Analysis of Spatial and Temporal Trend for Hydro-climatic Parameters in the Kilombero River Catchment, Tanzania

Abstract

Perceived and real-life water availability have raised social and economic conflicts that necessitate proper water management. This requires an understanding of spatial-temporal trends of hydro-climatic variables as the main contributor to available water for use by sectors of economy. In this study, we have analyzed the trend of hydro-climatic variables viz. precipitation, temperature, evapotranspiration and river discharge. One downstream river gauge station was used for discharge data whereas a total of 9 daily observed and 29 grided satellite stations were used for climate data. Climate Hazards Group InfraRed Precipitation was used for precipitation data and Observational-Reanalysis Hybrid was used for Temperature data. Mann-Kendall Statistical test, Sen’s slope estimator and ArcMap Inverse Distance Weighted Interpolation functionality were employed for temporal, magnitude and spatial trend analysis respectively. Results show that on the latter, there are three main climatic zones in the study area viz. Udzungwa escarpment, Kilombero valley and Mahenge escarpment. On the former, with exception of the declining potential evapotranspiration trend, all other variables are on increase. This is with catchment rates of 2.08 mm/y, 0.05 oC/y, 0.02 oC/y, 498.6 m3/s/y and − 2.27 mm/y for precipitation, Tmax, Tmin, river discharge and PET respectively. Furthermore, there is a synonymous seasonality pattern for all variables with significant range, especially in river discharge. However, it is recommended to improve water resources management practices to limit flow impairment as expansions in sectors of economy are expected. Furthermore, landuse change analysis is recommended to ascertain actual trend and hence future water uptake.

Application of Mann Kendal Sen's Slope Estimator in Trend Analysis of Historical And Future Precipitation and Temperature in the Kilombero River Basin

“…Commonly, rainfall was seen to be decreasing during the months of May, June, July and September while there was an increase in rainfall for the months of March, August, April, December, November and October for extended time (Smith, 2016). The results agreed with Senkondo, (2020) and Sigalla et al, (2023) which indicated that rainfall had shown a steady decrease in past ten years. Further, the aerial rainfall varies by more than 200mm in an analysis done for 40 years in areas.…”

“…The long rains fall between Januarys to May, whereas the short rains fall between Octobers to December. Similarly, Sigalla et al, (2023) and Kangalawe and Liwenga, (2015), showed the average rainfall within Kilombero basin range between 1000mm to 1400mm where the reported month of high rainfall intensity is April. It was only the month of April that showed the peak flow from 1989 to 2020.…”

“…Figure 4 and Table 2 shows the graphical trend for the yearly precipitation. Kilombero, Ifakara and Malinyi stations shows no increasing trend in precipitation and according to Sigalla et al, (2023), the possible reasons is that these stations located in the area where a lot of human activities which alter the hydrological systems conducted such as agriculture and built-up areas Figure 5. Since the basin plays a vital role in the country's water resources and supports agriculture and wildlife habitats, precipitation trends in the basin are essential to monitor, as they can impact water availability, agricultural productivity, and biodiversity.…”

“…Studies on rainfall and temperature trends have examined climate change and variability's impact on various sectors (Ayugi and Tan, 2018). However, a declining trend of annual and monthly rainfall has been noticed due to shifts in rainfall and temperature patterns (Sigalla et al, 2023).…”

Hydrological Droughts in the Eastern Mediterranean Basin Using Hybrid Trend Analysis Methods

“…This paradigm shift requests a re-evaluation of conventional hydrological models. It underlines the need for robust trend analysis methods, such as the Mann-Kendall (MK) test, to detect and interpret these changes (Sigalla, Valimba et al 2023). The MK test is known for its robustness in identifying trends in hydrological time series, and it is widely employed in various hydro-meteorological analyses.…”