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A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling

Theoretical and Applied Climatology volume 143, pages 639–658 (2021)Cite this article

Abstract

This study aims to propose a framework for assessing climate change in Mediterranean data-sparse contexts. For that purpose, the 309-km2 Lebanese Nahr Ibrahim watershed, extending over 3% of Lebanon’s surface, was chosen as a representative of the targeted settings. Generally, holistic climate change assessments encompass both climate trend analysis and future forecasting. According to the World Meteorological Organization, a continuous, homogenous, and uninterrupted climatic record for at least 30 years is needed to fulfill these tasks. Often, some Mediterranean watersheds lack such data and are hence characterized by climatic data scarcity. Such is the case of Lebanon where 30 years of wars have considerably disrupted the country’s climatic record. In an effort to overcome this state of data scarcity, remote sensing–derived drought indicators were used to determine the climate’s evolution during the last 28 years. For that purpose, several remote sensing indices were extracted from LANDSAT imageries for the period 1990–2018 at a 3-year interval, and were coupled to meteorological indicators. Forecasting was then performed using autoregressive integrated moving average (ARIMA) models. Meteorological indices showed increased variability of precipitations and aridity periods, while remote sensing indicators collectively revealed slight shifts towards increasing droughts. Projections using ARIMA models forecasted increases of 0.9 °C, 0.7 °C, and 0.8 °C for average, maximal, minimal temperatures, and an average 6 mm decrease of precipitations at the 95% confidence level for the year 2030. The presented approach can serve as a tool for proactive climate change mitigation or adaptation plans.

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References

  • Abdallah C, Faour G (2016) Landslide hazard mapping of Ibrahim River basin. Lebanon. Nat. Hazards 85:237–266. https://doi.org/10.1007/s11069-016-2560-1

    Article  Google Scholar 

  • Adhikari R, Agrawal RK (2013) An introductory study on time series modeling and forecasting. LAP Lambert Academic Publishing

  • AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson MC, Wardlow BD, Hain CR (2015) Remote sensing of drought: progress, challenges and opportunities. Rev Geophys 53:452–480. https://doi.org/10.1002/2014RG000456

    Article  Google Scholar 

  • Arnoldus H (1980) An approximation of the rainfall factor in the Universal Soil Loss Equation. In: De Boodt M, Gabriels D (eds) Assessment of erosion. John Wiley and Sons Ltd., Chichester, pp 127–132

    Google Scholar 

  • Asare YM, Forkuo EK, Forkuor G, Thiel M (2020) Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-off image for LULC classification in a heterogeneous landscape of West Africa. Int J Remote Sens 41:2544–2564. https://doi.org/10.1080/01431161.2019.1693076

    Article  Google Scholar 

  • Balsamo G, Agusti-Panareda A, Albergel C, Arduini G, Beljaars A, Bidlot J, Bousserez N, Boussetta S, Brown A, Buizza R, Buontempo C, Chevallier F, Choulga M, Cloke H, Cronin MF, Dahoui M, De Rosnay P, Dirmeyer PA, Drusch M, Dutra E, Ek MB, Gentine P, Hewitt H, Keeley SPE, Kerr Y, Kumar S, Lupu C, Mahfouf JF, McNorton J, Mecklenburg S, Mogensen K, Muñoz-Sabater J, Orth R, Rabier F, Reichle R, Ruston B, Pappenberger F, Sandu I, Seneviratne SI, Tietsche S, Trigo IF, Uijlenhoet R, Wedi N, Woolway RI, Zeng X (2018) Satellite and in situ observations for advancing global earth surface modelling: a review. Remote Sens 10:2038. https://doi.org/10.3390/rs10122038

    Article  Google Scholar 

  • Bates B, Kundzewicz ZW, Wu S, Palutikof J (2008) Climate change and water. Technical Paper of the Intergovernmental Panel on Climate Change, Geneva, Switzerland

    Google Scholar 

  • Bertule M, Appelquist LR, Spensley J, Trærup SLM, Naswa P (2018) Climate change adaptation technologies for water. A practicioner’s to adaptation technologies for increased water sector resilience

  • Bhuiyan C (2008) Desert vegetation during droughts: response and sensitivity. Int Arch Photogramm Remote Sens Spat Inf Sci, XXXVII

