Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India

Samantaray, Dibyendu; Chatterjee, Chandranath; Singh, Rajendra Prasad; Gupta, Praveen Kumar; Panigrahy, Sushma

doi:10.1007/s11069-014-1493-9

Cited by 43 publications

(20 citation statements)

References 37 publications

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“…Five 3-parameter distributions, namely, generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GNO), Pearson type-III (PE3), generalized pareto (GPA), and one 5-parameter distribution Wakeby (WAK) were employed. L-moments ratio diagrams were often used in conjunction with Z-dist statistics to identify the best fit distribution for a given sample (Kumar and Chatterjee, 2005;2011;Jena et al, 2014;Samantaray et al, 2015;Bisht et al, 2016). However, a sample can be fitted into more than one distribution in many cases, and in such cases Z-dist statistics is used to identify the best-fit distribution.…”

Section: Frequency Analysismentioning

confidence: 99%

“…The best fit for a sample is identified if |Z-dist| statistic is sufficiently close to 0 and less than 1.64 (Kumar and Chatterjee, 2011). For cases, where none of the 3-parameter distribution show |Z-dist| < 1.64, a 5-parameter distribution, Wakeby, is employed for the robustness of analysis (Hosking and Wallis, 1997;Samantaray et al, 2015). Owing to a large number of grids in the present study, only Z-dist statistics were used.…”

Section: Frequency Analysismentioning

confidence: 99%

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Drought characterization over India under projected climate scenario

Bisht

Sridhar

Mishra

et al. 2018

Intl Journal of Climatology

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109

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The study evaluates the drought characteristics in India over projected climatic scenarios in different time frames, that is, near‐future (2010–2039), mid‐future (2040–2069), and far‐future (2070–2099) in comparison with reference period (1976–2005). Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used owing to its robustness in capturing drought conditions while accounting the temperature. Gridded rainfall and temperature data provided by India Meteorological Department (IMD) was used to perform bias correction of nine Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Quantile mapping was used to correct the daily rainfall data at seasonal scale whereas daily temperature data was corrected at monthly scale. Multi‐Model Ensemble (MME) was prepared for different homogeneous monsoon regions of India, namely Hilly Regions (HR), Central Northeast (CNE), Northeast (NE), Northwest (NW), West Central (WC), and Peninsula (PS). Taylor diagram statistics were used for the preparation of MME. The regional climate cycle obtained from MME was found to be in good agreement with observed cycle derived from IMD data. The Mann–Kendal trend test was employed to detect the trend in drought severity and magnitude whereas L‐moments based frequency analysis was used to assess the magnitude of extreme drought severity under different time frames. The study reveals an increasing trend in drought severity, duration, occurrences, and the average length of drought under warming climate scenarios. Furthermore, the area under “above moderate drought” (i.e., severe and extreme drought combined) condition was also found to be increasing in projected climate.

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Section: Frequency Analysismentioning

confidence: 99%

Section: Frequency Analysismentioning

confidence: 99%

Drought characterization over India under projected climate scenario

Bisht

Sridhar

Mishra

et al. 2018

Intl Journal of Climatology

Self Cite

109

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“…The depth-damage curve was used as a continuous function for crop loss assessment, where x-axis and y-axis represent flood depth and corresponding loss, respectively. Many studies used three to four depth classes and associated potential damage in crop loss assessment [47,[60][61][62]. Waisurasingha et al [30] and Pacetti et al [63] used 80 and 100 cm flood-depth threshold to determine crop damage.…”

Section: Flood-intensity-based Crop Loss Assessmentmentioning

confidence: 99%

“…Categorical flood duration expressed in the range of days was used in most case studies of this kind. The ranges of duration classes were varied from a single day to several days (a few weeks) depending on the crop types and seasonality [61,62,65,66]. Only a few studies utilized flow velocity in crop loss assessment (Table 1).…”

Section: Flood-intensity-based Crop Loss Assessmentmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

