Nat Hazards (2015) 79:1239–1256 DOI 10.1007/s11069-015-1903-7 ORIGINAL PAPER
Flood forecasting in the upper Uruguay River basin A. S. Ferna´ndez Bou1 M. Cataldi1
•
R. Ventura De Sa´2
•
Received: 10 January 2015 / Accepted: 8 July 2015 / Published online: 16 July 2015 Springer Science+Business Media Dordrecht 2015
Abstract Floods are common in the upper Uruguay River, and they may occur almost at any moment, because there are not defined rainfall seasonal patterns. Moreover, there is not an official model for flood forecasting in this basin. This study developed a methodology for 1-month flood forecasting in the upper region of the Uruguay River basin (C3000 m3 s-1), from the headwaters to the Ita´ reservoir. The monthly version of the SMAP (Soil Moisture Accounting Procedure) model was presented and used to describe the transformation of rainfall into runoff, and the CFSv2 (Climate Forecast System version 2) model was used to provide rainfall forecasts. Twenty-five 1-month-lead rainfall forecasts were used to calculate 25 flow predictions for every month. Ensembles with different number of members were compared among them and with the official model currently used for 1-month flow forecasting in the upper Uruguay River. The best accuracy was achieved with the average of the first seven members, which showed a mean relative error of 10.8 % during the floods, while the official model presented 64.0 %, predicting remarkably lower flows. Furthermore, during the period assessed, the correlation between the natural flow and the first-seven-member ensemble was [0.77, while with the official model was 0.34. Thus, coupling SMAP and CFS is a valid approach that can be useful to anticipate mitigating actions to decrease the effects of severe floods in the upper Uruguay River and, probably, in other Brazilian basins. Keywords
Flood forecast Flood defense SMAP CFSv2
& M. Cataldi
[email protected] A. S. Ferna´ndez Bou
[email protected] 1
Fluminense Federal University, Rua Passo da Pa´tria, 156, bl E, sl 302, Sa˜o Domingos, Campus da Praia Vermelha, Nitero´i, RJ CEP: 24210-240, Brazil
2
Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
123
1240
Nat Hazards (2015) 79:1239–1256
1 Introduction Brazil has the largest supply of freshwater resources in the world (12.8 %). According to the Food and Agriculture Organization (FAO), the long-term average precipitation is 1761 mm per year, equivalent to 14,995 km3 per year (FAO 2015). Thus, water resources play a key role in the Brazilian energy sector, where about 90 % of country’s generation comes from hydroelectric plants (Cataldi et al. 2012). Southern Brazilian basins are complex, with short times of concentration, and their seasonality is not regular, making it difficult to generate reliable flow forecasts. Due to the intense rainfall and the geomorphological characteristics of the land, floods are common in some basins of the Brazilian southern states. Poor forecasting capability may also cause suboptimal operation of hydroelectric energy facilities, which are important in these southern basins. In a changing environment due to global warming, the frequency of great floods can increase remarkably (Milly et al. 2002; Alfieri et al. 2015). In fact, floods are the most frequent natural disaster in most of the countries in the world (Stillwell 1992; Ahern et al. 2005). In this context, tools to predict floods, such as flood warning systems, may be a significant advantage to preserve lives and properties, as well as to reduce the potential damages caused by them. Flood forecasting systems are traditionally based on a combination of hydrological and rainfall forecast models. Their aim is to maximize the lead time necessary until the alerts are delivered. Lead time depends on the lag time necessary for the transformation of rainfall into runoff (Werner et al. 2005). As the time of concentration in Brazilian southern basins goes from hours to a few days, the greater the lead time, the greater the probability to reduce the damages produced by a flood. Warning systems for excessive rain events have been developing over the past few decades and are commonly used in many places worldwide to warn people on imminent natural disasters, such as floods or landslides (Sorensen 2000; Alfieri et al. 2012). Some institutions provide online services of flood monitoring, such as the Dartmouth Flood Observatory, which is linked to NASA (http://floodobservatory.colorado.edu/), IFnet (International Flood network, www.internationalfloodnetwork.org), the Global Flood Partnership (http://portal.gdacs.org/Global-Flood-Partnership) (De Groeve et al. 2014); in the USA, NOAA’s AFWS (Automated Flood Warning System, http://water.weather.gov/ afws); and, in Europe, EFAS (European Flood Alert System, www.efas.eu) (Bartholmes et al. 2009; Thielen et al. 2009; Alfieri et al. 2014). Since hydropower is the main energy source in Brazil, the country has a National Interconnected System with hundreds of hydroelectric plants. Nonetheless, the system is managed exclusively by one nonprofit entity, the Electric System National Operator, ONS. The ONS is often involved in flood control, given that it manages energy generation in many regulating reservoirs, including the ones on the Uruguay River. Because of the system’s inertia, anticipating the possibility of floods may prevent them, while keeping optimal hydroelectric power generation rates. Thus, if it was possible to anticipate a flood in a basin, it would be possible to plan the correct operation of the dam to attenuate the flood, for example, by increasing the flow (and hence the energy generation) weeks earlier. However, the flow forecast model used by the ONS for horizons [7 days is stochastic (ONS 2007), and the only input used is a series of former flows, without considering climate. Besides, the model is used to plan energy generation, and not specifically for flood forecasting. Therefore, it is used just an indicator for flood events, because there are no other official 1-month flood forecast model in the Uruguay River basin.
