• Journal of Wetlands Research
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• v.12 no.1
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• pp.51-61
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• 2010

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.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.339-346
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• 2013

This study analyzes the application possibilities of the satellite-derived precipitation to water resources field. Precipitation observed by ground gauges and climate prediction center morphing method (CMORPH) which is global scale precipitation estimated by National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) using satellite images are compared to evaluate the quality of precipitation estimated from satellite images. Precipitation data from 10-years (2002 to 2011) is applied. The correlation coefficient of 1-day cumulative precipitation is 0.87, but the 1-year precipitation is 4 to 5 times different. The variability of root mean square error (RMSE) become smaller as temporal resolution lower. On the results for the watershed scale, the precipitation from gauges and CMORPH shows better agreement as the watershed become larger.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.164-164
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• 2018

강우침식인자는 토양침식에 영향을 주는 한 인자이다. 강우침식인자는 강우강도, 강우량, 강우빈도 등과 같은 강우패턴으로 산정되는 값으로 기후변화로 인해 많은 지역에서 강우패턴의 변화가 관측되었기에 강우침식인자 또한 기후변화로 인한 변화가 예상된다. 한국의 강우의 시공간적인 변동성에 대한 연구는 많이 진행되었으나, 강우침식인자에 대한 연구는 아직까지 미흡한 상태이기 때문에 본 연구에서는 한국의 강우침식인자의 시공간적 변동성을 분석하였다. 강우강도, 강우량, 강우빈도, 강우지속기간 등 강우패턴을 결정하는 인자들 중 어떤 인자가 강우침식인자의 시간적인 변동성에 영향을 주는지 조사하였다. 시간적인 변동성을 조사하기 위해서 경향성 검사를 진행하였다. 적용된 경향성 검사는 Mann-Kendall test, 수정된 Mann-Kendall test, Block Bootstrapping Mann-Kendall test, T-test를 적용하였다. 검사결과 대부분의 지점에서는 강우침식인자에서는 경향성이 발견되지 않았다. 경향성이 발견된 지점에 대하여 경향성의 원인을 검토해본 결과, 복합적인 강우패턴 인자의 영향으로 인하여 강우침식인자의 경향성이 발생하는 것을 확인하였다. 강우패턴 인자 중에서는 유효강우사상의 강우량이 가장 큰 영향인자인 것을 확인 할 수 있었다.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.221-241
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• 2005

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.

• Ocean and Polar Research
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• v.34 no.2
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• pp.201-218
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• 2012

The purpose of this study is to investigate climatological variations from the sea surface temperature (SST), chlorophyll-a concentration (Chl-a), and phytoplankton size class (PSC), using NOAA AVHRR, SeaWiFS, and MODIS data in the South Sea of Korea (SSK) and East China Sea (ECS). 26-year monthly SST and 13-year monthly Chl-a and PSC data, separated by whole and nine-different areas, were used to understand seasonal and inter-annual variations. SST and Chl-a clearly showed seasonal variations: higher SST and Chl-a were observed during the summer and spring, and lower values occurred during the winter and summer. The annual and monthly SST over 26 years increased by $0.2{\sim}1.0^{\circ}C$. The annual and monthly Chl-a concentration over 13 years decreased by $0.2{\sim}1.1mg/m^3$. To determine more detailed spatial and temporal variations, we used the combined data with monthly SST, Chl-a, and PSC. Between 1998 and 2010, the inter-annual trend of Chl-a decreased, with decreasing micro- and nano-size plankton, and increasing pico-size plankton. In regional analysis, the west region of the study area was spatially and temporally correlated with the area dominated by decreasing micro-size plankton; while the east region was less sensitive to coastal and land effects, and was dominated by increasing pico-size plankton. This phenomenon is better related to one or more forcing factors: the increased stratification of ocean driven by changes occurring in spatial variations of the SST caused limited contributions of nutrients and changed marine ecosystems in the study area.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.5
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• pp.637-647
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• 2019

