Assessment and Spatiotemporal Variation Analysis of Water Quality in the Zhangweinan River Basin, China

AbstractSpatiotemporal variation analysis of water quality and identification of water pollution sources in river basins is very important for water resources protection and sustainable utilization. In this study, fuzzy comprehensive analysis and two statistical methods including cluster analysis and seasonal Kendall test method were used to evaluate the spatiotemporal variation of water quality in the Zhangweinan River basin. The results for spatial cluster analysis and assessment on water quality at 19 monitoring sites indicated that water quality in the Zhangweinan River basin could be classified into two regions according to pollution levels. One is the Zhang River basin located in northwest of the Zhangweinan River basin where water quality is good. Another one includes the Wei River and eastern plain of the Zhangweinan River basin, and the water pollution in this region is serious, where the pollutants from point sources flow into the river and the water quality changes greatly. The results of temporal cluster analysis and seasonal Kendall test indicated that the sampling periods may be classified into three periods during 2002-2009 according to water quality. Results of temporal cluster analysis and seasonal Kendall test indicated that the study periods may be classified into three periods and two different trends was detected during the period of 2002-2009. The first period was the year of 2002-2003, during which water quality had deteriorated and serious pollution was observed in the Wei River basin and eastern plain of the Zhangweinan River basin. The second period was the year of 2004-2006, during which water quality became better. The year of 2007-2009 is the third period, during which water quality had been improved greatly. Despite that water quality in the Zhangweinan River basin had been improved during the period of 2004-2009, water quality in the Wei River (southwestern part of the basin), the Wei Canal River and the Zhangweixin River (eastern plain of the basin) is still poor. These results provide may useful information for better pollution control strategies in the Zhangweinan River basin

Coastal Fish Research

This book is a collection of a series of articles on several aspects of coastal fish biology and ecology. Coastal fish are key components of marine ecosystems, and the aim of this book is to present relevant research on these wonderful animals and to provide insights for future research in this field

Controlling Factors of Microplastic Riverine Flux and Implications for Reliable Monitoring Strategy

Embargo until December 12, 2022A significant proportion of marine plastic debris and microplastics is assumed to be derived from river systems. In order to effectively manage plastic contamination of the marine environment, an accurate quantification of riverine flux of land-based plastics and microplastics is imperative. Rivers not only represent pathways to the ocean, but are also complex ecosystems that support many life processes and ecosystem services. Yet riverine microplastics research is still in its infancy, and many uncertainties still remain. Major barriers exist in two aspects. First, nonharmonized sampling methodologies make it problematic for compiling data across studies to better estimate riverine fluxes of microplastics globally; Second, the significant spatiotemporal variation of microplastics in rivers which was affected by the river characteristics, MPs properties, etc. also have important influence on the estimation of riverine MPs fluxes. In this study, we made a comprehensive review from the above two aspects based on published peer-reviewed studies and provide recommendations and suggestions for a reliable monitoring strategy of riverine MPs, which is beneficial to the further establish sampling methods for rivers in different geographical locations. Besides, methods for achieving a high level of comparability across studies in different geographical contexts are highlighted. Riverine microplastic flux monitoring is another important part of this manuscript. The influential factors and calculation methods of microplastic flux in rivers are also discussed in this paper.acceptedVersio