123
Nat Hazards (2015) 79:1239–1256
1241
The main objective of this study was to develop an approach for providing 1-month flood forecasts for the upper reaches of the Uruguay River. The monthly version of the SMAP (Soil Moisture Accounting Procedure) model was introduced and used to represent how rainfall transformed into runoff (Lopes et al. 1982). For the operating period, from 2005 to 2010, the global model CFSv2 (Climate Forecast System version 2) was used to provide the input rainfall forecasts (Saha et al. 2014) and coupled with SMAP. The accuracy and the uncertainty of flood forecasting were evaluated using different ensembles and compared with the official model used by the ONS. This methodology aims to support companies involved in hydropower generation and government agencies in the development of better policies with respect to flood control, flood mitigation and hydroelectric power generation.
2 Data and methods 2.1 Uruguay River basin at Ita´ The Uruguay River headwaters are located in Serra do Mar, in the south of Brazil, where the river divides the states of Santa Catarina at north and Rio Grande do Sul at south.
Fig. 1 Detail of the Uruguay River basin. Studied basin remarked in blue, from the Uruguay River headwaters to the Ita´ hydroelectric plant (red square to the west of the basin). Rainfall stations are represented by red circles. Source: SRTM/USGS (topography)
123
1242
Nat Hazards (2015) 79:1239–1256
Subsequently, it divides the nations of Brazil and Argentina and of Argentina and Uruguay. The river is a tributary of the Rı´o de la Plata basin, where the capitals of Argentina (Buenos Aires) and Uruguay (Montevideo) are situated. Cities along the Uruguay River are often affected by floods, which produce remarkable losses in terms of properties and quality of life. The South Region of Brazil has uniform precipitation and climate, with average temperatures between 14 and 18 C and rainfall between 1250 and 2000 mm per year (FAO 2003). According to the Brazilian Environment Ministry (Ministe´rio do Meio Ambiente), the Uruguay River hydrographic region has average rainfall of 1784 mm per year, average temperature between 16 and 20 C and average evapotranspiration of 1041 mm per year (Ministe´rio do Meio Ambiente 2006). This study focused on the catchment area upstream of the Ita´ hydroelectric power plant. The basin is presented in Fig. 1, highlighted in blue. The surface of this area is 44,500 km2 (Conso´rcio Ita´ 2015).
2.2 Rainfall analysis and flow Twelve representative rainfall stations were selected from the national water agency (Agencia Nacional de A´guas, ANA) HidroWeb database (http://hidroweb.ana.gov.br). Statistical analyses were performed, such as seasonality, cluster patterns and the isohyetal lines behavior. The stations had at least 20 years (240 months) of good-quality data. There were no missing data for the period studied, from 1986 to 2010. The rainfall within the upper Uruguay River basin does not follow the seasonality typical of other Brazilian basins (Collischonn et al. 2005). With ill-defined rainfall patterns, floods may occur almost at any moment. Since 1932, nine of the 12 calendar months have had at least one flood. The seasonality graph for the period from 1986 to 2005 is
Fig. 2 Rainfall seasonality from 1986 to 2005 of the 12 rainfall stations studied within the Ita´ basin. The dotted black line represents the average of all rainfall stations; the blue lines represent the average monthly rainfall of each station; and the rectangles indicate the average minus and plus the standard deviation, exhibiting strong interannual variability. Source: HidroWeb
123
Nat Hazards (2015) 79:1239–1256
1243
presented in Fig. 2, exhibiting similar seasonal trends but marked ranges in precipitation between the stations and over time. Natural flow is the amount of water that would flow in the absence of human influence (FAO 2003). It is an input required by the SMAP model, and the estimated natural flow data are public and available at www.ons.org.br/operacao/vazoes_naturais.aspx. Those data are computed based on historic flow series, consumptive uses and reservoir regulation, among others. Within the context of this article, ‘‘natural flow’’ must be understood as ‘‘estimated natural flow,’’ and ‘‘predicted flow’’ or ‘‘calculated flow’’ as ‘‘calculated natural flow.’’
