Assessment of Wetland Functions considering Climate Change

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Assessment of the Impacts of Global Climate Change and Regional Water Projects on Streamflow Characteristics in the Geum River Basin in Korea

The impacts of two factors on future regional-scale runoff were assessed: the external factor of climate change and the internal factor of a recently completed large-scale water resources project. A rainfall-runoff model was built (using the Soil and Water Assessment Tool, SWAT) for the Geum River, where three weirs were recently constructed along the main stream. RCP (Representative Concentration Pathways) climate change scenarios from the HadGEM3-RA RCM model were used to generate future climate scenarios, and daily runoff series were constructed based on the SWAT model. The indicators of the hydrologic alteration (IHA) program was used to carry out a quantitative assessment on the variability of runoff during two future periods (2011–2050, 2051–2100) compared to a reference period (1981–2006). Analyses of changes in the runoff characteristics of the lower Geum River showed that climate change is likely to lead to an increase of the future runoff ratio and that weirs contributed to an increase in the minimum discharge and a decrease in the maximum discharge. The influence of the weirs on the runoff characteristics of the Geum River basin was projected to be greater than that of climate change

Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF

Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea

In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study applied Rgauge and Rradar to a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data for Rgauge, RZ, and RKDP were utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods using RZ(P) and RKDP(P), the simulation which utilized Rgauge reflected the rainfall-runoff characteristics better than the simulations which applied RZ(P) or RKDP(P). However, in the rainy or wet season, the simulations which utilized RZ(P) or RKDP(P) were similar to or better than the simulation that applied Rgauge. This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar

Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future

DualPIM: A Dual-Precision and Low-Power CNN Inference Engine Using SRAM- and eDRAM-based Processing-in-Memory Arrays

Recently, machine learning community has focused on developing deep learning models that are not only accurate but also efficient to deploy them on resource-limited devices. One popular approach to improve the model efficiency is to aggressively quantize both features and weight parameters. However, the quantization generally entails accuracy degradation thus additional compensation techniques are required. In this work, we present a novel network architecture, named DualNet, that leverages two separate bit-precision paths to effectively achieve high accuracy and low model complexity. On top of this new network architecture, we propose to utilize both SRAM-and eDRAM-based processing-in-memory (PIM) arrays, named DualPIM, to run each computing path in a DualNet at a dedicated PIM array. As a result, the proposed DualNet significantly reduces the energy consumption by 81% on average compared to other quantized neural networks (i.e., 4-bit and ternary), while achieving 13% higher accuracy on average

Case Study: On Objective Functions for the Peak Flow Calibration and for the Representative Parameter Estimation of the Basin

The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall–runoff model. However, it does not focus on peak flow and on representative parameter for various rain storm events of the basin, but it can estimate the optimal parameters by minimizing the overall error of observed and simulated flows. Therefore, the aim of this study is to suggest the objective functions that can fit peak flow in hydrograph and estimate the representative parameter of the basin for the events. The Streamflow Synthesis And Reservoir Regulation (SSARR) model was employed to perform flood runoff simulation for the Mihocheon stream basin in Geum River, Korea. Optimization was conducted using three calibration methods: genetic algorithm, pattern search, and the Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). Two objective functions of the Sum of Squared of Residual (SSR) and the Weighted Sum of Squared of Residual (WSSR) suggested in this study for peak flow optimization were applied. Since the parameters estimated using a single rain storm event do not represent the parameters for various rain storms in the basin, we used the representative objective function that can minimize the sum of objective functions of the events. Six rain storm events were used for the parameter estimation. Four events were used for the calibration and the other two for validation; then, the results by SSR and WSSR were compared. Flow runoff simulation was carried out based on the proposed objective functions, and the objective function of WSSR was found to be more useful than that of SSR in the simulation of peak flow runoff. Representative parameters that minimize the objective function for each of the four rain storm events were estimated. The calibrated observed and simulated flow runoff hydrographs obtained from applying the estimated representative parameters to two different rain storm events were better than those retrieved from parameters estimated using a single rain storm event. The results of this study demonstrated that WSSR is adequate in peak flow simulation, that is, the estimation of peak flood runoff. In addition, representative parameters can be applied to a flow runoff simulation for rain storm events that were not involved in parameter estimation

Urban Drainage System Improvement for Climate Change Adaptation

Recently, urban areas have experienced frequent, large-scale flooding, a situation that has been aggravated by climate change. This study aims to improve the urban drainage system to facilitate climate change adaptation. A methodology and a series of mitigation strategies are presented to efficiently improve the urban drainage system in light of climate change. In addition, we assess the impact of climate change and predict the scale of potential future flood damage by applying the methodology and mitigation strategies to urban areas. Based on the methodology presented, urban flood prevention measures for Gyeyang-gu (Province), Incheon, Korea, was established. The validity of the proposed alternatives is verified by assessing the economic feasibility of the projects to reduce flood damage. We expect that the methodology presented will aid the decision-making process and assist in the development of reasonable strategies to improve the urban drainage system for adaptation to climate change