Abstract
A physiographic-based multilinear regression model supported by GIS was developed to estimate spatial rainfall variability in the Southwest Iberian Peninsula. The area study includes a wide diversity of landscape features and comprises four Portuguese regions and one Spanish province (totalizing 28,860 km2). The region suffers a very strong Mediterranean influence, with a major cleavage between winter and summer seasons. Thus, the analysis was carried out separately for the wet (October to March) and dry (April to September) semesters. From an initial set of 10 explanatory physiographic variables, five were selected to be used in the multilinear regression, as they allowed generating models by map algebra that fitted well with the last 40 years of monthly rainfall data records. These records were obtained from 163 weather stations, filtered from an initial set of 230 (142 stations in Portugal and 88 in Spain). The correlation between the physiographic-based multilinear regression model and a model obtained by interpolation from rainfall historical data showed to be good or very good in approximately 75% of the area under study. Results show that physiographic-based models can be effectively used to estimate rainfall where there is a lack of rain gauges, or to densify spatial resolution of rainfall between rain gauges.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The authors confirm that the raw data supporting the findings of this study are available within the article. The raw data could be downloaded through the Portuguese and Spanish Official Networks of Hydrometeorological Records (Direção Regional de Agricultura e Pescas (DRAP); Junta de Andalucía (JA); Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO); Sistema Nacional de Informação de Recursos Hídricos (SNIRH)).
https://www.drapc.gov.pt/base/agrometeorologia.htm
https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/
https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/otros/default.aspx
References
Abatzoglou, J. T., & Ficklin, D. L. (2017). Climatic and physiographic controls of spatial variability in surface water balance over the contiguous United States using the Budyko relationship. Water Resources Research, 53(9), 7630–7643. https://doi.org/10.1002/2017WR020843
Adhikary, S. K., Muttil, N., & Yilmaz, A. G. (2017). Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments. Hydrological Processes, 31(12), 2143–2161. https://doi.org/10.1002/hyp.11163
Akbar, R., Short Gianotti, D., McColl, K. A., Haghighi, E., Salvucci, G. D., & Entekhabi, D. (2018). Hydrological storage length scales represented by remote sensing estimates of soil moisture and precipitation. Water Resources Research, 54(3), 1476–1492. https://doi.org/10.1002/2017WR021508
Almodóvar, G. R., Yesares, L., Sáez, R., Toscano, M., González, F., & Pons, J. M. (2019). Massive sulfide ores in the iberian pyrite belt: Mineralogical and textural evolution. Minerals, 9(11), 653. https://doi.org/10.3390/min9110653
Bárdossy, A., & Pegram, G. (2013). Interpolation of precipitation under topographic influence at different time scales. Water Resources Research, 49(8), 4545–4565. https://doi.org/10.1002/WRCR.20307
Barsi, J. A., Schott, J. R., Hook, S. J., Raqueno, N. G., Markham, B. L., & Radocinski, R. G. (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607–11626. https://doi.org/10.3390/rs61111607
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 1–12. https://doi.org/10.1038/sdata.2018.214
Berger, K. P., & Entekhabi, D. (2001). Basin hydrologic response relations to distributed physiographic descriptors and climate. Journal of Hydrology, 247(3–4), 169–182. https://doi.org/10.1016/S0022-1694(01)00383-3