2.3 Performance metrics Four performance metrics were used in this methodology. The SMAP model used the Nash–Sutcliffe efficiency coefficient (Nash and Sutcliffe 1970) and the mean absolute percentage error (MAPE). These metrics were used to compare natural and predicted flow, in order to optimize the parameters of the hydrological model. They are presented, respectively, in Eqs. 1 and 2. The root-mean-square error (RMSE) was used to evaluate the rainfall results before and after the bias removal (Eq. 3). The mean absolute error (MAE), presented in Eq. 4, was used to assess both the bias removal process and the flow calculated based on each member of the climate ensemble. Nash–Sutcliffe model efficiency coefficient P ðQo ðtÞQc ðtÞÞ2 ð1Þ E ¼ 1 P 2 Qo ðtÞQo Mean absolute percentage error MAPE ¼
1 X jQo ðtÞQc ðtÞj Qo ðtÞ n
ð2Þ
where E, Nash–Sutcliffe model efficiency coefficient (dimensionless) and E B 1. MAPE, mean absolute percentage error (dimensionless) and MAPE C 0. Qo (t), flow at time t. Qc (t), calculated flow at time t. Qo , mean flow. Root-mean-square error RMSE ¼
1 n
ð3Þ
where RMSE, root-mean-squared error. Po (t), observed precipitation at time t. Pc (t), precipitation forecast at time t. Mean absolute error 1X ð4Þ MAE ¼ jHo ðtÞHc ðtÞj n where MAE, mean absolute error, used for climate and hydrological modeling assessment. Ho (t), observed precipitation (climate modeling) or flow (hydrological modeling) at time t. Hc (t), forecasted precipitation (climate modeling) or calculated flow (hydrological modeling) at time t.
123
1244
Nat Hazards (2015) 79:1239–1256
2.4 The SMAP model, monthly basis version The SMAP model (Soil Moisture Accounting Procedure) is a conceptual deterministic hydrological model developed to simulate the transformation of rainfall into river flow (Lopes et al. 1982). It was originally developed for daily forecasts, and it is based on the principle of mass conservation. Its main advantages are the relatively simple utilization and the possibility of using only a few parameters that describe the entire basin. Hence, it is a lumped model, where the transfer equations are a function of time only. The spatial distribution of rainfall is represented by the spatial weight parameter for each rainfall station. The model’s inputs are precipitation, potential evapotranspiration and natural flow. In this study, a monthly version of the model (Fig. 3) was applied displaying two mathematical reservoirs, soil water (unsaturated zone) and groundwater (saturated zone), and the transference functions were updated for each time lap, i.e., each month of the series. The equations used for the calculations are 5–16. This SMAP version was adapted to be a conceptual and mathematical model (not only conceptual, as the original was), since the calibration process uses optimization tools. The calibration of the physical parameters and the spatial and temporal weights consists in finding the appropriate values to maximize global efficiency. The global efficiency coefficient is based on the difference between natural and calculated flow. Since the values of the parameters must be consistent with the physical characteristics of the basin, constraints were defined based on previous analyses, as mentioned in point 2.2. Furthermore, after the optimization process, some physical parameters were adjusted in order to better adjust the model to floods. Mathematical reservoirs: Soil water reservoir Rsoil ðtÞ ¼ Rsoil ðt 1Þ þ P ðtÞ Es ðtÞ ET ðtÞ GR ðtÞ
ð5Þ
Groundwater reservoir Rgrw ðtÞ ¼ Rgrw ðt 1Þ þ GR ðtÞ BF ðtÞ Soil water reservoir initialization Fig. 3 SMAP schematic representation showing the partitioning of precipitation into runoff and infiltration. The water in the soil may transform into evapotranspiration or groundwater recharge, and the latter may transform into base flow. The sum of runoff and base flow is the river flow. Source: based on Lopes et al. (1982)
123
ð6Þ
Nat Hazards (2015) 79:1239–1256
1245
Rsoil ð1Þ ¼ hð0Þ Str
ð7Þ
Groundwater reservoir initialization Rgrw ð1Þ ¼
BFð0Þ Uu ð1 kÞ DA
ð8Þ
Pondered precipitation PðtÞ ¼
0 X
S X
mth¼3
i¼1
! ðPi ðtÞ wi Þ wmth
ð9Þ
Transference functions: Runoff R ðtÞ ¼ P ðtÞ hðtÞk2t
ð10Þ
ET ðtÞ ¼ hðtÞ EP
ð11Þ
GR ðtÞ ¼ hfc h ðtÞ4 Rsoil
ð12Þ
BF ðtÞ ¼ ð1 kÞ Rgrw ðtÞ
ð13Þ
ðBFðtÞ þ RðtÞÞ DA Uu
ð14Þ
Evapotranspiration
Groundwater recharge
Base flow
Total flow QðtÞ ¼
