combined with a potential water demand increase (due to the increasing of number of inhabitants), may favor the
intensification of the aridity processes.
processes leading to aridity. Third, clustering techniques are used to group zones with similar climatic parameters. In this way, areas with the more extreme climate conditions are identified. Possible effects due to selected climate-change scenarios are considered by analyzing possible nonstationary conditions in extreme events and by performing calculations in both a historical period and a projection period (where different
scenarios are considered). An application of the proposed procedure is implemented in an area around Ouagadougou, Burkina Faso. From the analyses, it emerged that the eastern part of the case study area will experience both large rainfall deficit and the highest extreme temperatures. Those two aspects, combined with a potential water demand increase (due to the increasing of number of inhabitants), may favor the
intensification of the aridity processes.
Sponsors
FP7 European project CLUVA (Climate change and Urban Vulnerability in Africa), Grant No. 265137
References
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ing silhouette coefficients.” J. Comput. Sci., 43(3), 252–255.
Baltas, E. (2007). “Spatial distribution of climatic indices in northern
Greece.” Meteorol. Appl., 14(1), 69–78.
Bolshakova, N., and Azuaje, F. (2003). “Cluster validation techniques for
genome expression data.” Signal Process., 83(4), 825–833.
Bucchignani, E., Garcia-Aristizabal, A., and Montesarchio, M. (2014).
“Climate-related extreme events with high-resolution regional simula-
tions: Assessing the effects of climate change scenarios in Ouagadougou,
Burkina Faso.” Second Int. Conf. on Vulnerability and Risk Analysis and
Management (ICVRAM) and the Sixth Int. Symp. on Uncertainty, Mod-
eling, and Analysis (ISUMA), ASCE, Reston, VA, 1351–1362.
Chen, G., Jaradat, S. A., Banerjee, N., Tanaka, T. S., Ko, M. S. H., and
Zhang, M. Q. (2002). “Evaluation and comparison of clustering algo-
rithms in analyzing ES cell gene expression data.” Stat. Sin., 12(1),
241–262.
Coles, S. (2001). An introduction to statistical modeling of extreme values,
Springer, London.
De Martonne, E. (1926). “Aréisme et indice artidite.” Comptes Rendus de
L’Acad Sci., 182, 1395–1398 (in French).
De Paola, F., Giugni, M., Topa, M., and Bucchignani, E. (2014). “Intensity-
duration-frequency (IDF) rainfall curves, for data series and climate
projection in African cities.” SpringerPlus, 3(1), 133.
De Risi, R., Jalayer, F., De Paola, F., and Giugni, M. (2014). “Probabilistic
delineation of flood-prone areas based on a digital elevation model and
the extent of historical flooding: The case of Ouagadougou.” Bol. Geol.
Min., 125(3), 329–340.
Descroix, L., et al. (2009). “Spatio-temporal variability of hydrological
regimes around the boundaries between Sahelian and Sudanian areas of
West Africa: A synthesis.” J. Hydrol. (Amsterdam, Neth.), 375(1–2),
90–102.
Domínguez-López, D., Adame, J., Hernández-Ceballos, M., Vaca, F., De la
Morena, B., and Bolívar, J. (2014). “Spatial and temporal variation of
surface ozone, NO and NO 2 at urban, suburban, rural and industrial
sites in the southwest of the Iberian Peninsula.” Environ. Monitor.
Assess., 186(9), 5337–5351.
Ekström, M., Fowler, H., Kilsby, C., and Jones, P. (2005). “New estimates
of future changes in extreme rainfall across the UK using regional
climate model integrations: 2. Future estimates and use in impact
studies.” J. Hydrol. (Amsterdam, Neth.), 300(1–4), 234–251.
El Adlouni, S., Ouarda, T., Zhang, X., Roy, R., and Bobée, B. (2007).
“Generalized maximum likelihood estimators for the nonstationary
generalized extreme value model.” Water Resour. Res., 43(3), W03410.
Fan, F., Bradley, R., and Rawlins, M. (2014). “Climate change in the
northeastern US: Regional climate model validation and climate change
projections.” Clim. Dyn., 43(1–2), 145–161.