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“…1 引言随着我国城镇化快速发展，由于城市开发强度过高，大量硬质铺装，改变了原有的自然生态本底和水文特征。城市每逢遭遇高强度降雨时，大量降雨在短时间内形成地表径流，大大增加了城市排水压力，经常出现"城内看海"现象，"逢暴雨必涝"已成为中国城市的真实写照。2010 年住房和城乡建设部对全国 351 个城市的抽样调查显示，仅 2008-2010 年就有 62%的城市发生过不同程度的暴雨内涝 [1] 。 2013 年 3 月 25 日，国务院办公厅正式发布了《国务院办公厅关于做好城市排水防涝设施建设工作的通知(国办发[2013]23 号) 》；2013 年 6 月住房和城乡建设部颁布了《城市排水 (雨水) 防涝综合规划编制大纲》 (以下简称 "大纲" ) 。目前，城市内涝风险评估应用较多的方法有：历史灾情数理统计法、指标体系法和水文水力学模型与仿真模拟法。周魁一提出"历史模型"的概念，论述了基于历史数据的历史模型方法和在灾害问题中的应用 [2] ； Hans de Model 利用荷兰 1990-2000 年以及未来 100 年规划的空间地理信息分析了城市化对洪水产生的影响 [3] ；北美学者利用指标体系从国家、市级尺度对洪水灾害风险进行了区划和评估 [4][5][6][7] ；杜鹃等从孕灾环境的自然属性、承灾体的社会环境以及致灾因子的特点出发，构建了洪水灾害综合风险评估指标体系，并将理论成果运用在湘江流域风险评估中 [8] 。运用模型对城市内涝风险进行评估在国内起步相对较晚：1970 年以前主要运用基于物理机制的经验方程满足城市径流计算的需要； 1970-1990 年相继将一些新的理论和方法引入城市雨洪模型之中，使得雨洪模型应用于城市管网汇流以及水质模型 [9][10] ；2000 年以后，模型与 GIS、 RS 等结合广泛，开始研究城市防洪调度以及内涝积水的模拟与仿真 [11] 。大纲提出"在排水防涝设施普查的基础上，推荐使用水力模型对城市现有雨水管网和泵站等设施进行评估，分析实际排水能力"，"推荐使用水力模型进行城市内涝风险评估"。目前，国外应用水力模型进行城市内涝风险评估及排水系统相关规划设计已较成熟 [12][13][14] ，国内应用模型辅助进行方案设计尚处于研究阶段 [15][16][17] 。国际上用于评估城市内涝风险的水力学模型有丹麦水利研究院(DHI)开发的 MIKE 系列模型 [18][19]23] ，英国 HR Wallingford 公司的 InfoWorks 系列模型 [14,[20][21][22] ，美国环境保护署(EPA) 开发的 SWMM 模型 [23][24] ，欧洲委员会联合研究中心开发的 LISFLOOD 模型 [25][26]…”

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Research on Urban Waterlogging Disaster Risk Assessment Based on ARCGIS and MIKE FLOOD andash; A Case Study on Shijiazhuang

Zhang¹,

Hu²,

Wang³

et al. 2015

JRACR

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Urban flood risk assessment supported by accurate and precise modeling could make great contributions to urban flood management. In this paper, an urban drainage system model of Shijiazhuang city was built based on the MIKE21, MIKE URBAN and MIKE 11 modules in MIKE FLOOD platform in order to provide a storm sewer drainage capacity and waterlogging risk evaluation. The results show that in Shijiazhuang, 64.5% of storm sewer drainage criteria is less than 1-year return period storm, and only 28.9% of storm sewer can meet the drainage criteria of 2-year return period storm. The waterlogging risk area grows from 3.2, 15.1 to 18.5 square kilometer when the return period is 10, 20 and 50 years. Both the current condition and the drainage system model database indicate the reasons of the drainage issue in Shijiazhuang which can be summarized as the rapid urbanization, the low capacity of pipe network and pumping stations, the low construction standard, the lag between the sewer system construction and the demand, the lack of drainage outlet, and the imperfect management system.

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Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India

Cited by 43 publications

References 37 publications

Drought characterization over India under projected climate scenario

Drought characterization over India under projected climate scenario

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Research on Urban Waterlogging Disaster Risk Assessment Based on ARCGIS and MIKE FLOOD andash; A Case Study on Shijiazhuang

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