Fei Cheng
Research Subjects
Fei Cheng has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jichong Han
College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
1 shared publication - Juan Cao
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
1 shared publication - Qinghang Mei
Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
1 shared publication - Xingye Han
Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
1 shared publication - Yumiao Fan
Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
1 shared publication - Zhiwen Yang
Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, China
1 shared publication
Latest Publications
- Open Access
Drought-Index Forecasting in the Yellow River Basin: Evaluating Index Timescales, Lead Times, and Machine Learning Models
Drought forecasting for basin water management requires accounting for precipitation deficits, climatic water balance, and soil-water storage across appropriate timescales. This study evaluates index timescales, lead times, and five machine learning models for the Yellow River Basin using 21 ERA5-Land variables from 1990 to 2025. We constructed the standardized precipitation index (SPI), Penman–Monteith standardized precipitation evapotranspiration index (SPEI-PM), and root-zone standardized soil moisture index (SSI-rootzone). A staged comparison used training-window standardization and rolling-origin cross-validation, followed by evaluation during 2021–2025. Scale screening retained 90-day indices. At a seven-day lead, histogram gradient boosting achieved R2 values of 0.807, 0.852, and 0.995 for SPI, SPEI-PM, and SSI-rootzone, respectively. R2 remained positive at 30 days but became negative at 90 days. Across 8548 grid cells, forecasts reproduced drought-area dynamics but underestimated severe and extreme drought extent, particularly for SPEI-PM. GPM and SMAP, excluded from model development, provided external comparisons; independently derived indices correlated with SPI and SSI-rootzone forecasts at 0.669 and 0.517. These findings show that evaluating forecasts for drought preparedness requires consideration of temporal aggregation, lead time, spatial extremes, and product dependence rather than basin-average accuracy alone.
Open AccessDrought-Index Forecasting in the Yellow River Basin: Evaluating Index Timescales, Lead Times, and Machine Learning Models
Drought forecasting for basin water management requires accounting for precipitation deficits, climatic water balance, and soil-water storage across appropriate timescales. This study evaluates index timescales, lead times, and five machine learning models for the Yellow River Basin using 21 ERA5-Land variables from 1990 to 2025. We constructed the standardized precipitation index (SPI), Penman–Monteith standardized precipitation evapotranspiration index (SPEI-PM), and root-zone standardized soil moisture index (SSI-rootzone). A staged comparison used training-window standardization and rolling-origin cross-validation, followed by evaluation during 2021–2025. Scale screening retained 90-day indices. At a seven-day lead, histogram gradient boosting achieved R2 values of 0.807, 0.852, and 0.995 for SPI, SPEI-PM, and SSI-rootzone, respectively. R2 remained positive at 30 days but became negative at 90 days. Across 8548 grid cells, forecasts reproduced drought-area dynamics but underestimated severe and extreme drought extent, particularly for SPEI-PM. GPM and SMAP, excluded from model development, provided external comparisons; independently derived indices correlated with SPI and SSI-rootzone forecasts at 0.669 and 0.517. These findings show that evaluating forecasts for drought preparedness requires consideration of temporal aggregation, lead time, spatial extremes, and product dependence rather than basin-average accuracy alone.