University of Twente Student Theses
Simulating discharges and forecasting floods using a conceptual rainfall-runoff model for the Bolivian Mamoré basin
Maat, W.H. (2015) Simulating discharges and forecasting floods using a conceptual rainfall-runoff model for the Bolivian Mamoré basin.
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| Abstract: | Flood protection and awareness have continued to rise on the political agenda over the last decade accompanied by a drive to improve flood forecasts. Operational flood forecasting systems form a key part of ‘preparedness’ strategies for disastrous flood events by providing early warnings several days ahead giving flood forecasting services, civil protection authorities and the public adequate preparation time and thus reducing the impacts of the flooding. The River Mamoré in Bolivia, a major tributary of the Amazon, floods annually causing considerable damage, especially to cattle ranches and villages in the area. To limit the effects of flooding in Bolivia the Bolivian Vice-Ministry for Water Management and the Dutch Embassy together initiated the program ‘Vivir con el Agua’ (living with water). A part of the program is to set up a Flood Early Warning System, FEWS, which will warn inhabitants of any flood danger and give them time to take measures to limit the damage. This FEWS-project in Bolivia is being carried out by the consortium including RoyalHaskoningDHV, Deltares and the local organization Centro Agua Bolivia. During the FEWS project in Bolivia the curiosity arose how a semi-distributed hydrological model like HBV will perform in forecasting the discharge in this basin area, instead of the physically-based, distributed parameter, basin hydrological model TOPOG, used in the project. The goal of this research is to set up and evaluate the hydrological HBV-model to simulate discharges and forecast floods of the Mamoré River at the city of Trinidad, a city in Bolivia which suffer from the annual floods. A data analysis is executed to select the meteorological stations which are used in the research to determine the input data and to determine the sub basins with corresponding discharge stations. This data analysis showed that the Mamoré basin for this study should be divided into two sub basins: upstream sub basin Grande with outlet at discharge station Abapó and downstream sub basin Mamoré with outlet at discharge station Camiaco. In the model calibration procedure the objective function Y of sub basin Grande is a combination of the well-known Nash-Sutcliffe coefficient and Relative Volume error and the objective function of sub basin Mamoré is the Nash-Sutcliffe coefficient for high flows NSH, both with an optimum value of 1. The parameters for which the objective functions are the most sensitive, for both sub basins separately, are used for the final calibration step to obtain the sets of the optimum parameter values. The calibrated model is run for the validation period and sub basin Grande showed an improvement in performance in terms of the objective function with an Y value of 0.39 for the calibration period and an Y value of 0.54 for the validation period. The performance for sub basin Mamoré decreased in terms of the NSH values from 0.72 to 0.51 for the calibration period and validation period respectively. For flood forecasting, the optimized parameter set obtained during the calibration process is used to forecast the discharge for the validation period for 1 up to 10 days ahead, using perfect weather ‘forecasts’ in the absence of historical weather forecasts. By following an updating procedure the observations of the state of the basin up to the current time are used for the forecasting to improve the model prediction performance. Page | 5 The forecasted discharges of 1 up to 10 days ahead performed quite well considering the overall accuracy. The NSH values of the forecasts lie between 0.99 for one day ahead and 0.69 for 10 days ahead. These performances are an improvement compared to the performance of the simulated discharges, which had a NSH value of 0.51. Next to the objective function value, the evaluation of the performance of the forecasted discharges are based on contingency tables, which show the total number of hits (an event occurred and the event was forecasted), misses (an event occurred, but the event was not forecasted), false-alarms (an event did not occur, but an event was forecasted) and correct rejections (an event did not occur and an event was not forecasted) for four different events for the validation period. Two types of events are taken into account in this study for two different thresholds; a high water level threshold and a flood level threshold: - Event type 1: ‘the exceedance of a discharge threshold at time step t’ - Event type 2: ‘the exceedance of a discharge threshold at time step t, with the condition that at time step t-1 this threshold was not exceeded’. The accuracies A of the forecasts up to 10 days ahead (A ≥ 0.94) are at least higher than the accuracies of the simulated discharges in forecasting event type 1 for the high water level and the flood level threshold. Accuracy A has a value between 0 and 1, with 1 as optimum value, thus the performance of the forecasts in terms of accuracy are considered as good for the event type 1. The skill of the model in forecasting high water and flood levels up to 10 days ahead in terms of False-alarm rates F is better than the skill of the model in simulating high water and flood levels. Partly due to the high number of correct rejections the false-alarm rates F, which have a value between 0 and 1, of the forecasts are ≤0.02. The skill of the model in forecasting high water and flood levels up to 10 days ahead in terms of hit rate H are decreasing as the forecasting days ahead are increasing. Nevertheless, hit rates H of the high water level of the forecasts up to 7 days ahead are higher than the hit rate of the simulated discharges and the hit rates H of the flood level of the forecasts up to 3 days ahead are higher than the hit rate of the simulated discharges. At least the forecasts up to 3 days ahead perform well in terms of skill for the event type 1 for both thresholds. The reliability of the forecasts up to 10 days ahead for event type 1 in terms of the probability of a correct warning H’ is high (H’ ≥ 0.92) for the high water and flood level threshold. The reliabilities of simulated discharges are 0.76 and 0.79 for the high water level and the flood level respectively. These values are much higher than the base rates, which has a value of 0.31 and 0.19 for the high water level and the flood level respectively. The reliability of the forecasts up to 10 days ahead for event type 1 in terms of probability of an incorrect non-warning (miss rate F’), decreases sharply as the forecasting days ahead are increasing for the high water and flood level thresholds. This is not directly visible in the miss rate F’, because the number of correct rejections is high. The contingency tables of event type 2 show that the performance of the forecasts is very poor for the high water and flood level thresholds. Event type 2 is the start of a high water or flood period. In the validation period, 6 high water periods and 2 flood periods occurred. Out of all the 80 events (2 events for flood threshold plus 6 events for high water threshold times 10 forecasts) the model was able to forecast 2 events and missed 78 events. Due to the small number of event (and thus the small Page | 6 number of hits and misses) and the large number of correct rejections the evaluation in terms of accuracy, skill and reliability is not meaningful. In conclusion, the overall accuracy of the forecasts increase as the prediction days decrease and is higher than accuracy of the simulated discharges. The accuracy, skill and reliability of the forecasts up to 3 days ahead of event type 1 are higher than the simulated discharges for both the high water and flood level. However, as a decision maker, you are also interested in ability of a model to forecast the start of a high water or flood level threshold exceedance, event type 2. This to give a high water or flood warning to the people in the area, so they are able to evacuate and limit the flood damage. Unfortunately, the model is barely able to forecast and simulate events of type 2 for both high water and flood level thresholds. |
| Item Type: | Essay (Master) |
| Faculty: | ET: Engineering Technology |
| Subject: | 56 civil engineering |
| Programme: | Civil Engineering and Management MSc (60026) |
| Link to this item: | https://purl.utwente.nl/essays/67046 |
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