Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5
Abstract
1. Introduction
2. Study Area and Data
3. Methods of Hydrological Drought Forecasting
4. Selection of Drought Index Duration
5. Land Surface Model (LSM)-Based MSWSI(3) Performance
5.1. Evaluation of Runoff Simulation
5.2. Evaluation of MSWSI(3) Based On Simulated Runoff
6. Hydrological Drought Forecasting Performance for 2015-2016 Drought Events
6.1. Predictability Analysis of Forecast Lead Times
6.2. Predictability Analysis for Basin Types
7. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Yuan, X.; Wood, E.F.; Luo, L.; Pan, M. A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. J. Geophys. Res. 2011, 38, L13402. [Google Scholar] [CrossRef]
- Yoon, J.H.; Mo, K.; Wood, E.F. Dynamic-model-based seasonal prediction of meteorological drought over the contiguous United States. J. Hydrometeorol. 2012, 13, 463–482. [Google Scholar] [CrossRef]
- Yuan, X.; Wood, E.F.; Chaney, N.W.; Sheffield, J.; Kam, J.; Liang, M.; Guan, K. Probabilistic seasonal forecasting of African drought by dynamical models. J. Hydrometeorol. 2013, 14, 1706–1720. [Google Scholar] [CrossRef]
- Dutra, E.; Pozzi, W.; Wetterhall, F.; Di Giuseppe, F.; Magnusson, L.; Naumann, G.; Barbosa, P.; Vogt, J.; Pappenberger, F. Global meteorological drought-Part 2: Seasonal forecasts. Hydrol. Earth Syst. Sci. 2014, 18, 2669–2678. [Google Scholar] [CrossRef]
- Mo, K.C.; Lyon, B. Global meteorological drought prediction using the North American Multi-Model Ensemble. J. Hydrometeorol. 2015, 16, 1409–1424. [Google Scholar] [CrossRef]
- Palmer, W. Meteorological Drought; Research Paper; U.S. Weather Bureau: Washington, DC, USA, 1965; Volume 45.
- Narasimhan, B.; Srinivasan, R. Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. For. Meteorol. 2005, 133, 69–88. [Google Scholar] [CrossRef]
- Dutra, E.; Viterbo, P.; Miranda, P.M. ERA-40 reanalysis hydrological applications in the characterization of regional drought. Geophys. Res. Lett. 2008, 35, L19402. [Google Scholar] [CrossRef]
- Hao, Z.; AghaKouchak, A.; Nakhjiri, N.; Farahmand, A. Global integrated drought monitoring and prediction system. Sci. Data 2014, 1, 140001. [Google Scholar] [CrossRef]
- Shukla, S.; Wood, A.W. Use of a standardized runoff index for characterizing hydrologic drought. J. Geophys. Res. 2008, 35, L2045. [Google Scholar] [CrossRef]
- Wang, D.; Hejazi, M.; Cai, X.; Vaocchi, A.J. Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois. Water Resour. Res. 2011, 47, W09527. [Google Scholar] [CrossRef]
- Tigkas, D.; Vangelis, H.; Tsakiris, G. Drought and climatic change impact on streamflow in small watershed. Sci. Total Environ. 2012, 440, 33–41. [Google Scholar] [CrossRef] [PubMed]
- Jung, I.W.; Chang, H.J. Climate change impacts on spatial patterns in drought risk in the Willamette River Basin, Oregon, USA. Theor. Appl. Climatol. 2012, 108, 355–371. [Google Scholar] [CrossRef]
- Shafer, B.A.; Dezman, L.E. Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas. In Conference Proceedings, 50th Annual Western Snow Conference; Colorado State University: Reno, NV, USA, 1982; pp. 164–175. [Google Scholar]
- Kwon, H.J.; Park, H.J.; Hong, D.O.; Kim, S.J. A study on semi-distributed hydrologic drought assessment modifying. J. Korea Water Resour. Assoc. 2006, 39, 645–658. [Google Scholar] [CrossRef][Green Version]
- David, C.G. The surface water supply index; formulation and issues. In Remote Presentation for World Meteorological Organization Workshop on Hydrological Drought Indices Geneva. 2011, Switzerland September 2011; United States Department of Agriculture Natural Resources Conservation Service: Washington, DC, USA, 2011. [Google Scholar]
- Jung, M.S.; Park, S.; Hong, H.; Lee, J.H. Estimating Quantified Hydrological Input Value for Hydrological Drought. J. Korean Soc. Hazard Mitig. 2017, 18, 11–22. [Google Scholar] [CrossRef]
- Luo, L.; Wood, E.F. Use of Bayesian merging techniques in a multimodel seasonal hydrologic ensemble prediction system for the eastern United States. J. Hydrometeorol. 2008, 9, 866–884. [Google Scholar] [CrossRef]
- Fundel, F.; Jörg-Hess, S.; Zappa, M. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices. Hydrol. Earth Syst. Sci. 2013, 17, 395–407. [Google Scholar] [CrossRef]
- Sheffield, J.; Wood, E.F.; Chaney, N.; Guan, K.; Sadri, S.; Yuan, X.; Olang, L.; Amani, A.; Ali, A.; Demuth, S.; et al. A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull. Am. Meteorol. Soc. 2014, 95, 861–882. [Google Scholar] [CrossRef]
