• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3097-3100
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• 2015

Projections of cancer cases are particularly useful in developing countries to plan and prioritize both diagnostic and treatment facilities. In the prediction of cancer cases for the future period say after 5 years or after 10 years, it is imperative to use the knowledge of past time trends in incidence rates as well as in population at risk. In most of the recently published studies the duration for which the time trend was assessed was more than 10 years while in few studies the duration was between 5-7 years. This raises the question as to what is the optimum time period which should be used for assessment of time trends and projections. Thus, the present paper explores the suitability of different time periods to predict the future rates so that the valid projections of cancer burden can be done for India. The cancer incidence data of selected cancer sites of Bangalore, Bhopal, Chennai, Delhi and Mumbai PBCR for the period of 1991-2009 was utilized. The three time periods were selected namely 1991-2005; 1996-2005, 1999-2005 to assess the time trends and projections. For the five selected sites, each for males and females and for each registry, the time trend was assessed and the linear regression equation was obtained to give prediction for the years 2006, 2007, 2008 and 2009. These predictions were compared with actual incidence data. The time period giving the least error in prediction was adjudged as the best. The result of the current analysis suggested that for projections of cancer cases, the 10 years duration data are most appropriate as compared to 7 year or 15 year incidence data.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.185-201
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• 2020

Scenario population projection reflects the high probability of future realization and ease of statistical interpretation. Statistics Korea (2019) also presents the results of 30 combinations, including special scenarios, as official statistics. However, deterministic population projections provide limited information about future uncertainties with several limitations that are not probabilistic. The deterministic population projections are scenario-based estimates and show a perfect autocorrelation of three factors (birth, death, movement) of population variation over time. Therefore, international organizations UN, the Max Planck Population Research Institute (MPIDR) of Germany and the Vienna Population Research Institute (VID) of Austria have suggested stochastic based population estimates. In addition, some National Statistics Offices have also adopted this method to provide information along with the scenario results. This paper calculates the demographics of Korea based on a probabilistic or stochastic basis and then draws the pros and cons and show implications of the scenario (deterministic) population projections.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.439-449
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• 2015

In this study, reliable future runoff projections based on RCPs for Yongdam dam watershed was performed using SWAT model, which was validated by k-fold cross validation method, and investigated the factors that cause the differences with respect to runoff projections between this study and previous studies. As a result, annual average runoff compared to baseline runoff would increase 17.7% and 26.1% in 2040s and 2080s respectively under RCP8.5 scenario, and 21.9% and 44.6% in 2040s and 2080s respectively under RCP4.5 scenario. Comparing the results to previous studies, minimum and maximum differences between runoff projections over different studies were 10.3% and 53.2%, even though runoff was projected by the same rainfall-runoff model. SWAT model has 27 parameters and physically based complex structure, so it tends to make different results by the model users' setting. In the future, it is necessary to reduce the cause of difference to generate standard runoff scenarios.

• Proceedings of the KGS Conference
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• 2005.05a
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• pp.76-77
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• 2005

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.863-871
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• 2009

For the assessment of climate change impacts on river flow condition, CGCM 3.1 T63 is selected as future climate information. The projections come from CGCM used to simulate the GHG emission scenario known as A2. Air temperature and precipitation information from the GCM simulations are converted to regional scale data using the statistical downscaling method known as MSPG. Downscaled climate data from GCM are then used as the input data for the modified TANK model to generate regional runoff estimates for 44 river locations in Nakdong river basin. Climate change is expected to reduce the reliability of water supplies in the period of 2021~2030. In the period of 2051~2060, stream flow is expected to be reduced in spring season and increased in summer season. However, it should be noted that there are a lot of uncertainties in such multiple-step analysis used to convert climate information from GCM-based future climate projections into hydrologic information.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.327-327
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• 2019

The study will quantify the total uncertainties in streamflow and precipitation projections for Upper Awash River Basin located in central Ethiopia. Three hydrological models (GR4J, CAT, and HBV) will be used to simulate the streamflow considering two emission scenarios, six high-resolution GCMs, and two downscaling methods. The readily available hydrometeorological data will be applied as an input to the three hydrological models and the potential evapotranspiration will be estimated using the Penman-Monteith Method. The SCE-UA algorithm implemented in PEST will be used to calibrate the three hydrological models. The total uncertainty including the incremental uncertainty at each stage (emission scenarios and model) will be presented after assessing a total of 24 (=$2{\times}6{\times}2$) high-resolution precipitation projections and 72 (=$2{\times}6{\times}2{\times}3$) streamflow projections for the study basin. Finally, the primary causes that generate uncertainties in future climate change impact assessments will be identified and a conclusion will be made based on the finding of the study.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.54-59
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• 2016

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.69-80
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• 2018

This study investigated climate change influences over crop water requirement (CWR) and irrigation water requirement (IWR) of the wheat-rice cropping system of Upper Chenab Canal (UCC) command in Punjab Province, Pakistan. PRECIS simulated delta-change climate projections under the A1B scenario were used to project future climate during two-time slices: 2030s (2021-2050) and 2060s (2051-2080) against baseline climatology (1980-2010). CROPWAT model was used to simulate future CWRs and IWRs of the crops. Projections suggested that future climate of the study area would be much hotter than the baseline period with minor rainfall increments. The probable temperature rise increased CWRs and IWRs for both the crops. Wheat CWR was more sensitive to climate-induced temperature variations than rice. However, projected winter/wheat seasonal rainfall increments were satisfactorily higher to compensate for the elevated wheat CWRs; but predicted increments in summer/rice seasonal rainfalls were not enough to complement change rate of the rice CWRs. Thus, predicted wheat IWRs displayed a marginal and rice IWRs displayed a substantial rise. This suggested that future wheat production might withstand the climatic influences by end of the 2030s, but would not sustain the 2060s climatic conditions; whereas, the rice might not be able to bear the future climate-change impacts even by end of the 2030s. In conclusion, the temperature during the winter season and rainfall during the summer season were important climate variables controlling water requirements and crop production in the study area.