EFFICIENCY OF THE INTEGRATED RESERVOIR OPERATION FOR FLOOD CONTROL IN THE UPPER TONE RIVER OF JAPAN CONSIDERING SPATIAL DISTRIBUTION OF RAINFALL Dawen YANG, Eik Chay LOW and Toshio KOIKE Department of Civil Engineering, University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan This paper presents a new methodology of flood control by utilizing the distributed hydrological model and advanced radar rainfall observation system. It explores the efficiency of integrated reservoir operation for flood control in the upper Tone River of Japan. According to two heavy rainfall events in August and September of 2001 observed by the weather radar, different spatial distribution patterns are assumed by reversing the direction in east-west and south-north. Considering the spatial pattern of rainfall, reduction of flood peak using an integrated reservoir operation is examined. The result shows that the efficiency of flood control is greatly related to the spatial pattern of rainfall and is limited by the capacities of reservoirs. INTRODUCTION The conversion of rainfall into surface runoff is traditionally analyzed by means of lumped models. These models assumed that rainfall and geographical conditions over a whole watershed is uniform and the conversion process is described by the familiar convolution integral and an instantaneous unit hydrograph for linear system. In practice, the rainfall and geographical conditions over a middle or large watershed are nonuniform, so errors can be caused if the lumped model is used to simulate the rainfallrunoff process. On the other hand, distributed hydrological models have the potential to account for the spatial variability of the catchment conditions and meteorological inputs, allowing better representations of the hydrological processes [1]. The distributed hydrological model examines geographical information such as land use, soil properties and topography to reflect the rainfall-runoff process more accurately. Also, studies have shown that by using radar rainfall with spatial distribution patterns, we were able to obtain better simulations of flood hydrographs as compared to those obtained using the catchment mean rainfall [2]. The Tone River basin usually suffers from heavy rainfall events as a result of rainy season frontal activity, typhoons and cumulus cloud activity associated with the local circulation. The reservoirs located in the upper basin play an important role in flood regulation. During a flood period, reservoirs are operated manually and individually. The reservoir release is determined according to the present water level of the reservoir and the inflow discharge, which is called the reservoir operation rule. Each reservoir has its 1
operational rule, independent of the others. For the efficient control of flood it needs an integrated operation of reservoirs which considers the spatial distribution of rainfall. By utilizing weather radar measurement and a coupled distributed hydrological model with reservoir operation, the present research explores the efficiency of integrated reservoir operation for flood control in the upper Tone River of Japan. STUDY AREA The Tone River is located in the Northeast of Tokyo, Japan. The Okutone basin, which has been selected as the study area, is located in the high steep mountainous region of the upper Tone River (Fig. 1). The elevation varies from about 300 m to 2600 m. The longterm mean precipitation in this region is about 1500 mm per year. 2 Code in Fig. 1 Figure 1. Study area Table 1. Characteristics of reservoirs located in the Okutone basin Name Complete Drainage area Crest depth year (km 2 ) (m) 1 Yagisawa 1967 167.4 131.0 204.3 2 Naramata 1990 60.1 158.0 90.0 3 Fujiwara 1958 401.0 95.0 90.0 4 Aimata 1958 110.8 67.0 25.0 5 Sonohara 1966 493.9 76.5 20.31 Total capacity ( 10 6 m 3 ) This region usually experiences heavy snowfall during the winter (December to February) and suffers from several typhoons during the late summer (August and September). Snowmelt runoff is the major water resource used for supplying Tokyo, however Tokyo s high flood risk is a result of typhoons. The catchment area of the
Okutone basin is 1700 km2 at the Iwamoto gauge point. There are five reservoirs located in this basin (see Fig. 1) which play an important role in flood regulation for protecting the Lower Kanto plain. Table 1 shows the main characteristics of the five reservoirs. METHODOLOGY The Hydrological Model For easy use of spatially distributed rainfall, a grid-based distributed hydrological model is used in this study, which is modified from the geomorphology-based hydrological model (GBHM) [3]. A digital elevation model (DEM) is used to define the target basin. The target basin is divided into sub-catchments of an appropriate size. 3 Figure 2. Concept of the distributed hydrological model
