Remote Sensing of Water-Related Hazards. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: География
Год издания: 0
isbn: 9781119159148
Скачать книгу
in Central Mexico. Geomat Nat Haz Risk, 7(4), 1460–1488.

      54 Sorooshian, S., Hsu, K. L., Gao, X., Gupta, H. V., Imam, B., & Braithwaite, D. (2000). Evaluation of PERSIANN system satellite‐based estimates of tropical rainfall. Bull. Am. Meteorol. Soc., 81(9), 2035–2046.

      55 Su, C., Wang, L. L., Wang, X. Z., Huang, Z. C., & Zhang, X. C. (2015). Mapping of rainfall‐induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards, 76(3), 1759–1779.

      56 Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., & Watkins, M. M. (2004). GRACE measurements of mass variability in the Earth system. Science, 305(5683), 503–505.

      57 Wang, H. B., Ma, M. G., & Geng, L. Y. (2015). Monitoring the recent trend of aeolian desertification using Landsat TM and Landsat 8 imagery on the north‐east Qinghai‐Tibet Plateau in the Qinghai Lake basin. Nat Hazards, 79(3), 1753–1772.

      58 Wear, S. L., Acuna, V., McDonald, R., & Font, C. (2021). Sewage pollution, declining ecosystem health, and cross‐sector collaboration. Biol Conserv, 255.

      59 Weng, Q. (2017). Advances in environmental remote sensing: Sensors, algorithms, and applications. Boca Raton, FL: Taylor & Francis Group.

      60 Wigneron, J.‐P., Jackson, T., O'neill, P., De Lannoy, G., De Rosnay, P., Walker, J., et al. (2017). Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L‐band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 192, 238–262.

      61 Wu, C. H., Yeh, P.J.F., Chen, Y. Y., Hu, B. X., & Huang, G. R. (2020). Future precipitation‐driven meteorological drought changes in the CMIP5 multimodel ensembles under 1.5 degrees C and 2 degrees C global warming. J. Hydrometeorol., 21(9), 2177–2196.

      62 Xu, H. W., Windsor, M., Muste, M., & Demir, I. (2020). A web‐based decision support system for collaborative mitigation of multiple water‐related hazards using serious gaming. Journal of Environmental Management, 255. doi:10.1016/j.jenvman.2019.10988

      63 Xu, Y. K., George, D. L., Kim, J., Lu, Z., Riley, M., Griffin, T., & de la Fuente, J. (2021). Landslide monitoring and runout hazard assessment by integrating multi‐source remote sensing and numerical models: An application to the Gold Basin landslide complex, northern Washington. Landslides, 18(3), 1131–1141. doi:10.1007/s10346‐020‐01533‐0

      64 Zeng, Z. Y., Tang, G. Q., Long, D., Zeng, C., Ma, M. H., Hong, Y., et al.(2016). A cascading flash flood guidance system: Development and application in Yunnan Province, China. Nat Hazards, 84(3), 2071–2093.

      65 Zhang, D., Zhang, Q., Qiu, J. M., Bai, P., Liang, K., & Li, X. H. (2018). Intensification of hydrological drought due to human activity in the middle reaches of the Yangtze River, China. Sci. Total Environ., 637, 1432–1442.

      66 Zhang, K., Kimball, J. S., Hogg, E. H., Zhao, M., Oechel, W. C., Cassano, J. J., & Running, S. W. (2008). Satellite‐based model detection of recent climate‐driven changes in northern high‐latitude vegetation productivity. J. Geophys. Res., 113, G03033. doi:10.1029/2007JG000621

      67 Zhang, K., Kimball, J. S., Nemani, R. R., & Running, S. W. (2010). A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour. Res., 46, W09522. doi:10.1029/2009WR008800

      68 Zhang, K., et al. (2015). The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO2 and land‐use. Global Change Biol., 21(7), 2569–2587. doi:10.1111/gcb.12903

      69 Zhang, K., Xue, X. W., Hong, Y., Gourley, J. J., Lu, N., Wan, Z. M., et al. (2016). iCRESTRIGRS: A coupled modeling system for cascading flood‐landslide disaster forecasting. Hydrol. Earth Syst. Sci., 20(12), 5035–5048.

      70 Zhang, Q. Q., C. Xing, Y. Y. Cai, X. T. Yan, and G. G. Ying (2021). How much do human and livestock actually contribute to steroids emission and surface water pollution from past to the future: A global research. Sci. Total Environ., 772. doi:10.1016/j.scitotenv.2021.145558

      71 Zhang, Y. Q., et al. (2016). Multi‐decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6, 19124. doi:10.1038/srep19124

      72 Zhou, X., Matthes, H., Rinke, A., Klehmet, K., Heim, B., Dorn, W., et al. (2014). Evaluation of arctic land snow cover characteristics, surface albedo, and temperature during the transition seasons from regional climate model simulations and satellite data. Adv Meteorol. doi:10.1155/2014/604157

Part I Remote Sensing of Precipitation and Storms

       Guoqiang Tang1, Tsechun Wang2, Meihong Ma3, Wentao Xiong2, Feng Lyu2, and Ziqiang Ma2

       1 Center for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada

       2 Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

       3 School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China

      ABSTRACT

      Precipitation is one of the most essential environmental variables in global water and energy cycles. Precipitation storms often trigger various natural hazards such as flash floods. The advance of satellite remote sensing provides valuable sources of global precipitation data, which have been widely used in hydrometeorological studies and hazard monitoring. Particularly, the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely evaluated and applied since its release in 2014. It is acknowledged that satellite precipitation contains uncertainties varying with space and time. This work assesses the accuracy of the retrospective IMERG products in China and compares IMERG with nine satellite and reanalysis products to reveal the characteristics of modern precipitation data sets. We find that IMERG outperforms other products, except for Global Satellite Mapping of Precipitation (GSMaP), due to the deficiency of monthly‐scale gauge adjustment. Regarding snowfall, IMERG exhibits large underestimation in the whole China region compared with gauge and reanalysis data. The triple collocation analysis reveals that the performance of IMERG in snowfall estimation is still unsatisfying. Furthermore, IMERG Early and Final runs are applied in the early warning of flash floods in Yunnan Province, China, where flood hazards are common and destructive. IMERG could be better in monitoring floods at higher temporal resolutions (e.g., 1 h and 3 h) than the lower temporal resolution (e.g., daily). IMERG Early run has better timeliness but lower capability of capturing floods compared to IMERG Final run. The study is useful for both users and developers of satellite precipitation products.

      Precipitation is an indispensable element of global water and energy cycles (Trenberth et al., 2003). As an important source of land water resources, precipitation has a direct link with human society. Extreme precipitation can often trigger natural hazards such as floods and landslides (Hong et al., 2007; Zeng et al., 2017; Vionnet et al., 2019). More frequent and severe extreme precipitation is observed or predicted due to the warming global climate. Therefore, extensive research has been conducted on the occurrence/quantity estimation and temporal and spatial changes of precipitation (Hong et al., 2006; Yong et al., 2013; Behrangi et al., 2018; Hong et al., 2018).