Browse > Article
http://dx.doi.org/10.3741/JKWRA.2019.52.4.279

Meteorological drought outlook with satellite precipitation data using Bayesian networks and decision-making model  

Shin, Ji Yae (Department of Civil and Environmental Engineering, Hanyang University)
Kim, Ji-Eun (Department of Civil and Environmental System Engineering, Hanyang University)
Lee, Joo-Heon (Department of Civil Engineering, Joongbu University)
Kim, Tae-Woong (Department of Civil and Environmental Engineering, Hanyang University)
Publication Information
Journal of Korea Water Resources Association / v.52, no.4, 2019 , pp. 279-289 More about this Journal
Abstract
Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.
Keywords
Bayesian networks; Decision-making model; Drought outlook; Satellite image data;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
1 AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R. (2015). "Remote sensing of drought: progress, challenges and opportunities." Reviews of Geophysics, Vol. 53, No. 2, pp. 452-480.   DOI
2 Aviles, A., Celleri, R., Solera, A., and Paredes, J. (2016). "Probabilistic forecasting of drought events using markov chain-and bayesian network-based models: a case study of an andean regulated river basin." Water, Vol. 8, No. 2, pp. 1-37.
3 De Jesus, A., Brena-Naranjo, J. A., Pedrozo-Acuna, A., and Yamanaka, V. H. A. (2016). "The use of TRMM 3B42 product for drought monitoring in Mexico." Water, Vol. 8, No. 8, pp. 325.   DOI
4 De Linage, C., Famiglietti, J. S., and Randerson, J. T. (2014). "Statistical prediction of terrestrial water storage changes in the Amazon Basin using tropical Pacific and North Atlantic sea surface temperature anomalies." Hydrology and Earth System Sciences, Vol. 18, No. 6, pp. 2089-2102.   DOI
5 Hao, Z., Singh, V. P., and Xia, Y. (2018). "Seasonal drought prediction: advances, challenges, and future prospects." Reviews of Geophysics, Vol. 56, No. 1, pp. 108-141.   DOI
6 Jang, S., Rhee, J., Yoon, S., Lee, T., and Park, K. (2017). "Evaluation of GPM IMERG applicability using SPI based satellite precipitation." Journal of the Korean Society of Agricultural Engineer, Vol. 59, No. 3, pp. 29-39.   DOI
7 Jang, S., Yoon, S., Lee, S., Lee, T., and Park, K. (2018). "Evaluation of drought monitoring using satellite precipitation for un-gaged basins." Journal of the Korean Society of Agricultural Engineer, Vol. 60, No. 2, pp. 55-63.   DOI
8 Jeong, M.-S., Kim, J.-S., Jang, H.-W., and Lee, J.-H. (2016). "ROC evaluation for MLP ANN drought forecasting model." Journal of Korea Water Association, Vol. 49, No. 10, pp. 877-885.
9 Kim, T. W., and Valdes, J. B. (2003). "Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks." Journal of Hydrologic Engineering, Vol. 8, No. 6, pp. 319-328.   DOI
10 Lee, J. H., Jeong, S. M., Kim, J. H., and Ko, Y. S. (2006). "Development of drought monitoring system: II. Quantitative drought monitoring and drought outlook methodology." Journal of Korea Water Resources Association, Vol. 39, No. 9, pp. 801-812.   DOI
11 Liu, W., and Juarez, R. N. (2001). "ENSO drought onset prediction in northeast Brazil using NDVI." International Journal of Remote Sensing, Vol. 22, No. 17, pp. 3483-3501.   DOI
12 Lui, Z., Lu, G., He, H., Wu, Z., and He, J. (2018). "A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China." Hydrology and Earth System Sciences, Vol. 22, No. 1, pp. 529-546.   DOI
13 Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A. W., Cheng, L., Hsu, K. L., and Svoboda, M. (2016). "A hybrid statistical dynamical framework for meteorological drought prediction: application to the southwestern United States." Water Resources Research, Vol. 52, No. 7, pp. 5095-5110.   DOI
14 Madadgar, S., and Moradkhani, H. (2014). "Spatio-temporal drought forecasting within Bayesian networks." Journal of Hydrology, Vol. 512, pp. 134-146.   DOI
