Hydrologic Impact Assessment of Land Cover Change and Stormwater Management Using the Hydrologic Footprint Residence

Giacomoni, Marcio; Gómez, Romina Díaz; Berglund, Emily Zechman

doi:10.1111/jawr.12187

Cited by 27 publications

(8 citation statements)

References 48 publications

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“…SWAT has the capability to generate output for various water quality constituents (including but not limited to sediment, different forms of nitrogen and phosphorus, dissolved oxygen, and pesticides) at a user specified time step (subdaily, daily, monthly, or yearly). It has been shown to model agricultural watersheds with reasonable accuracy (Santhi et al, 2001) and continues to be one of the most widely used watershed models in the world (Fuka et al, 2014;Giacomoni et al, 2014;Park and Kim, 2014;Yen et al, 2014b).…”

Section: Swat Modelmentioning

confidence: 99%

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

Yen

Hoque

Wang

et al. 2016

J American Water Resour Assoc

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In the field of watershed modeling, the impact of measurement uncertainty (MU) on calibration results indicates the potential issue of inaccurate model predictions. It is important to note that MU refers to the uncertainty in measured data such as flow and nutrient values that are used to evaluate model outputs. The calculation of error statistics assuming measured data are deterministic may not be appropriate as has been frequently stated in literature. Although MU can affect model calibration results, it is rarely incorporated in modeling practice. MU can be incorporated in two schemes: explicitly incorporated (MU-EI) during model calibration and post-processed (MU-PP) after calibration is completed. In this study, both schemes are implemented in a case study of the Arroyo Colorado Watershed, Texas. Unexpectedly, no substantial differences were observed between each scheme for flow predictions. Although MU did not cause dramatic differences in most sediment and NH 4 -N predictions, error statistics were affected in cases with MU greater than 50%, especially for sediment and NH 4 -N. Therefore, it is concluded that MU may not exert a significant impact on model predictions until certain threshold is reached. This study demonstrates that high levels of uncertainty in measured calibration/validation data significantly affect parameter estimation, especially in the auto-calibration process. (KEY TERMS: measurement uncertainty; model calibration; SWAT; uncertainty analysis; IPEAT.) Yen, Haw, Yamen M. Hoque, Xiuying Wang, and Robert Daren Harmel, 2016. Applications of Explicitly Incorporated/Post-Processing Measurement Uncertainty in Watershed Modeling.

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Section: Swat Modelmentioning

confidence: 99%

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

Yen

Hoque

Wang

et al. 2016

J American Water Resour Assoc

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“…The metrics HFR and peak flow were computed for the four management scenarios for the 2, 10, and 100 yrs design storms and 78 historical rainstorm events. Another study (Giacomoni et al 2014) used the HFR to assess the hydrologic impact of land cover change in a 370 km² watershed. The following stormwater management scenarios were tested: existing, future, detention under three design storm events.…”

Section: Figure 1 -Computation Of the Hfr: Inputs (Hyetograph Cross-mentioning

confidence: 99%

Use of Multiobjective Evolutionary Algorithm Optimization for Low-Impact Development Placement

Giacomoni

2015

International Low Impact Development Conference 2015

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The transformation of natural land cover to urban areas severely alters the hydrologic flow regime of watersheds. The negative impacts include the increase of surface runoff and decrease of infiltration rates, which can result in more frequent and intense flood events and the reduction of groundwater recharge. Low Impact Developments (LIDs) are strategies designed to better mimic the natural flow regime by promoting higher infiltration and the treatment of stormwater. Examples of LID structures are bio-gardens, green roofs, bio-swales and pervious pavements. While the expansion of LIDs in urban catchments would be desirable, retrofitting large urban watersheds with LIDs can be cost prohibitive. This study combines hydrologic simulation with a Multi-Objective Evolutionary Algorithm (MOEA) to find solutions in terms of LID design and location in urban catchments that maximize the environmental benefits and characterize the tradeoffs between LID performance indicators and costs. The solutions are evaluated in terms of costs, peak flow, and the Hydrologic Footprint Residence (HFR). The HFR is a new metric designed to quantify the impacts of urbanization in the hydrologic regime by representing the dynamics of inundated areas and residence time of flood waves. The results provide a basis for better stormwater management and planning because tradeoff curves provide a wider spectrum of designs and placement guidelines, improving the sustainability of urban watersheds.

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“…The HFR better captures the impacts of urbanization than peak flow alone, because it accounts for the temporal shifts of the hydrographs, as well as the change in surface runoff that is represented by the alteration in the inundation area. (Giacomoni et al 2014;Giacomoni et al 2012).…”

Section: Introductionmentioning

confidence: 97%

Low Impact Development Placement Investigation Using a Multi-Objective Evolutionary Optimization Algorithm

Giacomoni¹

2015

World Environmental and Water Resources Congress 2015

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Low Impact Development (LID) controls, such as green roofs and pervious pavements, are alternative stormwater management strategies designed to better mimic the pre-development hydrologic flow regime and mitigate the impacts of urbanization. Many studies focused on monitoring and understanding the hydraulic behavior of individual LIDs, while the collective behavior of retrofitting urban watersheds with LID controls is less understood. This study applies a Multi-Objective Evolutionary Algorithm (MOEA) connected to a hydrologic model to find near-optimal solutions in terms of LID locations in urban watersheds. This methodology identifies the tradeoffs between the costs of retrofitting urban areas with LIDs and the environmental benefits provided by such measures, such as surface runoff volume and peak flow reduction. The benefit provided by LIDs is also evaluated through the use of the Hydrologic Footprint Residence (HFR), a new stormwater metric that better quantifies hydrologic alteration than peak flow alone. The HFR is a spatial and temporal metric that represents the dynamics of inundated areas and residence time of flood waves throughout downstream segments. The proposed methodology can help urban stormwater decision makers improve the sustainability of urban watersheds as it provides guidelines of LIDs placement under cost constraints.

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Hydrologic Impact Assessment of Land Cover Change and Stormwater Management Using the Hydrologic Footprint Residence

Cited by 27 publications

References 48 publications

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

Use of Multiobjective Evolutionary Algorithm Optimization for Low-Impact Development Placement

Low Impact Development Placement Investigation Using a Multi-Objective Evolutionary Optimization Algorithm

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