    Google Scholar 

  • Bhuyan MDI, Islam MM, Bhuiyan MEK (2018) A trend analysis of temperature and rainfall to predict climate change for northwestern region of Bangladesh. Am J Clim Chang 7:115–134

    Google Scholar 

  • Boschetto RG, Mohamed RM, Arrigotti J (2010) Vulnerability to desertification in a sub-Saharan region: a first local assessment in five villages of southern region of Malawi. Ital J Agron 5:91–102

    Google Scholar 

  • Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, revised. ed. Holden-Day

  • Box GE, Pierce DA (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J Am Stat Assoc 65:1509–1526

    Google Scholar 

  • Brekke LD, Kiang JE, Olsen R, Pulwarty RS, Raff DA, Turnipseed DP, Webb RS, White KD (2009) Climate change and water resources management : a federal perspective

  • Buma WG, Lee S. Il (2019) Multispectral image-based estimation of drought patterns and intensity around Lake Chad, Africa. Remote Sens 11:1–11. https://doi.org/10.3390/rs11212534

    Article  Google Scholar 

  • Burak S, Margat J (2016) Water management in the Mediterranean region: concepts and policies. Water Resour Manag 30:5779–5797

    Google Scholar 

  • Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire JM (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ 77:22–33. https://doi.org/10.1016/S0034-4257(01)00191-2

    Article  Google Scholar 

  • CEDRO (2013) Hydropower in Lebanon; History and Prospects

  • Chattopadhyay S, Edwards D (2016) Long-term trend analysis of precipitation and air temperature for Kentucky, United States. Climate 4:1–15. https://doi.org/10.3390/cli4010010

    Article  Google Scholar 

  • Cheval S (2015) The standardized precipitation index – an overview. Rom J Meteorol 12:17–64

    Google Scholar 

  • Chowdhurry RK, Eslamian S (2014) Climate change and hydrologic modeling. In: Eslamian S (ed) Handbook of engineering hydrology modeling, climate change, and variability. CRC-Press, pp 72–81

  • Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • CORINE (1992) CORINE soil erosion risk and important land resources - in the southern regions of the European Community. Europe

  • Daniels, R.C., 2012. Using ArcMap to extract shorelines from Landsat TM data., in: Thirty-Second ESRI International User Conference. ESRI, San Diego, USA, pp. 1–23

  • Darwish T, Zdruli P, Saliba R, Awad M, Shaban A, Faour G (2012) Vulnerability to desertification in Lebanon based on geo-information and socioeconomic conditions. J. Environ. Sci. Eng. B:851–861

  • Darwish T, Shaban A, Portoghese I, Vurro M, Khadra R, Saqallah S, Drapeau L, Gascoin S, Amacha N (2015) Inducing water productivity from snow cover for sustainable water management in Ibrahim River basin, Lebanon. Br J Appl Sci Technol 5:233–243. https://doi.org/10.9734/BJAST/2015/13777

    Article  Google Scholar 

  • Dash P, Göttsche FM, Olesen FS, Fischer H (2002) Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. Int J Remote Sens 23:2563–2594

    Google Scholar 

  • Donohue RJ, McVicar T, Roderick ML (2009) Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006. Glob Chang Biol 15:1025–1039

    Google Scholar 

  • Dubertret, L., 1955. Cartes geologiques du Liban à l’echelle 1 :50000

  • ESRI (2016) Raster Calculator [WWW Document]. Spat. Anal. tools URL https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/raster-calculator.htm

  • Farjalla, N., Haddad, E.A., Camargo, M., Lopes, R., Vieira, F., 2014. Climate change in Lebanon: higher-order regional impacts from agriculture (no. 23), climate change and environment in the Arab world. Beirut, Lebanon

  • Fatichi S, Vivoni ER, Ogden FL, Ivanov VY, Mirus B, Gochis D, Downer CW, Camporese M, Davison JH, Ebel B, Jones N, Kim J, Mascaro G, Niswonger R, Restrepo P, Rigon R, Shen C, Sulis M, Tarboton D (2016) An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. J Hydrol 537:45–60. https://doi.org/10.1016/j.jhydrol.2016.03.026

    Article  Google Scholar 

  • Feng G, Cobb S, Abdo Z, Fisher DK, Ouyang Y, Adeli A, Jenkins JN (2016) Trend analysis and forecast of precipitation, reference evapotranspiration, and rainfall deficit in the Blackland Prairie of Eastern Mississippi. J Appl Meteorol Climatol 55:1425–1439. https://doi.org/10.1175/JAMC-D-15-0265.1