Berndtsson, R., & Niemczynowicz, J. (1988). Spatial and temporal scales in rainfall analysis - some aspects and future perspectives. Journal of Hydrology, 100(1–3), 293–313. https://doi.org/10.1016/0022-1694(88)90189-8
Borges, P. D. A., Franke, J., da Anunciação, Y. M. T., Weiss, H., & Bernhofer, C. (2016). Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal Brazil. Theoretical and Applied Climatology, 123(1–2), 335–348 https://doi.org/10.1007/s00704-014-1359-9
Brown, D. P., & Comrie, A. C. (2002). Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA. Climate Research, 22(2), 115–128. https://doi.org/10.3354/cr022115
Castiglioni, S., Castellarin, A., Montanari, A., Skøien, J. O., Laaha, G., & Blöschl, G. (2011). Smooth regional estimation of low-flow indices: Physiographical space based interpolation and top-kriging. Hydrology and Earth System Sciences, 15(3), 715–727. https://doi.org/10.5194/hess-15-715-2011
CEDEX (Centro de Estudios y experimentación de obras públicas). (2013). Cálculo hidrometeorológico de aportaciones y crecidas. Manual CHAC (5.06 beta1). Ministerio de Transportes, movilidad y Agenda Urbana. https://ceh.cedex.es/chac/
Chandwani, V., Vyas, S. K., Agrawal, V., & Sharma, G. (2015). Soft computing approach for rainfall-runoff modelling: A review. Aquatic Procedia, 4, 1054–1061. https://doi.org/10.1016/j.aqpro.2015.02.133
Chow, V. Te, Maidment, D. R., & Mays, L. W. (1988). Applied hydrology (N. Y. McGraw-Hill Book Company (ed.)). McGraw. http://ponce.sdsu.edu/Applied_Hydrology_Chow_1988.pdf
Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression (C. and H. New York (ed.); Retrieved). Chapman and Hall. https://hdl.handle.net/11299/37076
Direção Regional de Agricultura e Pescas do Centro (DRAP). (n.d.). Agrometeorologia. Retrieved March 11, 2021, from https://www.drapc.gov.pt/base/agrometeorologia.htm
Durocher, M., Burn, D. H., Mostofi Zadeh, S., & Ashkar, F. (2019). Estimating flood quantiles at ungauged sites using nonparametric regression methods with spatial components. Hydrological Sciences Journal, 64(9), 1056–1070. https://doi.org/10.1080/02626667.2019.1620952
EC (European Commission). (2008). Natura 2000 - Protecting Europe´s biodiversity (Susanne Wegefelt, Nature & Biodiversity Unit, & D. Environment (eds.); Oxford, UK). https://doi.org/10.2779/45963
Edwards, K. A. (1973). Estimating areal rainfall by fitting surfaces to irregularly spaced data. In W. M. O. WMO (Ed.), Proc. of the International Symposium on the Distribution of Precipitation in Mountainous Areas (pp. 565–587). http://hydrologie.org/redbooks/a106/iahs_106_V1_0000.pdf
Fan, J. C., Chang, S. C., Liao, K. W., Guo, J. J., Liu, C. H., Chang, Y. C., Huang, C. L., & Yang, C. H. (2018). The impact of physiographic factors upon the probability of slides occurrence: A case study from the Kaoping River Basin. Taiwan. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers, Series A, 41(5), 419–429. https://doi.org/10.1080/02533839.2018.1482236
Fehmi, J. S., Niu, G. Y., Scott, R. L., & Mathias, A. (2014). Evaluating the effect of rainfall variability on vegetation establishment in a semidesert grassland. Environmental Monitoring and Assessment, 186(1), 395–406. https://doi.org/10.1007/S10661-013-3384-Z
Feidas, H., Karagiannidis, A., Keppas, S., Vaitis, M., Kontos, T., Zanis, P., Melas, D., & Anadranistakis, E. (2013). Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters. Theoretical and Applied Climatology, 118(1–2), 133–146. https://doi.org/10.1007/S00704-013-1052-4