Garcia-Aristizabal, A., Bucchignani, E., Palazzi, E., D’Onofrio, D.,
Gasparini, P., and Marzocchi, W. (2015). “Analysis of non-stationary
climate-related extreme events considering climate change scenarios:
An application for multi-hazard assessment in the Dar es Salaam region,
Tanzania.” Nat. Hazards, 75(1), 289–320.
Goutorbe, J., et al. (1997). “An overview of HAPEX-Sahel: A study in
climate and desertification.” J. Hydrol. (Amsterdam, Neth.), 188–189,
4–17.
Han, J., and Kamber, M. (2006). Data mining—Concepts and techniques,
Morgan Kaufmann, San Francisco.
Ibrahim, B., Polcher, J., Karambiri, H., and Rockel, B. (2008). “Characteri-
zation of the rainy season in Burkina Faso and it’s representation by
regional climate models.” Clim. Dyn., 39(6), 1287–1302.
IPCC (Intergovernmental Panel on Climate Change). (2007). “Climate
change 2007: The physical science basis.” Contribution of Working
Group I to the Fourth Assessment Rep. of the Intergovernmental Panel
on Climate Change, Cambridge University Press, Cambridge, U.K.
IPCC (Intergovernmental Panel on Climate Change). (2012). Managing
the risks of extreme events and disasters to advance climate change
adaptation, Cambridge University Press, Cambridge, MA.
Jeffreys, H. (1961). Theory of probability, 3rd Ed., Oxford University Press,
Oxford.
Jenkinson, A. F. (1955). “The frequency distribution of the annual
maximum (or minimum) values of meteorological elements.” Q. J. R.
Meteorol. Soc., 81(348), 158–171.
Kass, R. E., and Raftery, A. E. (1995). “Bayes factors.” J. Am. Stat. Assoc.,
90(430), 773–795.
Kaufman, L., and Rousseeuw, P. J. (2008). “Partitioning around medoids
(program PAM).” Finding groups in data: An introduction to cluster
analysis, Wiley, Hoboken, NJ.
Khaliq, M., Ouarda, T., Ondo, J.-C., Gachon, P., and Bobée, B. (2006).
“Frequency analysis of a sequence of dependent and/or non-stationary
hydro-meteorological observations: A review.” J. Hydrol. (Amsterdam,
Neth.), 329(3–4), 534–552.
Lewis, S. M., and Raftery, A. E. (1997). “Estimating Bayes factors via
posterior simulation with the Laplace-metropolis estimator.” J. Am. Stat.
Assoc., 92(438), 648–655.
MacQueen, J. (1967). “Some methods for classification and analysis of
multivariate observations.” 5th Berkeley Symp. on Mathematical Statis-
tics and Probability, 281–297.
Maliva, R., and Missimer, T. (2012). “Aridity and drought.” Arid lands
water evaluation and management: Environmental science and engi-
neering, Springer, Berlin, 21–39.
Maurer, E. P., Das, T., and Cayan, D. R. (2013). “Errors in climate model
daily precipitation and temperature output: Time invariance and impli-
cations for bias correction.” Hydrol. Earth Syst. Sci., 17(6), 2147–2159.
Mitchell, T. D., and Jones, P. D. (2005). “An improved method of construct-
ing a database of monthly climate observations and associated high-
resolution grids.” Int. J. Climatol., 25(6), 693–712.
Moss, R. H., et al. (2010). “The next generation of scenarios for climate
change research and assessment.” Nature, 463(7282), 747–756.
Munich Re. (2011). “Topics geo—Natural catastrophes 2010: Analyses,
assessments, positions.” Rep. No. 302-06735, Germany.
Ouedraogo, I., Savadogo, P., Tigabu, M., Cole, R., Odén, P. C., and
Ouadba, J.-M. (2009). “Is rural migration a threat to environmentalsustainability in Southern Burkina Faso?” Land Degrad. Dev., 20(2),
217–230.
Paeth, H., et al. (2011). “Progress in regional downscaling of West African
precipitation.” Atmos. Sci. Lett., 12(1), 75–82.
Panitz, H.-J., Dosio, A., Büchner, M., Lüthi, D., and Keuler, K. (2014).
“COSMO-CLM (CCLM) climate simulations over CORDEX-Africa
domain: Analysis of the ERA-Interim driven simulations at 0.44 and
0.22 resolution.” Clim. Dyn., 42(11–12), 3015–3038.
