Understanding the evolution and physical drivers of drought is critical to informing forecasting efforts. One aspect that has seldom been explored is the joint evolution of droughts in space and time. Most studies fix the reference area and focus on their temporal variability, or study their spatial heterogeneity over fixed durations. This work implements a Lagrangian approach by aggregating contiguous areas under drought into clusters. These clusters become the frame of reference and are tracked as they evolve through space and time. Clusters were identified from soil moisture data from the Climate Forecast System Reanalysis (1979-2009). Evapotranspiration, moisture fluxes, and precipitation were used to explore the relevance of possible mechanisms of drought propagation. While most droughts remain near their origin, the centroid of 10% of clusters traveled at least 1,400-3,100 km, depending on the continent. This approach also revealed that large-scale droughts often lock into further growth and intensification.
Droughts and heat waves have important impacts on multiple sectors including water resources, agriculture, electricity generation, and public health, so it is important to understand how they will be affected by climate change. However, there is large uncertainty in the projected changes of these extreme events from climate models. We compare historical biases in models against their future projections to understand and attempt to constrain these uncertainties. Historical biases in precipitation, near-surface air temperature, evapotranspiration, and a land-atmospheric coupling metric are calculated for 24 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) against the North American Land Data Assimilation System Phase 2 (NLDAS-2) as reference for 1979-2005. Biases are highly correlated across variables, with some models being hotter and drier, and others wetter and cooler. Models that overestimate summer precipitation project larger increases in precipitation, evapotranspiration, and land-atmospheric coupling over important agricultural regions by the end of the 21st century (2070-2099) under RCP8.5, although the percentage variance explained is low. Changes in the characteristics of droughts and heat waves are calculated and linked to historical biases in precipitation and temperature. A method to constrain uncertainty by ranking models based on historical performance is discussed but the rankings differ widely depending on the variable considered. Despite the large uncertainty that remains in the magnitude of the changes, there is consensus amongst models that droughts and heat waves will increase in multiple regions in the US by the end of the 21st century unless climate mitigation actions are taken.
The spatial heterogeneity of soil moisture remains a persistent challenge in the design of in situ measurement networks, spatial downscaling of coarse estimates (e.g., satellite retrievals), and hydrologic modeling. To address this challenge, we analyze high-resolution (∼9 m) simulated soil moisture fields over the Little River Experimental Watershed (LREW) in Georgia, USA, to assess the role and interaction of the spatial heterogeneity controls of soil moisture. We calibrate and validate the TOPLATS distributed hydrologic model with high to moderate resolution land and meteorological data sets to provide daily soil moisture fields between 2004 and 2008. The results suggest that topography and soils are the main drivers of spatial heterogeneity over the LREW. We use this analysis to introduce a novel network design method that uses land data sets as proxies of the main drivers of local heterogeneity (topography, land cover, and soil properties) to define unique and representative hydrologic similar units (subsurface, surface, and vegetation) for probe placement. The calibration of the hydrologic model and network design method illustrates how the use of hydrologic similar units in hydrologic modeling could minimize computation and guide efforts toward improved macroscale land surface modeling.
Understanding how global change is impacting African agriculture requires a full physical accounting of water supply and demand, but accurate, gridded data on key drivers (e.g., humidity) are generally unavailable. We used a new bias-corrected meteorological dataset to analyze changes in precipitation (supply), potential evapotranspiration (, demand), and water availability (expressed as the ratio ) in 20 countries (focusing on their maize-growing regions and seasons), between 1979 and 2010, and the factors driving changes in . Maize-growing areas in Southern Africa, particularly South Africa, benefitted from increased water availability due in large part to demand declines driven primarily by declining net radiation, increasing vapor pressure, and falling temperatures (with no effect from changing windspeed), with smaller increases in supply. Sahelian zone countries in West Africa, as well as Ethiopia in East Africa, had strong increases in availability driven primarily by rainfall rebounding from the long-term Sahelian droughts, with little change or small reductions in demand. However, intra-seasonal supply variability generally increased in West and East Africa. Across all three regions, declining net radiation contributed downwards pressure on demand, generally over-riding upwards pressure caused by increasing temperatures, the regional effects of which were largest in East Africa. A small number of countries, mostly in or near East Africa (Tanzania and Malawi) experienced declines in water availability primarily due to decreased rainfall, but exacerbated by increasing demand. Much of the reduced water availability in East Africa occurred during the more sensitive middle part of the maize-growing season, suggesting negative consequences for maize production.