    Article  Google Scholar 

  • Filho WL (2015) Handbook of climate change adaptation. Springer, Berlin

    Google Scholar 

  • Gao B (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Google Scholar 

  • Gaussen, H., 1963. Bioclimatic map of Mediterranean zone. Paris

  • Giorgi F, Lionello P (2008) Climate change projections for the Mediterranean region. Glob Planet Change 63:90–104. https://doi.org/10.1016/j.gloplacha.2007.09.005

    Article  Google Scholar 

  • Gu Y, Hunt E, Wardlow B, Basara JB, Brown JF, Verdin JP (2008) Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophys Res Lett 35. https://doi.org/10.1029/2008GL035772

  • Gulácsi A, Kovács F (2015) Drought monitoring with spectral indices calculated from MODIS satellite images in Hungary. J Environ Geogr 8:11–20

    Google Scholar 

  • Halwani, J., 2009. Climate change and water resources in Lebanon, in: IOP Conference Series: Earth and Environmental Science. p. 292011. https://doi.org/10.1088/1755-1307/6/9/292011

  • Herrero, A., Bluendià, C., Bussi, G., Sabater, S., Vericat, D., Palau, A., Batalla, R.J., 2017. Modeling the sedimentary response of a large Pyrenean basin to global change. J. Soils Sediments 17, 2677–2690

  • Hintze, J.L., 2007. The Box-Jenkins method, in: User’s Guide I Quick Start & Self Help, Introduction, Data, Tools, and Graphics. NCSS, Kaysville, Utah, p. 630

  • Hipel KW, McLeod IA (1994) Time series modelling of water resources and environmental systems (developments in water science). Elsevier Science Ltd, Amsterdam

  • Huntra P, Keener TC (2017) Evaluating the impact of meteorological factors on water demand in the Las Vegas Valley using time-series analysis: 1990–2014. Int J Geo-Information 6:1–49

    Google Scholar 

  • Ioras F, Bandara I, Kemp C (2014) Introduction to climate change and land degradation. Buckinghamshire New University, Buckinghamshire

    Google Scholar 

  • IPCC (2014a) Climate change 2014 synthesis report summary chapter for policymakers

  • IPCC (2014b) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, p 151

  • JRC European Comission and the Institute for Environment and Sustainability (2011) NDWI: Normalized Difference Water Index [WWW document]. Prod. FACT SHEET NDWI – Eur. URL http://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf. Accessed 13 Jan 2019

  • Karam, F., 2002. Climate change and variability in Lebanon: impact on land use and sustainable agriculture development

  • Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Sp Res 15:91–100

    Google Scholar 

  • Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636

    Google Scholar 

  • Li Z, Fang H (2017) Geomorphology modeling the impact of climate change on watershed discharge and sediment yield in the black soil region , northeastern China. Geomorphology 293:255–271. https://doi.org/10.1016/j.geomorph.2017.06.005

    Article  Google Scholar 

  • Lima TA, Beuchle R, Langner A, Grecchi RC, Griess VC, Achard F (2019) Comparing Sentinel-2 MSI and Landsat 8 OLI imagery for monitoring selective logging in the Brazilian Amazon. Remote Sens 11:1–21. https://doi.org/10.3390/rs11080922

    Article  Google Scholar 

  • Lionello P, Malanotte-Rizzoli P, Boscolo R, Alpert P, Artale V, Li L, Luterbacher J, May W, Trigo R, Tsimplis M, Ulbrich U, Xoplaki E (2006) The Mediterranean climate: an overview of the main characteristics and issues. Dev Earth Environ Sci:1–26. https://doi.org/10.1016/S1571-9197(06)80003-0

  • Mamuye M, Kebebewu Z (2018) Review on impacts of climate change on watershed hydrology. J Environ Earth Sci 8:91–99

    Google Scholar 

  • McKee, T.B., Doescken, N., Kleist, J., 1993. The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, U.S.A., pp. 179–184

  • Mediterranean Water Scarcity & Drought Working Group (2007) Mediterranean water scarcity and drought report. Technical report on water scarcity and drought management in the Mediterranean and the Water Framework Directive. Technical Report - 009 - 2007, pp 134