Fernandez, H. M., Martins, F. M. G., Isidoro, J. M. G. P., Zavala, L., & Jordán, A. (2016). Soil erosion, Serra de Grândola (Portugal). Journal of Maps, 12(5), 1138–1142. https://doi.org/10.1080/17445647.2015.1135829
Fernandez, H., Martins, F., & Isidoro, J. M. G. P. (2020). Mapping rainfall aggressiveness from physiographical data: Application to the Grândola Mountain Range (Alentejo, Portugal). Physical Geography, 41(5), 451–466. https://doi.org/10.1080/02723646.2019.1674557
Fraga, I., Cea, L., & Puertas, J. (2019). Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models. Hydrological Processes, 33(1), 160–173. https://doi.org/10.1002/hyp.13319
Fu, P., & Rich, P. M. (2002). A geometric solar radiation model with applications in agriculture and forestry. Computers and Electronics in Agriculture, 37(1–3), 25–35. https://doi.org/10.1016/S0168-1699(02)00115-1
Ghiglieri, G., Carletti, A., & Pittalis, D. (2014). Runoff coefficient and average yearly natural aquifer recharge assessment by physiography-based indirect methods for the island of Sardinia (Italy) and its NW area (Nurra). Journal of Hydrology, 519(PB), 1779–1791. https://doi.org/10.1016/j.jhydrol.2014.09.054
Ghumman, A. R., Hassan, I., Khan, Q. U. Z., & Kamal, M. A. (2013). Investigation of impact of environmental changes on precipitation pattern of Pakistan. Environmental Monitoring and Assessment, 185(6), 4897–4905. https://doi.org/10.1007/S10661-012-2911-7
González-Hidalgo, J. C., de Luis Arrillaga, M., Stepánek, P., Raventós Bonvehí, J., & Cuadrats Prats, J. M. (2002). Reconstrucción, estabilidad y proceso de homogeneizado de series de precipitación en ambientes de elevada variabilidad pluvial. In S. M. V. y M. A. S. J. M. Cuadrat (Ed.), VII Reunión Nacional de Climatología: La información climática como herramienta de gestión ambiental (pp. 47–58). Universidad de Zaragoza.
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1–2), 113–129. https://doi.org/10.1016/S0022-1694(00)00144-X
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Guillemette, N., St-Hilaire, A., Ouarda, T. B. M. J., Bergeron, N., Robichaud, É., & Bilodeau, L. (2009). Feasibility study of a geostatistical modelling of monthly maximum stream temperatures in a multivariate space. Journal of Hydrology, 364(1–2), 1–12. https://doi.org/10.1016/j.jhydrol.2008.10.002
Harris, R., & Jarvis, C. (2011). Statistics for geography and environmental science (Routledge (ed.); 1st Editio). Taylor & Francis. https://www.routledge.com/Statistics-for-Geography-and-Environmental-Science/Harris-Jarvis/p/book/9780131789333
Hevesi, J. A., Istok, J. D., & Flint, A. L. (1992). Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: Structural analysis. Journal of Applied Meteorology and Climatology, 31(7), 661–676. https://journals.ametsoc.org/view/journals/apme/40/11/1520-0450_2001_040_1835_gaoor_2.0.co_2.xml
Hu, Q., Li, Z., Wang, L., Huang, Y., Wang, Y., & Li, L. (2019). Rainfall spatial estimations: a review from spatial interpolation to multi-source data merging. In Water (Switzerland) (Vol. 11, Issue 3, p. 579). MDPI AG. https://doi.org/10.3390/w11030579
Hundecha, Y., Ouarda, T. B. M. J., & Bárdossy, A. (2008). Regional estimation of parameters of a rainfall-runoff model at ungauged watersheds using the “spatial” structures of the parameters within a canonical physiographic-climatic space. Water Resources Research, 44(1), 1427. https://doi.org/10.1029/2006WR005439
Hurtado, S. I., Zaninelli, P. G., Agosta, E. A., & Ricetti, L. (2021). Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina. Atmospheric Research, 254, 105482. https://doi.org/10.1016/J.ATMOSRES.2021.105482
