기후변화로 인하여 국지성 집중호우가 크게 늘어나고 그로인해 막대한 인적 및 물적 피해를 야기하고 있다. 따라서 강우의 시간적 공간적 특성을 파악하는 것이 중요하다고 할 수 있다. 본 연구에서는 레이다 강우를 이용하여 시공간적 변동성을 고려한 격자형 면적강우량을 산정하기 위하여 추계학적 방법인 칼만필터 기법을 이용하여 지상 강우 관측망과 레이다 강우 관측망을 조합하여 면적강우량을 산정하였다. 또한 전통적인 지상 강우량을 면적강우량으로 전환하는 기법인 Thiessen법, 역거리법, 크리깅 기법을 이용하여 면적강우량을 산정한 후 칼만필터 기법에 의해 보정된 면적 레이다 강우와 비교하였다. 그 결과, 칼만필터 기법에 의해 보정된 레이다 강우는 실제 강우 분포와 유사한 공간분포를 가지는 원시 레이다 강우 분포를 잘 재현하면서도 강우 체적은 우량계 자료의 체적과 유사하게 나타났다. 그리고 안성천 유역을 대상유역으로 선정하여 칼만필터 기법에 의해 보정된 레이다 강우를 물리적 기반의 분포형 모형인 $Vflo^{TM}$ 모형과 준분포형 모형인 ModClark 모형에 적용하여 홍수유출을 모의하였다. 그 결과, $Vflo^{TM}$ 모형은 첨두시간과 첨두치가 관측 수문곡선과 유사하게 모의되었으며 ModClark 모형은 총 유출체적에서 좋은 결과를 나타냈다. 그러나 매개변수 검증에서는 $Vflo^{TM}$ 모형이 ModClark 모형보다 관측 수문곡선을 잘 재현하였다. 이를 통해 지상강우와 레이더 강우를 적절하게 조합하여 정확도 높은 면적강우량을 산정하고 분포형 수문모형과 연계하여 홍수유출모의를 실시할 경우 충분한 적용성을 가지고 있음을 확인할 수 있었다.
기후변화로 인하여 국지성 집중호우가 크게 늘어나고 그로인해 막대한 인적 및 물적 피해를 야기하고 있다. 따라서 강우의 시간적 공간적 특성을 파악하는 것이 중요하다고 할 수 있다. 본 연구에서는 레이다 강우를 이용하여 시공간적 변동성을 고려한 격자형 면적강우량을 산정하기 위하여 추계학적 방법인 칼만필터 기법을 이용하여 지상 강우 관측망과 레이다 강우 관측망을 조합하여 면적강우량을 산정하였다. 또한 전통적인 지상 강우량을 면적강우량으로 전환하는 기법인 Thiessen법, 역거리법, 크리깅 기법을 이용하여 면적강우량을 산정한 후 칼만필터 기법에 의해 보정된 면적 레이다 강우와 비교하였다. 그 결과, 칼만필터 기법에 의해 보정된 레이다 강우는 실제 강우 분포와 유사한 공간분포를 가지는 원시 레이다 강우 분포를 잘 재현하면서도 강우 체적은 우량계 자료의 체적과 유사하게 나타났다. 그리고 안성천 유역을 대상유역으로 선정하여 칼만필터 기법에 의해 보정된 레이다 강우를 물리적 기반의 분포형 모형인 $Vflo^{TM}$ 모형과 준분포형 모형인 ModClark 모형에 적용하여 홍수유출을 모의하였다. 그 결과, $Vflo^{TM}$ 모형은 첨두시간과 첨두치가 관측 수문곡선과 유사하게 모의되었으며 ModClark 모형은 총 유출체적에서 좋은 결과를 나타냈다. 그러나 매개변수 검증에서는 $Vflo^{TM}$ 모형이 ModClark 모형보다 관측 수문곡선을 잘 재현하였다. 이를 통해 지상강우와 레이더 강우를 적절하게 조합하여 정확도 높은 면적강우량을 산정하고 분포형 수문모형과 연계하여 홍수유출모의를 실시할 경우 충분한 적용성을 가지고 있음을 확인할 수 있었다.
Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall wh...
Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.
Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall. In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR rainfall adjusted with Kalman-filter method was applied in $Vflo^{TM}$ model, a physical-based distributed model, and ModClark model, a semi-distributed model. As a result, $Vflo^{TM}$ model simulated peak time and peak value similar to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for verifying the parameter, $Vflo^{TM}$ model showed better reproduction of observed hydrograph than ModClark model. These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is performed in link to the distributed hydrological model.
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문제 정의
The objective of this paper is to link adjusted RADAR rainfall with gridded rainfallrunoff model and simulate flood runoff for basins in South Korea. First, Kalman-Filter method, a stochastical technique, was used for adjusting the data from Imjin River rainfall RADAR in real-time.
제안 방법
(2007) performed sensitivity analysis of VfloTM for Jungrang basin. Based on the analysis result, parameter was adjusted to make it suitable for Anseong basin for this research. To simulate flood runoff using ModClark model, a cell size 1㎞ RADAR rainfall adjusted by Kalman-filter.
In this study, RADAR rainfall was estimated using the data obtained from Anseong basin, which has flood forecasting in operation, for flood simulation using RADAR rainfall and distributed models. Figure 3 is a basin map of Anseong observed from a RADAR.
In this study, the data whose cell size are 500m was inputted to VfloTM model, and four rainfall event data mentioned above were used to simulate the runoff amount. Of the five stage stations located in Anseong basin, flood runoff was simulated at Pyeongtaek and Gongdo location.
First, Kalman-Filter method, a stochastical technique, was used for adjusting the data from Imjin River rainfall RADAR in real-time. The adjusted RADAR rainfall data was inputted to ModClark (Modified Clark), a distributed model, and VfloTM model, a physical-based distributed model, to simulate flood runoff. In addition, methods used in today's practices, Thiessen polygon technique, inverse distance weighting (IDW) method, and Kriging method, were used for inputting the areal rainfall estimated from ground point rainfall data to the two models (as described before) to estimate flood runoff hydrograph and to compare the estimated hydrograph with the actual runoff hydrograph.
이론/모형
But, raw RADAR rainfall often did not estimates accuracy the rainfall amount. Because of this, raw RADAR data has been adjusted with Kalman-filter method.
The objective of this paper is to link adjusted RADAR rainfall with gridded rainfallrunoff model and simulate flood runoff for basins in South Korea. First, Kalman-Filter method, a stochastical technique, was used for adjusting the data from Imjin River rainfall RADAR in real-time. The adjusted RADAR rainfall data was inputted to ModClark (Modified Clark), a distributed model, and VfloTM model, a physical-based distributed model, to simulate flood runoff.
For the each event of rainfall, Thiessen polygon method, inverse distance weighting (IDW) method, and Kriging method were used to estimate adjusted RADAR rainfall and areal rainfall from the location of rain gauge station. The estimations were compared with gauge areal rainfall.
In addition, methods used in today's practices, Thiessen polygon technique, inverse distance weighting (IDW) method, and Kriging method, were used for inputting the areal rainfall estimated from ground point rainfall data to the two models (as described before) to estimate flood runoff hydrograph and to compare the estimated hydrograph with the actual runoff hydrograph.
In this paper, Kalman-filter method was applied into estimation of areal rainfall distribution by adjusting RADAR rainfall in Anseong-Chen basin. The adjusted RADAR rainfall was used for simulating flood runoff with physical-based distributed model VfloTM and conceptual distributed model ModClark.
The concept of ModClark model is based on fundamental principles of the conceptual rainfall-runoff model by Clark and is added with a function for simulating spatially distributed rainfall data.
Based on the analysis result, parameter was adjusted to make it suitable for Anseong basin for this research. To simulate flood runoff using ModClark model, a cell size 1㎞ RADAR rainfall adjusted by Kalman-filter. ModClark adjusted parameters for time of concentration, storage constant, and Muskingum K.
성능/효과
(1) As a result of using Kalman-Filter method, a stochastical technique, for adjusting the data from Imjin RADAR station, the adjusted RADAR rainfall maintained the features of spatial variability of raw RADAR rainfall distribution and well-reproduced the rainfall intensity of rain gauge rainfall.
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