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012

    Article  Google Scholar 

  • Mishra N, Khare D, Shukla R, Kumar K (2014) Trend analysis of air temperature time series by Mann Kendall test - a case study of upper Ganga canal command (1901-2002). Br J Appl Sci Technol 4:4066–4082

    Google Scholar 

  • MoE, GEF, and the UNDP (2011) Lebanon’s second national communication to the UNFCCC. Beirut, Lebanon

  • MoE, the UNDP and GEF (2016) Lebanon’s third national communication to the UNFCCC. Beirut, Lebanon

    Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50:885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  • Mukundan R, Pradhanang SM, Schneiderman EM, Pierson DC, Anandhi A, Zion MS, Matonse AH, Lounsbury DG, Steenhuis TS (2013) Suspended sediment source areas and future climate impact on soil erosion and sediment yield in a New York City water supply watershed, USA. Geomorphology 183:110–119. https://doi.org/10.1016/j.geomorph.2012.06.021

    Article  Google Scholar 

  • Olsson T, Jakkila J, Veijalainen N, Backman L, Kaurola J, Vehviläinen B (2015) Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data. Hydrol Earth Syst Sci 19:3217–3238

    Google Scholar 

  • Pechlivanidis IG, Jackson BM, McIntryre NR, Wheater HS (2011) Catchment scale hydrological modelling : a review of model types, calibration approaches and uncertainty analysis method in the context of recent developments in technology and applications. Glob NEST J 13:193–214

    Google Scholar 

  • Pechlivanidis IG, Arheimer B, Donnelly C, Hundecha Y, Huang S, Aich V, Samaniego L, Eisner S, Shi P (2017) Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions. Clim Chang 141:467–481. https://doi.org/10.1007/s10584-016-1723-0

    Article  Google Scholar 

  • Pendergrass AG, Knutti R, Lehner F, Deser C, Sanderson BM (2017) Precipitation variability increases in a warmer climate. Sci Rep 7:1–9

    Google Scholar 

  • Phan DB, Wu CC, Hsieh SC (2011) Impact of climate change on stream discharge and sediment yield in northern. Viet Nam 1(38):827–836. https://doi.org/10.1134/S0097807811060133

    Article  Google Scholar 

  • Romilly P (2005) Time series modelling of global mean temperature for managerial decision-making. J Environ Manag 76:61–70

    Google Scholar 

  • Rouse JWJ, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS (No. A20). Texas

  • Rustum R, Adeloye AJ, Mwale F (2017) Spatial and temporal trend analysis of long term rainfall records in data-poor catchments with missing data, a case study of Lower Shire floodplain in Malawi for the period 1953-2010. Hydrol Earth Syst Sci Discuss 601:1–30

    Google Scholar 

  • Salvati L, Zitti M, Di Bartolomei R, Perini L (2013) Climate aridity under changing conditions and implications for the agricultural sector: Italy as a case study. Geogr J 1–7:1–7. https://doi.org/10.1155/2013/923173

    Article  Google Scholar 

  • Sehgal V, Sridhar V (2019) Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.S. Weather Clim. Extrem 23:1–14. https://doi.org/10.1016/j.wace.2018.100191

    Article  Google Scholar 

  • Shaban A (2009) Indicators and aspects of hydrological drought in Lebanon. Water Resour Manag 23:1875–1891

    Google Scholar 

  • Sholihah RI, Trisasongko BH, Shiddiq D, Iman LOS, Kusdaryanto S, Manijo PDR (2016) Identification of agricultural drought extent based on vegetation health indices of Landsat data: case of Subang and Karawang, Indonesia, in: The 2nd International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2015, LISAT-FSEM 2015. In: Procedia Environmental Sciences, pp 14–20. https://doi.org/10.1016/j.proenv.2016.03.051

    Chapter  Google Scholar 

  • Singh RP, Singh N, Singh S, Mukherjee S (2016) Normalized difference vegetation index (NDVI) based classification to assess the change in land use/land cover (LULC) in Lower Assam, India. Int J Adv Remote Sens GIS 5:1963–1970

    Google Scholar 

  • Stancalie G, Nertan A, Serban F (2014) Agricultural drought monitoring using satellite - based products in Romania. In: Proceedings of the Third International Conference on Telecommunications and Remote Sensing (ICTRS 2014), pp 100–106

