Trend analysis of selected hydro-meteorological variables for the Rietspruit sub-basin, South Africa

Abstract

Identifying hydro-meteorological trends is critical for assessing climate change and variability both at a basin and regional level. This study examined the long- and short-term trends from stream discharge, temperature, and rainfall data around the Rietspruit sub-basin in South Africa. The data were subjected to homogeneity testing before performing the trend tests. Inhomogeneity was widely detected in discharge data, hence no further analyses were performed on such data. Temperature and rainfall trends and their magnitudes at yearly, seasonal, and monthly time steps were identified after applying the non-parametric Mann-Kendall and Sen's slope estimator. The possible starting point of a trend was determined by performing the sequential Mann-Kendall test. This study revealed a combination of upward and downward trends in both temperature and rainfall data for the time steps under observation. For rainfall on an annual basis, there were no statistically significant monotonic trends detected, although non-significant downward trends were dominant. However, significant decreasing rainfall trends were observed in dry and low rainfall months, which were April, August, September, and November. In contrast, significant upward temperature trends were detected at the Vereeniging climate station at an annual scale and in October, November, spring, and winter. The findings are critical for climate risk management and reduction decisions for both near and long-term timescales.

Trends in climatic and hydrological parameters in the Ajora-Woybo watershed, Omo-Gibe River basin, Ethiopia

“…(1) [30,31]. MI using chained equations produces m imputations based on sequential imputation regression models of each variable conditioned by all other variables [30,32]. The multiple imputations have been implemented in software such as XLSTAT [32].…”

“…MI using chained equations produces m imputations based on sequential imputation regression models of each variable conditioned by all other variables [30,32]. The multiple imputations have been implemented in software such as XLSTAT [32]. MI is a widely used method in hydrology and its advantages were does not overestimate error, simulate missing data multiple times [30,32] and leads to best estimation of missing values [33].…”

Trend Analysis of Climatic and Hydrological Parameters in Ajora-Woybo Watershed, Omo-Gibe River Basin, Ethiopia

“…(1)(Aieb et al, 2019;Baddoo et al, 2021). The multiple imputations have been implemented in software such as XLSTAT(Banda et al, 2021). MI is a widely used method in hydrology and its advantages were does not overestimate error, simulate missing data multiple times(Aieb et al, 2019;Banda et al, 2021) and leads to best estimation of missing values(Sattari et al, 2017).…”

“…The multiple imputations have been implemented in software such as XLSTAT(Banda et al, 2021). MI is a widely used method in hydrology and its advantages were does not overestimate error, simulate missing data multiple times(Aieb et al, 2019;Banda et al, 2021) and leads to best estimation of missing values(Sattari et al, 2017). To apply this method, we can proceed as follows: (i) Find k similar databases for each missing value; then the observed values are used to impute MD.…”

“…NMA, (2014) states that there are three main seasons: Kiremt (the main rainy season from June to September), Belg (a small rainy season from February to May) and Bega (a dry season from October to January). Trend analyses were carried out on monthly, seasonal and annual basis with reference to other researchers (e.g Banda et al, 2021;Belihu et al, 2018;Jaweso et al, 2019)Yue & Wang (2004).…”