Implications of spatial scale on climate change assessments

• Autorzy: Pingale S. , Adamowski J. , Jat M. , Khare D.

Identyfikatory

DOI

10.1515/jwld-2015-0015

Warianty tytułu

PL

Konsekwencje różnej skali przestrzennej w ocenach zmian klimatu

• Języki publikacji: EN

Abstrakty

EN

While assessing the effects of climate change at global or regional scales, local factors responsible for climate change are generalized, which results in the averaging of effects. However, climate change assessment is required at a micro-scale to determine the severity of climate change. To ascertain the impact of spatial scales on climate change assessments, trends and shifts in annual and seasonal (monsoon and non-monsoon), rainfall and temperature (minimum, average and maximum) were determined at three different spatial resolutions in India (Ajmer city, Ajmer District and Rajasthan State). The Mann–Kendall (MK), MK test with pre-whitening of series (MK–PW), and Modified Mann–Kendall (MMK) test, along with other statistical techniques were used for the trend analysis. The Pettitt–Mann–Whitney (PMW) test was applied to detect the temporal shift in climatic parameters. The Sen’s slope and % change in rainfall and temperature were also estimated over the study period (35 years). The annual and seasonal average temperature indicates significant warming trends, when assessed at a fine spatial resolution (Ajmer city) compared to a coarser spatial resolution (Ajmer District and Rajasthan State resolutions). Increasing trend was observed in minimum, mean and maximum temperature at all spatial scales; however, trends were more pronounced at a finer spatial resolution (Ajmer city). The PMW test indicates only the significant shift in non-monsoon season rainfall, which shows an increase in rainfall after 1995 in Ajmer city. The Kurtosis and coefficient of variation also revealed significant climate change, when assessed at a finer spatial resolution (Ajmer city) compared to a coarser resolution. This shows the contribution of land use/land cover change and several other local anthropogenic activities on climate change. The results of this study can be useful for the identification of optimum climate change adaptation and mitigation strategies based on the severity of climate change at different spatial scales.

PL

W szacowaniu skutków zmian klimatu w skali globalnej lub regionalnej czynniki lokalne warunkujące zmiany klimatu są uogólniane, co skutkuje uśrednianiem efektów. Zmiany klimatu powinny jednak być oceniane w skali mikro, aby ustalić ich natężenie. W celu określenia wpływu skali przestrzennej na oceny zmian klimatycznych oznaczono roczne i sezonowe (pora monsunowa i pozamonsunowa) trendy temperatury i opadów (minimalne, średnie, maksymalne) w trojakiej rozdzielczości: dla miasta Ajmer, dystryktu Ajmer i stanu Rajasthan w Indiach. W analizie trendu wykorzystano test Manna–Kendalla (MK), test MK z wstępnym wygładzaniem (MK–KW), zmodyfikowany test Manna–Kendalla (MMK) i inne techniki statystyczne. Do wykrycia czasowych zmian parametrów klimatycznych użyto testu Pettitta–Manna–Whitneya (PMW). Dla okresu badawczego (35 lat) określono także nachylenie Sena i zmiany opadów i temperatury (w %). Średnie roczne i sezonowe wartości temperatury wskazywały istotną tendencję do ocieplania klimatu, kiedy oceny dokonywano w skali miasta, niż gdy analizie poddawano obszary o większej skali przestrzennej (dystrykt Ajmer i stan Rajasthan). Zaobserwowano rosnące trendy dla minimalnej, średniej i maksymalnej temperatury we wszystkich skalach przestrzennych, jednak silniej przejawiały się one w mniejszej skali (miasto Ajmer). Test PMW wykazał istotną zmianę jedynie w wielkości opadów w sezonie pozamonsunowym – wzrost opadów po 1995 r. w mieście Ajmer. Kurtoza i współczynnik zmienności wykazały także istotne zmiany klimatyczne, kiedy rozpatrywano je w mniejszej skali (miasto Ajmer). Takiej prawidłowości nie zaobserwowano w skali regionalnej. Wyniki świadczą o wpływie zmian w użytkowaniu/pokryciu terenu i innych czynników antropogenicznych na zmiany klimatu. Z tego powodu mogą one być użyteczne w opracowaniu optymalnych strategii adaptacji i łagodzenia skutków zmian klimatycznych na podstawie ich intensywności w różnych skalach przestrzennych.