    Google Scholar 

  • Taweesin K, Seeboonraung U, Saraphirom P (2018) The influence of climate variability effects on groundwater time series in the lower central plains of Thailand. Water 10:1–23

    Google Scholar 

  • Terray L, Boé J (2013) Quantifying 21st-century France climate change and related uncertainties. Comptes Rendus Geosci 345:136–149

    Google Scholar 

  • Tiao GC, Grupe MR (1980) Hidden periodic autoregressive-moving average models in time series data. Biometrika 67:365–373

    Google Scholar 

  • Trzaska S, Schnarr E (2014) A review of downscaling methods for climate change projections. African and Latin American reslience to climate change

  • Tseng F, Tzeng G (2002) A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst 126:367–376

    Google Scholar 

  • U.S. Geological Survey (1998) Landsat 7 science data users handbook. Greenbelt, Maryland

    Google Scholar 

  • UNDP (2014) Assessment of groundwater resources of Lebanon. Beirut, Lebanon

    Google Scholar 

  • UNFCC (1992) United Nations framework convention on climate change. Review of European Community and International Environmental Law 1:270–277. https://doi.org/10.1111/j.1467-9388.1992.tb00046.x

    Article  Google Scholar 

  • UNFCCC (2011) Climate change and freshwater resources - a synthesis of adaptation actions undertaken by Nairobi work programme partner organizations

  • USGS (2012) Earth explorer. Sioux Falls

  • Vasiliades L, Loukas A, Patsonas G (2009) Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts. Nat Hazards Earth Syst Sci 9:879–894

    Google Scholar 

  • Wang HR, Wang C, Lin X, Kang J (2014) An improved ARIMA model for precipitation simulations. Nonlin Process Geophys 21:1159–1168

    Google Scholar 

  • Wilhite D (2005) Drought and water crises: science, technology, and management issues. Taylor & Francis, Florida

    Google Scholar 

  • Wilhite D, Michael HG (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10:111–120

    Google Scholar 

  • World Meteorological Organization (2011) Guide to climatological practices. Switzerland, Geneva

    Google Scholar 

  • World Meteorological Organization (2018) Guide to climatological practices. Switzerland, Geneva

    Google Scholar 

  • Yan N, Bingfang W, Boken VK, Chang S, Yang L (2016) A drought monitoring operational system for China using satellite data: design and evaluation. Geomatics Nat Hazards Risk 7:264–277

    Google Scholar 

  • Ye L, Yang GX, van Ranst E, Tang HJ (2013) Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. Adv Atmos Sci 30:382–396

    Google Scholar 

  • Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens 6:9829–9852

    Google Scholar 

  • Zarei R, Sarajian M, Bazgeer S (2013) Monitoring meteorological drought in Iran using remote sensing and drought indices. Desert 18:89–97

    Google Scholar 

  • Zargar A, Sadiq R, Naser B, Khan FI (2011) A review of drought indices. Environ Rev 19:333–349

    Google Scholar 

Download references

Acknowledgments

This research is part of a PhD thesis funded by the National Council of Scientific Research-Lebanon (CNRS-L), Agence Universitaire de la Francophonie (AUF) Lebanon and the Lebanese University. Thanks are extended to the Lebanese Agriculture Institute (LARI)’s Dr. Ihab Jomaa for providing the authors with weather station data. Authors would also like to thank the editor and reviewers for their valuable comments in leveraging the scientific quality of work.

Funding

This research paper was funded by the National Council of Scientific Research-Lebanon (CNRS-L), Agence Universitaire de la Francophonie (AUF) Lebanon and the Lebanese University.

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  1. National Council for Scientific Research – Remote Sensing Center, P.O. Box: 11-8281, Blvrd. Sport City- Birr Hassan, Beirut, Lebanon

    Mario J. Al Sayah, Chadi Abdallah & Talal Darwich

  2. Centre de Recherches en Sciences et Ingénierie, Lebanese University Faculty of Engineering II, Roumieh, Lebanon

    Mario J. Al Sayah & Michel Khouri

  3. Centre d’Études et de Développement des Territoires et de l’Environnement, Université d’Orléans, Orléans, France

    Mario J. Al Sayah & Rachid Nedjai

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Al Sayah, M.J., Abdallah, C., Khouri, M. et al. A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling. Theor Appl Climatol 143, 639–658 (2021). https://doi.org/10.1007/s00704-020-03442-7

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