고해상도 초분광영상을 활용한 하천 부유사농도 계측기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022. 8. 서일원.기존의 하천 부유사 농도 계측은 샘플링 기반 직접계측 방식에 의존하여 시공간적 고해상도 자료 취득이 어려운 실정이다. 이러한 한계점을 극복하기 위해 최근 위성과 드론을 활용하여 촬영된 다분광 혹은 초분광 영상을 통해 고해상도의 부유사농도 시공간분포를 계측하는 기법에 대한 연구가 활발히 진행되고 있다. 하지만, 다른 하천 물리량 계측에 비해 부유사 계측 연구는 하천에 따라 부유사가 다양하게 분포하고 다른 부유물질 혹은 하상에 의한 바닥 반사의 영향 때문에 분광 자료를 통해 정확한 부유사농도 분포를 재현하기 어려운 실정이다. 특히, 부유사 분광 특성에 영향을 미치는 입도분포, 광물특성, 침강성 등이 하천에 따라 강한 지역성을 나타내기에 이러한 요인에서 야기되는 분광다양성으로 인해 특정 시기와 지역에만 적합한 원격탐사 기반 계측 모형들이 개발되어 왔다. 본 연구에서는 이러한 분광다양성을 반영하여 다양한 하천 및 유사 조건에서 적용 가능한 고해상도 초분광영상을 활용한 하천 부유사농도 계측방법을 개발하기 위해 초분광 군집화 기법과 다양한 파장대의 분광 밴드를 학습할 수 있는 기계학습 회귀 모형을 결합하여 CMR-OV라는 방법론을 제시하였다. CMR-OV 개발 및 검증은 1) 실험적 연구를 통한 하천 부유사 분광 특성의 주요 교란 요인 분석, 2) 최적 회귀모형 선정 및 초분광 클러스터링과의 결합, 3) 현장적용성 평가의 과정을 거쳐 수행되었다. 실험적 연구에서는 우선 실내 실험실에서 횡방향 혼합기를 활용하여 바닥 반사를 제거하고 완전 혼합된 상태에서 부유사의 고유 초분광 스펙트럼 자료를 수집하였다. 이를 바탕으로 실제 하천과 유사한 조건의 실규모 옥외 수로 실험에서 다양한 유사 특성(입도 및 광물)과 하상 특성(식생 및 모래)에 대한 초분광 자료를 수집하여 고유 초분광 스펙트럼과 비교하였다. 그 결과, 부유사의 분광 특성은 유사의 종류 및 입도에 따라 농도 증가에 따른 초분광 스펙트럼의 반사율 변화가 상이하게 나타났다. 또한, 1 m 이하의 얕은 수심 조건에서는 바닥 반사의 영향으로 하상 종류에 따라 초분광 스펙트럼의 개형이 크게 변화하였으며, 고농도의 부유사가 분포할 때도 바닥 반사가 크게 영향을 미치는 것을 확인하였다. 이러한 분광다양성이 반영된 부유사농도와 초분광 자료의 관계를 구축하기 위하여 기계학습 기반 랜덤포레스트 회귀 모형과 가우시안 혼합 모형 기반 초분광 군집 기법을 결합한 CMR-OV를 적용한 결과, 기존 연구들에서 주로 활용된 밴드비 기반의 모형과 단일 기계학습모형에 비해 정확도가 크게 향상하였다. 특히, 기존 최적 밴드비 분석 (OBRA) 방법은 비선형성을 고려해도 좁은 영역의 파장대만을 고려하는 한계점으로 인해 분광다양성을 반영하지 못하는 것으로 밝혀졌다. 하지만, CMR-OV는 폭 넓은 파장대 영역을 고려함과 동시에 높은 정확도를 산출하였다. 최종적으로 CMR-OV를 황강의 직선구간 및 사행구간과 낙동강과 황강의 합류부에 적용하여 현장검증을 수행한 결과, 기존 모형 대비 정확도와 부유사 농도 맵핑의 정밀성에서 큰 개선이 있었으며, 비학습지역에서도 높은 정확도를 산출하였다. 특히, 하천 합류부에서는 초분광 군집을 통해 두 하천 흐름의 경계층을 명확히 구별하였으며, 이를 바탕으로 지류와 본류에 대해 각각 분리된 회귀모형을 구축하여 복잡한 합류부 근역 경계층에서의 부유사 분포를 보다 정확하게 재현하였다. 또한, 나아가서 재현된 고해상도의 부유사 공간분포를 바탕으로 혼합도를 산정한 결과, 기존 점계측 대비 상세하게 부유사 혼합에 대한 정량적 평가가 가능한 것으로 나타났다. 따라서, 본 연구에서 개발한 초분광영상 기반 부유사 계측 기술을 통해 추후 하천 조사 및 관리 실무의 정확성 및 효율성을 크게 증진할 수 있을 것으로 기대된다.The conventional measurement method of suspended sediment concentration (SSC) in the riverine system is labor-intensive and time-consuming since it has been conducted using the sampling-based direct measurement method. For this reason, it is challenging to collect high-resolution datasets of SSC in rivers. In order to overcome this limitation, remote sensing-based techniques using multi- or hyper-spectral images from satellites or UAVs have been recently carried out to obtain high-resolution SSC distributions in water environments. However, suspended sediment in rivers is more dynamic and spatially heterogeneous than those in other fields. Moreover, the sediment and streambed properties have strong regional characteristics depending on the river type; thus, only models suitable for a specific period and region have been developed owing to the increased spectral variability of the water arising from various types of suspended matter in the water and the heterogeneous streambed properties. Therefore, to overcome the limitations of the existing monitoring system, this study proposed a robust hyperspectral imagery-based SSC measurement method, termed cluster-based machine learning regression with optical variability (CMR-OV). This method dealt with the spectral variability problem by combining hyperspectral clustering and machine learning regression with the Gaussian mixture model (GMM) and Random forest (RF) regression. The hyperspectral clustering separated the complex dataset into several homogeneous datasets according to spectral characteristics. Then, the machine learning regressors corresponding to clustered datasets were built to construct the relationship between the hyperspectral spectrum and SSC. The development and validation of the proposed method were carried out through the following processes: 