The primary objective of this study is to monitor and collect data from the main
tributaries of Smaquli stream during rainfall storm events, which can be used to
establish relationship between suspended sediment concentration and discharge. The
Smaquli catchment is divided into two sub-catchments namely Sarwchawa and
Krosh with areas of 80.64 and 34.82 km2 respectively. Jali dam is built at watershed
outlet. Rainfall, stream discharge, and suspended sediment concentration are
monitored during ten rainfall storms in the water years (2012-2013) and (2013-
2014). Analysis of the data from the two sampling sites, shows two different
responses of suspended sediment concentrations. The Krosh sub-catchment reacts
rapidly to rainfall storms and the same behavior is shown in the suspended sediment
that is resulted seven single and three double-peaked graphs. On the other hand, the
reaction of Sarwchawa sub-catchment was slower. Moreover, analysis of cumulative
flow and suspended sediment showed the Krosh sub-catchment ranks almost equally
as one of the flow and sediment contributors to the Smaquli stream, in spite of the
area difference between Sarwchawa and Krosh sub-catchments. The peak stream
flows are highly correlated with total and peak suspended sediments at Krosh station
having correlation coefficients of (R=0.96 and 0.84) respectively. In contrast, at
Sarwchawa sub-catchment, the peak stream flow was weakly correlated with peak
suspended sediment (R=0.47). To reduce sedimentation, this study suggests
changing the land use practices for example types of crops and frequent planting and
less grazing of sheep and goats.
‎ Since the first outbreak in Wuhan, China, in December 31, 2019, COVID-19 pandemic ‎has been spreading to many countries in the world. The ongoing COVID-19 pandemic has caused a ‎major global crisis, with 554,767 total confirmed cases, 484,570 total recovered cases, and ‎‎12,306 deaths in Iraq as of February 2, 2020. In the absence of any effective therapeutics or drugs ‎and with an unknown epidemiological life cycle, predictive mathematical models can aid in ‎the understanding of both control and management of coronavirus disease. Among the important ‎factors that helped the rapid spread of the ep
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