Objective: The study aimed at quantifying seasonal and spatial variations in availability and nutritive value of herbaceous vegetation on native pastures and supplement feedstuffs for domestic ruminants in Western Kenya. Methods: Samples of herbaceous pasture vegetation (n = 75) and local supplement feedstuffs (n = 46) for cattle, sheep, and goats were collected in 20 villages of three geographic zones (Highlands, Mid-slopes, Lowlands) in Lower Nyando, Western Kenya, over four seasons of one year. Concentrations of dry matter (DM), crude ash (CA), ether extract (EE), crude protein (CP), neutral detergent fibre (NDF), gross energy (GE), and minerals were determined. Apparent total tract organic matter digestibility (dOM) was estimated from in vitro gas production and proximate nutrient concentrations or chemical composition alone using published prediction equations. Results: Nutrient, energy, and mineral concentrations were 52 to 168 g CA, 367 to 741 g NDF, 32 to 140 g CP, 6 to 45 g EE, 14.5 to 18.8 MJ GE, 7.0 to 54.2 g potassium, 0.01 to 0.47 g sodium, 136 to 1825 mg iron, and 0.07 to 0.52 mg selenium/kg DM. The dOM was 416 to 650 g/kg organic matter but differed depending on the estimation method. Nutritive value of pasture herbage was superior to most supplement feedstuffs, but its value strongly declined in the driest season. Biomass yields and concentrations of CP and potassium in pasture herbage were highest in the Highlands amongst the three zones. Conclusion: Availability and nutritive value of pasture herbage and supplement feedstuffs greatly vary between seasons and geographical zones, suggesting need for season- and region-specific feeding strategies. Local supplement feedstuffs partly compensate for nutritional deficiencies. However, equations to accurately predict dOM and improved knowledge on nutritional characteristics of tropical ruminant feedstuffs are needed to enhance livestock production in this and similar environments.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.23-43
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• 2017

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

• Atmosphere
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• v.24 no.3
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• pp.343-364
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• 2014

This study presents local and synoptic conditions associated with extreme heavy snowfall events in the Yeongdong region, as well as the temporal and spatial variability of these conditions. During the last 12 years (2001~2012), 3 extreme snowfall events occurred in the Yeongdong region, which recorded daily snowfall greater than 50 cm, respectively. In these events, one of the noticeable features is the occurrence of heavy hourly snowfall greater than 10 cm. It was reported from satellite analysis that these heavy snowfall may be closely related to mesoscale convective clouds. In this paper the 3 extreme events are examined on their synoptic environments associated with the developments of mesoscale convective system using numerical model output. These 3 events all occurred in strongly forced synoptic environments where 500 and 300 hPa troughs and 500 hPa thermal troughs were evident. From the analysis of diagnostic variables, it was found in all 3 events that absolute vorticity and cold air advection were dominant in the Yeongdong region and its surrounding sea at upper levels, especially at around 500 hPa (absolute vorticity: $20{\sim}60{\times}10^{-5}s^{-1}$, cold air advection: $-10{\sim}-20^{\circ}C$ $12hr^{-1}$). Moreover, the spatial distributions of cold advection showed mostly the shape of a narrow band along the eastern coast of Korea. These features of absolute vorticity and cold advection at 500 hPa were sustained for about 10 hours before the occurrence of maximum hourly snowfall.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.192-195
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• 2015

BACKGROUND: The growing season (GS) has been understood as a useful indicator for climate change due to high relationship with increasing temperature. Hear this study was conducted to examine changes in the thermal GS over South Korea from 1970 to 2013 based on daily mean air temperature for assessing the temporal and spatial variability in GS. METHODS AND RESULTS: Three GS parameters (starting date, ending date, and length) were determined at 19 stations throughout South Korea. The results show that the GS has been extended by 4.2 days/decade between 1970 and 2013 on average. The growing season start (GSS) has been advanced by 2.7 days/decade and the growing season end (GSE) has been delayed by 1.4 day/decade. Spatial variation in the GS parameters in Korea are shown. The GS parameters, especially GSS, of southeastern part of Korea have been changed more than that of northwestern part of Korea. The extension of GS may be more influenced on earlier onset in spring rather than later GSE. CONCLUSION: Under climate change scenarios, the GS will be more extended due to delayed GSE as well as advanced GSS. And These are more notable in the northeastern part of Korea.