Raftery, A. E. (1996). “Hypothesis testing and model selection.” Markov
chain Monte Carlo in practice, W. R. Gilks, S. Richardson, and D. J.
Spiegelhalter, eds., Chapman and Hill, London, 163–187.
Rockel, B., Will, A., and Hense, A. (2008). “The regional climate model
COSMO-CLM (CCLM).” Meteorol. Z., 17(4), 347–348.
Rousseeuw, P. J. (1987). “Silhouettes: A graphical aid to the interpretation
and validation of cluster analysis.” J. Comput. Appl. Math., 20, 53–65.
Salinger, M., and Griffiths, G. (2001). “Trends in New Zealand daily tem-
perature and rainfall extremes.” Int. J. Climatol., 21(12), 1437–1452.
Scoccimarro, E., et al. (2011). “Effects of tropical cyclones on ocean heat
transport in a high-resolution coupled general circulation model.”
J. Clim., 24(16), 4368–4384.
Stanelle, T., Vogel, B., Vogel, H., Bäumer, D., and Kottmeier, C. (2010).
“Feedback between dust particles and atmospheric processes over
West Africa during dust episodes in March 2006 and June 2007.”
Atmos. Chem. Phys., 10(22), 10771–10788.
Tanre, D., Geleyn, J. F., and Slingo, J. M. (1984). First results of the
introduction of an advanced aerosol-radiation interaction in the
ECMWF low resolution global model, Deepak Publishing, Hampton,
VA, 133–177.
Traore, S., and Owiyo, T. (2013). “Dirty droughts causing loss and damage
in Northern Burkina Faso.” Int. J. Global Warming, 5(4), 498–513.
UNEP-GEF. (2013). “Volta basin transboundary diagnostic analysis.”
〈http://www.gefvolta.iwlearn.org〉 (Nov. 19, 2015).
Xu, C., Sheng, S., Chi, T., Yang, X., An, S., and Liu, M. (2014). “Devel-
oping a quantitative landscape regionalization framework integrating
driving factors and response attributes of landscapes.” Landscape Ecol.
Eng., 10(2), 295–307.
Xu, R., and Wunsch, D. I. (2005). “Survey of clustering algorithms.” IEEE
Trans. Neural Networks, 16(3), 645–678.
Zhang, X., Zwiers, F., and Li, G. (2004). “Monte Carlo experiments on the
detection of trends in extreme values.” J. Clim., 17(10), 1945–1952.
ing silhouette coefficients.” J. Comput. Sci., 43(3), 252–255.
Baltas, E. (2007). “Spatial distribution of climatic indices in northern
Greece.” Meteorol. Appl., 14(1), 69–78.
Bolshakova, N., and Azuaje, F. (2003). “Cluster validation techniques for
genome expression data.” Signal Process., 83(4), 825–833.
Bucchignani, E., Garcia-Aristizabal, A., and Montesarchio, M. (2014).
“Climate-related extreme events with high-resolution regional simula-
tions: Assessing the effects of climate change scenarios in Ouagadougou,
Burkina Faso.” Second Int. Conf. on Vulnerability and Risk Analysis and
Management (ICVRAM) and the Sixth Int. Symp. on Uncertainty, Mod-
eling, and Analysis (ISUMA), ASCE, Reston, VA, 1351–1362.
Chen, G., Jaradat, S. A., Banerjee, N., Tanaka, T. S., Ko, M. S. H., and
Zhang, M. Q. (2002). “Evaluation and comparison of clustering algo-
rithms in analyzing ES cell gene expression data.” Stat. Sin., 12(1),
241–262.
Coles, S. (2001). An introduction to statistical modeling of extreme values,
Springer, London.
De Martonne, E. (1926). “Aréisme et indice artidite.” Comptes Rendus de
L’Acad Sci., 182, 1395–1398 (in French).
De Paola, F., Giugni, M., Topa, M., and Bucchignani, E. (2014). “Intensity-
duration-frequency (IDF) rainfall curves, for data series and climate
projection in African cities.” SpringerPlus, 3(1), 133.
