Dry‐Season Water Deficits in the Southwestern Amazon Under High Emissions

Abstract

Understanding future changes in cumulative water deficit (CWD) is essential for assessing the vulnerability of Amazonian ecosystems to climate change. This study evaluates the performance of CMIP6 models in simulating CWD in the southwestern Amazon from 1985 to 2024 and projects future changes through 2100 under three emission scenarios (SSP1‐2.6, SSP3‐7.0, SSP5‐8.5). CWD was calculated using a fixed evapotranspiration threshold of 100 mm/month, and the maximum cumulative water deficit (MCWD) was derived to quantify overall water stress over time. Model performance was assessed using statistical metrics (MAE, RMSE, bias and Pearson correlation), and the best‐performing models were selected for future projections. Additionally, time series decomposition with breakpoint detection (BFAST) was applied. Results show that most models reproduce the seasonal cycle but diverge during peak dry months. Under SSP5‐8.5, deficits intensify significantly, exceeding 150 mm between July and September. CAS‐ESM2‐0 and CESM2‐WACCM exhibit large negative biases, while BCC‐CSM2‐MR and IPSL‐CM6A‐LR perform best. SSP1‐2.6 shows minimal changes, whereas SSP3‐7.0 and SSP5‐8.5 exhibit significant negative trends, with depletion rates reaching −21 mm/month by 2100. Temporal decomposition reveals increased hydrological instability, structural breaks after 2040 (SSP3‐7.0) and 2050 (SSP5‐8.5), and amplified seasonality under high emissions. Residual variability also increases, indicating a higher frequency of non‐seasonal anomalies. These findings highlight growing water stress under higher warming scenarios and emphasise CWD as a key indicator of ecological risk. Reliable projections depend on careful model evaluation, underscoring the need for model selection to guide climate adaptation and forest conservation strategies.

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