Drought Resistance Information Platform Driven by Digital Twins for the Changjiang River Basin: Design and Development

doi:10.11988/ckyyb.20230594

Abstract

Abstract:

In the context of global climate change, the Changjiang River basin has suffered from multiple severe high temperatures and drought disasters. Drought resistance management in the basin is faced with such bottleneck problems as low efficiency of drought monitoring and warning, inadequate accuracy of drought prediction and early-warning, and insufficient ability to deduce drought plans. To address these issues, digital transformation has become exigent. In line with the comprehensive requirements of smart water conservancy and digital twin technology, a digital twin platform for drought prevention has been constructed by utilizing WebGL and GIS to meet the operational management requirements of drought resistance and disaster mitigation in the Changjiang River Basin, aligning with the demands of the “forecast, early warning, preview, and contingency planning” principles. Key technologies have been developed, including remote sensing drought monitoring and evaluation, dynamic loading of drought-specific models, early-warning of water levels exceeding drought limit, and visualization of drought reduction plans. Consequently, the business application covering the full chain of “forecast, early warning, preview, and contingency planning” has been achieved. This has effectively improved the intelligent and refined level of drought resistance management in the Changjiang River Basin, providing technical support for drought prevention and disaster reduction in the basin.

Key words: Changjiang River Basin, drought defense information platform, digital twin, forecast, early-warning, preview, and contingency planning

CLC Number:

TV87防洪工程

LI Zhe, XIANG Da-xiang, CHEN Zhe, CUI Chang-lu. Drought Resistance Information Platform Driven by Digital Twins for the Changjiang River Basin: Design and Development[J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(8): 180-188.

Figures/Tables 11

Fig.1 Implementation of forecast, early warning, preview, and contingency planning by the digital twin platform

Fig.2 Overall architecture of the digital twin platform

Fig.3 Main functions of the digital twin platform

Fig.4 Main interface of the digital twin platform

Fig.5 Demonstration of the digital twin platform

Table 1 Remote sensing drought indices in the digital twin platform

序号	遥感指数	计算公式	适用范围
1	陆面温度 (T_s)	T_s= $a (1 - C - D) + [b (1 - C - D) + C + D] T 10 - D T a C$ $C = ε τ$ $D = (1 - T) [1 + (1 - ε) τ]$ 式中:T_s是地表真实温度(K);a和b是常量,分别是-67.355 351和0.458 606; $ε$ 是地表比辐射率; $τ$ 是大气透过率;T₁₀是卫星高度上传感器探测到的像元亮度温度(K);T_a是大气平均作用温度(K)	地表水分亏缺状况
2	归一化水体指数 (NDWI)	NDWI= $G r e e n - N I R G r e e n + N I R$ 式中:Green为绿波段反射率;NIR为近红外波段反射率	湖库水体分布
3	归一化植被指数 (NDVI)	NDVI= $N I R - R N I R + R$ 式中:NIR为近红外波段反射率;R为红光波段反射率	作物长势状况
4	植被健康指数 (VHI)	VCI=100 $(N D V I - N D V I m i n) (N D V I m a x + N D V I m i n)$ TCI=100 $(L S T m a x - L S T) (L S T m a x + L S T m i n)$ VHI=aVCI+(1-a)TCI 式中:NDVI、NDVI_max和NDVI_min(LST、LST_max、LST_min)分别是NDVI(LST)观测值、多年平均NDVI(LST)的最大值和最小值;a=0.5	作物长势状况
5	垂直干旱指数(PDI)	PDI= $1 s 2 + 1$ (R_red+sR_SWIR) 式中:s为土壤背景线斜率;R_red为红波段的反射率;R_SWIR为短波红外的反射率	土壤含水量状况
6	综合干旱指数 (ZH)	ZH=aPDI+(1-a)VHI 式中:PDI和VHI分别为垂直干旱指数和植被健康指数;a=0.3	区域旱情综合情况