De Risi, R., Jalayer, F., De Paola, F., and Giugni, M. (2014). “Probabilistic
delineation of flood-prone areas based on a digital elevation model and
the extent of historical flooding: The case of Ouagadougou.” Bol. Geol.
Min., 125(3), 329–340.
Descroix, L., et al. (2009). “Spatio-temporal variability of hydrological
regimes around the boundaries between Sahelian and Sudanian areas of
West Africa: A synthesis.” J. Hydrol. (Amsterdam, Neth.), 375(1–2),
90–102.
Domínguez-López, D., Adame, J., Hernández-Ceballos, M., Vaca, F., De la
Morena, B., and Bolívar, J. (2014). “Spatial and temporal variation of
surface ozone, NO and NO 2 at urban, suburban, rural and industrial
sites in the southwest of the Iberian Peninsula.” Environ. Monitor.
Assess., 186(9), 5337–5351.
Ekström, M., Fowler, H., Kilsby, C., and Jones, P. (2005). “New estimates
of future changes in extreme rainfall across the UK using regional
climate model integrations: 2. Future estimates and use in impact
studies.” J. Hydrol. (Amsterdam, Neth.), 300(1–4), 234–251.
El Adlouni, S., Ouarda, T., Zhang, X., Roy, R., and Bobée, B. (2007).
“Generalized maximum likelihood estimators for the nonstationary
generalized extreme value model.” Water Resour. Res., 43(3), W03410.
Fan, F., Bradley, R., and Rawlins, M. (2014). “Climate change in the
northeastern US: Regional climate model validation and climate change
projections.” Clim. Dyn., 43(1–2), 145–161.
Garcia-Aristizabal, A., Bucchignani, E., Palazzi, E., D’Onofrio, D.,
Gasparini, P., and Marzocchi, W. (2015). “Analysis of non-stationary
climate-related extreme events considering climate change scenarios:
An application for multi-hazard assessment in the Dar es Salaam region,
Tanzania.” Nat. Hazards, 75(1), 289–320.
Goutorbe, J., et al. (1997). “An overview of HAPEX-Sahel: A study in
climate and desertification.” J. Hydrol. (Amsterdam, Neth.), 188–189,
4–17.
Han, J., and Kamber, M. (2006). Data mining—Concepts and techniques,
Morgan Kaufmann, San Francisco.
Ibrahim, B., Polcher, J., Karambiri, H., and Rockel, B. (2008). “Characteri-
zation of the rainy season in Burkina Faso and it’s representation by
regional climate models.” Clim. Dyn., 39(6), 1287–1302.
IPCC (Intergovernmental Panel on Climate Change). (2007). “Climate
change 2007: The physical science basis.” Contribution of Working
Group I to the Fourth Assessment Rep. of the Intergovernmental Panel
on Climate Change, Cambridge University Press, Cambridge, U.K.
IPCC (Intergovernmental Panel on Climate Change). (2012). Managing
the risks of extreme events and disasters to advance climate change
adaptation, Cambridge University Press, Cambridge, MA.
Jeffreys, H. (1961). Theory of probability, 3rd Ed., Oxford University Press,
Oxford.
Jenkinson, A. F. (1955). “The frequency distribution of the annual
maximum (or minimum) values of meteorological elements.” Q. J. R.
Meteorol. Soc., 81(348), 158–171.
Kass, R. E., and Raftery, A. E. (1995). “Bayes factors.” J. Am. Stat. Assoc.,
90(430), 773–795.
Kaufman, L., and Rousseeuw, P. J. (2008). “Partitioning around medoids
(program PAM).” Finding groups in data: An introduction to cluster
analysis, Wiley, Hoboken, NJ.
Khaliq, M., Ouarda, T., Ondo, J.-C., Gachon, P., and Bobée, B. (2006).
“Frequency analysis of a sequence of dependent and/or non-stationary
hydro-meteorological observations: A review.” J. Hydrol. (Amsterdam,
Neth.), 329(3–4), 534–552.
Lewis, S. M., and Raftery, A. E. (1997). “Estimating Bayes factors via
posterior simulation with the Laplace-metropolis estimator.” J. Am. Stat.
Assoc., 92(438), 648–655.
MacQueen, J. (1967). “Some methods for classification and analysis of
multivariate observations.” 5th Berkeley Symp. on Mathematical Statis-
tics and Probability, 281–297.
Maliva, R., and Missimer, T. (2012). “Aridity and drought.” Arid lands
water evaluation and management: Environmental science and engi-
neering, Springer, Berlin, 21–39.
Maurer, E. P., Das, T., and Cayan, D. R. (2013). “Errors in climate model
daily precipitation and temperature output: Time invariance and impli-
cations for bias correction.” Hydrol. Earth Syst. Sci., 17(6), 2147–2159.
Mitchell, T. D., and Jones, P. D. (2005). “An improved method of construct-
ing a database of monthly climate observations and associated high-
resolution grids.” Int. J. Climatol., 25(6), 693–712.
Moss, R. H., et al. (2010). “The next generation of scenarios for climate
change research and assessment.” Nature, 463(7282), 747–756.
Munich Re. (2011). “Topics geo—Natural catastrophes 2010: Analyses,
assessments, positions.” Rep. No. 302-06735, Germany.
Ouedraogo, I., Savadogo, P., Tigabu, M., Cole, R., Odén, P. C., and
Ouadba, J.-M. (2009). “Is rural migration a threat to environmentalsustainability in Southern Burkina Faso?” Land Degrad. Dev., 20(2),
217–230.
Paeth, H., et al. (2011). “Progress in regional downscaling of West African
precipitation.” Atmos. Sci. Lett., 12(1), 75–82.
Panitz, H.-J., Dosio, A., Büchner, M., Lüthi, D., and Keuler, K. (2014).
“COSMO-CLM (CCLM) climate simulations over CORDEX-Africa
domain: Analysis of the ERA-Interim driven simulations at 0.44 and
0.22 resolution.” Clim. Dyn., 42(11–12), 3015–3038.
Raftery, A. E. (1996). “Hypothesis testing and model selection.” Markov
chain Monte Carlo in practice, W. R. Gilks, S. Richardson, and D. J.
Spiegelhalter, eds., Chapman and Hill, London, 163–187.
Rockel, B., Will, A., and Hense, A. (2008). “The regional climate model
COSMO-CLM (CCLM).” Meteorol. Z., 17(4), 347–348.
Rousseeuw, P. J. (1987). “Silhouettes: A graphical aid to the interpretation
and validation of cluster analysis.” J. Comput. Appl. Math., 20, 53–65.
Salinger, M., and Griffiths, G. (2001). “Trends in New Zealand daily tem-
perature and rainfall extremes.” Int. J. Climatol., 21(12), 1437–1452.
Scoccimarro, E., et al. (2011). “Effects of tropical cyclones on ocean heat
transport in a high-resolution coupled general circulation model.”
J. Clim., 24(16), 4368–4384.
Stanelle, T., Vogel, B., Vogel, H., Bäumer, D., and Kottmeier, C. (2010).
“Feedback between dust particles and atmospheric processes over
West Africa during dust episodes in March 2006 and June 2007.”
Atmos. Chem. Phys., 10(22), 10771–10788.
Tanre, D., Geleyn, J. F., and Slingo, J. M. (1984). First results of the
introduction of an advanced aerosol-radiation interaction in the
ECMWF low resolution global model, Deepak Publishing, Hampton,
VA, 133–177.
Traore, S., and Owiyo, T. (2013). “Dirty droughts causing loss and damage
in Northern Burkina Faso.” Int. J. Global Warming, 5(4), 498–513.
UNEP-GEF. (2013). “Volta basin transboundary diagnostic analysis.”
〈http://www.gefvolta.iwlearn.org〉 (Nov. 19, 2015).
Xu, C., Sheng, S., Chi, T., Yang, X., An, S., and Liu, M. (2014). “Devel-
oping a quantitative landscape regionalization framework integrating
driving factors and response attributes of landscapes.” Landscape Ecol.
Eng., 10(2), 295–307.
Xu, R., and Wunsch, D. I. (2005). “Survey of clustering algorithms.” IEEE
Trans. Neural Networks, 16(3), 645–678.
Zhang, X., Zwiers, F., and Li, G. (2004). “Monte Carlo experiments on the
detection of trends in extreme values.” J. Clim., 17(10), 1945–1952.
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