15 Mishra, A., Desai, V., and Singh, V. (2007). "Drought forecasting using a hybrid stochastic and neural network model." Journal of Hydrologic Engineering, Vol. 12, No. 6, pp. 626-638.   DOI
16 Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J. (2008). "Stationarity is dead: whither water management?" Science, Vol. 319, No. 5863, pp. 573-574.   DOI
17 Mishra, A. K., and Desai, V. R. (2005). "Drought forecasting using stochastic models." Stochastic Environmental Research and Risk Assessment, Vol. 19, No. 5, pp. 326-339.   DOI
18 Mishra, A. K., and Singh, V. P. (2011). "Drought modeling: a review." Journal of Hydrology, Vol. 403, No. 1-2, pp. 157-175.   DOI
19 Morid, S., Smakhtin, V., and Bagherzadeh, K. (2007). "Drought forecasting using artificial neural networks and time series of drought indices." International Journal of Climatology, Vol. 27, No. 15, pp. 2103-2111.   DOI
20 Mwangi, E., Wetterhall, F., Dutra, E., Di Giuseppe, F., and Pappenberger, F. (2014). "Forecasting droughts in East Africa." Hydrology and Earth System Sciences, Vol. 18, No. 2, pp. 611-620.   DOI
21 Nam, W.-H., Tadesse, T., Wardlow, B. D., Jang, M.-W., and Hong, S.-Y. (2015). "Satellite-based hybrid drought assessment using vegetation drought response index in South Korea (VegDRISKorea)." Journal of the Korean Society of Agricultural Engineers, Vol. 57, No. 4, pp. 1-9.   DOI
22 Pathiraja, S., Marshall, L., Sharma, A., and Moradkhani, H. (2016). "Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation." Advances in Water Resources, Vol. 94, pp. 103-119.   DOI
23 Sohn, S. J., Ahn, J. B., and Tam, C. Y. (2013a). "Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble." Geophysical Research Letters, Vol. 40, No. 3, pp. 579-583.   DOI
24 Rhee, J., Im, J., and Kim, J. (2014). "Hydrological drought assessment and monitoring based on remote sensing for ungauged area." Korean Journal of Remote Sensing, Vol. 30, No. 4, pp. 525-536.   DOI
25 Shin, J. Y., Ajmal, M., Yoo, J., and Kim, T.-W. (2016). "A Bayesian network-based probabilistic framework for drought forecasting and outlook." Advances in Meteorology, Vol. 2016, No. 9472605, pp. 1-10.
26 Shin, J. Y., Kwon, H.-H., Lee, J.-H., and Kim, T.-W. (2017). "Bayesian networks-based probabilistic forecasting of hydrological drought considering drought propagation." Journal of Korea Water Resources Association, Vol. 50, No. 11, pp. 769-779.   DOI
27 Sohn, S. J., Min, Y. M., Lee, J. Y., Tam, C. Y., Kang, I. S., Wang, B., Ahn, J. B., and Yamagata, T. (2012). "Assessment of the long-lead probabilistic prediction for the Asian summer monsoon precipitation (1983-2011) based on the APCC multimodel system and a statistical model." Journal of Geophysical Research, Vol. 117, No. D4, pp. 1-12.
28 Sohn, S. J., Tam, C. Y., and Ahn, J. B. (2013b). "Development of a multimodel-based seasonal prediction system for extreme droughts and floods: a case study for South Korea." International Journal of Climatology, Vol. 33, No. 4, pp. 793-805.   DOI
29 Son, K.-H., Bae, D.-H., and Cheong, H.-S. (2015). "Construction & evaluation of GloSea5-based hydrological drought outlook system." Atmosphere, Vol. 25, No. 2, pp. 271-281.   DOI
30 Tadesse, T., Demisse, G. B., Zaitchik, B., and Dinku, T. (2014). "Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in eastern Africa: a case study for Ethiopia." Water Resources Research, Vol. 50, No. 3, pp. 2176-2190.   DOI
31 Russell, S. and Norvig, P. (1995). Artificial intelligence: a modern approach. Englewood Cliff, Prentice Hall.
32 Yan, H., Moradkhani, H., and Zarekarizi, M. (2017). "A probabilistic drought forecasting framework: a combined dynamical and statistical approach." Journal of Hydrology, Vol. 548, pp. 291-304.   DOI
33 Yoon, J. H., Mo, K., and Wood, E. F. (2012). "Dynamic-modelbased seasonal prediction of meteorological drought over the contiguous United States." Journal of Hydrometeorology, Vol. 13, No. 2, pp. 463-482.   DOI