where Rsoil, soil reservoir (vadose zone) [L] (mm). Rgrw, groundwater reservoir (saturation zone) [L] (mm). P (t), pondered precipitation [L] (mm). ET, evapotranspiration [L] (mm). R, runoff [L] (mm). GR, groundwater recharge [L] (mm). BF, base flow [L] (mm). DA, drainage area [L2] (km2). EP, potential evaporation [L] (mm). Uu, unit-updating coefficient [T] (month); in this study, Uu = 2629.8 (month). 3 ðBFðmmÞ þ RðmmÞÞ AD km2 m ¼ ! Q s UuðtÞ m3 mm km2 1m 1;000;000 m2 m3 month ¼ ! ¼ 365:25 day 2 h s t s s t 1000 mm 1km 12 month 24 day 3600 h t ¼ month ! Uu ¼ 2629:8 ðmonthÞ t: Uu units Parameters: Str, saturation capacity [L] (mm). k2t, runoff parameter [dimensionless]. hfc, field capacity [dimensionless]. k, recession coefficient (month - 1) [dimensionless]. h, soil moisture [dimensionless] and
123
1246
Nat Hazards (2015) 79:1239–1256
h¼
Rsoil Str
ð15Þ
h (0), initial moisture rate [dimensionless]. BF (0), initial basic flow [L3 T-1] (m3 s-1). Equation 8 updates the units; hence, BF (t) dimension is [L] (mm). wi, weight of rainfall station i [dimensionless], considering 1 B i B S, and S, the number of rainfall stations. wmth, weight of month mth [dimensionless], considering that mth = 0 is the current month at that time step and -3 B mth B 0; this states that the natural flow may be influenced by the precipitation of the past 3 months and the present month. Optimization equation: The optimization process used Eqs. 1 and 2, i.e., the Nash–Sutcliffe efficiency coefficient and the mean absolute percentage error, respectively. Global efficiency coefficient GEC ¼ E þ 1 MAPE
ð16Þ
where GEC, global efficiency coefficient [dimensionless] and GEC B2. SMAP uses the first 3 months of the series to prepare the flow forecast, i.e., to initialize the variables. Therefore, the predicted results start on the fourth month instead of the first one. For example, the operation series began in April 2005, but the forecast did in July 2005.
2.5 Climate forecast model CFSv2 global model reforecast results were used to obtain the rainfall forecast for the drainage area (Saha et al. 2014). CFSv2 is an acronym of Climate Forecast System version 2 and was developed by the Environmental Modeling Center at the National Centers for Environmental Prediction (NCEP). It is a fully coupled forecast model that represents the interaction between oceans, atmosphere, ice and land. The model’s domain is almost global, from 74S to 64N. According to Saha et al. (2006), CFS model shows remarkably low bias for tropical sea surface temperature (SST) forecasts. In addition, the model shows better performance to predict SST at El Nin˜o 3.4 region, when compared to other statistical methods used at the Climate Prediction Center (CPC). This represents one of the greatest advances in coupled modeling performed by the NCEP. Weather forecast horizons equal to or [30 days have remarkable numeric modeling uncertainty, and using just one forecast does not represent the solutions domain. Thus, it is more suitable to use ensembles to represent probable solutions, by slightly modifying the initial conditions for each variable, therefore, for each forecast. This technique has been largely used by researchers (Cloke and Pappenberger 2009), and it is mainly based on the concept of atmospheric nonlinearity, proposed by Lorenz (1967). Bias removal based on Block et al. (2009) was performed to correct systematic errors in weather forecasting.
2.6 Climate forecast CFSv2 forecasts were obtained directly from the model’s Web site (http://cfs.ncep.noaa. gov/cfsv2/downloads.html) for the period from 1999 to 2014, and the weather in the basin was represented by 12 grid points with 1 of spatial resolution (Fig. 4). Each point was pondered by its specific weight, based on the spatial distribution. For example, grid point P10 had small weight, since it represented a small area of the basin; however, the area
123
Nat Hazards (2015) 79:1239–1256
1247
represented by P7 was almost completely inside the basin, so its weight was greater than P10. The total precipitation was calculated as the sum of the rainfall forecasts of all grid points pondered by their respective weights. For each month, 25 rainfall forecast members were generated after 25 different initial conditions with a 24-h lag obtained the month before (lead 1). The initialization always happened at 00Z each day for the last 25 days of the month. For example, to obtain the rainfall forecast for July, each day from June 6th to June 30th, 31 rainfall forecasts corresponding to all the days in July were summed. Those 25 forecasts were the members of the ensemble, and the SMAP model used each one of them to generate 25 different calculated flows. Nevertheless, after analyzing the results, other ensembles presented better performance than the 25-member ensemble, especially from the first-five- to the first-nine-member ensembles. Thus, in addition, the flood forecast using ensembles with different number of members was studied.