Jin, Q., Zhang, J., Shi, M., & Huang, J. (2016). Estimating loess plateau average annual precipitation with multiple linear regression kriging and geographically weighted regression kriging. Water (switzerland), 8(6), 266. https://doi.org/10.3390/W8060266
Junta de Andalucía (JA). (n.d.). Red de Información Agroclimática de Andalucía (RIA) | Instituto de Investigación y Formación Agraria y Pesquera (IFAPA). Retrieved March 11, 2021, from https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/
Kim, D. (2013). Incorporation of multi-scale spatial autocorrelation in soil moisture-landscape modeling. Physical Geography, 34(6), 441–455. https://doi.org/10.1080/02723646.2013.857267
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130
Kuentz, A., Arheimer, B., Hundecha, Y., & Wagener, T. (2017). Understanding hydrologic variability across Europe through catchment classification. Hydrology and Earth System Sciences, 21(6), 2863–2879. https://doi.org/10.5194/hess-21-2863-2017
Kumari, M., Singh, C. K., & Basistha, A. (2017). Clustering data and incorporating topographical variables for improving spatial interpolation of rainfall in mountainous region. Water Resources Management, 31(1), 425–442. https://doi.org/10.1007/s11269-016-1534-0
Kyriakidis, P. C., Kim, J., & Miller, N. L. (2001). Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. Journal of Applied Meteorology and Climatology, 40(11), 1855–1877. https://journals.ametsoc.org/view/journals/apme/40/11/1520-0450_2001_040_1855_gmopfr_2.0.co_2.xml
Lyon, S. W., Seibert, J., Lembo, A. J., Steenhuis, T. S., & Walter, M. T. (2008). Incorporating landscape characteristics in a distance metric for interpolating between observations of stream water chemistry. Hydrology and Earth System Sciences, 12(5), 1229–1239. https://doi.org/10.5194/hess-12-1229-2008
Marquínez, J., Lastra, J., & García, P. (2003). Estimation models for precipitation in mountainous regions: The use of GIS and multivariate analysis. Journal of Hydrology, 270(1–2), 1–11. https://doi.org/10.1016/S0022-1694(02)00110-5
Mello, C. R., Viola, M. R., Beskow, S., & Norton, L. D. (2013). Multivariate models for annual rainfall erosivity in Brazil. Geoderma, 202–203, 88–102. https://doi.org/10.1016/j.geoderma.2013.03.009
Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO). (n.d.). Estaciones Climatológicas de la Agencia Estatal de Meteorología (AEMET). Retrieved March 11, 2021, from https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/otros/default.aspx
Mohamoud, Y. (2004). Comparison of hydrologic responses at different watershed scales. Office of Research and Development, United States Environmental Protection Agency, 1–81.
Moliba Bankanza, J. C. (2014). Spatial modeling of summer precipitation over the Czech Republic using physiographic variables. Geographical Research, 52(1), 85–105. https://doi.org/10.1111/1745-5871.12041
Montanari, A., & Di Baldassarre, G. (2013). Data errors and hydrological modelling: The role of model structure to propagate observation uncertainty. Advances in Water Resources, 51, 498–504. https://doi.org/10.1016/j.advwatres.2012.09.007
Moral, F. J. (2010). Comparison of different geostatistical approaches to map climate variables: Application to precipitation. International Journal of Climatology, 30(4), 620–631. https://doi.org/10.1002/joc.1913
Narbondo, S., Gorgoglione, A., Crisci, M., & Chreties, C. (2020). Enhancing physical similarity approach to predict runoff in ungauged watersheds in sub-tropical regions. Water (Switzerland), 12(2). https://doi.org/10.3390/w12020528
National Administration Space Aeronautics (NASA), & Ministry of Economy Trade and Industry (METI). (n.d.). Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). Retrieved April 23, 2021, from https://gdemdl.aster.jspacesystems.or.jp/index_en.html
