Academic Commons

Theses Doctoral

Quantifying the Hydrological Impact of Landscape Re-greening Across Various Spatial Scales

Hakimdavar, Raha

The conversion of natural landscapes for human use over the past century has led to significant ecological consequences. By clearing tropical forests, intensifying agriculture and expanding urban centers, human actions have transformed local, regional and global hydrology. Urban landscapes, designed and built atop impervious surfaces, inhibit the natural infiltration of rainfall into the subsurface. Deforestation, driven by the demand for natural resources and food production, alters river flow and regional climate. These land cover changes have manifested into a number of water management challenges, from the city to the watershed scale, and motivated investment into landscape re-greening programs. This movement has prompted the need for monitoring, evaluation and prediction of the hydrological benefits of re-greening. The research presented in this dissertation assesses the contribution of different re-greening strategies to water resources management, from multiple scales. Specifically, re-greening at the city scale is investigated through the study of vegetated rooftops (green roofs) in a dense urban environment. Re-greening at the watershed scale is investigated through the study of forest regeneration on deforested and ecologically degraded land in the tropics.
First, the benefits of city re-greening for urban water management are investigated through monitoring and modeling the hydrological behavior of a number of green roofs in New York City (NYC). Influence of green roof size and rainfall characteristics on a green roof’s ability to retain/ detain rainwater are explored and the ability of a soil infiltration model to predict green roof hydrology is assessed. Findings from this work present insight regarding green roof design optimization, which has utility for scientific researchers, architects, and engineers.
Next, a cost effective tool is developed that can be used to evaluate green roof hydrologic performance, citywide. This tool, termed the Soil Water Apportioning Method (SWAM), generates green roof runoff and evapotranspiration based on minimally measured parameters. SWAM is validated using measured runoff from three extensive green roofs in NYC. Additional to green roofs, there is potential for SWAM to be used in the hydrologic performance evaluation of other types of green infrastructure, making SWAM a relevant tool for city planners and agencies as well as for researchers from various disciplines of study.
Finally, the impact of degraded landscape re-greening is investigated using a case study of 15 watersheds in Puerto Rico that have experienced extensive reforestation. The study provides evidence of improved soil conditions following reforestation, which in effect positively impacts streamflow generation processes. Findings from this work fill a gap in knowledge regarding the hydrological benefits of forest regeneration in mesoscale watersheds and provide guidance for future investment into reforestation programs.
Land cover will inevitably continue to change to meet the needs of a growing and increasingly urban population. Yet there is potential to offset some of the ecological effects – especially those on hydrology – that result from land cover change. As a whole, this dissertation aims to contribute knowledge that can be used to make the re-greening of altered landscapes more realizable.

Files

  • thumnail for Hakimdavar_columbia_0054D_13155.pdf Hakimdavar_columbia_0054D_13155.pdf binary/octet-stream 11 MB Download File

More About This Work

Academic Units
Civil Engineering and Engineering Mechanics
Thesis Advisors
Culligan, Patricia J.
Degree
Ph.D., Columbia University
Published Here
February 5, 2016
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.