2.7 Bias removal The bias removal consisted in matching the average of observed rainfall to the average of rainfall forecast for each month from 1999 to 2004. Therefore, 12 correction coefficients were calculated according to Eq. 17. The observed rainfall considered the spatial weights given by SMAP to each station during calibration (Eq. 18). The average rainfall forecast was obtained as the mean of the 25-member ensemble generated by CFSv2. The rainfall forecast data to be used during the operative process, i.e., from 2005 to 2010, were rectified using the correction coefficients, as presented in Eq. 19. Correction coefficient for the month m
Fig. 4 CFSv2 grid. The studied basin area corresponds to twelve CFSv2 grid points
123
1248
Nat Hazards (2015) 79:1239–1256
Cm ¼ 1 þ
ðPOBSm PENSm Þ PENSm
ð17Þ
Observed average monthly rainfall for the month m S X
ð18Þ
Pj ðtÞ ¼ PENSj ðtÞ Cm
ð19Þ
POBSm ¼
Pi wi
i¼1
Corrected precipitation
where Cm, correction coefficient for the month m [dimensionless], considering m, {January,…, December}. POBSm, observed average rainfall for the month m within the basin [L] (mm). This amount considers spatial weights. Thus, it is pondered by each station’s weight. PENSm, 25-member-ensemble average rainfall for month m [L] (mm). Pi , average monthly rainfall for the month m at the rainfall station i [L] (mm). wi, weight of rainfall station i [dimensionless], considering 1 B i B S, and S, the number of rainfall stations. This weight was used in Eq. 9. Pj (t), precipitation of the ensemble member j after bias removal in the time step (month) t [L] (mm). PENSj (t), rainfall calculated by CFSv2 for the ensemble member j in the time step (month) t [L] (mm). The input precipitation for SMAP during the operative process was the corrected precipitation, Pj. Nevertheless, similar to Eq. 9, the SMAP model pondered the rainfall temporal characteristics during the calibration by using temporal weights, wmth. Equation 20 presents this calculation, which substitutes Eq. 9 in the operative process (Eq. 9 is used during the calibration and validation process). Equation 20 is equal to Eq. 9 if the latter used just one rainfall data source. Operative pondered precipitation P0j ðtÞ ¼
0 X
Pj ðtÞ wmth
ð20Þ
mth¼3
where Pj0 (t), corrected precipitation forecast of each ensemble member j for the time step t and pondered by temporal weights to be used by SMAP during the operation process [L] (mm). wmth, weight of month mth [dimensionless], considering that mth = 0 is the current month at that time step and -3 B mth B 0. This weight was used in Eq. 9.
Fig. 5 Comparison of the observed and forecasted rainfall monthly average from 1999 to 2004
123
Nat Hazards (2015) 79:1239–1256
1249
3 Results and discussion 3.1 Bias removal and SMAP calibration Figure 5 presents the observed rainfall from 1999 to 2004, compared to the average monthly rainfall forecast given by the mean rainfall forecast of the 25-member ensemble generated by CFSv2. CFSv2 forecasts captured the lack of seasonality of the upper Uruguay River basin and were systematically lower than the observed. This reinforces the usefulness of adjustment techniques, such as bias removal, to make the rainfall forecasts compatible with climate. Figure 6 presents the raw and processed forecasted rainfall data, i.e., before and after applying the bias removal technique, compared with the observed rainfall, during the bias removal period. Moreover, it shows the mean absolute error (MAE, Eq. 4) of the raw and processed forecasted rainfall data. It also illustrates the improvement in the quality of corrected data, since rainfall forecast (understood as the average of the 25-member ensemble) was closer to observed values after the bias removal. The monthly rainfall forecast was systematically lower than the observed rainfall. The root-mean-square error (RMSE, Eq. 3) was 63.9 mm before the bias removal and 51.8 mm after the application of the technique.
3.2 SMAP calibration Figure 7a shows the results of the training process corresponding to the period from 1986 to 1997, and Fig. 7b shows the results of the validation period from 1998 to 2005. The blue line represents the natural flow, and the red line represents the flow calculated by SMAP. The fitness is good in general, except for a few events, such as the second trimester of 1989, when the flow variability was not correctly calculated. In most flood events (flow similar or[3000 m3 s-1), the model predicted a flow peak with small relative error, except for the flood events occurred in June 1990 and October 2001. Moreover, for all flood events, the model predicted flows [2000 m3 s-1 (except in October 2001, when it predicted 1961 m3 s-1), showing a clear correlation between natural and calculated data. Considering this correlation, it is acceptable to state that, whenever a flood event occurred,
Fig. 6 Comparison of monthly rainfall during the bias removal period
123
1250
Nat Hazards (2015) 79:1239–1256
Fig. 7 a SMAP calibration results, from 1986 to 1998. b SMAP validation results, from 1998 to 2006
Fig. 8 Comparison of observed and calculated flow using the 25-member ensemble. Natural flow is represented by a black solid line, and the mean of the 25 flow members by dotted black line. Each member of the ensemble is represented either by a solid blue line (more recent initial conditions) or by a red dasheddouble-dotted line (older initial conditions)
123
Nat Hazards (2015) 79:1239–1256
1251
the calculated flow was similar or [2000 m3 s-1. The opposite statement was almost always true, since there were few false alarms (the calculated flow was [2000 m3 s-1, but the natural flow did not rise 3000 m3 s-1). Nevertheless, the risk of a flood occurrence could be detected both in the calibration and in the validation stages.