Neves, M. C., Nunes, L. M., & Monteiro, J. P. (2020). Evaluation of GRACE data for water resource management in Iberia: A case study of groundwater storage monitoring in the Algarve region. Journal of Hydrology: Regional Studies, 32, 100734. https://doi.org/10.1016/j.ejrh.2020.100734
Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., Mizukami, N., Brekke, L., & Arnold, J. R. (2015). Gridded ensemble precipitation and temperature estimates for the Contiguous United States. Journal of Hydrometeorology, 16(6), 2481–2500. https://doi.org/10.1175/JHM-D-15-0026.1
Pandey, R. P., Pandey, A., Galkate, R. V., Byun, H.-R., & Bimal, ·, Mal, C., Pandey, R. P., Pandey, A., Galkate, R. V, Byun, H.-R., & Mal, B. C. (2010). Integrating hydro-meteorological and physiographic factors for assessment of vulnerability to drought. Water Resources Management, 24, 4199–4217. https://doi.org/10.1007/s11269-010-9653-5
Parveen, U., & Sreekesh, S. (2018). Physiographic influence on rainfall variability: a case study of Upper Ganga Basin. In E. E. and D. R. R. Climate change (Ed.), Sustainable Development Goals Series (pp. 59–73). Springer, Cham. https://doi.org/10.1007/978-3-319-56469-2_4
Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5), 1633–1644. https://doi.org/10.5194/hess-11-1633-2007
Penagos Cruz, G. (2014). Variables hidrometereológicas asociadas al cambio climático en Girardot y la Región del Alto Magdalena. Ambiente y Desarrollo, 18(35), 134. https://doi.org/10.11144/Javeriana.AyD18-35.vhac
Portalés, C., Boronat, N., Pardo-Pascual, J. E., & Balaguer-Beser, A. (2010). Seasonal precipitation interpolation at the Valencia region with multivariate methods using geographic and topographic information. International Journal of Climatology, 30(10), 1547–1563. https://doi.org/10.1002/JOC.1988
Pulido-Calvo, I., Gutiérrez-Estrada, J. C., & Sanz-Fernández, V. (2020). Drought and ecological flows in the Lower Guadiana River Basin (Southwest Iberian Peninsula). Water, 12(3), 677. https://doi.org/10.3390/w12030677
Rich, P. M., Hetrick, W. A., Saving, S. C., & Dubayah, R. O. (1994). Using Viewshed models to calculate intercepted solar radiation: applications in ecology. American Society for Photogrammetry and Remote Sensing Technical Papers, 524–529.
Ruiz-Ortiz, V., Garciá-López, S., Solera, A., & Paredes, J. (2019). Contribution of decision support systems to water management improvement in basins with high evaporation in Mediterranean climates. Hydrology Research, 50(4), 1020–1036. https://doi.org/10.2166/nh.2019.014
Ruiz-Ortiz, V., García-López, S., Vélez-Nicolás, M., Sánchez-Bellón, Á., de Villar, A. C., & Contreras, F. (2021). Learning from hydrological and hydrogeological problems in civil engineering. Study of reservoirs in Andalusia, Spain. Engineering Geology, 282, 105916. https://doi.org/10.1016/j.enggeo.2020.105916
Segarra, J., González-Torralba, J., Aranjuelo, Í., Araus, J. L., & Kefauver, S. C. (2020). Estimating wheat grain yield using Sentinel-2 imagery and exploring topographic features and rainfall effects on wheat performance in Navarre, Spain. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142278
Shiau, J.-T., Chen, C.-N., & Tsai, C.-T. (2011). Physiographic drainage-inundation model based flooding vulnerability assessment. Water Resources Management, 26(5), 1307–1323. https://doi.org/10.1007/S11269-011-9960-5
Singh, A., Singh, R. M., Senthil Kumar, A. R., Kumar, A., Hanwat, S., & Tripathi, V. K. (2021). Evaluation of soft computing and regression-based techniques for the estimation of evaporation. Journal of Water and Climate Change, 12(1), 32–43. https://doi.org/10.2166/wcc.2019.101
