Abstract
This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 0.02 units per year while that of NADI-MERRA–2 was 0.01 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.
Highlights
Climate change is expected to alter precipitation, temperature and increase the probability of occurrence of extreme events such as heatwaves, sea surface temperature, and droughts amongst others
This study aimed to investigate the trend in the Non-linear Aggregated Drought Index (NADI) time series
The results showed that the indices in this study. and TheNADI-MERRA–2 trend analysis was deduced by fittinga the line of positive best fit in both the
Summary
Climate change is expected to alter precipitation, temperature and increase the probability of occurrence of extreme events such as heatwaves, sea surface temperature, and droughts amongst others. The severity of drought is being aggravated by the rise in water demand and global climate change [4], putting water supplies and agricultural production and food security at risk [2]. In disciplines, such as atmospheric science and agriculture, dynamics of soil moisture in the root zone of vegetation is essential, since this part of the vadose zone is the core component controlling the partitioning of water into evaporative fluxes, drainage, recharge, and runoff [5]. Precipitation is the main source of soil moisture, while temperature affects soil moisture by controlling evapotranspiration [7]
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