The locations of dams are defined in the river network. Each sub-catchment is divided into a discrete grid system, and a grid is represented by a number of geometrically-symmetrical hillslopes (see Fig. 2). The components of the hydrological model include the data input, the hydrological simulation and the resulting output. The hydrological simulation part includes runoff generation from the hillslope and the flow routing in the river network. The reservoir operation is coupled with the river flow routing [3]. Reservoir Routing Reservoir is an effective tool in flood control because of its large storage capacity. Basically, a reservoir is formed when a river is dammed. Depth and velocity change quickly with time during a flood, and therefore when the water travels downstream, we must solve this as an unsteady flow problem. In simplest terms, neglecting any lateral flow, the continuity equation for unsteady reservoir can be written as dv = I Q (1) dt where V is the volume of water, I is inflow and O is outflow. Typically, to solve this kind of equation, a numerical method is used. That is, to rewrite the equation in terms of finite differences. The equation then becomes Vn+ 1 Vn I n + I n+ 1 Qn + Qn+ 1 = (2) t 2 2 4 Figure 3. Flowchart for determining reservoir release
At the initial time step (n=1), volume and outflow must be specified. In addition to these initial conditions, inflow is known from the hydrological simulation. Rearranging the equation, we get I n + I n+ 1 Vn+ 1 = Vn + Qn t (3) 2 where the lower index n indicates the current time level, n+1 indicates the next time level, and t is the time interval. The reservoir release Q n+ 1 for the next time step is then determined according to the operational rule and considering the inflows and the situations of all reservoirs and the downstream condition simultaneously. Figure 3 shows the flowchart for the integrated reservoir operation. Rainfall Data The gauge data are available from the AMeDAS (Automated Meteorological Data Acquisition System) provided by the Japanese Meteorological Agency (JMA), from the Ministry of Land, Infrastructure and Transportation (MLIT) and from the Japan Water Agency. These data sources are available at 1-hr resolution. A few of the AMeDAS gauges measure four parameters including precipitation, temperature, wind speed and sunshine duration. The availability of meteorological gauges in the Okutone basin is shown in Fig. 1. Thiessen polygons are used for estimating the distribution of rainfall from point measurements. The rainfall interpreted from the radar measurement used in this study is obtained from the Foundation of Rivers & Basin Integrated Communications (FRICS) of Japan. This data is available in 5-min temporal and 1-km spatial resolutions from 2001. According to the availability of precipitations from both gauge and radar measurements, two flood events on August 22 nd and September 11 th of the 2001 are selected for this study. Based on the observed rainfall events, different spatial patterns of the rainfall are assumed by changing the direction in east-west and south-north. MODEL APPLICATION, RESULTS AND DISCUSSION In this application, there are two main tasks: (1) to test the effect of rainfall spatial distribution on the flood forecasting, (2) to validate efficiency of the integrated reservoir operation for flood control. Effect of Rainfall Spatial Distribution on Flood Forecasting For the first task, simulations of inflows to four reservoirs in the uppermost stream, i.e., the Yagisawa, Naramata, Aimata and Sonohara, are examined. Figure 4 (a)-(d) compares the simulated results using both gauge and radar rainfall inputs. In the Yagisawa subcatchment, hydrograph simulated using gauge rainfall is better than the hydrograph simulated using radar rainfall. But in the other three sub-catchments, hydrographs simulated using radar rainfall are better than ones simulated using gauge rainfall. Above 5
each hydrograph, the spatially-averaged rainfall is illustrated. The general patterns of rainfall are similar between gauge and radar data, but the differences between their simulated hydrographs are much larger. There are no rain gauges located inside the drainage basin of the Aimata dam, and the nearest gauge is very far from the center of this sub-basin. In the drainage basin of the Sonohara dam, there are three rain gauges, but they are too close to each other and are concentrated in the middle of this basin. For the Aimata and Sonohara sub-basins, the simulated flood peaks from gauge rainfall are about two times higher than the observed. Due to their sparse distributions and locations in valleys, the spatial representativeness of rain gauges is poor, and the situation becomes worse in the case of a moving rainfall field during a typhoon. In contrast, radars are able to measure the spatial patterns of rainfall better than rain gauges. From the above results, it is can be seen that the spatial variability of rainfall is important for flood simulation. The accuracy of flood simulations can be greatly improved by utilizing radar measurements. However, the present algorithm for rainfall interpretation from radar measurements should also be improved for better