Citation: | Kareem Hayder H, Nassrullah SA. 2025. Impact of climate changes on Arizona State precipitation patterns using high-resolution climatic gridded datasets. Journal of Groundwater Science and Engineering, 13(1): 34-46 doi: 10.26599/JGSE.2025.9280037 |
All about Arizona. 2023. Sheppard Software. Archived from the original on November 20: [updated 15 September, 2023; cited 15 September, 2023]. https://en.wikipedia.org/wiki/Arizona. 2017.
|
Arizona Climate. 2023. Desert Research Institute, Western Regional Climate Center, Reno, Nevada. December 7, 2001. Archived from the original on December 22, 2011. [updated 15 September, 2023; cited 15 September, 2023]. https://en.wikipedia.org/wiki/Arizona.
|
Beck HE, van Dijk AIJM, Levizzani V, et al. 2017a. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences, 21(1): 589−615. DOI: 10.5194/hess-21-589-2017.
|
Beck HE, Vergopolan N, Pan M, et al. 2017b. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12): 6201−6217. DOI: 10.5194/hess-21-6201-2017.
|
Behrangi A, Khakbaz B, AghaKouchak A. 2021. A satellite precipitation fusion framework (SPFusion) to produce high-resolution rainfall products. Advances in Water Resources, 148: 103869.
|
Burrough PA, McDonnell RA, Lloyd ACD. 2015. Principles of geographical information systems. Third edition. Oxford: Oxford University Press.
|
Camera C, Bruggeman A, Hadjinicolaou P, et al. 2017. Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain. Stochastic Environmental Research and Risk Assessment, 31(3): 757−773. DOI: 10.1007/s00477-016-1239-1.
|
Cai WJ, Borlace S, Lengaigne M, et al. 2014. Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4: 111−116. DOI: 10.1038/nclimate2100.
|
Chen FR, Liu Y, Liu Q, et al. 2014. Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity. International Journal of Remote Sensing, 35(9): 3074−3093. DOI: 10.1080/01431161.2014.902550.
|
Cressie NAC. Statistics for Spatio-Temporal Data. Wiley. 2015. DOI: 10.1080/09332480.2014.914769.
|
Donat MG, Alexander LV, Yang H, et al. 2013. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. Journal of Geophysical Research: Atmospheres, 118(5): 2098−2118. DOI: 10.1002/jgrd.50150.
|
Esri ArcGIS Desktop Documentation. 2023. https://desktop.arcgis.com/en/.
|
Fick SE, Hijmans RJ. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12): 4302−4315. DOI: 10.1002/joc.5086.
|
Goddard L, Mason SJ, Zebiak SE, et al. 2001. Current approaches to seasonal to interannual climate predictions. International Journal of Climatology, 21(9): 1111−1152. DOI: 10.1002/joc.636.abs.
|
Groisman PY, Knight RW, Easterling DR, et al. 2005. Trends in intense precipitation in the climate record. Journal of Climate, 18(9): 1326−1350. DOI: 10.1175/jcli3339.1.
|
Harris I, Osborn TJ, Jones P, et al. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1): 109. DOI: 10.1038/s41597-020-0453-3.
|
Haylock MR, Hofstra N, Klein Tank AMG, et al. 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres, 113(D20): e2008jd010201. DOI: 10.1029/2008jd010201.
|
Herold N, Alexander LV, Donat MG, et al. 2016. How much does it rain over land? Geophysical Research Letters, 43(1): 341−348. DOI: 10.1002/2015gl066615.
|
Hu ZY, Zhou QM, Chen X, et al. 2018. Evaluation of three global gridded precipitation data sets in central Asia based on rain gauge observations. International Journal of Climatology, 38(9): 3475−3493. DOI: 10.1002/joc.5510.
|
Huffman GJ, Bolvin DT, Braithwaite D, et al. 2017. Algorithm Theoretical Basis Document (ATBD) Version 4.6 for the NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (I-MERG).
|
Jensen JR. 2016. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson.
|
IPCC Fifth Assessment Report. https://www.ipcc.ch/report/ar5/. 2014.
|
IPCC Sixth Assessment Report: https://www.ipcc.ch/report/ar6/. 2021.
