A drought is defined as a prolonged period of abnormally low precipitation, resulting in a measurable water shortage that affects vegetation, agriculture, and communities. Unlike sudden events such as floods or wildfires, a drought is a creeping phenomenon that develops slowly over weeks or months, making its onset difficult to pinpoint. Scientists have developed sophisticated methods to forecast these dry periods, allowing for preparation and mitigation. While predicting the exact end date remains challenging, the probability, duration, and severity can be projected with varying degrees of accuracy depending on the time frame.
Defining the Predictable Factors
The ability to forecast drought hinges on understanding large-scale climate drivers that persist long enough to influence weather patterns weeks or months in advance. These drivers are known as teleconnections, which describe atmospheric and oceanic connections between distant regions. Since the atmosphere is inherently chaotic, the long-term signal for drought prediction must come from the slower, more stable ocean systems.
The most powerful predictable signal originates from the tropical Pacific Ocean in the form of the El Niño-Southern Oscillation (ENSO). This oscillation involves cyclical warming (El Niño) and cooling (La Niña) of sea surface temperatures (SSTs), which alters atmospheric circulation across the globe. A strong ENSO event can influence rainfall and temperature anomalies in distant regions, providing a reliable lead time for seasonal drought forecasts.
Slower oceanic patterns also play a role in multi-year and decadal drought predictability. The Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO) involve changes in sea surface temperatures (SSTs) that span decades. Drought conditions can be intensified when these oscillations align; for instance, a cold PDO phase combined with a warm AMO phase has been linked to severe, multi-year droughts over parts of the United States.
Monitoring and Measurement Tools
Drought forecasting relies on a steady stream of data to establish the current water deficit, which serves as the starting point for model projections. Measurements are collected from two primary sources: ground-based sensors and remote sensing satellites. Ground-based monitoring networks provide direct measurements of the water cycle’s terrestrial components. These include streamflow gauges, snowpack measurements that indicate future meltwater reserves, and wells that monitor deeper groundwater levels.
Satellite remote sensing offers a complementary, broad-scale view of drought conditions impossible to achieve from ground stations alone. The Gravity Recovery and Climate Experiment (GRACE and its follow-on mission, GRACE-FO) satellites monitor tiny changes in Earth’s gravitational field caused by variations in water mass, providing an estimate of total terrestrial water storage, including deep groundwater. Other satellite systems, like the Soil Moisture Active Passive (SMAP) mission, use microwave sensors to measure water content in the top layer of the soil.
Scientists also use satellite data to track the health of vegetation, an indicator of agricultural drought. The Normalized Difference Vegetation Index (NDVI) is calculated from satellite measurements of reflected red and near-infrared light, indicating the vigor and greenness of plant life. By combining these disparate data streams—from deep groundwater to surface vegetation health—forecasters establish the initial conditions required to run prediction models.
Modeling Approaches for Forecasting
The actual generation of a drought forecast requires turning observational data and climate drivers into future projections through computational models. These models fall into two distinct categories: statistical and dynamical. Statistical models are the simpler of the two, relying on historical relationships and probability. They analyze past data to determine how frequently a drought followed a specific set of initial conditions or climate signals, such as an ENSO event.
These models are computationally efficient and are often used to generate quick, first-pass probabilistic forecasts, such as the likelihood of a region experiencing below-average rainfall. They are limited, however, by the assumption that future climate behavior will mirror historical patterns, which may not hold true in a changing climate. Statistical downscaling methods are also frequently used to refine the large-scale outputs of dynamical models to a local scale, making the predictions relevant for specific regions.
Dynamical models, conversely, are based on the fundamental physical laws governing fluid motion, energy transfer, and atmospheric circulation. These sophisticated climate models simulate the entire Earth system, from the ocean surface to the upper atmosphere, using massive supercomputers. They are computationally expensive but provide a detailed, physics-based forecast of how moisture and weather systems are expected to evolve. Operational seasonal forecasts often use multi-model ensembles, combining the outputs of several different dynamical models to reduce uncertainty and improve prediction skill.
Time Horizons of Drought Forecasting
The reliability of a drought forecast is directly tied to its time horizon, as the predictability of the climate system naturally decreases with time. Short-term forecasts, covering the next few weeks, have relatively high reliability and are largely an extension of standard numerical weather prediction. These predictions are generally accurate because they rely on the immediate, observable state of the atmosphere and local conditions.
The most valuable window for drought prediction is the seasonal time scale, typically looking out one to nine months. This moderate reliability window is where the slow-moving ocean signals, like ENSO, provide their greatest benefit, acting as the primary source of predictability. Seasonal forecasts allow water managers and farmers to make informed decisions about reservoir levels, crop selection, and irrigation planning. Prediction skill tends to be highest when the teleconnection signal is strongest, such as during a well-established El Niño or La Niña event.
For long-term forecasts that span years or decades, reliability is significantly lower. Specific event prediction is virtually impossible due to the inherent chaotic nature of the atmosphere, often referred to as the “butterfly effect.” Long-term projections are primarily used for climate change risk assessment and strategic planning, focusing on general trends and changes in drought frequency. Models struggle to maintain accuracy beyond a few months because small errors in the initial conditions grow exponentially.