Drought modelling. Application to case studies.

Transcription

1 Drought modelling. Application to case studies. Extended Abstract Diana da Silva Serra February, 2014

2 Drought modelling. Application to case studies. Diana da Silva Serra Scope Droughts are natural hazard with severe consequences in different areas, such as economy, agriculture and water supply. Therefore is important to study the phenomena in order to understand it better and consequently to reduce its impacts, whenever possible. Drought characterization, in this research, is based on the SPI index (Standardized Precipitation Index) that allows the determination of duration and intensity of such event. The main purpose of this research is, by using the SPI index, to compare drought events resulting from the analysis of rainfall and runoff series and then study the effect of dams on the propagation of droughts downstream of such infrastructures. If the correlation between SPI applied to rainfall and flow series is high, than droughts events can be characterized through any of such series. The advantage of using runoff series to define those events is the possibility of detecting droughts stem from either natural hydrological variability or due to Human activities such as the construction of dams. In fact after the construction of a dam creating an artificial reservoir, the droughts that may occur result from the flow released downstream of the dam and not from the rainfall over the watershed. For that reason in order to study the propagations of droughts downstream the dam, the SPI has to be applied to runoff series. Therefore, to materialize this research nine study cases were selected to compare the SPI resulting from both rainfall and flow series. In order to study the dam effects in droughts propagation, the Portuguese dam of Vilarinho das Furnas, located in the Homem River, in the North of Portugal, was elected. To analyse the effect of the dam along the Homem River the stream gauging station of Covas located downstream of such infrastructure was chosen. Models The model applied to characterize droughts was the SPI. This index can be associated to different time scales the most common are 1, 3, 6 (which represent the meteorological and agricultural droughts) and 12 months (which represents the hydrologic droughts). In the time scale of one month each precipitation will be associated to one SPI value, thus if there is N years of rainfall records, there will be 12 times N SPI results. To other scales the rainfall must be accumulated in sets with as many elements as the correspondent time scale number. The data set is moved in the sense that for each month a new value is determined. Consequently the number of SPI values will be lower as the time scale rises that means that to the SPI 3, 6 and 12 will be (12N-2), (12N-5) and (12N-11) respectively (Santos & Portela, 2010). 1

3 In general terms the method consist in applying to the rainfall sample the Pearson III function (to adjust that sample into a distributed probability function) and then, by applying the standardized inverse normal, the distributed probability density is obtained with mean of zero and standard deviation of unity (McKee, et al., 1993). In other words, the SPI is the normal standard value of the probability of non-exceedance of an observed precipitation. This means that when SPI is null, there isn t any deviation to the mean calculated to a certain historic record. If it is positive the observed rainfall is higher than the mean and the inverse if it is negative. (Santos & Portela, 2010). In order to classify the drought category there were established some limit values according to Agnew, 2000, which are expressed in Table 1. Table 1 Drought category for SPI values according to Agnew, SPI Drought Category <SPI Moderate drought <SPI < Severe drought <SPI < Extreme drought The reason why this index was chosen was due to its numerous advantage such as: Its robutness and versatility in drought analysis (López-Moreno, et al., 2009). SPI values are comparable in both time and space, and are not affected by geographical or topographical differences (López-Moreno, et al., 2009). The SPI is uniquely related to probability (McKee, et al., 1993). As SPI is normally distributed it can be used to monitor wet and dry periods (McKee, et al., 1993). Another model applied was a flow regionalization model aiming at reconstructing the natural river regime downstream dams creating artificial reservoirs. For that purpose the transposition procedure presented by Portela & Quintela, 2005, was applied. This model uses the Equation 1,!!,! =!!,!! (1)! where! represents the average annual flow upstream to the section k: 1 (the stream gauging station with records) and 2 (the stream gauging station which the natural regime is going to be reconstructed) (m 3!! ),!,! is the monthly flow registered in the gauging station and!,! represents the transposed runoff. The index i and j refer to the year and to the month, respectively (Portela & Quintela, 2005, 2006). In order to assure that the runoff serie transposed to a section in a watercourse is adequate to the hydrological conditions in the respective watershed, the annual flow depth expressed in mm must be close enough to the annual runoff depth from the stream gauging station with historic data. It is also better if both watershed (from the watercourse section and from the gauging station) are located as 2

