Evaluation of Extreme Precipitation in Madeira Island

Transcription

1 Evaluation of Extreme Precipitation in Madeira Island Sílvia Sepúlveda Abstract The extreme natural phenomena can cause serious imbalances in social, economic and environmental structures of an island community. In Madeira Island, the most relevant risks result of weather conditions and geomorphologic characteristics of the island. It is so appropriate to identify and map areas of greatest susceptibility to the occurrence of extreme precipitation in the island, which is the main objective of this work. Data about the spatial characterization of rainfall (mm / day), corresponding to three sets of observations recorded between 2006 and 2008 in eleven local weather stations, were analyzed. After this, the indicators of extreme precipitation R30 (wet extreme) and RL10 (dry extreme), were characterized and normalized by the number of days per year with record rainfall. The method used to quantify the spatial variability of extreme precipitation was based on its modeling geo-statistics, by applying a stochastic simulation algorithm. The spatial extent of extreme rainfall was mapped to identify critical areas and to create images that were interpreted and analyzed with other information such as the mapping of land use and fire risk maps. It was concluded that the areas of Madeira Island with higher precipitation events are located at the highlands in the northwest-southeast direction, mostly with vegetation, although a significant part is human hand. The dry areas are more frequent are the lower lands located nearby the coast. They are intensely human hand across the south and the surrounding of the S. Jorge Santana station, at the north they are preferably occupied by forest. Introduction The characterization of extreme precipitation is an important element of support for designers of hydraulic structures, technical planning and management of water resources and planning, civil protection officials and the general public, as it enables the early prediction of cases of rupture, and for the management of natural resources such as forests, soils, and in particular, water. This knowledge is an important asset in decision support, deal with situations such as floods, droughts and water erosion. Information on rainfall intensities associated with the frequency of occurrence and duration is crucial for its quantification and the zoning of flood problems. On the island of Madeira, where the major disasters often strike with loss of life, environmental changes and substantial material damage, the risks considered most relevant are those arising from weather conditions and geomorphological features associated with the island, including the full fast, slips and landslides, and storms, as concluded Fernandes (2009). The waterways of the island is endowed with huge torrential, particularly by geology and topography of the island - some extensions exceeding tens of kilometers, descending from height with slopes between 30% and 40% at the top, and 4% to 10%, close to sea level. The degradation of forest

2 ecosystems in some watersheds of the region intensifies erosion and torrential, exacerbating the risks to which the island is exposed. In this context, it is pertinent to identify and map the areas of greatest susceptibility to extremes of precipitation (wet and dry) on the island of Madeira, which is the main objective of this work. The topic in question is justified, thus the need to contribute to knowledge about the parts of the island more sensitive to extreme precipitation events, so that, by crossing this information with the relevant land use, provide useful information for risk management in this region before such uncontrollable natural phenomena. Methodology This work presents a methodology based on exploratory data analysis, by reference, on the one hand, the geostatistical models to construct maps of the incidence of extreme precipitation (using software GeoMS), and secondly, the Geographic Information Systems for integrating relational databases of geographic data on land occupation and extreme precipitation (using ArcGis 9.3). The main sources of information are grounded in a set of data on the variable, geographically referenced. The data on the spatial characteristics of precipitation (mm/day) provided by the Institute of Meteorology and the Regional Civil Engineering Laboratory (LREC), consist of series of observations ordered in time, recorded between 2006 and 2008 in eleven measuring stations located at Madeira Island. The data analysis starts from basic series quality assessment and characterization of extreme rainfall indices R30 (to the extreme wet) and RL10 (to the extreme dry). Then we elaborate a descriptive analysis of data. Exploratory analysis proceeds with the study of the structures of spatial continuity, which identify and characterize the spatial covariance structures evidenced by the data. We experience various statistical parameters for analysis of the structure of spatial continuity, which fit the theoretical models appropriate to the variable. The results are used in the process of sequential stochastic simulation to evaluate the spatial uncertainty. By analyzing the maps obtained, we conclude about the areas of incidence of the phenomena under study, using a geographical information software to integrate the simulated images with the available information on land use. Characterization of baseline data To conduct the present study, we used the daily rainfall records from a network of automatic measurement of precipitation in Madeira Island, consisting of 11 stations udometer in the period covering the calendar years 2006, 2007 and Table 1 presents the identification and geographic location of udometer stations used, whose spatial distribution is presented in Figure 1.