3.3 SMAP tests with rainfall forecast Figure 8 presents the results of the SMAP simulations using CFSv2 in 1-month horizon (lead 1). The analysis of this ensemble hydrograph suggests that, in general, the flow variability was correctly represented by the ensemble mean (dotted thick black line). However, there were two main exceptions in October 2006 and in May 2010, when the forecast presented the opposite behavior compared with the natural flow. Nevertheless, in October 2006 and May 2010, the members of the ensemble calculated with most recent initial conditions (blue thin solid lines) showed more accuracy than the ones calculated with older initial conditions (red dash-double-dot lines). Still, all four times in which the natural monthly flow was equal to or [3000 m3 s-1, indicating a critical flood condition, the flow forecasted using the 25-member ensemble represented the event by presenting values [2000 m3 s-1. Just one false alarm occurred, in October 2009, right after a flood event. Hence, the likelihood of a flood event could have been detected with this methodology.
Fig. 9 Efficiency assessment of the 25 members that integrate the ensemble. The Nash–Sutcliffe coefficient is represented by a red dotted line, and the accumulated coefficient by a blue solid line. The coefficient ‘‘1 - MAPE’’ is represented by gray dashed line, and the accumulated coefficient by a solid black line. The coefficient ‘‘1 - MAPE’’ is symmetrical to the Nash–Sutcliffe coefficient, and the greater the distance between the accumulated coefficients, the better the performance of the model for flow forecasting
123
1252
Nat Hazards (2015) 79:1239–1256
3.4 Analysis of the ensemble members The distribution of the ensemble members was organized according to the generation moment of the initial conditions, being the first members of the ensemble the closest to the forecasted moment. Figure 9 presents the statistical analysis of the Nash–Sutcliffe efficiency coefficient on the left axis and the ‘‘1 - MAPE’’ coefficient on the right axis, during the period from July 2005 to December 2010. The behavior of the first members of the ensemble is more accurate than the rest. The Nash–Sutcliffe efficiency coefficient (represented by a shaded area limited by a red dashed line) shows that the highest values, therefore the most reliable, are mainly concentrated from the first to the sixth members. Negative efficiency values were observed for a few members. The blue line represents the accumulated Nash–Sutcliffe coefficient, i.e., the coefficient corresponding to the average of the members from the first to each one indicated in the abscissas axis. For example, for the seventh member, the blue line value represents the Nash–Sutcliffe coefficient of the mean flow calculated as the average of the first seven members of the ensemble. Similarly, the black line represents the accumulated value of the coefficient defined as ‘‘1 - MAPE.’’ For the first members, both coefficients were greater; thus, the average forecasts presented greater efficiency when the members of the different ensembles were closer to the first one. The official model for flow forecasting used by Electric System National Operator, ONS, was completely stochastic, based on a series of former flows, until 2008, when it began considering climate for horizons up to 7 days (ONS 2007). Therefore, during the studied period, the ONS changed their flow forecasting methodology. The model was not designed specifically for flood forecasting, but it was used for this purpose. The data are public and available at http://www.ons.org.br/operacao/decomp.aspx. To find the correct data for an specific month, it is necessary to select the year from the dropdown list; then, a new page will appear where the month must be chosen from the list ‘‘Valor esperado’’ (expected value). This procedure will download a zip file with two folders. The file with a 6-week forecast is stored within the folder with the word ‘‘entrada’’ (input) with the name ‘‘Prevs.RV0.’’ The forecasts at the Ita´ power plant can be located at the line corresponding to the code number 93 in the second column. The 6 weekly forecasts are displayed in the columns from 3 to 8. This study used the averages of the first-five-week forecasts displayed for each month at the file ‘‘Prevs.RV0,’’ which is prepared by the ONS during the last week of the month before the forecasted month. Tables 1 and 2 present the efficiency of different ensembles and the stochastic model used by the ONS to predict floods. The former shows the Nash–Sutcliffe coefficient, the
Table 1 Efficiency coefficients during the operation period Nash–Sutcliffe
MAPE (%)
MAE (m3 s-1)
Correlation
25-Member average
0.4083
56.2
476.3
0.645
First-5-member average
0.5691
44.9
411
0.794
First-6-member average
0.5709
47.2
411
0.790
First-7-member average
0.5432
49.5
423
0.775
