Literature DB >> 31713135

Ensemble streamflow projections for a small watershed with HSPF model.

Mine Albek1, Erdem Ahmet Albek1, Serdar Göncü1, Burcu Şimşek Uygun2.   

Abstract

A watershed modeling tool, Hydrological Simulation Program-FORTRAN (HSPF), was utilized to model the hydrological processes in the agricultural Sarısu watershed in western Turkey. The meteorological input data were statistically downscaled time series from General Circulation Model simulations. The input data were constructed as an ensemble of 400 individual time series of temperature, precipitation, dewpoint temperature, solar radiation, potential evapotranspiration, cloudiness, and wind velocity, as required by HSPF. The ensemble was divided into four subsets, each comprising of 100 time series, of different Special Report on Emissions Scenarios. Yearly and monthly total streamflow time series were obtained from the calibrated and validated HSPF model spanning a period of 116 years between the water years of 1984 and 2099. The projections in the watershed showed a median increase of 3 °C in yearly average temperatures between the beginning and end 30-year periods of the 116-year simulation periods based on 400 ensemble members while the corresponding change in total yearly precipitation was - 71 mm. These changes led to a decrease in yearly streamflows by 40% which reflected itself to varying degrees in monthly flows. Correlations were established between the principal drivers of the watershed hydrological cycle, namely temperature and precipitation, and streamflow. The results showed that the changes in the climatic conditions will greatly affect water-related issues in the watershed and emphasize the necessity of preparing carefully to adapt to a warmer and drier climate.

Entities:  

Keywords:  Climate change; Correlation; HSPF; Hydrological modeling; Nonparametric analysis; Statistical downscaling; Streamflow; Watershed

Mesh:

Year:  2019        PMID: 31713135     DOI: 10.1007/s11356-019-06749-9

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages.

Authors:  Manizhe Zarei; Omid Bozorg-Haddad; Sahar Baghban; Mohammad Delpasand; Erfan Goharian; Hugo A Loáiciga
Journal:  Sci Rep       Date:  2021-12-21       Impact factor: 4.379

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.