Green roof systems (GRs) are effective tools for urban stormwater management. However, there is limited documentation of the long-term hydrological performance of GRs to support decision-making. This study evaluated long-term field monitoring records (7 years) from a 12-year-old GR, situated in a Moist Subtropical Mid-Latitude Climate, to analyze seasonality in and evolution of hydrological performance. The monitoring system was built within a pan lysimeter buried under substrate layers matching the surrounding GR. The monitoring results highlight the efficacy of this GR in long-term stormwater runoff control. The GR can retain 87% of the annual precipitation and return 54% of the precipitation to the atmosphere through evapotranspiration (ET) and sustain long-term event-based mean runoff volume reductions, peak flow reductions, and flow delays of 82%, 93%, and 4.3 h, respectively. The initial moisture content prior to events was highly correlated with hydrological performance, with a seasonal mean Spearman correlation coefficient of 0.47, suggesting the potential of enhancing ET from the GR to improve performance. Substrate water holding capacity increased over time, but no obvious changes in water retention performance were observed. These monitoring results from the aging GR demonstrate the effectiveness of GR systems for long-term stormwater management.
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http://dx.doi.org/10.1016/j.jenvman.2024.122831 | DOI Listing |
Environ Sci Pollut Res Int
January 2025
Department of Environmental Health Engineering, School of Public Health, Mazandaran University of Medical Sciences, Sari, Iran.
Climate change significantly impacts the risk of eutrophication and, consequently, chlorophyll-a (Chl-a) concentrations. Understanding the impact of water flows is a crucial first step in developing insights into future patterns of change and associated risks. In this study, the Statistical DownScaling Model (SDSM)-a widely used daily downscaling method-is implemented to produce downscaled local climate variables, which serve as input for simulating future hydro-climate conditions using a hydrological model.
View Article and Find Full Text PDFSci Total Environ
January 2025
University of Tokyo, Japan.
Over the last 20 years, we have dramatically improved hydrometeorological data including isotopes, but are we making the most of this data? Stable isotopes of oxygen and hydrogen in the water molecule (stable water isotopes - SWI) are well known tracers of the global hydrological cycle producing critical climate science. Despite this, stable water isotopes are not explicitly included in influential climate reports (e.g.
View Article and Find Full Text PDFSci Rep
January 2025
School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
Hydrological forecasting is of great significance to regional water resources management and reservoir operation. Climate change has increased the complexity and difficulty of hydrological forecasting. In this study, a hybrid explainable streamflow forecasting model based on CNN-LSTM-Attention was established for five typical river source regions in the eastern Qinghai-Tibet Plateau (EQTP).
View Article and Find Full Text PDFPLoS One
December 2024
São Paulo State University (Unesp), School of Sciences and Engineering, Tupã, São Paulo, Brasil.
Meteorological data acquired with precision, quality, and reliability are crucial in various agronomy fields, especially in studies related to reference evapotranspiration (ETo). ETo plays a fundamental role in the hydrological cycle, irrigation system planning and management, water demand modeling, water stress monitoring, water balance estimation, as well as in hydrological and environmental studies. However, temporal records often encounter issues such as missing measurements.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Agreste Academic Center, Federal University of Pernambuco, Av. Marielle Franco, Caruaru, 55014-900, PE, Brazil.
Arid and semiarid regions have particularities that make more difficult hydrological modeling, such as shallow soils, pronounced temporal and spatial irregularity of precipitation, and sometimes, lack of consistent data. In order to contribute to the hydrological studies in these regions, this research used the CAWM IV model (Campus Agreste Watershed Model Version IV), specially developed for applications in these areas. This model was used to simulate the input of natural flows in the Castanhão reservoir, the most important reservoir in the state of Ceará, northeast of Brazil.
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