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- Author or Editor: Wondmagegn Yigzaw x
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Abstract
Since historical (predam) data are traditionally the sole criterion for dam design, future (postdam) meteorological and hydrological variability due to land-use and land-cover change cannot be considered for assessing design robustness. For example, postdam urbanization within a basin leads to definite and immediate increase in direct runoff and reservoir peak inflow. On the other hand, urbanization can strategically (i.e., gradually) alter the mesoscale circulation patterns leading to more extreme rainfall rates. Thus, there are two key pathways (immediate or strategic) by which the design flood magnitude can be compromised. The main objective of the study is to compare the relative contribution to increase in flood magnitudes through direct effects of land-cover change (urbanization and less infiltration) with gradual climate-based effects of land-cover change (modification in mesoscale storm systems). The comparison is cast in the form of a sensitivity study that looks into the response to the design probable maximum flood (PMF) from probable maximum precipitation (PMP). Using the American River watershed (ARW) and Folsom Dam as a case study, simulated peak floods for the 1997 (New Year's) flood event show that a 100% impervious watershed has the potential of generating a flood that is close to design PMF. On the other hand, the design PMP produces an additional 1500 m3 s−1 peak flood compared to the actual PMF when the watershed is considered 100% impervious. This study points to the radical need for consideration future land-cover changes up front during the dam design and operation formulation phase by considering not only the immediate effects but also the gradual climatic effects on PMF. A dynamic dam design procedure should be implemented that takes into account the change of land–atmospheric and hydrological processes as a result of land-cover modification rather than relying on historical records alone.
Abstract
Since historical (predam) data are traditionally the sole criterion for dam design, future (postdam) meteorological and hydrological variability due to land-use and land-cover change cannot be considered for assessing design robustness. For example, postdam urbanization within a basin leads to definite and immediate increase in direct runoff and reservoir peak inflow. On the other hand, urbanization can strategically (i.e., gradually) alter the mesoscale circulation patterns leading to more extreme rainfall rates. Thus, there are two key pathways (immediate or strategic) by which the design flood magnitude can be compromised. The main objective of the study is to compare the relative contribution to increase in flood magnitudes through direct effects of land-cover change (urbanization and less infiltration) with gradual climate-based effects of land-cover change (modification in mesoscale storm systems). The comparison is cast in the form of a sensitivity study that looks into the response to the design probable maximum flood (PMF) from probable maximum precipitation (PMP). Using the American River watershed (ARW) and Folsom Dam as a case study, simulated peak floods for the 1997 (New Year's) flood event show that a 100% impervious watershed has the potential of generating a flood that is close to design PMF. On the other hand, the design PMP produces an additional 1500 m3 s−1 peak flood compared to the actual PMF when the watershed is considered 100% impervious. This study points to the radical need for consideration future land-cover changes up front during the dam design and operation formulation phase by considering not only the immediate effects but also the gradual climatic effects on PMF. A dynamic dam design procedure should be implemented that takes into account the change of land–atmospheric and hydrological processes as a result of land-cover modification rather than relying on historical records alone.
Abstract
Recent research in mesoscale hydrology suggests that the size of the reservoirs and the land-use/land-cover (LULC) patterns near them impact the extreme weather [e.g., probable maximum flood (PMF)]. A key question was addressed by W. Yigzaw et al.: How do reservoir size and/or LULC modify extreme flood patterns, specifically PMF via modification of probable maximum precipitation (PMP)? Using the American River watershed (ARW) as a representative example of an impounded watershed with Folsom Dam as the flood control structure, they applied the distributed Variable Infiltration Capacity (VIC) model to simulate the PMF from the atmospheric feedbacks simulated for various LULC scenarios. The current study presents a methodology to extend the impacts of these modified extreme flood patterns on the downstream Sacramento County, California. The research question addressed is, what are the relative effects of downstream flood hazards to population on the American River system under various PMF scenarios for the Folsom Dam? To address this goal, a two-dimensional flood model, the Flood in Two Dimensions–Graphics Processing Unit (Flood2D-GPU), is calibrated using synthetic aperture radar (SAR) and Landsat satellite observations and observed flood stage data. The calibration process emphasized challenges associated with using National Elevation Dataset (NED) digital elevation model (DEM) and topographic light detection and ranging (lidar)–derived DEMs to achieve realistic flood inundation. Following this calibration exercise, the flood model was used to simulate four land-use scenarios (control, predam, reservoir double, and nonirrigation). The flood hazards are quantified as downstream flood hazard zones by estimating flood depths and velocities and its impacts on risk to population using depth–velocity hazard relationships provided by U.S. Bureau of Reclamation (USBR). From the preliminary application of methodology in this study, it is evident from comparing downstream flood hazard that similar trends in PMF comparisons reported by W. Yigzaw et al. were observed. Between the control and nonirrigation, the downstream flood hazard is pronounced by −3.90% for the judgment zone and −2.40% for high hazard zones. Comparing the control and predam scenarios, these differences are amplified, ranging between 0.17% and −1.34%. While there was no change detected in the peak PMF discharges between the control and reservoir-double scenarios, it still yielded an increase in high hazard areas for the latter. Based on this preliminary bottom-up vulnerability assessment study, it is evident that what was observed in PMF comparisons by W. Yigzaw et al. is confirmed in comparisons between control versus predam and control versus nonirrigation. While there was no change detected in the peak PMF discharges between the control and reservoir-double scenarios, it still yielded a noticeable change in the total areal extents: specifically, an increase in high hazard areas for the latter. Continued studies in bottom-up vulnerability assessment of flood hazards will aid in developing suitable mitigation and adaptation options for a much needed resilient urban infrastructure.