3 Table 1 - Identification and geographic location of stations used udometer Name of station Height (m) Rectangular coordinates UTM Median Parallel Areeiro Bica de Cana Parque Ecológico do Funchal Pico Verde Encumeada Santa de Porto Moniz Ponta do Pargo - Calheta LREC S. Jorge - Santana Funchal Lugar de Baixo Source: Santos (2010) Figure 1 - Geographical location of weather stations used Source: Santos (2010) To study the wet end is accounted for each year and each season, the number of days with rainfall less than 29.4 mm, since it is considered as a reference to this end the minimum rainfall intensity of 30 mm per day, with a view to using the index of extreme rainfall R30. After they are found in several stations, the lack of records of measurements on various days of the year, we decided to determine the annual percentage of days of rainfall less than 29.4 mm, taking as reference the number to 100% total number of days with recorded measurements at each station. However, having regard to the elimination of close approaches, were excluded stations where failure rates equal or exceed 180 consecutive days, what happened, for the year 2006, with the stations of LREC and Ecological Park Funchal. This standardization intended to assign the correct weight to the contribution of each station to obtain the medium images after spatial inference by stochastic simulation. This standardization, resulting in a new variable: the index Normalized R30 (%R30), which is the variable to be used in the study of extreme damp, whose values are shown in Table 2.

4 Station Table 2 - Determination of the values of extreme precipitation index %R30 A. Number of days with precipitation 29.4 mm B. Number of days with recording measurements C. Index %R30 [C = (A/B) x 100] Areeiro Bica de Cana Parque Ecológico Funchal 11* * * Pico Verde Encumeada Santa de Porto Moniz Ponta do Pargo - Calheta LREC 4* * * S. Jorge - Santana Funchal Lugar de Baixo * Station excluded for having 180 or more consecutive days without recording measurements For the study of extremely dry, we applied the same procedure, although it has counted the number of days with rainfall less than or equal to 10.0 mm, since it is considered as a reference to this end the maximum intensity of precipitation 10 mm per day, with a view to using the index of extreme rainfall RL10. After normalization of the day recorded by the same procedure, we obtained the new variable: Normalized index RL10 (%RL10), which is the variable to be used in the study of extremely dry, and whose values are shown in Table 3. Table 3 - Determination of the values of extreme precipitation index %RL10 Station A. Number of days with precipitation 10.0 mm B. Number of days with recording measurements C. Index %RL10 [C = (A/B) x 100] Areeiro Bica de Cana Parque Ecológico Funchal 99* * * Pico Verde Encumeada Santa de Porto Moniz Ponta do Pargo - Calheta LREC 156* * * S. Jorge - Santana Funchal Lugar de Baixo * Station excluded for having 180 or more consecutive days without recording measurements

5 Geostatistical modeling of extreme rainfall rates on Madeira island The method used to quantify the spatial variability of extreme precipitation in the Madeira Island is based on geostatistical modeling of this climatic phenomenon, namely, the application of a stochastic simulation algorithm. Using this algorithm allows to map the spatial extent of extreme precipitation and thus identify critical areas and provide images that can be interpreted and analyzed with other information (such as land cover letter). Statistical analysis of data A stage prior to application of the simulation algorithm, it is important to characterize the major statistical experimental data available, in particular, the calculated values for the index %R30 (extreme wet) and %RL10 (extremely dry). Thus, we present the basic statistical location, shape and dispersion of the distribution of values of %R30 and %RL10, and its histogram, the joint analysis of the values of% R30 and RL10% for the period between 2006 and Subsequently, we studied this data set about its spatial continuity and its spatial variability, obtained through simulation. This treatment option is justified by the small number of stations with measurements of precipitation values and intent to give more robustness to the geostatistical analysis. Regarding the lack of measurement points, it is also noted that to the east of Madeira Island (from the area of Funchal) were not provided any data of precipitation. All results were obtained using the software included in the module Geodata GeoMS. The major statistical series analyzed in each study variable %R30 are summarized in Table 4, the average global histogram is shown in Figure 2, and global spatial distribution in Figure 3. Table 4 - Statistical principal of each series analyzed in the study of the index %R30 Period Nº of obs. Average Median P.75 Variance Coef. Variation Min Max asymemtry Global