4 closely as possible to each other and have similar geomorfological condition as well as identical soil use and occupation (Portela & Quintela, 2006). Comparison of the SPI based on rainfall and runoff series In order to evaluate the capacity of the SPI index applied both to rainfall and runoff series to detect similar drought events, nine case studies, identified in Table 2, were selected. Table 2 General characteristics of the stream gauging station of each case study in the period between 1910/11 and 2003/04. Watershed )Gauging)station Watershed)area)) (km 2 ) Average) annual)flow) (mm) Average)annual) precipitation) (mm) Cunhas , ,43 Castro6Daire , ,84 )Douro)River Cabriz , ,95 Vinhais6Quinta6da6 Ranca , ,62 Vale6Giestoso , ,18 Sado)River) Torrão6do6Alentejo ,63 729,58 )Tejo)River Couto6Andreiros ,62 647,59 Guadiana) Vascão ,10 655,43 River) Albernoa ,61 525,02 Those case studies were chosen due to the amount of records available, as Santos, 2007, had obtained the monthly flows and precipitations to a period of 94 years between 1910/11 and 2003/04. For each case study the monthly precipitations were computed (based on the Thiessen method) and used to calculate the SPI in four different time scales (1, 3, 6, 12 months), which was also applied to the corresponding monthly flow series. According to the established Agnew, 2000, a drought occurs whenever the SPI is below The results are synthetized in Table 3, where are indicated the number of occurrence of drought events, the minimum and mean value as well as the standard deviation of SPI. With the aim of easing the table reading, the more severe characteristics were shaded. By more sever is understood as the highest number of occurrence and standard deviation, and the lowest minimum and mean values. The different colors used in the table have no specific meaning besides the fact they facilitate the reading. The case studies are organized by descending order of average annual precipitation. 3

5 Table 3 Resume table of the analysis of the SPI results applied to rainfall and runoff series, in four different time scales. For each case study is indicated, between brackets, the annual precipitation and flow in mm, respectively. Stream gauging station Cunhas ( ; ) Castro Daire ( ; ) Cabriz ( ; ) SPI Drought event Precipitation Flow Precipitation Flow Precipitation Flow Number of occurrences SPI 1 Minimum value Mean value Standard deviation Number of occurrences SPI 3 Minimum value Mean value Standard deviation Number of occurrences SPI 6 Minimum value Mean value Standard deviation Number of occurrences SPI 12 Minimum value Mean value Standard deviation Stream gauging station Vinhais Quinta da Ranca ( ; ) Vale Giestoso ( ; ) Torrão do Alentejo (729.59;156.63) SPI Drought event Precipitation Flow Precipitation Flow Precipitation Flow Number of occurrences SPI 1 Minimum value Mean value Standard deviation Number of occurrences SPI 3 Minimum value Mean value Standard deviation Number of occurrences SPI 6 Minimum value Mean value Standard deviation Number of occurrences SPI 12 Minimum value Mean value Standard deviation Stream gauging station Vascão (655.43; ) Couto Andreiros (647.59; ) Albernoa (525.02; 96.61) SPI Drought event Precipitation Flow Precipitation Flow Precipitation Flow Number of occurrences SPI 1 Minimum value Mean value Standard deviation Number of occurrences SPI 3 Minimum value Mean value Standard deviation Number of occurrences SPI 6 Minimum value Mean value Standard deviation Number of occurrences SPI 12 Minimum value Mean value Standard deviation The results were also classified in drought categories, according to Agnew, 2000, and are represented in Figure 1. Once more the stream gauging stations were organized by descending order of average annual precipitation. 4

6 Figure 1 Characterization of the three drought categories in number of occurrence (column) and mean value of SPI (line), divided by time scale, to all the case studies which are organized by descending order of average annual precipitation. 5

7 As it can be observed such in the figure as in the table, in watersheds with low average annual precipitation, as the last three study cases, the SPI index fails to detect drought events when applied to flow series. However, to high average annual precipitation, the SPI resulting from both series, flow and precipitation, identifies similar droughts events, and that similarity increases with the rise of the time scale. Influence of Vilarinho das Furnas dam in the drought events downstream the dam With the aim of analyzing the influence of Vilarinho das Furnas dam in the drought events downstream its infrastructure, the stream gauging station of Covas, located downstream of the dam in the Homem River, was selected. The monthly flow series at Covas is presented in the Figure 2. The blue line is related to the monthly record available at the gauging station. Those records, before 1972 (year of the dam construction) are in natural conditions and after that year in modified condition due to the dam operation. Such records were obtained from the website SNRIH (Sistema Nacional de Informação de Recursos Hídricos). Figure 2 Time series of monthly flow at the gauging station Covas in natural and modified conditions. The natural regime after the dam construction and operation was obtained by transposition from the Fragas da Torres gauging station, according to the procedure developed by Portela & Quintela, 2005, The mean annual flow depth in Covas watershed and in Fragas da Torre, during the period of 1955/56 to 1971/72, are mm and mm, respectively. Those values are close enough to enable the flow transposition. Prior to the effect of the dam in drought propagation, it was verified that also for Covas watershed the drought events could be characterized based either on the stream flow or rainfall series. With that goal, the SPI was applied to the natural flow and corresponding precipitation during the period of 47 years between 1955/56 and 2001/02. The monthly rainfall series at Covas watershed were evaluated by applying the Thiessen method to the following rain gages: Portela do Homem, Leonte, Covide, São Bento da Porta Aberta e Seixas (weight of 27, 21, 26, 14, 13 respectively). The scatter plot between SPI values based on rainfall and runoff series are exemplified in Figure 3 for the time scale 6 and 12 months. The closer the values are to the 45 degrees line more correlated are the values, meaning that both series can be used to characterize drought events. 6