1) analysis of confounding factors in the spectral variability through experimental studies, 2) selection of an optimal regression model and validation of hyperspectral clustering, and 3) evaluation of field applicability. In the experimental studies, the intrinsic hyperspectral spectra of suspended sediment were collected in a completely mixed state after removing the bottom reflection using a horizontal rotating cylinder. Then, hyperspectral data on various sediment properties (particle size and mineral contents) and river bed properties (sand and vegetation) were collected from sediment tracer experiments in field-scale open channels under different hydraulic conditions and compared with intrinsic hyperspectral spectra. Consequently, the change of the hyperspectral spectrum was different according to the sediment type and particle size distribution. In addition, under the shallow water depth condition of 1 m or less, the shape of the hyperspectral spectrum changed significantly depending on the bed type due to the bottom reflectance. The bottom reflectance substantially affected the hyperspectral spectrum even when the high SSC was distributed. As a result of combining the GMM and RF regression with building a relationship between the SSC and hyperspectral data reflecting the spectral variability, the accuracy was substantially improved compared to the other methods. In particular, even when nonlinearity is considered based on the existing optimal band ratio analysis (OBRA) method, spectral variability could not be reflected due to the limitation of considering only a narrow wavelength range. On the other hand, CMR-OV showed high accuracy while considering a wide range of wavelengths with clusters having distinct spectral characteristics. Finally, the CMR-OV was applied to the straight and meandering reaches of the Hwang River and the confluence of the Nakdong and Hwang Rivers in South Korea to assess field applicability. There was a remarkable improvement in the accuracy and precision of SSC mapping under various river conditions compared to the existing models, and CMR-OV showed robust performance even with non-calibrated datasets. At the river confluence, the mixing pattern between the main river and tributary was apparently retrieved from CMR-OV under optically complex conditions. Compared to the non-clustered model, hyperspectral clustering played a primary role in improving the performance by separating the water bodies originating from both rivers. It was also possible to quantitatively evaluate the complicated mixing pattern in detail compared to the existing point measurement. Therefore, it is expected that the accuracy and efficiency of river investigation will be significantly improved through the SSC measurement method presented in this study.Abstract of dissertation i List of figures ix List of tables xvii List of abbreviations xix List of symbols xxii 1. Introduction 1 2. Theoretical research 13 2.1.1 Pre-processing of hyperspectral image (HSI) 19 2.1.2 Optical characteristics of suspended sediment in rivers 28 2.1.2.1 Theory of solar radiation transfer in rivers 28 2.1.2.2 Heterogeneity of sediment properties 33 2.1.2.3 Effects of bottom reflectance 38 2.1.2.4 Vertical distribution of suspended sediment 41 2.1.3 Retrieval of suspended sediment from remote sensing data 46 2.1.3.1 Remote sensing-based regression approach 46 2.1.3.2 Clustering of remote sensing data 52 2.2 Mapping of suspended sediment concentration in rivers 56 2.2.1 Traditional method for spatial measurement 56 2.2.2 Spatial measurement at river confluences 57 2.2.2.1 Dynamics of flow and mixing at river confluences 57 2.2.2.2 Field experiments in river confluences 64 3. Experimental studies 68 3.1 Experimental