Słowa kluczowe

EN

climate change; India; Rajasthan State; scale effects; trend and shift analysis

PL

analiza trendu; analiza zmian; efekt skali; Indie; stan Rajasthan; zmiany klimatu

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2015
• Tom: no. 26
• Strony: 37--55
• Opis fizyczny: Bibliogr. 79 poz., rys., tab.

Twórcy

autor

Pingale S.

• Arba Minch University, Department of Water Resource and Irrigation Engineering, Arba Minch, P.O. Box 21, Ethiopia

autor

Adamowski J.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Jat M.

• Malaviya National Institute of Technology, Department of Civil Engineering, Jaipur, Rajasthan, India

autor

Khare D.

• Indian Institute of Technology Roorkee, Department of Water Resource Development and Management, Roorkee 247 667 (UA), India

Bibliografia

• ADAMOWSKI J., ADAMOWSKI K., BOUGADIS J. 2010. Influence of trend on short duration design storms. Water Resources Management. Vol. 24. Iss. 3 p. 401–413.
• ADAMOWSKI J., CHAN H., PRASHER S., SHARDA V.N. 2012a. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan microwatersheds with limited data. Journal of Hydroinformatics. Vol. 14. Iss. 3 p. 731–744.
• ADAMOWSKI J., PROKOPH A. 2013. Assessing the impacts of the urban heat island effect on streamflow patterns in Ottawa, Canada. Journal of Hydrology. Vol. 496 p. 225–237.
• ADAMOWSKI K., PROKOPH A., ADAMOWSKI J. 2009. Development of a new method of wavelet aided trend detection and estimation. Hydrological Processes. Vol. 23 p. 2686–2696.
• ADAMOWSKI K., PROKOPH A., ADAMOWSKI J. 2012b. Influence of the 11 year solar cycle on annual streamflow maxima in Canada. Journal of Hydrology. Vol. 442–443 p. 55–62.
• ARAGHI A., ADAMOWSKI J., NALLEY D., MALARD J. 2015. Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data. Journal of Atmospheric Research. Vol. 155 p. 52–72.
• ARORA M., GOEL N.K., SINGH P. 2005. Evaluation of temperature trends over India. Hydrological Sciences Journal. Vol. 50. Iss. 1 p. 81–93.
• BASISTHA A., ARYA D.S., GOEL N.K. 2009. Analysis of historical changes in rainfall in the Indian Himalayas. International Journal of Climatology. Vol. 29. Iss. 4 p. 555–572.
• BASISTHA A., GOEL N.K., ARYA D.S., GANGWAR S.K. 2007. Spatial pattern of trends in Indian sub-divisional rainfall. Jalvigyan Sameeksha. Vol. 22 p. 47–57.
• BLÖSCHL G. 1999. Scaling issues in snow hydrology. Hydrological Processes. Vol. 13. Iss. 1415 p. 2149–2175.
• BLÖSCHL G., SIVAPALAN M. 1995. Scale issues in hydrological modelling: a review. Special issue: Scale issues in hydrological modelling. Hydrological Processes. Vol. 9. Iss. 3–4 p. 251–290.
• BORMANN H., DIEKKRÜGER B., RENSCHLER C. 1999. Regionalization concept for hydrological modeling on different scales using a physically based model: results and evaluation. Physics and Chemistry of the Earth. Part B: Hydrology, Oceans and Atmosphere. Vol. 24. Iss. 7 p. 799–804.
• BURN D.H., ELNUR M.A. 2002. Detection of hydrologic trends and variability. Journal of Hydrology. Vol. 255. Iss. 1 p. 107–122.
• CAMPISI S., ADAMOWSKI J., ORON G. 2012. Forecasting urban water demand via wavelet- denoising and neural network models. Case study: city of Syracuse, Italy. Water Resources Management. Vol. 26. Iss. 12 p. 3539–3558.
• CARTWRIGHT I., SIMMONDS I. 2008. Impact of changing climate and land use on the hydrogeology of southeast Australia. Australian Journal of Earth Sciences. Vol. 55. Iss. 8 p. 1009–1021.
• Census of India 2011. Census Organization of India. Government of India.
• CHUNG Y.S., YOON M.B., KIM H.S. 2004. On climate variations and changes observed in South Korea. Climate Change. Vol. 66. Iss. 1–2 p. 151–161.
