Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Interventional Pulmonology (middletown, de.)
The use of rainfall data for flood early warning prediction have been highlighted by several researchers, in the last couple of decades. This study investigates the involvement of antecedent daily rainfall, for the determination of rainfall thresholds, to be used for flood early warning purposes at the upper watershed, Java island, Indonesia. An inventory of 70 flood occurrences for the period of 1992–2017 was compiled, and rainfall data were retrieved from 37 stations. First, calculate the critical discharge to determine the flood status in each watershed based on the results of statistical analysis of the frequency of the data series of discharge. Second, a procedure for the calculation of rainfall thresholds for flood occurrence was followed consisting of four steps: i) determining the rainfall associated with each inventory of flood occurrence and nonoccurrence; ii) the antecedent daily rainfall was calculated for 1 to 7 days for the selected dates and watersheds; iii) the optimum number of antecedent rainfall days was evaluated; and (iv) empirical rainfall thresholds for flood occurrence were determined. The results showed flood occurrences are best predicted using a combination of daily rainfall and 7 days of antecedent rainfall for all alert zones (A, B, C and D) including regional model (RRTM) with a negative relationship between antecedent rainfall and daily rainfall. Rainfall threshold models have an overall accuracy of more than 95%. It has provided evidence that the flood event in the study area is preceded by soil conditions that is saturated due to rain a few days before the flood
This paper intends to formulate the base flow model due to the watershed morphometry factors. The methodology consists of to carry out the filtering of rainfall-single peak flood hydrograph pair data, then to separate the base flow and direct run-off from the observed hydrograph. However, the whole data that are used in this research are formerly evaluated regarding to the data validity condition. The next step is to find out the average of each base flow peak discharge. Finally, is to characterize the watershed physical parameter to formulate the base flow modeling. The result shows that the base flow formulation is QBF = 0.355 * P1,124 * Dd0,310 with the determination coefficient is 0.782