Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Kaiyu Guan x
  • All content x
Clear All Modify Search
Stephen P. Good, Kaiyu Guan, and Kelly K. Caylor

Abstract

Interannual variation in precipitation totals is a critical factor governing the year-to-year availability of water resources, yet the connection between interannual precipitation variability and underlying event- and season-scale precipitation variability remains unclear. In this study, tropical and midlatitude precipitation characteristics derived from extensive station records and high-frequency satellite observations were analyzed to attribute the fraction of interannual variability arising as a result of individual variability in precipitation event intensity, frequency, and seasonality, as well as the cross-correlation between these factors at the global scale. This analysis demonstrates that variability in the length of the wet season is the most important factor globally, causing 52% of the total interannual variability, while variation in the intensity of individual rainfall events contributes 31% and variability in interstorm wait times contributes only 17%. Spatial patterns in the contribution of each of these intra-annual rainfall characteristics are informative, with regions such as Indonesia and southwestern North America primarily influenced by seasonality, while regions such as the eastern United States, central Africa, and the upper Amazon basin are strongly influenced by storm intensity and frequency. A robust cross-correlation between climate characteristics is identified in the equatorial Pacific, revealing an increased interannual variability over what is expected based on the variability of individual events. This decomposition of interannual variability identifies those regions where accurate representation of daily and seasonal rainfall statistics is necessary to understand and correctly model rainfall variability at longer time scales.

Full access
Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

Abstract

As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982–2007) seasonal hydrologic hindcasts run at 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode from observations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land–atmosphere coupling, is necessary.

Restricted access
Justin Sheffield, Eric F. Wood, Nathaniel Chaney, Kaiyu Guan, Sara Sadri, Xing Yuan, Luke Olang, Abou Amani, Abdou Ali, Siegfried Demuth, and Laban Ogallo

Drought is one of the leading impediments to development in Africa. Much of the continent is dependent on rain-fed agriculture, which makes it particularly susceptible to climate variability. Monitoring drought and providing timely seasonal forecasts are essential for integrated drought risk reduction. Current approaches in developing regions have generally been limited, however, in part because of unreliable monitoring networks. Operational seasonal climate forecasts are also deficient and often reliant on statistical regressions, which are unable to provide detailed information relevant for drought assessment. However, the wealth of data from satellites and recent advancements in large-scale hydrological modeling and seasonal climate model predictions have enabled the development of state-of-the-art monitoring and prediction systems that can help address many of the problems inherent to developing regions. An experimental drought monitoring and forecast system for sub-Saharan Africa is described that is based on advanced land surface modeling driven by satellite and atmospheric model data. Key elements of the system are the provision of near-real-time evaluations of the terrestrial water cycle and an assessment of drought conditions. The predictive element takes downscaled ensemble dynamical climate forecasts and provides, when merged with the hydrological modeling, ensemble hydrological forecasts. We evaluate the overall skill of the system for monitoring and predicting the development of drought and illustrate the use of the system for the 2010/11 Horn of Africa drought. A key element is the transition and testing of the technology for operational usage by African collaborators and we discuss this for two implementations in West and East Africa.

Full access