Snow Changes Drought Characterization but Not Drought Predictability: A Leakage-Free Multiscale Study in Ohio

Abstract

We examine whether accounting for snow accumulation and melt, which precipitation-based drought indices ignore, changes drought characterization and one-month-ahead drought forecasting at three stations in Ohio, USA (1953--2026). The Standardized Precipitation Index (SPI) is compared with the Standardized Snow Melt and Rain Index (SMRI), and both are decomposed with the Haar maximal overlap discrete wavelet transform. Nonlinear autoregressive networks without (NAR) and with (NARX) wavelet inputs are compared over 100 paired repetitions in a leakage-free design (past-only wavelet inputs, untouched test period), together with persistence, climatology, and linear autoregressive benchmarks. Snow changes the 3-month index mainly from December to May (mean absolute SMRI--SPI differences of up to 0.17--0.28). These differences are attributable to the snow-adjusted input rather than to the fitted distribution, are robust to the snow-model parameters, and change the drought classification of 21--32\% of the December--May drought months. Wavelet inputs reduce NARX error relative to NAR by 6--10\% for the 3-month indices in 92--100\% of the repetitions, but not for the 6-month indices. A linear autoregression with 7--12 lags is at least as accurate as two-lag NARX for every series, and networks given six lags gain at most 4.5\% from the wavelet inputs and only match the autoregression. The forecast gain therefore reflects the memory of the accumulated index rather than multiscale information, and it is similar for the SPI and the SMRI. Snow matters for drought monitoring in winter and spring, but not for one-month predictability.