Table 1 Remote sensing drought indices in the digital twin platform

序号	遥感指数	计算公式	适用范围
1	陆面温度 (T_s)	T_s= $a (1 - C - D) + [b (1 - C - D) + C + D] T 10 - D T a C$ $C = ε τ$ $D = (1 - T) [1 + (1 - ε) τ]$ 式中:T_s是地表真实温度(K);a和b是常量,分别是-67.355 351和0.458 606; $ε$ 是地表比辐射率; $τ$ 是大气透过率;T₁₀是卫星高度上传感器探测到的像元亮度温度(K);T_a是大气平均作用温度(K)	地表水分亏缺状况
2	归一化水体指数 (NDWI)	NDWI= $G r e e n - N I R G r e e n + N I R$ 式中:Green为绿波段反射率;NIR为近红外波段反射率	湖库水体分布
3	归一化植被指数 (NDVI)	NDVI= $N I R - R N I R + R$ 式中:NIR为近红外波段反射率;R为红光波段反射率	作物长势状况
4	植被健康指数 (VHI)	VCI=100 $(N D V I - N D V I m i n) (N D V I m a x + N D V I m i n)$ TCI=100 $(L S T m a x - L S T) (L S T m a x + L S T m i n)$ VHI=aVCI+(1-a)TCI 式中:NDVI、NDVI_max和NDVI_min(LST、LST_max、LST_min)分别是NDVI(LST)观测值、多年平均NDVI(LST)的最大值和最小值;a=0.5	作物长势状况
5	垂直干旱指数(PDI)	PDI= $1 s 2 + 1$ (R_red+sR_SWIR) 式中:s为土壤背景线斜率;R_red为红波段的反射率;R_SWIR为短波红外的反射率	土壤含水量状况
6	综合干旱指数 (ZH)	ZH=aPDI+(1-a)VHI 式中:PDI和VHI分别为垂直干旱指数和植被健康指数;a=0.3	区域旱情综合情况

Fig.6 Display of Perpendicular Drought Index (PDI) in digital watershed scenes

Fig.7 XML basic information file

Fig.8 XML input/output file

Fig.9 Rolling warning of drought-limit water level

Fig.10 Emergency response planning for flood and drought disasters in the Changjiang River Basin

[1]	LIU Cheng-kun, ZHANG Hang, JING Zheng, YANG Xing-yue, ZHANG Li. Three-dimensional Water Quality Deduction Method Based on Digital Twin Technology [J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(8): 189-196.
[2]	TANG Wen-jian, SHEN Shao-hong, YANG Heng-ling, YUAN Yuan, NIU Guang-li. Practices of Applying Key Technologies of Digital Twin Water Conservancy to the Jiangya and Zaoshi Project as an Example [J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(8): 172-179.
[3]	LU Han-you, ZHAO Wen-gang, JIANG Jie-yu, WANG Zai-ai, LIU Xiao-qun, LI Zhi-wei. Research and Application of UE5-based Digital Simulation Technology for Water Flow of Hudu River [J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(8): 157-163.
[4]	HUANG Wei, CHEN Duan, YANG Qing-yuan, HUANG Yan. Research and Application Advances of Hydrodynamic Models in Digital Twin of Water Resources [J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(8): 197-206.
[5]	MEI Jun-ya. Research and Practice on Hydrological Intelligent Perception Based on New Quality Productive Forces [J]. Journal of Changjiang River Scientific Research Institute, 2024, 41(11): 1-6.
[6]	XU Ji-jun, YUAN Zhe. Drought Characteristics of Changjiang River Basin in 2022 and Drought Mitigation Response Pattern under New Circumstances [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(4): 1-8.
[7]	ZHAO Ke-feng, CAO Hui-qun, LIN Li, JING Zheng, LUO Ping-an. Typical Applications of Artificial Intelligence Video Recognition in Water Conservancy Digital Twin [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(3): 186-190.
[8]	NIU Guang-li, LI Tian-yang, YANG Heng-ling, HU Lei, ZHOU Hua-yan, FANG Hao-wen. Research and Application of Safety Intelligent Analysis and Early Warning Technology for Digital Twin Water Conservancy Project [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(3): 181-185.
[9]	LI Zhe, YE Song, YANG Xing-yue, YAO Zheng-li. Design and Development of a Digital-Twins-Driven Water Quality Information Platform for the Middle Route of South-to-North Water Diversion Project [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(3): 174-180.
[10]	CHEN Jin. Regulation and Management of Water Resources in the Changjiang River under Severe Drought Scenarios [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(3): 1-5.
[11]	LU Jin-you, WANG Zhi-hong. Construction Scheme for Digital Twin of Jiangya and Zaoshi Water Conservancy Project [J]. Journal of Changjiang River Scientific Research Institute, 2023, 40(1): 1-9.
[12]	CHEN Jin, LIU Zhi-ming. Standard-reaching Rate of Water Quality in Water Function Regions in Changjiang River Basin in Recent Years [J]. Journal of Changjiang River Scientific Research Institute, 2019, 36(1): 1-6.
[13]	CHEN Jin, LIU Zhi-ming. Analysis of Water Resources Utilization in the Changjiang River Basin in Recent Two Decades [J]. Journal of Changjiang River Scientific Research Institute, 2018, 35(1): 1-4.
[14]	DENG Min-hui, ZHOU Yan-lai, SANG Lian-hai. Multi-objective Evaluation of Water Intake Quantity and Quota for Fossil-fuel Power Stations in Changjiang River Basin [J]. Journal of Changjiang River Scientific Research Institute, 2016, 33(5): 23-27.
[15]	YU Huihua, ZHANG Pingcang, LIU Jigen. Preliminary Research on Assessment Method of Soil Erosion State in Changjiang River Basin [J]. Journal of Changjiang River Scientific Research Institute, 2008, 25(3): 76-81.