• CUI L., SHI J. 2012. Urbanization and its environmental effects in Shanghai, China. Urban Climate. Vol. 2 p. 1–15.
• CUNDERLIK J.M., BURN D.H. 2004. Linkages between regional trends in monthly maximum flows and selected climatic variables. Journal of Hydrologic Engineering. Vol. 9. Iss. 4 p. 246–256.
• DAS P.K., CHAKRABORTY A., SESHASAI M.V.R. 2013. Spatial analysis of temporal trend of rainfall and rainy days during the Indian Summer Monsoon season using daily gridded 0.5°×0.5°. rainfall data for the period of 1971–2005. Meteorological Applications. Vol. 21. Iss. 3. DOI: 10.1002/met.1361 p. 481–493.
• DASH S.K., JENAMANI R.K., KALSI S.R., PANDA S.K. 2007. Some evidence of climate change in twentieth-century India. Climatic Change. Vol. 85. Iss. 3–4 p. 299–321.
• DE U.S., RAO G.S.P. 2004. Urban climate trends – The Indian scenario. Journal of Indian Geophysical Union. Vol. 8. No. 3 p. 199–203.
• EZBER Y.O., SEN L., KINDAP T., KARACA M. 2007. Climatic effects of urbanization in Istanbul: a statistical and modeling analysis. International Journal of Climatology. Vol. 27. Iss. 5 p. 667–679.
• FISCHER T., GEMMER M., LIU L., SU B. 2012. Change-points in climate extremes in the Zhujiang River Basin, South China, 1961–2007. Climatic Change. Vol. 110. Iss. 3–4 p. 783–799.
• FUJIBE F. 1995. Temperature rising trends at Japanese cities during the last hundred years and their relationships with population, population increasing rates and daily temperature ranges. Meteorology and Geophysics. Vol. 46. Iss. 2 p. 35–55.
• GADGIL A., DHORDE A. 2005. Temperature trends in twentieth century at Pune. Atmospheric Environment. Vol. 39. Iss. 35 p. 6550–6556.
• GOWDA K.K., MAJUANTHA K., MANJUNATH B.M., PUTTY Y.R. 2008. Study of climate changes at Davangere region by using climatological data. Water and Energy International. Vol. 65. Iss. 3 p. 66–77.
• HAIDARY A., AMIRI B.J., ADAMOWSKI J., FOHRER N., NAKANE K. 2013. Assessing the impacts of four land use types on the water quality of wetlands in Japan. Water Resources Management. Vol. 27. Iss. 7 p. 2217–2229.
• HALBE J., PAHL-WOSTL C., SENDZIMIR J., ADAMOWSKI J. 2013. Towards adaptive and integrated management paradigms to meet the challenges of water governance. Water Science and Technology: Water Supply. Vol. 67 p. 2651–2660.
• HAMED K.H., RAO A.R. 1998. A modified Mann–Kendall trend test for auto correlated data. Journal of Hydrology. Vol. 204. Iss. 1 p. 182–196.
• HINGANE L.S. 1995. Is a signature of socio-economic impact written on the climate? Climatic Change. Vol. 32. Iss. 1 p. 91–101.
• HUTH R., POKORNA L. 2004. Parametric versus nonparametric estimates of climatic trends. Theoretical and Applied Climatology. Vol. 77. Iss. 1–2 p. 107–112.
• IPCC 2007. AR4 Working group I report. The physical science basis. Cambridge. Cambridge Univ. Press. ISBN 978 0521 88009-1 pp. 966.
• JAIN S.K., KUMAR V., SAHARIA M. 2013. Analysis of rainfall and temperature trends in northeast India. International Journal of Climatology. Vol. 33. Iss. 4 p. 968–978.
• JEGANATHAN A., ANDIMUTHU R. 2013. Temperature trends of Chennai City, India. Theoretical and Applied Climatology. Vol. 111. Iss. 3 p. 417–425.
• JHA R., PATRA K.C. 2009. Hydrological estimates of flow in ungauged sub-basin and impact of climate change in a typical river system of India. Joint International Convention of 8th IAHS Scientific Assembly and 37th IAH Congress Water: A vital resource under stress – How science can help? September 6–12, 2009, Hyderabad, India.
• JIANG F., HU R., WANG S., ZHANG Y., TONG L. 2013. Trends of precipitation extremes during 1960–2008 in Xinjiang, the Northwest China. Theoretical and Applied Climatology. Vol. 111. Iss. 1 p. 133–148.