estimates of rainfall values. As shown in Fig. 4(a), radar rainfall doesn t display a second peak as shown by the actual hydrograph. This might have been caused by a mountain effect on the radar signal. Efficiency of the Integrated Reservoir Operation for Flood Control The efficiency of flood control was investigated by operating the reservoirs as an integrated system. The period of examination runs from 1 st August to 30 th September 2001, focusing mainly on two floods occurring on August 21 st -25 th and September 9 th - 14 th. Based on the two rainfall events, different spatial distributions of rainfalls were made using a west-east reversion and a south-north reversion. Totally 6 cases were examined in the present study (see Table 2). Using radar measured rainfall from the 21 st to 25 th August 2001 as the input, the integrated reservoir operation was carried out for reducing the peak discharge at the Iwamoto gauge. As a result, flood peak at the Iwamoto gauge was reduced from 1,860 m 3 / sto 1,600 m 3 / s, a reduction of 260 m 3 / s(or 14.0%). By the same way, reductions of peak floods were examined using the integrated reservoir operation manually for other five cases. The results are then complied into Table 2. Table 2 shows that the peak discharge can be reduced by 6-18% when the integrated operations were carried out for the six cases. However, by the present study, it is difficult to conclude that the peak discharge can be reduced only by 6-18%. This is because the reduction of peak discharge is strongly affected by the flood control capacity of the reservoirs and the spatial patterns of rainfall. For example, when substantial rain fell mainly over the drainage areas of the Sonohara dam (such as the case-4 and case-6), being small in flood storage capacity, it had no choice but to engage in early release to maintain the dam safety. This means that the peak reduction percentage is small. On the other hand, when the heavy rainfall field was over the drainage areas of the Fujiwara dam, the Fujiwara reservoir was able to make full use of its large capacity in holding back the 6
release and hence made good contributions to a big peak reduction percentage (such as the case-1 and case-5). Table 2. Efficiency of the integrated reservoir operation Case 21 st -25 th August 2001 9 th -14 th September 2001 Case-2: Case-3: Case-5: Case-6: Case-1: Case-4: E-W N-S E-W N-S Original Rev. * Original Rev. Rev. Rev. Individual operation 1,860 3,330 3,500 1,555 1,740 1,900 ( m 3 / s) Integrated operation 1,600 3,070 3,075 1,450 1,430 1,780 ( m 3 / s) Peak reduction ( m 3 / s) 260 260 425 115 310 120 Peak reduction 14.0% 8.5% 12.1% 7.4% 17.8% 6.3% * Rev.: reversion CONCLUSION A coupled distributed hydrological model with reservoir operation has been developed and applied to the Okutone basin for investigating its efficiency for flood control together with weather radar measurements. Comparing the gauge and radar rainfall on August 22, 2001 and their simulated flood hydrographs, radar measurements captured better the spatial pattern than the rain gauges, and greatly improved the accuracy of simulations of the inflows into the reservoirs. Simultaneously, the coupled model successfully simulated releases from the reservoirs in terms of the independent operational rule used in normal flood regulation. On efficient flood control in the Okutone basin, an integrated reservoir operation was carried out manually. The coupled distributed hydrological mode with reservoir operation provided the forecast of inflows and the situations of all reservoirs and the downstream condition simultaneously. The results showed that the peak discharge could be reduced by 6-18% when the integrated operations were carried out for the case studies. And the efficiency of flood control was greatly related to the spatial pattern of rainfall and was limited by the capacities of reservoirs. This research showed the advantages of using a distributed hydrological and radar measurements in practical applications of flood management. 7
Figure 4. Comparison of inflow simulations using different rainfall data 8
9 Figure 4. Comparison of inflow simulations using different rainfall data (Continued) ACKNOWLEDGEMENT The authors would like to thank the Tonegawa Integrated Dam and Reservoir Group Management Office for providing the radar precipitation data required for this study. REFERENCES [1] Yang D., S. Herath, K. Musiake (2002). Hillslope-based hydrological model using catchment area and width functions. Hydrological Sciences Journal 47(1): 49-65. [2] Yang D, T. Koike, H. Tanizawa (2003). Effect of precipitation spatial distribution on the hydrological response in the upper Tone River of Japan. IAHS publication, no. 282, 194-202. [3] Yang, D., T. Oki, S. Herath, and K. Musiake (2002). A Geomorphology-Based Hydrological Model and Its Applications. In V.P. Singh & D.K. Frevert (ed.) Mathematical Models of Small Watershed Hydrology and Applications, Water Resources Publications, Littleton, Colorado. Chapter 9, 259-300.