Jaepil Cho

Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested.

Hydrothermal Assessment of Temporal Variability in Seedbed Microclimate

Abstract The microclimatic requirements for successful seedling establishment are much more restrictive than those required for adult plant survival. The purpose of the current study was to use hydrothermal germination models and a soil energy and water flux model to evaluate intra- and interannual variability in seedbed microclimate relative to potential germination response of six perennial grasses and cheatgrass. We used a 44-yr weather record to parameterize a seedbed microclimate model for estimation of hourly temperature and moisture at seeding depth for a sandy loam soil type at the Orchard Field Test Site in southwestern Ada County, Idaho. Hydrothermal germination response was measured in the laboratory for two seed lots of cheatgrass (Bromus tectorum L.), four seed lots of bluebunch wheatgrass (Pseudoroegneria spicata [Pursh] Löve), three seed lots of bottlebrush squirreltail (Elymus elymoides [Raf] Swezey), and one seed lot each of Sandberg bluegrass (Poa secunda J. Presl.), big squirreltail (Elymus multisetus [J.G. Smith] M.E. Jones), thickspike wheatgrass (Elymus lanceolatus [Scribn. And J.G. Smith] Gould) and Idaho fescue (Festuca idahoensis Elmer). Germination response models were developed to estimate potential germination rate for 13 subpopulations of each seed lot for every hour of the 44-yr simulation. Seedbed microclimate was assessed seasonally and for each day, month, and year, and germination rate-sum estimates integrated for a numerical index of relative site favorability for germination for each time period. The rate-sum favorability index showed a consistent pattern among seed lots for different years, and provides a relatively sensitive indicator of annual and seasonal variability in seedbed microclimate. This index could be used with field data to define minimum weather thresholds for successful establishment of alternative plant materials, in conjunction with weather forecast models for making restoration and fire-rehabilitation management decisions in the fall season, for evaluation of potential climate-change impacts on plant community trajectories, and in optimization schemes for selecting among alternative restoration/rehabilitation management scenarios.

Evaluation of Agro-Climatic Index Using Multi-Model Ensemble Downscaled Climate Prediction of CMIP5

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on , plant period based on , crop period based on , and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information

It is highly anticipated that meteorological changes resulting from global climate change will affect the pattern of rice disease epidemics worldwide. Here, we evaluated the potential impacts of climate change on two representative rice diseases, leaf blast and sheath blight, in Korea. This study involves analyses of disease simulation using an epidemiological model, EPIRICE, which was validated for Korean rice paddy fields. The goal of our study was to assess likely changes in national disease probabilities using individual climate scenarios across different models and multi-model ensemble scenarios constructed by running 11 global climate models. In this way, the results from this study emphasize the uncertainties in climate change scenarios resulting from the variations in initial conditions as well as the structural differences in the global climate models. Observed and simulated epidemics for both diseases were compared using the area under the disease progress curve from EPIRICE model runs. Overall, the simulated incidence of epidemics for both diseases gradually decreased towards 2100 both from individual global climate models and multi-model ensembles. It was noted that while each individual model resulted in different magnitudes of impact, the multi-model ensemble gave the most reliable result that accounts for uncertainty compared to the individual models. In conclusion, we found that in modeling climate impacts on rice diseases, ensembles account for uncertainty better than individual climate models and can lead to better decision making.

Assessing Applicability of SWAT Calibrated at Multiple Spatial Scales from Field to Stream

