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.

We assess uncertainties in the influence of sea surface temperatures on annual meteorological droughts over the contiguous U.S. within a Bayesian approach. Observational data for 1901–2012 indicate that a negative phase of the Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation (ENSO) elevated annual drought risk over the southern U.S., such that the 4 year return period event becomes a 3 year event, while a positive phase of the Atlantic Multidecadal Oscillation has a weak influence. In recent decades, the impacts of the negative phases of the PDO and ENSO on U.S. drought have weakened and shifted toward the southwestern U.S. These changes indicate an increasing of role of atmospheric variability on the U.S. drought overall with implications for long‐term changes in drought and the potential for seasonal forecasting.

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

This study introduces “Google Trends” as a social data source in monitoring and modeling the dynamics of drought awareness during the 2011–17 California drought. In this study, drought awareness is defined and operationalized as the relative search interest activities within California, using the search term “drought” from Google Trends. First, the 2011–17 California drought is characterized in the duration–intensity curve with other historical California droughts for comparative purposes, using the 12-month standard precipitation index data (1895–2017). Second, the potential triggers of the peaks of drought awareness during the 2011–17 California drought are investigated through Google Trends and Google Search. The Google Trends data show that the first peak of drought awareness occurred when the drought condition reached its peak and the governor declared the drought emergency (January 2014). The other peaks in August 2014, April 2015, and January 2017 are related to public interest in drought recovery driven by the forecast of the strong El Niño winter of 2014/15, the governor’s issue of water use rules, and California floods in early 2017, respectively. Last, a power-law decay model of drought awareness is fitted to the Google Trends data. According to the fitted power-law model, Californians remained interested in drought after the social trigger–related peaks longer than they did after the natural trigger–related peaks. The findings of this study suggest that it is necessary to develop a more realistic social dynamic modeling for communities that can respond to natural and human triggers and capture interactions with awareness of related disasters.