First-8-member average
0.5065
51.6
435
0.752
First-9-member average
0.5071
51.4
437
0.743
ONS stochastic model
0.0382
65.2
585
0.344
123
September 05 (%)
32.4
7.7
6.1
1.3
0.8
6.2
81.1
Flood event
25-Member average
First-5-member average
First-6-member average
First-7-member average
First-8-member average
First-9-member average
ONS stochastic model
MAPE
Table 2 MAPE and MAE during flood events
52.9
29.8
37.9
37.6
38.7
42.5
6.0
October 05 (%)
55.2
5.7
4.9
2.0
1.2
6.7
16.0
October 08 (%)
66.6
14.7
9.9
2.5
3.4
3.1
44.9
September 09 (%)
64.0
14.1
13.4
10.8
12.3
15.0
24.8
Mean (%)
2630
201
25
42
199
249
1051
September 05
MAE (m3 s-1)
1785
1006
1278
1267
1305
1433
204
October 05
1657
171
146
59
35
202
481
October 08
2830
625
421
106
144
131
1908
September 09
Nat Hazards (2015) 79:1239–1256 1253
123
1254
Nat Hazards (2015) 79:1239–1256
MAPE, the MAE and the correlation during the operative series (from 2005 to 2010), and the latter shows the MAPE and the MAE in the flood events occurred in the same period. For the flood events, the coefficients calculated with the mean of the first-seven-member ensemble were the best according to the relative error, better than the ones calculated using all 25-member average or other ensembles, and remarkably better than the results obtained by the stochastic model currently used to predict floods. Figure 10 compares the natural flow at Ita´ to the mean of the first-seven-member ensemble and to the official stochastic model used by ONS for flow forecasting. The flows during the four flood events are emphasized to note the differences between natural flow and the models. In the flood events occurred during the studied operating period, the stochastic official model currently used to predict floods in the upper Uruguay River calculated remarkably lower flows; it did not predict any of the floods or showed any correlation to infer a flood event possibility. The mean of the first seven SMAP results was very accurate in three of the four flood events, with an average MAPE lower than 2 %. In the second flood, occurred in October 2005 right after the first event, the first-sevenmember ensemble was less precise, although it detected the flood; therefore, it could have been prevented. This ensemble produced a false alarm in October 2009, just after the fourth flood event, occurred in September 2009. Nevertheless, while the correlation of the stochastic model was 0.34 for this period, the mean of the first-seven-member ensemble presented 0.77 (Table 1).
Fig. 10 Comparison of natural and calculated flow using the average of the first seven members and the model used by the Electric System National Operator. The four flood events that occurred from 2005 to 2010 are represented by black circumferences. Blue circles represent the flow calculated by coupling SMAP and CFSv2. Red diamonds represent the flow calculated by the ONS model
123
Nat Hazards (2015) 79:1239–1256
1255
4 Conclusions This study presented a methodology for 1-month flood forecasting for the upper reaches of the Uruguay River. The SMAP model was coupled with CFSv2 using 1-month-lead rainfall forecasts. The accuracy of the methodology was evaluated for the period from 2005 to 2010, when four flood events occurred in the upper Uruguay River. The results were satisfactory: The model showed high correlation with the natural flow, and the ensemble structure represented the forecasts uncertainty as a correct tool to analyze the potential risk of a flood. The uncertainty analysis indicated that evaluating the number of members to be used in an ensemble forecast may have significant improvements in flood prediction. The greatest accuracy during the flood events corresponded to the mean of the first-seven-member ensemble, with an average MAPE of 10.8 % and an average MAE of 369 m3 s-1, and all flood events would have been detected. The methodology presented remarkably better results than the current official model used for flow forecasting in the basin. During the flood events, that model had an average MAPE of 64 % and an average MAE of 2226 m3 s-1, predicting lower flows. None of the floods was detected with that model. Coupling CFSv2 with SMAP improved the information available for the generation of flow scenarios by anticipating the flow trend. This illustrates how relevant may be the contribution of rainfall forecasts to mitigate the impact of extreme flood events in Southern Brazilian basins. Acknowledgments The authors express their sincere gratitude to Danielle Cristine Carvalho Muniz e Silva and to Prof. Thomas C. Harmon. They also thank Talmo Manha˜es de Franc¸a Rodrigues, Carlos Henrique Silva and Yan Ferreira. Furthermore, the authors gratefully acknowledge the financial support provided by the Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior, CAPES, and by the Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico, CNPq.