6 Figure 2 - Histogram of the average global of %R30 Global Figure 3 - Spatial distribution point of index R30% Global Visual inspection of the distributions of the variable in space to the set of all years, shows that the variable %R30 shows lower values in the regions bordering the study area and highest in the northwest-southeast direction, corresponding to higher altitudes. For the study of variable %RL10, are summarized in Table 7, the main statistical each series analyzed, the average global histogram on the set of three, is shown in Figure 4, and global spatial distribution in Figure 5. Tabel 7 - Statistical principal of each series analyzed in the study of the index %RL10 Period Nº of obs. Average Median P.75 Variance Coef. Variation Min Max asymemtry Ano 2006 Ano 2007 Ano Global ,11 Figure 4 - Histogram of the average global of %RL10 Global Figure 5 - Spatial distribution point of index %RL10 Global

7 Overall, visual inspection of the distributions of the variable in space to the set of all years, shows that the variable %RL10 has a tendency to high values of neighboring regions of the island, especially the south and west of the study area and more low northwest-southeast direction, corresponding to higher altitudes. Analysis of spatial continuity For both extremes, we chose to make the adjustment to the omnidirectional semivariogram obtained for the experimental values, using the spherical model. It is presented in Figure 6 the average semivariogram spatial variable %R30 for the set of all years for which was also obtained a range of 9 km. In Figure 7, shows the average semivariogram %RL10 of the variable space for the set of all years, which was obtained for a range of greater continuity of 14 km. The results were obtained using all the modules and geovar GEOMOD GeoMS included in the software. Figure 61 - Overall average of the experimental semivariogram %R30 and adjustment of the spherical model Figura 7 - Overall average of the experimental semivariogram %RL10 and adjustment of the spherical model Then, we proceeded the average simulated images obtained from the global series, for each of the three years observed, shown in Figure 8 for the extreme wet and Figure 9 for the extreme dry. Figure 8 - Averages images of the simulated index %R30 These images show that the zones of Madeira target of more intense precipitation events are located at higher places in the northwest-southeast direction, and lower rates of heavy precipitation dominate the southern coast of the island.

8 Figura 9 - Averages images of the simulated index %RL10 These images show that the zones of Madeira target a greater number of drought events are located at lower altitudes, especially on the south coast, as mentioned above. It is also clearly marked the area surrounding the station S. Jorge Santana, on the north, target of drought events. In northwestsoutheast direction arise areas with lower values of the %RL10, being the least affected by such events. Delimitation of the critical areas of extreme precipitation in Madeira Island Was defined, as a criterion for classifying an area as critical, accounting for the precipitation index values above a cutoff value, considered as representative of an extreme situation. This value corresponds to the 75th percentile (P75), calculated for all the experimental values. Thus, based on all the thirty simulated images for the index %R30 and the index %RL10 (from the overall series), the values were recorded in space that exceed both the P75 with a probability of 0.5. For all the three years analyzed, identified critical areas in terms of intense precipitation, are shown in Figure 10, and for the greater incidence of drought are shown in Figure 11. Figure 10 Critical areas of intense precipitation for all three years Figure 11 Drought Critical areas for all three years For each of the levels of precipitation, the integration was made between the respective critical areas and the Charter of Madeira Land Use (COS 2007, provided by the Regional Geographic Information