• KARPOUZOS D.K., KAVALIERATOU S., BABAJIMOPOULOS C. 2010. Trend analysis of precipitation data in Pieria region Greece. European Water. Vol. 30 p. 31–40.
• KENDALL M.G. 1975. Rank correlation methods. 4th ed. London. Charles Griffin.
• KIELY G., ALBERTSON J.D., PARLANGE M.B. 1998. Recent trends in diurnal variation of precipitation at Valentia on the West Coast of Ireland. Journal of Hydrology. Vol. 207. Iss. 3–4 p. 270–279.
• KOLINJIVADI V., ADAMOWSKI J., KOSOY N. 2014. Recasting payments for ecosystem services (PES) in water resource management: A novel institutional approach. Ecosystem Services. Vol. 10 p. 144–154.
• KOLINJIVADI V., GRANT A., ADAMOWSKI J., KOSOY N. 2015. Juggling multiple dimensions in a complex socio-ecosystem: The issue of targeting in payments for ecosystem services. GeoForum. Vol. 58 p. 1–13.
• KOTHAWALE D.R., KUMAR R. 2005. On the recent changes in surface temperature trends over India. Geophysical Research Letters. Vol. 32. Iss. 18. L18714, DOI: 10.1029/2005GL023528.
• KOTHYARI U.C., SINGH V.P. 1996. Rainfall and temperature trends in India. Hydrological Processes. Vol. 10. Iss. 3 p. 357–372.
• KUMAR V., JAIN S.K., SINGH Y. 2010. Analysis of long-term rainfall trends in India. Hydrological Sciences Journal. Vol. 55. Iss. 4 p. 484–496.
• LIU Z., XU Z., CHARLES S.P., FU G., LIUA L. 2011. Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. International Journal of Climatology. Vol. 31. Iss. 13 p. 2006–2020.
• LUDWIG W., SERRAT P., CESMAT L., ESTEVES J.G. 2004. Evaluating the impact of the recent temperature increase on the hydrology of the Têt River Southern France. Journal of Hydrology. Vol. 289 p. 204–221.
• LUO Y., LIO S., SHENG L., FU S., LIU J., WANG G., ZHOU G. 2008. Trends of precipitation in Beijiang River Basin, Guangdong Province, China. Hydrological Processes. Vol. 22. Iss. 13 p. 2377–2386.
• MANN H.B. 1945. Non-parametric test against trend. Econometrica. Vol. 13 p. 245–259.
• MATONDO J.I., MSIBI K.M. 2001. Estimation of the impact of climate change on hydrology and water resources in Swaziland. Water International. Vol. 26. Iss. 3 p. 425–434.
• MCBEAN E., MOTIEE H. 2008. Assessment of impacts of climate change on water resources: a long term analysis of the Great Lakes of North America. Hydrology and Earth System Sciences. Vol. 12. Iss. 1 p. 239–255.
• MCAVANEY B., COVEY C., JOUSSAUME S., KATTSOV V., KITOH A., OGANA W., PITMAN A., WEAVER A., WOOD R., ZHAO Z.C. 2001. Climate change 2001: The scientific basis. Chap. 8: Model evaluation. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change IPCC. Cambridge. Cambridge University Press p. 471–523.
• MODARRES R., DA SILVA R.V.P. 2007. Rainfall trends in arid and semi-arid regions of Iran. Journal of Arid Environment. Vol. 70. Iss. 2 p. 344–355.
• MOTIEE H., MCBEAN E. 2009. An assessment of long-term trends in hydrologic components and implications of water levels in Lake Superior. Hydrology Research Journal. Vol. 406 p. 564–579.
• NALLEY D., ADAMOWSKI J., KHALIL B. 2012. Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario 1954–2008. Journal of Hydrology. Vol. 475 p. 204–228.
• NALLEY D., ADAMOWSKI J., KHALIL B., OZGA-ZIELINSKI B. 2013. Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Journal of Atmospheric Research. Vol. 132–133 p. 375–398.
• OKE T.R. 1987. Boundary layer climates. London, New York. Methuen. ISBN 0416044220 pp. 435.
• PANT G.B., KUMAR K.R. 1997. Climates of South Asia. Chichester. John Wiley & Sons Ltd. ISBN 0471949485 pp. 320.