|
Koziel S. 2011. Accurate modeling of microwave devices using kriging-corrected space mapping surrogates. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 25: 1−14. DOI: 10.1002/jnm.803.
|
Krige DG. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of Chemistry Metals and Mining Society, 52(6): 119−139.
|
Liebmann B, Allured D. 2005. Daily precipitation grids for South America. Bulletin of the American Meteorological Society, 86(11): 1567−1570. DOI: 10.1175/bams-86-11-1567.
|
Liu XM, Yang TT, Hsu K, et al. 2017. Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau. Hydrology and Earth System Sciences, 21(1): 169−181. DOI: 10.5194/hess-21-169-2017.
|
Maurer EP, Hidalgo HG. 2008. Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrology and Earth System Sciences, 12(2): 551−563. DOI: 10.5194/hess-12-551-2008.
|
Mishra V, Thapa S, Jha M. 2022. Spatio-temporal trends of precipitation over India using satellite-based high-resolution gridded datasets. Remote Sensing of Environment, 270: 112605. DOI: 10.1007/s13143-019-00120-1.
|
Morice CP, Kennedy JJ, Rayner NA, et al. 2021. An updated assessment of near surface temperature change from 1850: The HadCRUT5 data set. Journal of Geophysical Research (Atmospheres), 126(3): e2019JD032361. DOI: 10.1029/2019JD032361.
|
Nastos PT, Kapsomenakis J, Philandras KM. 2016. Evaluation of the TRMM 3B43 gridded precipitation estimates over Greece. Atmospheric Research, 169: 497−514. DOI: 10.1016/j.atmosres.2015.08.008.
|
Osborn TJ, Hulme M. 1997. Development of a relationship between station and grid-box rainday frequencies for climate model evaluation. Journal of Climate, 10(8): 1885−1908. DOI: 10.1175/1520-0442(1997)0102.0.co;2.
|
Osborn TJ, Jones PD, Lister DH, et al. 2021. Land surface air temperature variations across the globe updated to 2019: The CRUTEM5 data set. Journal of Geophysical Research: Atmospheres, 126(2): e2019jd032352. DOI: 10.1029/2019jd032352.
|
Osborn TJ, Jones PD. 2014. The CRUTEM4 land-surface air temperature data set: Construction, previous versions and dissemination via google earth. Earth System Science Data, 6(1): 61−68. DOI: 10.5194/essd-6-61-2014.
|
Seneviratne SI, Neville N. 2012. Changes in climate extremes and their impacts on the natural physical environment. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press. DOI: 10.1017/CBO9781139177245.006.
|
Shekhar MS, Karmakar S, Ghosh S. 2020. Future changes in precipitation extremes over South Asia using a multimodel ensemble of CMIP5 and CMIP6 simulations. Journal of Climate, 33(10): 4033−4053. DOI: 10.1088/1748-9326/abd7ad.
|
Shi HY, Li TJ, Wei JH. 2017. Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters Region. Journal of Hydrology, 548: 322−332. DOI: 10.1016/j.jhydrol.2017.03.017.
|
Song XM, Mo YC, Xuan YQ, et al. 2021. Impacts of urbanization on precipitation patterns in the greater Beijing–Tianjin–Hebei metropolitan region in Northern China. Environmental Research Letters, 16(1): 014042. DOI: 10.1088/1748-9326/abd212.
|
Sun QH, Miao CY, Duan QY, et al. 2018. A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Reviews of Geophysics, 56(1): 79−107. DOI: 10.1002/2017rg000574.
|
Swain S, Mishra SK, Pandey A, et al. 2022. Spatiotemporal assessment of precipitation variability, seasonality, and extreme characteristics over a Himalayan catchment. Theoretical and Applied Climatology, 147(1): 817−833. DOI: 10.1007/s00704-021-03861-0.
|
Timmermans B, Wehner M, Cooley D, et al. 2019. An evaluation of the consistency of extremes in gridded precipitation data sets. Climate Dynamics, 52(11): 6651−6670. DOI: 10.1007/s00382-018-4537-0.