9 and Spatial Planning), in order to identify the potentially affected by these climatic phenomena (Figure 12 to extreme wet and Figure 13 to dry extreme). Figure 12 Areas of heavy precipitation and its land use Figure 13 Drought and critical areas of their land use Figure 12 shows that the areas most affected by heavy precipitation events (Areeiro, Parque Ecológico, Bica de Cana, Pico Verde) have, mostly vegetation (permanent pastures, forests, open woodlands, shrub and herbaceous ), although a significant part is artificialized (Bica de Cana). Figure 13 shows that the areas most affected by drought events are densely artificialized stations in the surrounding area of Funchal and the LREC, or correspond to a mixture of artificialized areas and vegetation cover (forest, shrub and herbaceous), as Lugar de Baixo and in Ponta do Pargo (this predominantly open forest, shrub and herbaceous). Since most of the surrounding station S. Jorge - Santana, emphatically signaled, is occupied by forest. Flood Risk Analysis: Early Warning System The previous section analyzed the areas at risk of extreme precipitation through the characterization of areas of high probability (above 50%) of the index %R30 exceed the cutoff value equivalent to P75. Along with the maps mean, is characterized in this way a standard space-time (for all three time periods) of extreme rainfall. The purpose of this section is to analyze the consequences of extreme rainfall - such as the risk of floods and mudslides - from the default of %R30 analyzed. In other words, whenever the limits are exceeded this standard, we assume that we are in presence of a critical period of potential risk of flooding or mudslides. Specifically, in each space location x 0, which one knows the probability distribution Fz (x 0 ), estimated by the set of simulated values, can quantify the extremes P75 sites (x 0 ), P90 (x 0 ) or the maximum value Max (x 0 ). These parameters, which are the local extreme of %R30, can act as early warning of risk of flooding and mudslides. For example, if a given spatial location x0, for which the value P90 (x0)

10 or Max (x0) is equal to two days (two days with precipitation exceeding 30 mm), this limit works as a warning if it is exceeded. As an example of a map of early warning of risk of flooding and mudslides shows the map of %R30 maximum values for all three years. Note that given the gaps identified in the data sets, all %R30 index analysis was based on the total number of days and not on consecutive days with precipitation exceeding 30mm. However it was found a good correlation between the number of days and the total number of consecutive days. So it may be adopted such a warning system based on the maximum values of %R30. Conclusions and final remarks The study demonstrates and confirms the high susceptibility of Madeira to the occurrence of the phenomenon of floods and complexity of the processes associated with it. Critical areas in terms of extreme precipitation appeared a bit all over the island, mainly due to heavy rainfall, but also to drought events. Given the low density of stations measuring precipitation considered in this work, and the fact that identified critical areas are located in the envelope 10 of the 11 stations, it is expected that the real situation (it would be better represented if the data base include more stations) is even more critical than that presented in this paper. In order to improve the consistency of results, it is proposed that the procedures adopted in this work the baseline data more consistent, suggesting as minimum data recorded by 20 stations in the last 10 years, to subdaily or daily intervals, if possible, from the same source (the LREC currently manages the latest network that covers the entire island, with udometer associated with digital data loggers, most of which measure rainfall in 10 minute intervals). References - Fernandes, M. J. P. (2009). Riscos no Concelho da Ribeira Brava Movimentos de Vertente Cheias Rápidas e Inundações. Mestrado em Dinâmicas Naturais e Riscos Naturais - Riscos Geomorfológicos e Hidrológicos. Faculdade de Letras Universidade de Coimbra. - Horta, A. (2010). Soil contamination risk assessment. Dissertation submitted for the degree of Doctor of Earth and Space Sciences. Instituto Superior Técnico. - Ribeiro, M. (2006). A modelação geoestatística e os Sistemas de Informação Geográfica - uma abordagem exploratória aplicada em Saúde Pública. Dissertação de Mestrado ISEGI/UNL. - Soares, A. (2000). Geoestatística para as Ciências da Terra e do Ambiente. Lisboa, IST Press. - Soares, A. (2001). Direct Sequential Simulation and Cosimulation. Mathematical Geology.