• PATRA J.P., MISHRA A., SINGH R., RAGHUWANSHI N.S. 2012. Detecting rainfall trends in twentieth century (1871–2006) over Orissa State, India. Climatic Change. Vol. 111. Iss. 3–4 p. 801–817. DOI 10.1007/s10584-011-0215-5
• PINGALE S., KHARE D., JAT M., ADAMOWSKI J. 2014. Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centres of the arid and semi-arid state of Rajasthan, India. Journal of Atmospheric Research. Vol. 138 p. 73–90.
• RAI R.K., UPADHYAY A., OJHA C.S.P. 2010. Temporal variability of climatic parameters of Yamuna River Basin: spatial analysis of persistence, trend and periodicity. The Open Hydrology Journal. Vol. 4. Iss. 1 p. 184–210.
• RAO A.R., HAMED K.H., CHEN H.L. 2003. Nonstationarities in hydrologic and environmental time series. Kluwer Academic Publishers: The Netherlands pp. 362.
• RATHOR M.S. 2005. State level analysis of drought policies and impacts in Rajasthan, India. Colombo, Sri Lanka: IWMI. 40p. Working paper 93: Drought Series Paper 6. ISBN 929090593X pp. 29.
• RENSCHLER C.S. 2003. Designing geo-spatial interfaces to scale process models: The GeoWEPP approach. Hydrological Processes. Vol. 17. Iss. 5 p. 1005–1017.
• RENSCHLER C.S. 2005. Scales and uncertainties in volcano hazard prediction-optimizing the use of GIS and models. Journal of Volcanology and Geothermal Research. Vol. 139. Iss. 1–2 p. 73–87.
• SAADAT H., ADAMOWSKI J., BONNELL R., SHARIFI F., NAMDAR M., ALE-EBRAHIM S. 2011. Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. Journal of Photogrammetry and Remote Sensing. Vol. 66. Iss. 5 p. 608–619.
• SABOOHI R., SOLTANI S., KHODAGHOLI M. 2012. Trend analysis of temperature parameters in Iran. Theoretical and Applied Climatology. Vol. 109. Iss. 3 p. 529–547.
• SCHUBERT S. 1998. Downscaling local extreme temperature changes in south-eastern Australia from the CSIRO Mark2 GCM. International Journal of Climatology. Vol. 18. Iss. 13 p. 1419–1438.
• SEN P.K. 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association. Vol. 63. Iss. 324 p. 1379–1389.
• STATHOPOULOU M., CARTALIS C. 2007. Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece. Solar Energy. Vol. 81. Iss. 3 p. 358–368.
• STRAITH D., ADAMOWSKI J., REILLY K. 2014. Exploring the behavioural attributes, strategies and contextual knowledge of champions of change in the Canadian water sector. Canadian Water Resources Journal. Vol. 39. Iss. 3 p. 255–269.
• THEIL H. 1950. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Nedal. Akad. Wetensch. Proc. 53 p. 386–392.
• TIWARI M., ADAMOWSKI J. 2013. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resources Research. Vol. 49. Iss. 10 p. 6486–6507.
• TOZER C.R., KIEM A.S., VERDON-KIDD D.C. 2012. On the uncertainties associated with using gridded rainfall data as a proxy for observed. Hydrology and Earth System Sciences. Vol. 16. Iss. 5 p. 1481–1499.
• WAGESHO N., GOEL N.K., JAIN M.K. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrological Sciences Journal. Vol. 58. Iss. 2 p. 354–373.
• YOON W.S., LEE D.K. 2003. The development of the evaluation model of climate changes and air pollution for sustainability of cities in Korea. Landscape and Urban Planning. Vol. 63. Iss. 3 p. 145–160.
• YUE S., HASHINO M. 2003. Long term trends of annual and monthly precipitation in Japan. Journal of the American Water Resources Association. Vol. 39. Iss. 3 p. 587–596.
• YUE S., PILON P. 2004. A comparison of the power of the t test, Mann-Kendall and bootstrap tests for trend detection. Hydrological Sciences Journal. Vol. 491. Iss. 1 p. 21–37.
• ZHANG X.B., ZWIERS F.W., LI G.L. 2004. Monte Carlo experiments on the direction of trends in extreme values. Journal of Climate. Vol. 17. Iss. 10 p. 1945–1952.