To reduce the risk to food security posed by drought, it’s crucial that we develop systems able to disseminate accurate information in a timely manner. Although doing this may seem straightforward in an era of near-ubiquitous satellite measurements and increasingly high-powered computational models, there are challenges relating both to the analysis and dissemination of information in an operational context. I’ll explore the challenges to producing operational drought forecasts and monitors in this post and write about the challenges of disseminating those forecasts in my next post.
Broadly, most operational drought systems may be subdivided into those that monitor current conditions (and perhaps make historical readings available) and those that provide projections for future conditions. Coincidentally, while doing some research for this post I started to compile a list of available hydrological monitors and data portals, which you can find here. I focused on drought, but included a few others as well.
Drought monitoring, despite the availability of near real-time satellite data, faces great challenges of data availability. In fact, methods using satellite measurements currently perform comparably to those using only a handful of gauge stations. Satellite measurements are additionally challenged with a short climatology, and inconsistencies between products. Never-the-less, these products provide unparalleled coverage for regions with sparse in-situ measurements (as is often the case in developing countries), where drought monitoring has a crucial role to play in maintaining adequate levels of food security.
Although it is often described as a “slow onset” phenomenon, the development and evolution of drought can be remarkably difficult to predict. Part of this stems from an incomplete understanding of the oceanic forcings of drought (ENSO), and part of it stems from the inherently chaotic nature of the atmosphere. A chaotic systems sensitive to initial conditions creates an environment in which errors propagate through models and forecasts quickly diverge from one another. Shukla et al., 2013 analyzed the skill of a forecast as it relates to either (1) the initial conditions of the model or (2) the forecast skill, and found that the skill was dependent on both region and time of the year.
One means of improving both the monitoring and the forecasting of drought is to further explore the limitations to current model skill under different climate regimes. While Smith et al., explored times of the year and regions, the underlying factors of substance are the moisture-temperature-atmosphere regimes. It shouldn’t be ignored that during a drought, the region of interest (which may normally be energy-limited) will be abnormally arid (and therefore potentially moisture-limited). This shift may in fact mean that where the initial condition of soil moisture was once not a limiting factor for forecast skill, it may become one during the forecast of drought recovery. In that sense, forecasting the onset as opposed to the recovery of a drought may be two problems with distinct characteristics.
A second aspect of the drought system that warrants further exploration is the dynamics of vegetation during the evolution of multi-seasonal droughts. Previous studies have pointed towards dynamic vegetation as one source of increased interannual variability in precipitation, however, the impact of this dynamic vegetation on evapotranspiration and therefore on the atmospheric boundary layer may also play a significant role in determining how a drought develops. This is particularly true during multi-year droughts when drying of the soil occurs more deeply than during one-season droughts.