유역 내부의 수문과정에 대한 관측치를 이용한 모형의 보정은 분산형 수문수질 모형의 적용성을 높이는 방법으로 권장되지만, 관측치가 충분하지 않은 경우가 많아 적용사 례가 드문 실정이다. 본 연구는 경지에서 유역의 출구까지 여러 단계의 수문과정을 고려하여 분산형 수문수질모형을 보정하는 방법을 제시하고, 이와 같은 방법으로 보정된 모형의 적용성을 검토하고자 하였다. 이를 위해 본 연구는 SWAT 모형을 이용하여 미국 조지아의 South Atlantic Coastal Plain에 있는 한 농업유역의 수문과정을 모의 하였다. 또한, 유사와 영양물질의 구체적인 하천운송과정을 모의하기 위해 SWAT모형의 QUAL2E 모듈을 이용하였다. 모형보정은 유역의 유출량 및 유사와 영양물질의 양 뿐만 아니라 경지에서 관측된 바이오매스, 토양 침식량 및 영양물질 발생량, 수변지역 (riparian buffer)에서 발생하는 유사와 영양물질 감소, 하천운송과정 등을 고려하 여 수행되었다. 모형의 보정 및 검증기간은 자료기간과 토양보전농법의 시행기간을 고려하여 선정되었으며, 보정된 모형의 적용성은 복수의 통계치 (NSE, RE, RSR 등) 를 이용하여 분석되었다. 보정된 모형은 유역의 출구에서 하천유량과 총질소량을 각각 NSE 0.93 및 0.59의 정확도로 모의하였다. 그러나 유사 (NSE: 0.40)와 총인량 (NSE: 0.45)모의에서는 상대적으로 낮은 정확도를 보였다. SWAT의 QUAL2E 모듈을 이용하여 하천에서의 유사 및 영양물질 운송과정을 고려하는 것은 총인과 총질소 모의결과의 정확도를 향상시킬 수 있는 것으로 나타났다. 본 연구에서 다단계 수문과정을 고려하여 보정된 SWAT모형은 유역 내부에서 발생하는 수문과정을 모의하는데 적용성이 있는 것으로 나타났다. 하지만, 계측방법 및 계측된 자료의 분석방법의 변화에 따른 오차와 토지이용의 시공간적 변화를 모의에서 고려하지 못해서 발생한 오차를 감안하더라도, 보정된 모형은 유역출구에서 관측되는 유사와 영양물질 양을 정확하게 모의하는데 실패 (NSE < 0.5)하였다고 평가할 수 있으며, 이는 SWAT 모형의 보정 에서 다단계 또는 유역내부의 수문과정을 고려하는 것이 유역출구에서 높은 모의 정확도를 얻는데 기여하지 못할 수도 있음을 보여주었다.

Assessment of Climate Change Impacts on Extreme Precipitation Events: Applications of CMIP5 Climate Projections Statistically Downscaled over South Korea

Climate change may accelerate the water cycle at a global scale, resulting in more frequent extreme climate events. This study analyzed changes in extreme precipitation events employing climate projections statistically downscaled at a station-space scale in South Korea. Among the CMIP5 climate projections, based on spatial resolution, this study selected 26 climate projections that provide daily precipitation under the representative concentration pathway (RCP) 4.5. The results show that a 20-year return period of precipitation event during a reference period (1980∼2005) corresponds to a 16.6 yr for 2011 to 2040, 14.1 yr for 2041 to 2070, and 12.8 yr for 2071 to 2100, indicating more frequent extreme maximum daily precipitation may occur in the future. In addition, we found that the probability density functions of the future periods are located out of the 10% confidence interval of the PDF for the reference period. The result indicates that the design standard under the reference climate is not managed to cope with climate change, and accordingly the revision of the design standard is required to improve sustainability in infrastructures.

The influence of climate change on irrigation water requirements for corn in the coastal region of Ecuador

This study investigated the irrigation water requirements (IWR) for corn in five provinces of the coastal region of Ecuador that have been influenced by climate change. The weather data were statistically downscaled from six General Circulation Models and compared with the current climate period from 1986 to 2012. CROPWAT 8.0 was used to estimate future IWR for corn cultivation from 2011 to 2100 based on the Representative Concentration Pathways 4.5 and 8.5 scenarios. Also the frequency of predicted rainfall for future periods was analysed to identify the possibility of obtaining the amount of water required for corn plantation from rainfall. The projected trend of future climate showed increases in temperature and rainfall. The predicted IWR showed a decreasing trend in the rainy season and a similar or slightly lower trend in the dry season. Sufficient rain for corn cultivation was predicted for the wet region of the study area, with lower IWR, whereas most of the dry region was shown to have similar patterns of current water demands, except an increase in predicted rainfall resulted in lower IWR in some parts of this region.

Development of an Agricultural NPS-Web Data Management System for Small Watersheds

The objective of this study was to develop an agricultural Nonpoint Pollution Source(NPS)-web data management system for the effective handling of data. The system consists of the relational database for agricultural NPS data, a data processing module, and a web site for the visual presentation online. The data collected from the Yongnam Irrigation District were used in this study. The database was constructed using MySQL Server 5.5 with meteorological, hydrological, water quality, soil characteristics, and farming factors. The data process module and web site were developed using Microsoft C# and ASP.NET, respectively. The data process module was composed of two submodule; data input module and data analysis module to validate data. The results of this study showed the applicability to a variety of agricultural NPS data. Furthermore, the system developed in this study are expected to provide an appropriate data management tool for agricultural NPS studies.