8 Figure 3 Scatter plot between SPI obtained from flow series (vertical axis) and rainfall series (horizontal axis), at the time scale 6 and 12 months, for the period of 1955/ /02. The values are pretty closed to the 45 degree line, denoting a reasonable adjustment especially for the higher time scales. That being said, drought events in Covas watershed can be characterized by applying the SPI index to flow series. This possibility allows the study of propagation of drought downstream a dam through the index SPI. The Figure 4 exemplifies the results achieved for the higher time scales (6 and 12 months) when applying the SPI to the natural regime after the dam construction (values obtained by transposition) and to the modified conditions (values registered in Covas gauging station) due to the dam operation. The record period is from 1972/73 to 2001/02. Figure 4 Characterization of the SPI series calculated from monthly flow in natural conditions and in modified conditions. Period of records between 1972/73 and 2001/02. In the graphic is possible to note that there are some higher peaks in the modified regime than in the natural regime. This indicates that the drought intensity is more severe in modified conditions. In order to synthetize the information and to evaluate the difference between both regimes in each drought category, the results were organized in a graphic, Figure 5, where the number of occurrence and the SPI mean value are represented in column and lines, respectively. 7

9 Figure 5 Characterization of the three drought categories in number of occurrence (column) and mean value of SPI (line), divided by time scale. Conclusion and recommendations This study allowed achieving two sets of conclusions. The first one, referring to the possibility of detecting similar drought events by applying SPI to flow series or to rainfall series, Figure 1, is: To stream gauging station with low average annual flows, the SPI resulting from runoff series fail to detect droughts when applied to small time scales, such as one and three months. Independently of the average annual flow, the differences between SPI applied to runoff and rainfall series diminish with the increase of time scales used. When exists a large stream flow, the drought events are more easily detected using flow series to compute SPI than with rainfall series, on the other hand, when there is low water availability, the drought events are better characterized using precipitation series. The second part of this research aimed to evaluate the propagation of droughts downstream of the dam Vilarinho das Furnas, by analyzing the flow records in Covas gauging station, situated downstream of the dam, and comparing it, through the SPI index, with the natural runoff that would occur if there was no dam, obtained by transposition. As the average annual flow in Covas is high was expected to equal characterized drought events whether using flow or precipitation series to calculate the SPI. That was the conclusion achieve with the Figure 3, where is showed that the correlation between SPI from each series is high, meaning that both detect similar drought events. That being said, the study could be continued and the following conclusions were reached by analyzing the results presented in Figure 4 and 5: The number of occurrence of droughts in natural regime is higher than in modified conditions. The SPI minimum and mean values are more negative in modified conditions than in natural. When analyzing by drought categories, for moderate and severe droughts it is observed that the SPI mean values are pretty closed between both regimes, but the number of occurrence is higher for the natural regime. For extreme droughts the number of occurrence and the SPI mean value are more severe for modified conditions. Indeed, the operation of the dam Vilarinho das Furnas regulates the flow, reducing its temporal variability. Consequently, the SPI detects a lower number of drought occurrences in modified conditions than in natural ones, because occur less deviation to the mean value. However, in very dry years, and because the dam retains the flow upstream, the number of drought events as well as the their intensity are more severe in modified conditions. 8

10 These results allow to conclude that the dam Vilarinho das Furnas induces more severe droughts both in number and intensity downstream of the infrastructure and with bigger occurrence in the extreme drought category. Meaning that in case of dry year the river may run out of water. However, it is not possible to generalize those conclusions. In order to do that more dams should be studied from North and South of Portugal and even outside of Portugal, so that the drought propagation could be studied in different hydrological and meteorological conditions. That could be an interesting future investigation. An extended study of the application of SPI to flow series is also advised specially for cases with low average annual flow, as it can be an advantage to use the SPI index obtained from runoff because it acknowledges the hydrological droughts either natural or humanly induced. Bibliography Agnew, C. T., Using the SPI to Identify Drought, s.l.: University of Nebraska - Lincoln. López- Moreno, J. I. et al., Dam effects on droughts magnitude and duration in a transboundary basin: The Lower River Tagus, Spain and Portugal. s.l., Water Resources Research. McKee, T. B., Doesken, N. J. & Kleist, J., The Relationship of drought frequency and duration to time scales. Anaheim, Department of Atmospheric Science Colorado State University. Portela, M. M. & Quintela, A. C., Estimação em Portugal Continental de escoamentos e de capacidades úteis de albufeiras de regularização na ausência de informação. Recursos Hídricos, 27(2)( Número Temático: Hidrologia e Modelação Hidrológica, ISSN ), pp Portela, M. M. & Quintela, A. d. C., Estimação de Séries de caudais médios diários na ausência de informação hidrométrica. Évora, s.n. Santos, J. F., Alteraçõs em séries de variáveis hidroclimatológicas: seus efeitos nos volumes de água a fornecer para a rega e na fiabilidade do fornecimento desses volumes a partir das albufeiras. Lisboa: Universidade técnica de Lisboa: Instituto Superior Técnico. Santos, J. F. & Portela, M. M., Caraterização de secas em bacias hidrográficas de Portugal Continental: aplicação do índice de precipitação padronizada, SPI, a série de precipitações e de escoamento. Associação Portuguesa dos Recursos Hídricos. 9