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This case study is an application of the integrated climatic modelling framework (GEO-CWB) in the metropolitan area of torino (north Italy) for the projected period of 2080. the model was developed and validated at trinity college of dublin and uses Geographical Information System (GIS) as the climate change downscaling environment. the main goal of this research is to investigate the impacts of climate and land-use changes on the water resources vulnerability using GEO-CWB model with a wide range of input parameters and grids, including seasonal climate variables and changes, land use/land cover, seasonal parameters and future changes, seasonal groundwater depth, soil properties, topography and slope. An intense data collection activity was carried out for the year 2015, using all the possible sources available; additionally, potential evapotranspiration, as input data, was calculated using the method of blaney–criddle and modelled in GIS platform. In order to parametrize the hydrological response of the metropolitan area of torino to the changes in climate and land use, GEO-CWB has a number of simulation stages (WBt) as follows: wbt stage (1) – dynamical water balance (DWB), WBt stage (2) – surface runoff iteration and WBt stage (3) – climate and land-use vulnerability parameters. as a result, GEO-CWB gives a wide range of seasonally and yearly gridded output layers as surface runoff, subsurface water, interception, evapotranspiration, soil evaporation, transpiration including total uncertainties or error in the water balance. GEO-CWB outputs could allow the scientific community, modelers, planners and decision makers to study the impact of climate and land-use changes on regional water resources vulnerability.
climate change, GIS, north Italy, water balance, water vulnerability
[1] Siebert, S., Burke, J., Faures, J.M., Frenken, J., Hoogeveen, K., Döll, K. & Portmann, F.T., Groundwater use for irrigation – a global inventory. Hydrology and Earth Systems Science, 14, pp. 1863–1880, 2010.
[2] R odell, M., Velicogna, I. & Famiglietti, J.S., Satellite-based estimates of groundwater depletion in India. Nature, 460, pp. 999–1002, 2009.
[3] Gleeson, T., Wada, Y., Bierkens, M.F. & Van Beek, L.P., Water balance of global aquifers revealed by groundwater footprint. Nature, 488(7410), pp. 197–200, 2012.
[4] M isstear, B., Brown, L. & Johnston, P., Estimation of groundwater recharge in a major sand and gravel aquifer in Ireland using multiple approaches. Hydrogeology Journal, 17, pp. 693–706, 2009 (a).
[5] B erkhout, F., Hertin, J. & Jordan, A., Socio-economic futures in climate change impact assessment: using scenarios as ‘learning machines’. https://doi.org/10.1016/S0959-3780(02)00006-7; 2002.
[6] Grotch, S.L. & MacCracken, M.C., The use of general circulation models to predict regional climatic change. United States: N. p., Web. doi:10.1175/1520-0442(1991)004<0286:TUOGCM>2.0.CO;2; 1991.
[7] Kim, T.W. & Valdes, J.B., Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic engineering, 8(6), 2003.
[8] L eavesley, G.H., Modeling the effects of climate change on water resources – a review. Climatic Change, 28, pp. 159–177, 1994.
[9] B ahremand, A. & De Smedt F., Distributed hydrological modeling and sensitivity analysis in Torysa watershed, Slovakia. Water Resources Management, 22, pp. 393–408, 2008.
[10] Pruitt, W.O. & Doorenbos, J., Empirical calibration – a requisite for evapotranspiration formulae based on daily or Longer Mean Climatic Data. International Round table Conference on Evapotranspiration, International Commission on Irrigation and Drainage, Budapest, Hungary, 1977.
[11] Gharbia, S.S., Gill, L., Johnston, P. & Pilla F., Multi-GCM ensembles performance for climate projection on a GIS platform. Journal of Modeling Earth Systems and Environment, 2, pp. 1–21, 2016.
[12] A gkun, A. & Erkan, O., Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arabian Journal of Geosciences, 9(165), 2016.
[13] M üller M.F. & Thompson, S.E., Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes. Hydrology and Earth Systems Science, 20, pp. 669–683, 2016.
[14] W ood, A.W., Leung, L.R., Sridhar, V. & Lettenmaier, D.P., Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, pp. 189–216, 2004.
[15] M isstear, B., Brown, L. & Daly, D., A methodology for making initial estimates of groundwater recharge from groundwater vulnerability mapping. Hydrogeology Journal, 17, pp. 275–285, 2009 (b).
[16] T ague, C., Grant, G., Farrell, M., Choate, J. & Jefferson A., Deep groundwater mediates streamflow response to climate warming in the Oregon Cascades. Climatic Change, 86, pp. 189–210, 2008.
[17] C hen, J., Brissette, F.P. & Leconte, R., Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401, pp. 190–202, 2011.
[18] Salem S.G., Gill, L., Johnston P. & Pilla, F., GEO-CWB: GIS-based algorithms for parametrising the responses of catchment dynamic water balance regarding climate and land use changes. Hydrology, 7(3), pp. 39, 2020. https://ind01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Fhydrology7030039&data=02%7C01%7Csaranya.kumaresan%40integra.co.in%7C62b129f6db69403e89a008d82d9301ba%7C70e2bc386b4b43a19821a49c0a744f3d%7C0%7C0%7C637309456736367137&sdata=O1IPmbjP%2FwWMnBIk1X%2BLMH909tJMyazWg5XXh0QNubo%3D&reserved=0.