References Ahern M, Kovats RS, Wilkinson P, Few R, Matthies F (2005) Global health impacts of floods: epidemiologic evidence. Epidemiol Rev 27:36–46. doi:10.1093/epirev/mxi004 Alfieri L, Salamon P, Pappenberger F, Wetterhall F, Thielen J (2012) Operational early warning systems for water-related hazards in Europe. Environ Sci Policy 21:35–49. doi:10.1016/j.envsci.2012.01.008 Alfieri L, Pappenberger F, Wetterhall F, Haiden T, Richardson D, Salamon P (2014) Evaluation of ensemble streamflow predictions in Europe. J Hydrol 517:913–922. doi:10.1016/j.jhydrol.2014.06.035 Alfieri L, Burek P, Feyen L, Forzieri G (2015) Global warming increases the frequency of river floods in Europe. Hydrol Earth Syst Sci 19:2247–2260. doi:10.5194/hess-19-2247-2015 Bartholmes JC, Thielen J, Ramos MH, Gentilini S (2009) The European flood alert system EFAS—Part 2: statistical skill assessment of probabilistic and deterministic operational forecasts. Hydrol Earth Syst Sci 13:141–153 Block PJ, Souza Filho FA, Sun L, Kwon H-H (2009) A streamflow forecasting framework using multiple climate and hydrological models. J Am Water Resour As 45:828–843. doi:10.1111/j.1752-1688.2009. 00327.x Cataldi M, Saturnino Braga R, Dias TL, Sa´ RV, Rocha VF, Souza Filho FA (2012) Assessment of precipitation climate forecasting of models ECHAM 4.5, CFS, COLA/IRI and CCM3 for hydrographic basins in Southwest Brazil. In: CLIVAR VAMOS Workshop on Modeling and Predicting Climate in the Americas Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626 Collischonn W, Tucci CEM, Clarke RT, Dias PL, Oliveira GS (2005) Previsa˜o sazonal de vaza˜o na bacia do rio Uruguai 2: previsa˜o clima´tica-hidrolo´gica. Revista Brasileira de Recursos Hı´dricos 10:60–72 (In Portuguese)
123
1256
Nat Hazards (2015) 79:1239–1256
´ (2015) Ficha te´cnica da UHE ITA ´ . http://www.consorcioita.com.br/paginas/visualizar/ficha_ Conso´rcio ITA tecnica/#conteudo. Accessed Jan 2015 (in Portuguese) De Groeve T, Thielen J, Brakenridge R, Adler R, Alfieri L, Kull D, Lindsay F, Imperiali O, Pappenberger F, Rudari R, Salamon P, Villars N, Wyjad K (2014) Joining forces in a global flood partnership. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-14-00147.1 FAO (2003) Review of water resources by country. ftp://fao.org/agl/aglw/docs/wr23e.pdf. Accessed Jan 2015 FAO (2015) Freshwater Availability - Precipitation and Internal Renewable Water Resources (IRWR). AQUASTAT database. http://www.fao.org/nr/water/aquastat/data/query/index.html. Accessed January 2015 Lopes J, Braga B, Conejo J (1982) SMAP—a simplified hydrological model, applied modelling in catchment hydrology. In: Singh (ed) Water Resources Publications Lorenz EN (1967) The nature and theory of the general circulation of the atmosphere. World Meteorological Organization, Geneva Milly PCD, Wetherald R, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing climate. Nature 415:514–517. doi:10.1038/415514a Ministe´rio do Meio Ambiente (2006) Uruguay river hidrologic region report. Brası´lia, Environment Ministry (in Portuguese) Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. doi:10.1016/0022-1694(70)90255-6 ONS (2007) Technical note 133/2007. Electric System National Operator, Rio de Janeiro (in Portuguese) Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van Den Dool HM, Pan H-L, Moorthi S, Behringer D, Stokes D, Pena M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Clim 19:3483–3517. doi:10.1175/JCLI3812.1 Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y-T, Chuang H, Iredell M, Ek M, Meng J, Yang R, Pen˜a Mendez M, van den Dool H, Zhang Q, Wang W, Chen M, Becker E (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208. doi:10.1175/JCLI-D-12-00823.1 Sorensen JH (2000) Hazard warning systems: review of 20 years of progress. Nat Hazards Rev 1:119–125. doi:10.1061/(ASCE)1527-6988(2000)1:2(119) Stillwell HD (1992) Natural hazards and disasters in Latin America. Nat Hazards 6:131–159. doi:10.1007/ BF00124620 Thielen J, Bartholmes J, Ramos M-H, De Roo A (2009) The European flood alert system—part 1: concept and development. Hydrol Earth Syst Sci 13:125–140 Werner M, Reggiani P, De Roo AD, Bates P, Sprokkereef E (2005) Flood forecasting and warning at the river basin and at the European scale. Nat Hazards 36:25–42. doi:10.1007/s11069-004-4537-8
123