|
Trenberth KE, Dai AG, Rasmussen RM, et al. 2003. The changing character of precipitation. Bulletin of the American Meteorological Society, 84(9): 1205−1218. DOI: 10.1175/bams-84-9-1205.
|
Trenberth KE. 1997. Short-term climate variations: Recent accomplishments and issues for future progress. Bulletin of the American Meteorological Society, 78(6): 1081−1096. DOI: 10.1175/1520-0477(1997)0782.0.co;2.
|
Urban and Community Forestry Division. 2023. Arizona state forestry division. Archived from the original on July 14, 2014. [updated 15 September, 2023; cited 15 September, 2023]. Available on: https://en.wikipedia.org/wiki/Arizona.
|
Yatagai A, Arakawa O, Kamiguchi K, et al. 2009. A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Sola, 5: 137−140. DOI: 10.2151/sola.2009-035.
|
Zambrano F, Wardlow B, Tadesse T, et al. 2017. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmospheric Research, 186: 26−42. DOI: 10.1016/j.atmosres.2016.11.006.
|
Zhang L, Li X, Zheng DH, et al. 2021. Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach. Journal of Hydrology, 594: 125969. DOI: 10.1016/j.jhydrol.2021.125969.
|
2305-7068/© Journal of Groundwater Science and Engineering Editorial Office. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)
[1] | Stephen Pitchaimani V, Narayanan MSS, Abishek RS, Aswin SK, Jerin Joe RJ, 2024: Delineation of groundwater potential zones using remote sensing and Geographic Information Systems (GIS) in Kadaladi region, Southern India, Journal of Groundwater Science and Engineering, 12, 147-160. doi: 10.26599/JGSE.2024.9280012 |
[2] | Parvaiz Ahmad Ganie, Ravindra Posti, Garima, Kishor Kunal, Nityanand Pandey, Pramod Kumar Pandey, 2024: Morphometric analysis and hydrological implications of the Himalayan River Basin, Goriganga, India, using Remote Sensing and GIS techniques, Journal of Groundwater Science and Engineering, 12, 360-386. doi: 10.26599/JGSE.2024.9280028 |
[3] | Edmealem Temesgen, Demelash Wendmagegnehu Goshime, Destaw Akili, 2023: Determination of groundwater potential distribution in Kulfo-Hare watershed through integration of GIS, remote sensing, and AHP in Southern Ethiopia, Journal of Groundwater Science and Engineering, 11, 249-262. doi: 10.26599/JGSE.2023.9280021 |
[4] | Xiu-bo Sun, Chang-lai Guo, Jing Zhang, Jia-quan Sun, Jian Cui, Mao-hua Liu, 2023: Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis, Journal of Groundwater Science and Engineering, 11, 37-46. doi: 10.26599/JGSE.2023.9280004 |
[5] | Temesgen Mekuriaw Manderso, Yitbarek Andualem Mekonnen, Tadege Aragaw Worku, 2023: Application of GIS based analytical hierarchy process and multicriteria decision analysis methods to identify groundwater potential zones in Jedeb Watershed, Ethiopia, Journal of Groundwater Science and Engineering, 11, 221-236. doi: 10.26599/JGSE.2023.9280019 |
[6] | Muthamilselvan A Dr, Sekar Anamika, Ignatius Emmanuel, 2022: Identification of groundwater potential in hard rock aquifer systems using Remote Sensing, GIS and Magnetic Survey in Veppanthattai, Perambalur, Tamilnadu, Journal of Groundwater Science and Engineering, 10, 367-380. doi: 10.19637/j.cnki.2305-7068.2022.04.005 |
[7] | Wondesen Fikade Niway, Dagnachew Daniel Molla, Tarun Kumar Lohani, 2022: Holistic approach of GIS based Multi-Criteria Decision Analysis (MCDA) and WetSpass models to evaluate groundwater potential in Gelana watershed of Ethiopia, Journal of Groundwater Science and Engineering, 10, 138-152. doi: 10.19637/j.cnki.2305-7068.2022.02.004 |