- Ghafouri-Azar, M.; Bae, D.H.; Kang, S.U. Trend Analysis of Long-Term Reference Evapotranspiration and Its Components over the Korean Peninsula. Water 2018, 10, 1373. [Google Scholar] [CrossRef]
- Bae, D.H.; Jung, I.W.; Chang, H. Long-term trend of precipitation and runoff in Korean river basins. Hydrol. Process. 2008, 22, 2644–2656. [Google Scholar] [CrossRef]
- MacLachlan, C.; Arribas, A.; Peterson, K.A.; Maidens, A.; Fereday, D.; Scaife, A.A.; Gordon, M.; Vellinga, M.; Willianms, A.; Comer, R.E.; et al. Global Seasonal forecast system version 5 (GloSea5): A high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc. 2014, 141, 1072–1084. [Google Scholar] [CrossRef]
- Liang, X.; Lettenmaier, D.P.; Wood, E.F.; Burges, S.J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 1994, 99, 14415–14428. [Google Scholar] [CrossRef]
- McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Paper Presented at Eighth Conference on Applied Climatology; American Meteorological Society: Anaheim, CA, USA, 1993. [Google Scholar]
- Zargar, A.; Sadiq, R.; Naser, B.; Khan, F.I. A review of drought indices. Environ. Rev. 2011, 19, 333–349. [Google Scholar] [CrossRef]
- Hao, Z.; Singh, V.P. Drought characterization from a multivariate perspective: A review. J. Hydrol. 2015, 527, 668–678. [Google Scholar] [CrossRef]
- Yuan, X.; Roundy, J.K.; Wood, E.F.; Sheffield, J. Seasonal forecasting of global hydrologic extremes: System development and evaluation over GEWEX basins. Bull. Am. Meteorol. Soc. 2015, 96, 1895–1912. [Google Scholar] [CrossRef]
- Hao, Z.; Singh, V.P.; Xia, Y. Seasonal drought prediction: Advances, challenges, and future prospects. Rev. Geophys. 2018, 56, 108–141. [Google Scholar] [CrossRef]
- Vahid, N.; Mahsa, G.; Ali, D.M.; Elnaz, S. Investigating the effect of hydroclimatological variables on Urmia Lake water level using wavelet coherence measure. J. Water Clim. Chang. 2019, 10, 13–29. [Google Scholar]
- Shahabbodin, S.; Sajjad, H.; Hana, S.; Saeed, S.; Asadi, E.; Shadkani, S.; Kargar, K.; Mosavi, A.; Chau, K.-W.; Narjes, N. Predicting Standardized Streamflow index for hydrological drought using machine learning models. Eng. Appl. Comput. Fluid Mech. 2020, 14, 339–350. [Google Scholar]
- Wu, C.L.; Chau, K.W. Prediction of rainfall time series using modular soft computing methods. Eng. Appl. Artif. Intell. 2013, 26, 997–1007. [Google Scholar] [CrossRef]
- Taormina, R.; Chau, K.W. ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng. Appl. Artif. Intell. 2015, 45, 429–440. [Google Scholar] [CrossRef]
- | Major Characteristics and Information | |
---|---|---|
Composition of Model | Atmosphere | ⦁ UM (v8.0) |
Ocean | ⦁ NEMO (v3.2)-CICE (v4.1) | |
Coupler | ⦁ OASIS3 | |
Spatial Resolution | Atmosphere | ⦁ N216 (0.83°×0.56°) |
Ocean | ⦁ ORCA tri-polar grid at 0.25° | |
Initial Input Data | Atmosphere | ⦁ Hindcast: ERA interim ⦁ Forecast: KMA numerical analysis field |
Ocean | ⦁ Hindcast: Seasonal ODA reanalysis ⦁ Forecast: NEMO VAR | |
Production Period of Data and Ensemble Number | ⦁ Hindcast: - Fixed start dates of 1st, 9th, 17th, 25th of each month - Three member runs per start date ⦁ Forecast: - Two member runs each day |
Values | Drought Classification |
---|---|
4.0 ≤ MSWSI | Extreme wet |
3.0∼4.0 | Very wet |
2.0∼3.0 | Moderate wet |
1.0∼2.0 | Mild wet |
−1.0∼1.0 | Near normal |
−2.0∼-1.0 | Mild dry |
−3.0∼-2.0 | Moderate dry |
−4.0∼-3.0 | Severe dry |
−4.0 ≥ MSWSI | Extreme dry |
- | Formula | Range | Ideal Value |
---|---|---|---|
Correlation Coefficient (CC) | 1 | ||
Root Mean Square Error(RMSE) | 0 | ||
Nash–Sutcliffe Efficiency(NSE) | 1 | ||
Volume Error(VE) | 0 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
So, J.-M.; Lee, J.-H.; Bae, D.-H. Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5. Water 2020, 12, 2785. https://doi.org/10.3390/w12102785
So J-M, Lee J-H, Bae D-H. Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5. Water. 2020; 12(10):2785. https://doi.org/10.3390/w12102785
Chicago/Turabian StyleSo, Jae-Min, Joo-Heon Lee, and Deg-Hyo Bae. 2020. "Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5" Water 12, no. 10: 2785. https://doi.org/10.3390/w12102785
APA StyleSo, J.-M., Lee, J.-H., & Bae, D.-H. (2020). Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5. Water, 12(10), 2785. https://doi.org/10.3390/w12102785