Sistema Nacional de Informação de Recursos Hídricos (SNIRH). (n.d.). Redes de Monitorização. Retrieved March 11, 2021, from https://snirh.apambiente.pt/
Squintu, A. A., van der Schrier, G., Brugnara, Y., & Klein Tank, A. (2019). Homogenization of daily temperature series in the European Climate Assessment & Dataset. International Journal of Climatology, 39(3), 1243–1261. https://doi.org/10.1002/joc.5874
Strahler, A. N. (1969). Physical geography. John Wiley. https://onlinelibrary.wiley.com/doi/abs/10.1002/sce.3730550116
Tobin, C., Nicotina, L., Parlange, M. B., Berne, A., & Rinaldo, A. (2011). Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region. Journal of Hydrology, 401(1–2), 77–89. https://doi.org/10.1016/j.jhydrol.2011.02.010
Unwin, D. J. (1977). Statistical Methods in Physical Geography: Progress in Physical Geopraphy, 1(2), 185–221. https://doi.org/10.1177/030913337700100201
Vélez-Nicolás, M., García-López, S., Ruiz-Ortiz, V., Zazo, S., & Molina, J. L. (2022). Precipitation Variability and Drought Assessment Using the SPI: Application to Long-Term Series in the Strait of Gibraltar Area. https://doi.org/10.3390/w14060884
Vera, J. A. (2004). Geología de España (J. A. Vera (ed.); SGE-IGME). https://books.google.pt/books?hl=es&lr=&id=n1SO6IjVhZEC&oi=fnd&pg=PA1&dq=Vera+libro+geología&ots=7GeUv01izE&sig=S-utdKDNq9YpHesSHL1Vb3nDePI&redir_esc=y#v=onepage&q=Vera libro geología&f=false
Waldron, B., Gui, D., Liu, Y., Feng, L., & Dai, H. (2020). Assessing water distribution and agricultural expansion in the Cele Oasis, China. Environmental Monitoring and Assessment, 192(5). https://doi.org/10.1007/S10661-020-8233-2
WMO (World Meteorological Organization). (2020). Climate data and monitoring. Tenth Seminar for Homogenization and Quality Control in Climtologicla Database and Fith Conference on Spatial Interpolation Techniques in Climatology and Meteorology.
Wood, J. (1996). The geomorphological characterisation of digital elevation models. In Ph. D. Thesis, University of Leicester, Department of Geography, Leicester, UK.
Zazo, S., Molina, J.-L., Ruiz-Ortiz, V., Vélez-Nicolás, M., & García-López, S. (2020). Modeling river runoff temporal behavior through a hybrid causal–hydrological (HCH) method. Water, 12(11), 3137. https://doi.org/10.3390/w12113137
Zhang, C., Zhou, X., & Lei, W. (2019). Necessary length of daily precipitation time series for different entropy measures. Earth Science Informatics, 12(4), 475–487. https://doi.org/10.1007/s12145-019-00392-1
Zhao, G., Xue, H., & Ling, F. (2010). Assessment of ASTER GDEM performance by comparing with SRTM and ICESat/GLAS data in Central China. In Y. Liu & A. Chen (Eds.), 18th International Conference on Geoinformatics. IEEE GRSS; Geograph Soc China.
Acknowledgements
This work is the result of a research stay of the first author in the University of Algarve, which was supported by the University of Cadiz (2021-025/PU/PP-EST/MV).
Funding
This work is the result of a research stay of the first author in the University of Algarve, which was supported by the University of Cadiz (2021-025 / PU / PP-EST / MV). This work was supported by the FCT—Foundation for Science and Technology, strategic projects UIDB/04292/2020 granted to MARE and UIDB/04020/2020 granted to CinTurs.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ruiz-Ortiz, V., G. P. Isidoro, J.M., Fernandez, H.M. et al. Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian Peninsula. Environ Monit Assess 194, 722 (2022). https://doi.org/10.1007/s10661-022-10312-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-022-10312-4