• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.148-148
• /
• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Journal of Korea Water Resources Association
• /
• v.46 no.8
• /
• pp.857-871
• /
• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Atmosphere
• /
• v.28 no.3
• /
• pp.273-289
• /
• 2018

This paper evaluates daily precipitation products from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA), and the Climate Prediction Center Morphing Method (CMORPH), validated against gauge observation over South Korea and gauge-based analysis data East Asia during one year from June 2014 to May 2015. It is found that the three products effectively capture the seasonal variation of mean precipitation with relatively good correlation from spring to fall. Among them, IMERG and TMPA show quite similar precipitation characteristics but overall underestimation is found from all precipitation products during winter compared with observation. IMERG shows reliably high performance in precipitation for all seasons, showing the most unbiased and accurate precipitation estimation. However, it is also noticed that IMERG reveals overestimated precipitation for heavier precipitation thresholds. This assessment work suggests the validity of the IMERG product for not only seasonal precipitation but also daily precipitation, which has the potential to be used as reference precipitation data.

• Atmosphere
• /
• v.16 no.2
• /
• pp.55-65
• /
• 2006

The characteristics of Depth-Area-Duration (DAD) for 50 storms over the Geumgang river basin have been analysed in terms of various storm causes using the precipitation data during the period from 1984 to 2003. Results show that the ratio of the precipitation depth to duration, and the ratio of decrease in the precipitation depth to area are the largest in the case of the tropical cyclone. Storm maximization ratios are in the range 1.03 to 2.66 for the 50 selected heavy precipitation cases over Geumgang river basin, with the largest value for the tropical cyclone case, suggesting that the tropical cyclone could cause heavier precipitation than the other storms. In addition, the 24-hour probable maximum precipitation for the Geumgang river basin is estimated to be about 745 mm in the maximum precipitation area.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.330-330
• /
• 2022

우리나라에서 발생하는 기상 재해 현상은 주로 태풍, 집중호우, 장마 등 인명 및 경제적인 피해가 크며, 단기간에 국지적으로 나타난다. 현재 재해 감시 및 예보는 주로 종관기상관측체계를 이용하고 있다. 하지만, 우리나라의 복잡한 지형, 인구 밀집 지형, 관측 시기가 일정하지 않은 지형과 같은 조건에서 미계측 자료 및 지역이 다수 존재 때문에 강수의 공간 분포와 강도에 대한 정밀한 정보를 제공하지 못하는 실정이다. 최근 광범위한 관측영역과 공간 분해능의 개선, 자료추출 알고리즘의 개발로 전세계적으로 위성영상 기반 기상관측 자료의 활용성이 증대되고 있다. 본 연구에서는 한반도 지역의 지상 관측데이터와 전지구 격자형 위성 강우자료를 비교하여 한반도의 적용성을 분석하고자 한다. 다양한 위성영상 기반 기상자료인 Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) 4개의 강우위성영상을 수집하여, 1991년부터 2020년까지 30년 데이터를 활용하였다. 강수량 변동성 비교를 위하여 기상청의 종관기상관측장비 (Automated Synoptic Observation System, ASOS), 자동기상관측시설 (Automatic Weather System, AWS) 데이터와 상관 분석을 수행하고, 강우위성영상의 국내 적합성을 판단하고자 한다.

• Journal of Ecology and Environment
• /
• v.42 no.2
• /
• pp.77-84
• /
• 2018

Background: For understanding and evaluating a more realistic and accurate assessment of ecosystem carbon balance related with environmental change or difference, it is necessary to analyze the various interrelationships between soil respiration and environmental factors. However, the soil temperature is mainly used for gap filling and estimation of soil respiration (Rs) under environmental change. Under the fact that changes in precipitation patterns due to climate change are expected, the effects of soil moisture content (SMC) on soil respiration have not been well studied relative to soil temperature. In this study, we attempt to analyze relationship between precipitation and soil respiration in temperate deciduous broad-leaved forest for 2 years in Gwangneung. Results: The average soil temperature (Ts) measured at a depth of 5 cm during the full study period was $12.0^{\circ}C$. The minimum value for monthly Ts was $-0.4^{\circ}C$ in February 2015 and $2.0^{\circ}C$ in January 2016. The maximum monthly Ts was $23.6^{\circ}C$ in August in both years. In 2015, annual precipitation was 823.4 mm and it was 1003.8 mm in 2016. The amount of precipitation increased by 21.9% in 2016 compared to 2015, but in 2015, it rained for 8 days more than in 2016. In 2015, the pattern of low precipitation was continuously shown, and there was a long dry period as well as a period of concentrated precipitation in 2016. 473.7 mm of precipitation, which accounted for about 51.8% of the precipitation during study period, was concentrated during summer (June to August) in 2016. The maximum values of daily Rs in both years were observed on the day when precipitation of 20 mm or more. From this, the maximum Rs value in 2015 was $784.3mg\;CO_2\;m^{-2}\;h^{-1}$ in July when 26.8 mm of daily precipitation was measured. The maximum was $913.6mg\;CO_2\;m^{-2}\;h^{-1}$ in August in 2016, when 23.8 mm of daily precipitation was measured. Rs on a rainy day was 1.5~1.6 times higher than it without precipitation. Consequently, the annual Rs in 2016 was about 12% higher than it was in 2015. It was shown a result of a 14% increase in summer precipitation from 2015. Conclusions: In this study, it was concluded that the precipitation pattern has a great effect on soil respiration. We confirmed that short-term but intense precipitation suppressed soil respiration due to a rapid increase in soil moisture, while sustained and adequate precipitation activated Rs. In especially, it is very important role on Rs in potential activating period such as summer high temperature season. Therefore, the accuracy of the calculated values by functional equation can be improved by considering the precipitation in addition to the soil temperature applied as the main factor for long-term prediction of soil respiration. In addition to this, we believe that the accuracy can be further improved by introducing an estimation equation based on seasonal temperature and soil moisture.

• Journal of Korea Water Resources Association
• /
• v.43 no.12
• /
• pp.1083-1091
• /
• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Korean Journal of Soil Science and Fertilizer
• /
• v.47 no.1
• /
• pp.48-58
• /
• 2014

In order to evaluate water balance at upland according to RCP8.5 climate change scenario distributed by Korean Meteorological Administration (KMA), we simulated soil moisture using estimation model, called AFKAE0.5 for 66 sites from 2011 to 2020, and established the water balance maps. The amount of annual average precipitation by RCP8.5 scenario was highest in 2016 as recorded 2,062 mm and lowest in 2011 with 1,134 mm. As result of analysis for monthly precipitation and runoff, the amounts of precipitation and runoff have been especially intensive in July in 2014, 2016, 2019, and 2020. Overall, the area of Kyeongbuk and Gyeonggi was estimated more dried status of soil compared with precipitation. Except 2015 and 2020, soil water balance was recorded as negative value in other years which was calculated by subtracting output from input. The status of soil moisture was the most dry in 2020 among those in other years.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.134-134
• /
• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.499-499
• /
• 2002

The goal of this study is to apply and evaluate the precipitation outflow in river basin using satellite data and GIS for proposing the efficient watershed management method. Not only precipitation outflow data but also various spatial data such as digital map, soil map, geologic map and multi-temporal TM images were used. Using landcover classification result and soil map were applied to estimate the average CN. The CN value of 63.37 by SCS method was produced in AMC-2 condition otherwise the result of direct estimation with observation method was 63 CN value. The relative error of two results was 0.59%. It can be possible to apply the satellite data for precipitation outflow analysis. For more accurate and credible analysis of this, the more multi-temporal satellite and real observation data will be needed.