Evaluating probabilistic distributions for drought forecasting system in the Netherlands

In the Netherlands, flood forecasting and early warning systems are well established and operationally embedded. However, despite an increasing frequency of drought events and impacts over the past decades, drought early warning systems remain comparatively less developed. This gap is critical, as growing climate variability is expected to intensify agricultural, ecological, and hydrological stress even in temperate regions. Standardized drought indices such as the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) provide an established framework for drought monitoring and forecasting, but they strongly depend on the underlying probability distributions used to represent hydroclimatic variability and extremes. Poor distribution choices can distort index values and reduce forecast reliability, especially for moderate to extreme drought events.

In this study, we develop an enhanced drought early warning approach for the Netherlands using SPI (1-, 3-, 6-, and 12-month) and SSI (1- and 3-month) accumulation periods. Forecasts are derived from the operational European Flood Awareness System (EFAS) and ECMWF SEAS5 seasonal predictions. Reference indices are computed from historical precipitation and streamflow using ERA5-Land and EFAS datasets. For each grid cell, candidate distributions are fitted to accumulated monthly variables, and the dominant distribution is selected for standardization. To ensure the selected distributions remain valid under forecast conditions, we evaluate distribution performance using ECMWF hindcasts, applying a lead-month climatology framework (fitting and testing distributions per initialization month and lead time). Forecast indices are then evaluated against reference indices.

The use of correct distributions is expected to improve SPI/SSI forecast performance and enhance skill in predicting moderate to extreme drought events, particularly at short to medium lead times. This work supports operational integration of drought early warning into the Dutch forecasting center.