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The occurrence of drought indicates that there is no rainfall or little rainfall within a certain period. Nevertheless, no or little rainfall within a certain time implies that drought may not happen. The drought occurrence in different regions or seasons shows a certain lag to some extent. The lag time and intensity (i.e., the contribution rate of rainfall in previous period to runoff in current period) are deeply affected by watershed water storage capacity. Subsequently, rainfall deficit is a necessary and insufficient for the droughts. To reveal the mechanism of watershed lagged effects and the impact of human activities on droughts, the characteristics of lagged effects and human activities in Central Guizhou of China are analyzed by the Lagged index and Landscape index based on land use data, rainfall and runoff data during the period 1971–2016. The results demonstrate that the impact of the same land use type in different ages (1970s–2010s) on watershed lagged intensity has a significant difference (P < 0.001). Especially for the influence of the woodland and grassland on lagged intensity is increasing with the increase of time scales and the impact difference gradually decreases in cultivated land, water body and construction land. The impact (R-value) of land use type transfer on watershed lagged effect is particularly significant (p < 0.001), and the influence (R-value) of land use type transfer on lagged frequency is shown with a W-shaped pattern in cultivated land and grassland with the ages changing, a single peak character in woodland and a peak valley alternation in water body. The impact of principal component factors (Zs) of land use morphological characteristics on watershed lagged effect has a particularly significant difference (p < 0.001) in different lag periods (Xt−0–Xt−3), different ages (1970s–2010s) and different time scales (1–12 months). Among them, the impact (F-value) of the Zs in the Xt−0–Xt−3 on lagged intensity/frequency is the largest in grassland, followed by in water body and woodland, and the smallest in cultivated land and construction land. The F-value in the 1970s–2010s is larger in construction than in cultivated land and woodland, and it is the smaller in water body and grassland. The F-value in the 1–12 month scales is grassland > water body > cultivated land > construction land > woodland. As a subsequence, this study enriches the research content of landscape ecology, and it provides a theoretical basis for revealing the mechanism of agricultural and hydrological droughts in Karst drainage basins. [ABSTRACT FROM AUTHOR]

Droughts are becoming more frequent in the karst region of southwest China due to climate change, and accurate monitoring of karst agricultural droughts is crucial. To this end, in this study, based on random forest (RF) and support vector regression (SVR) algorithms, the monthly precipitation, monthly potential evapotranspiration, monthly normalised difference vegetation Index (NDVI), elevation, and karst development intensity from January to December 2001–2020 were used as independent variables, and the standardised soil moisture index (SSI) calculated by GLDAS soil moisture was used as the dependent variable to construct karst agricultural drought monitoring models at different timescales, using Guizhou Province as an example. The performance of the models constructed by the two algorithms was also evaluated using root mean square error (RMSE), coefficient of determination (R2), and correlation analysis, and the spatial and temporal evolution trends of karst agricultural drought at different timescales were analysed based on the model with better performance. The prediction of karst agricultural drought from January to December 2021–2025 was based on the seasonal difference autoregressive moving average (SARIMA) model and the analysis of change trends was performed using the Bayesian estimator of abrupt change, seasonal change, and trend (RBEAST). The results showed that (1) the drought model constructed by the RF regression algorithm performed better than the SVR algorithm at 1-, 3-, 6-, 9-, and 12-month timescales and was superior for monitoring karst agricultural drought. (2) The model showed that the overall trend of agricultural drought at different timescales was alleviated; 2010, 2011, and 2012 were typical drought years. At the same time, most regions showed a trend of drought mitigation, whereas a few regions (Bijie City, Liupanshui City, and Qianxinan Prefecture) showed a trend of aggravation. (3) The study predicted an overall high west–east distribution of drought intensity by 2021–2025. The 1- and 3-month timescales showed a trend of agricultural drought mitigation, and the 6-, 9-, and 12-month timescales showed a trend of aggravation; in 2021, 2022, and 2024, the abrupt change rates of autumn and winter droughts were higher. The results can provide a reference basis for the monitoring of agricultural drought in karst agriculture and the formulation of drought prevention and anti-drought measures. [ABSTRACT FROM AUTHOR]