Abstract: This study was designed to find the best-fit probability distribution of annual rainfall based on 50 years sample (1966-2015) in the Karkheh river basin at Iran using six probability distributions: Normal, 2-Parameter Log Normal, 3-Parameter Log Normal, Pearson Type 3, Log Pearson Type 3 and Gumbel distribution. The best fit probability distribution was selected using Stormwater Management and Design Aid (SMADA) software and based on the Residual Sum of Squares (R.S.S) between observed and estimated values Based on the R.S.S values of fit tests, the Log Pearson Type 3 and then Pearson Type 3 distributions were found to be the best-fit probability distribution at the Jelogir Majin and Pole Zal rainfall gauging station. The annual values of expected rainfall were calculated using the best fit probability distributions and can be used by hydrologists and design engineers in future research at studied region and other region in the world.
Abstract: The probability distributions are the best method for forecasting of extreme hydrologic phenomena such as rainfall and flood flows. In this research, in order to determine suitable probability distribution for estimating of annual extreme rainfall and flood flows (discharge) series with different return periods, precipitation with 40 and discharge with 58 years time period had been collected from Karkheh River at Iran. After homogeneity and adequacy tests, data have been analyzed by Stormwater Management and Design Aid (SMADA) software and residual sum of squares (R.S.S). The best probability distribution was Log Pearson Type III with R.S.S value (145.91) and value (13.67) for peak discharge and Log Pearson Type III with R.S.S values (141.08) and (8.95) for maximum discharge in Jelogir Majin and Pole Zal stations, respectively. The best distribution for maximum precipitation in Jelogir Majin and Pole Zal stations was Log Pearson Type III distribution with R.S.S values (1.74&1.90) and then Pearson Type III distribution with R.S.S values (1.53&1.69). Overall, the Log Pearson Type III distributions are acceptable distribution types for representing statistics of extreme hydrologic phenomena in Karkheh River at Iran with the Pearson Type III distribution as a potential alternative.
Abstract: The advancements in technology allow the
development of a new system that can continuously measure surface
soil erosion. Continuous soil erosion measurements are required in
order to comprehend the erosional processes and propose effective
and efficient conservation measures to mitigate surface erosion.
Mitigating soil erosion, especially in Mediterranean countries such as
Greece, is essential in order to maintain environmental and
agricultural sustainability. In this paper, we present the Automated
Soil Erosion Monitoring System (ASEMS) that measures surface soil
erosion along with other factors that impact erosional process.
Specifically, this system measures ground level changes (surface soil
erosion), rainfall, air temperature, soil temperature, and soil moisture.
Another important innovation is that the data will be collected by
remote communication. In addition, stakeholder’s awareness is a key
factor to help reduce any environmental problem. The different
dissemination activities that were utilized are described. The overall
outcomes were the development of a new innovative system that can
measure erosion very accurately. These data from the system help
study the process of erosion and find the best possible methods to
reduce erosion. The dissemination activities enhance the stakeholders
and public's awareness on surface soil erosion problems and will lead
to the adoption of more effective soil erosion conservation practices
in Greece.