[8] | Kessar Cherif, Benkesmia Yamina, Blissag Bilal, Wahib Kébir Lahsen, 2021: Delineation of groundwater potential zones in Wadi Saida Watershed of NW-Algeria using remote sensing, geographic information system-based AHP techniques and geostatistical analysis, Journal of Groundwater Science and Engineering, 9, 45-64. doi: 10.19637/j.cnki.2305-7068.2021.01.005 |
[9] | Mehdi Bahrami, Elmira Khaksar, Elahe Khaksar, 2020: Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran), Journal of Groundwater Science and Engineering, 8, 230-243. doi: 10.19637/j.cnki.2305-7068.2020.03.004 |
[10] | Negar Fathi, Mohammad Bagher Rahnama, Mohammad Zounemat Kermani, 2020: Spatial analysis of groundwater quality for drinking purpose in Sirjan Plain, Iran by fuzzy logic in GIS, Journal of Groundwater Science and Engineering, 8, 67-78. doi: 10.19637/j.cnki.2305-7068.2020.01.007 |
[11] | Fatima Zahra FAQIHI, Anasse BENSLIMANE, Abderrahim LAHRACH, Mohamed CHIBOUT, Mohamed EL MOKHTAR, 2020: Recognition of the hydrogeological potential using electrical sounding in the KhemissetTiflet region, Morocco, Journal of Groundwater Science and Engineering, 8, 172-179. doi: 10.19637/j.cnki.2305-7068.2020.02.008 |
[12] | SUN Dong, LIU Xin-ze, YANG Hai-jun, CAO Nan, ZHANG Zhi-peng, CHEN Yin-song, LI Da-meng, 2019: Analysis of hydrogeolgical characteristics and water environmental impact pathway of typical shale gas exploration and development zones in Sichuan Basin, China, Journal of Groundwater Science and Engineering, 7, 195-213. doi: 10.19637/j.cnki.2305-7068.2019.03.001 |
[13] | LU Chuan, Brian McPherson, WANG Gui-ling, 2018: Hysteresis effects in geological CO2 sequestration processes: A case study on Aneth demonstration site, Utah, USA, Journal of Groundwater Science and Engineering, 6, 243-260. doi: 10.19637/j.cnki.2305-7068.2018.04.001 |
[14] | Alhassan H Ismai, Muntasir A Shareef, Wesam Mahmood, 2018: Hydrochemical characterization of groundwater in Balad district, Salah Al-Din Governorate, Iraq, Journal of Groundwater Science and Engineering, 6, 306-322. doi: 10.19637/j.cnki.2305-7068.2018.04.006 |
[15] | JIANG Ti-sheng, QU Ci-xiao, WANG Ming-yu, SUN Yan-wei, HU Bo, CHU Jun-yao, 2017: Analysis on temporal and spatial variations of groundwater hydrochemical characteristics in the past decade in southern plain of Beijing, China, Journal of Groundwater Science and Engineering, 5, 235-248. |
[16] | WU Jian-qiang, WU Xia-yi, 2016: Geological environment impact analysis of a landfill by the Yangtze River, Journal of Groundwater Science and Engineering, 4, 96-102. |
[17] | GUO Li-jun, YAN Ya-ya, GUO Li-na, MA Jin-long, LV Ming-yu, 2016: GIS-based spatial and temporal changes of land occupation caused by mining activities-a study in eastern part of Hubei Province, Journal of Groundwater Science and Engineering, 4, 60-68. |
[18] | LIU Jun, CHENG Jian-mei, JIANG Fang-yuan, 2015: Methodological study of coastal geological hazard assessment based on GIS, Journal of Groundwater Science and Engineering, 3, 77-85. |
[19] | GU Ming-xu, LIU Yu, HAN Chong, SHANG Lin-qun, JIANG Xian-qiao, WANG Lin-ying, 2014: Analysis of impact of outfalls on surrounding soil and groundwater environment, Journal of Groundwater Science and Engineering, 2, 54-60. |
[20] | Jiankang Zhang, Yanpei Cheng, Hua Dong, Qingshi Guo, Kun Liu, Fawang Zhang, 2013: Study on Ecological Environment and Sustainable Land Use Based on Satellite Remote Sensing, Journal of Groundwater Science and Engineering, 1, 89-96. |
JGSE-ScholarOne Manuscript Launched on June 1, 2024.