cases 68 3.2 Laboratory experiment 74 3.2.1 Experimental setup 74 3.2.2 Experimental method 78 3.3 Field-scale experiments in River Experiment Center 83 3.3.1 Experiments in the straight channel 83 3.3.2 Experiments in the meandering channel 96 3.4 Field survey 116 3.4.1 Study area and field measurement 116 3.4.2 Hydraulic and sediment data in rivers with simple geometry 122 3.4.3 Hydraulic and sediment data in river confluences 126 3.5 Analysis of hyperspectral data of suspended sediment 141 3.5.1 Hyperspectral data of laboratory experiment 142 3.5.2 Hyperspectral data of field-scale experiments 146 3.5.2.1 Effect of bottom reflectance 146 3.5.2.2 Principal component analysis of the effect of suspended sediment properties 154 4. Development of suspended sediment concentration estimator using UAV-based hyperspectral imagery 164 4.1 Outline of Cluster-based Machine learning Regression with Optical Variability (CMR-OV) 164 4.2 Pre-processing of hyperspectral images 168 4.3 Regression models and clustering technique 173 4.3.1 Index-based regression models 173 4.3.2 Machine learning regression models 175 4.3.3 Relevant band selection 183 4.3.4 Gaussian mixture model for clustering 185 4.3.5 Performance criteria 188 4.4 Model development and evaluation 189 4.4.1 Comparison of regression models 189 4.4.1.1 OBRA-based explicit models 189 4.4.1.2 Machine learning-based implicit models 194 4.4.2 Assessment of hyperspectral clustering 200 4.4.3 Spatio-temporal SSCV mapping using CMR-OV 215 5. Evaluation of field applicability of CMR-OV 225 5.1 Outline of field applicability test 225 5.2 Cross-applicability validation of CMR-OV 227 5.3 Assessment of field applicability in rivers with simple geometry 234 5.4 Assessment of field applicability in river confluences 241 5.4.1 Classification of river regions using hyperspectral clustering 241 5.4.2 Retrievals of SSCV map 258 5.4.3 Mixing pattern extraction from SSCv map 271 6. Conclusions and future study 274 6.1 Conclusions 274 6.2 Future directions 278 Reference 280 Appendix 308 Appendix A. Breakthrough curve (BTC) analysis 308 Appendix B. Experimental data 310 Appendix B. 1. BTCs of in-situ measured SSC from field-scale experiments 310 Appendix B. 2. Dataset of spectra from hyperspectral images and corresponding SSC in rivers 330 Appendix C. CMR-OV code 331 국문초록 337박

The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Flow-3D CFD model of bifurcated open channel flow: setup and validation

Bifurcation is a morphological feature present in most of fluvial systems; where a river splits into two channels, each bearing a portion of the flow and sediments. Extensive theoretical studies of river bifurcations were performed to understand the nature of flow patterns at such diversions. Nevertheless, the complexity of the flow structure in the bifurcated channel has resulted in various constraints on physical experimentation, so computational modelling is required to investigate the phenomenon. The advantages of computational modelling compared with experimental research (e.g. simple variable control, reduced cost, optimize design condition etc.) are widely known. The great advancement of computer technologies and the exponential increase in power, memory storage and affordability of high-speed machines in the early 20th century led to evolution and wide application of numerical fluid flow simulations, generally referred to as Computational Fluid Dynamics {CFD). In this study, the open-channel flume with a lateral channel established by Momplot et al (2017) is modelled in Flow-3D. The original investigation on divided flow of equal widths as simulated in ANSYS Fluent and validated with velocity measurements

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes