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Maria Neto Paixão Vazquez Fernandez Martins, Helena
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- Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian PeninsulaPublication . Ruiz-Ortiz, Verónica; G. P. Isidoro, Jorge M.; Fernandez, Helena Maria; Granja-Martins, Fernando M.; García-López, SantiagoA 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 km(2)). 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.
- Mapping rainfall aggressiveness from physiographical data: application to the Grândola Mountain Range (Alentejo, Portugal)Publication . Fernandez, Helena Maria; Granja-Martins, Fernando M.; Isidoro, Jorge M. G. P.The South of the Iberian Peninsula is subject to long periods of drought followed by heavy rain events over shallow soils, promoting soil loss. The Modified Fournier Index (MFI) is a good indicator of this process; however, MFI is sometimes difficult to assess due to the scarcity of rainfall data. This study proposes a methodology using MFI and supported by a geographic information system (GIS) and geostatistics to map rainfall aggressiveness with scarce spatial rainfall data, where physiographic variables are used to overcome the lack of rainfall data. The Grândola Mountain Range in the Alentejo region, Portugal, is presented as a case study. This area is a CORINE biotope, currently under application to the Natura 2000 network, and should be considered as a priority for the conservation of the environment. The model allowed us to create a map of rainfall aggressiveness, classified according to CORINE-CEC, found to be Moderate in the mountains and Low in the coastal area of the Grândola Mountain Range. This cartography is an important tool for local and national stakeholders and authorities with responsibilities in planning and protection of the territory. The methodology can be used in regions with scarce spatial rainfall data to assess areas susceptible to rainfall-induced soil erosion.
- A multimethod interpolation approach for mapping the spatial distribution of rainfall in southwest Iberian PeninsulaPublication . Ruiz-Ortiz, Verónica; Maria Neto Paixão Vazquez Fernandez Martins, Helena; Granja Martins, Fernando Miguel; Vélez-Nicolás, Mercedes; Isidoro, Jorge; García-López, SantiagoEight spatial interpolation models were used to map the spatial distribution of precipitation in the southwestern sector of the Iberian Peninsula (22330 km2) over 40 years (1980/1981-2019/2020). Rainfall data from 103 meteorological stations were used to generate the interpolation models, namely inverse distance weight (IDW) with 6, 12 and 24 points, regression spline (RS), thin spline (TS), universal kriging with spherical and Gaussian variogram (UK_Sphe and UK_gauss, respectively) and multilinear regression (MR), based on physiographic and geographic variables. Furthermore, 32 rainfall stations were used to assess the performance of the previous methods through 7 statistical metrics Ordinary Least Squares (OLS), Root Mean Square Error (RMSE), Normalized root mean square error (NRMSE), Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (Bias and MAE). Based on these metrics, UK_gauss and IDW_6 provided the best adjustments, whereas MR presented the highest errors. All methods were suitable to predict the spatial distribution of rainfall, but adjustments are conditioned by the features of the study area, gauge density and gauge spatial distribution.