Abstract
Recent research in mesoscale hydrology suggests that the size of the reservoirs and the land-use/land-cover (LULC) patterns near them impact the extreme weather [e.g., probable maximum flood (PMF)]. A key question was addressed by W. Yigzaw et al.: How do reservoir size and/or LULC modify extreme flood patterns, specifically PMF via modification of probable maximum precipitation (PMP)? Using the American River watershed (ARW) as a representative example of an impounded watershed with Folsom Dam as the flood control structure, they applied the distributed Variable Infiltration Capacity (VIC) model to simulate the PMF from the atmospheric feedbacks simulated for various LULC scenarios. The current study presents a methodology to extend the impacts of these modified extreme flood patterns on the downstream Sacramento County, California. The research question addressed is, what are the relative effects of downstream flood hazards to population on the American River system under various PMF scenarios for the Folsom Dam? To address this goal, a two-dimensional flood model, the Flood in Two Dimensions–Graphics Processing Unit (Flood2D-GPU), is calibrated using synthetic aperture radar (SAR) and Landsat satellite observations and observed flood stage data. The calibration process emphasized challenges associated with using National Elevation Dataset (NED) digital elevation model (DEM) and topographic light detection and ranging (lidar)–derived DEMs to achieve realistic flood inundation. Following this calibration exercise, the flood model was used to simulate four land-use scenarios (control, predam, reservoir double, and nonirrigation). The flood hazards are quantified as downstream flood hazard zones by estimating flood depths and velocities and its impacts on risk to population using depth–velocity hazard relationships provided by U.S. Bureau of Reclamation (USBR). From the preliminary application of methodology in this study, it is evident from comparing downstream flood hazard that similar trends in PMF comparisons reported by W. Yigzaw et al. were observed. Between the control and nonirrigation, the downstream flood hazard is pronounced by −3.90% for the judgment zone and −2.40% for high hazard zones. Comparing the control and predam scenarios, these differences are amplified, ranging between 0.17% and −1.34%. While there was no change detected in the peak PMF discharges between the control and reservoir-double scenarios, it still yielded an increase in high hazard areas for the latter. Based on this preliminary bottom-up vulnerability assessment study, it is evident that what was observed in PMF comparisons by W. Yigzaw et al. is confirmed in comparisons between control versus predam and control versus nonirrigation. While there was no change detected in the peak PMF discharges between the control and reservoir-double scenarios, it still yielded a noticeable change in the total areal extents: specifically, an increase in high hazard areas for the latter. Continued studies in bottom-up vulnerability assessment of flood hazards will aid in developing suitable mitigation and adaptation options for a much needed resilient urban infrastructure.
More than a decade ago, a National Research Council (NRC) report popularized the term “valley of death” to describe the region where research on weather satellites had struggled to reach maturity for societal applications. A similar analogy can be drawn for other satellite missions, since their vantage point in space can be highly useful for some of the world's otherwise fundamentally intractable operational problems. One such intractable problem is flood forecasting for downstream nations where the f looding is transboundary. Bangladesh fits in this category by virtue of its small size and location at the sink of the mighty Ganges and Brahmaputra. There has been the claim made that satellites can be a solution for Bangladesh in achieving forecasts with lead times beyond three days. This claim has been backed up by scientific research done by numerous researchers, who have shown proof of concept of using satellite data for extending flood forecasting range. This article aims to take the reader on a journey that had its humble beginnings with this promising research and ended with making the dream of an operational system that is independently owned by the stakeholders a reality. The idea behind this article is to shed light on some of the commonly experienced but less familiar (in the academic community) roadblocks to making an operational system based on recent research survive in developing nations without long-term incubation.
More than a decade ago, a National Research Council (NRC) report popularized the term “valley of death” to describe the region where research on weather satellites had struggled to reach maturity for societal applications. A similar analogy can be drawn for other satellite missions, since their vantage point in space can be highly useful for some of the world's otherwise fundamentally intractable operational problems. One such intractable problem is flood forecasting for downstream nations where the f looding is transboundary. Bangladesh fits in this category by virtue of its small size and location at the sink of the mighty Ganges and Brahmaputra. There has been the claim made that satellites can be a solution for Bangladesh in achieving forecasts with lead times beyond three days. This claim has been backed up by scientific research done by numerous researchers, who have shown proof of concept of using satellite data for extending flood forecasting range. This article aims to take the reader on a journey that had its humble beginnings with this promising research and ended with making the dream of an operational system that is independently owned by the stakeholders a reality. The idea behind this article is to shed light on some of the commonly experienced but less familiar (in the academic community) roadblocks to making an operational system based on recent research survive in developing nations without long-term incubation.
