{"title":"Mechanical and micro-structural properties of polymer concrete using solid wastes for pavement sub-base layer","authors":"Sadia Tasnim, Faiz Shaikh, S. Doh","doi":"10.1016/j.pce.2022.103308","DOIUrl":"https://doi.org/10.1016/j.pce.2022.103308","url":null,"abstract":"","PeriodicalId":302795,"journal":{"name":"Physics and Chemistry of the Earth, Parts A/B/C","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130719544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Abreu, M. Fraga, L. Almeida, F. Silva, R. Cecílio, G. Lyra, R. Delgado
{"title":"Streamflow In The Sapucaí River Watershed, Brazil: Probabilistic Modeling, Reference Streamflow, And Regionalization","authors":"M. Abreu, M. Fraga, L. Almeida, F. Silva, R. Cecílio, G. Lyra, R. Delgado","doi":"10.22541/au.162134118.84540536/v1","DOIUrl":"https://doi.org/10.22541/au.162134118.84540536/v1","url":null,"abstract":"This work aims to study the streamflow statistic patterns in the Sapucaí\u0000River watershed, state of Minas Gerais, Brazil. This study embraces the\u0000streamflow probabilistic modeling to determine the reference streamflow\u0000and, later, the streamflow regionalization to improve the water\u0000resources management. A 26-year-data series (1989 - 2014) of maximum,\u0000average, and minimum streamflow were used. Probability density functions\u0000were applied to the maximum and minimum daily streamflow to determine\u0000the recurrence periods. Long-term average annual and monthly streamflow\u0000were also calculated. Linear and non-linear regressions were adjusted\u0000for the streamflow regionalization. The drainage area and the streamflow\u0000equivalent to the total rainfall (with and without abstractions) were\u0000used as predictor variables. The probability density functions that best\u0000adjusted the maximum streamflow data set were the Generalized Extreme\u0000Values, and for the minimum streamflow was the normal distribution.\u0000Linear and non-linear regressions were efficient (R²> 0.90\u0000and d Willmott> 0.97) in the regionalization process\u0000regardless of the predictor variables. However, a small statistical\u0000advantage was found for the adjustment of non-linear regressions that\u0000used the predictor variables drainage area and the streamflow equivalent\u0000to the total rainfall (without abstractions).","PeriodicalId":302795,"journal":{"name":"Physics and Chemistry of the Earth, Parts A/B/C","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127267571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}