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Xiaochun Wang, Yi Chao, C. K. Shum, Yuchan Yi, and Hok Sum Fok

Abstract

Two methods to assess ocean tide models, the current method and the total discrepancy method, are compared from the perspective of their relationship to the root-mean-square difference of tidal sea surface height (total discrepancy). These two methods are identically the same when there is only one spatial location involved. When there is more than one spatial location involved, the current method is the root-mean-square difference of total discrepancy at each location, and the total discrepancy method is the averaged total discrepancy. The result from the current method is always larger than or equal to that from the total discrepancy method. Monte Carlo simulation indicates that the difference between their results increases with increasing spatial variability of total discrepancy. Both of these two methods are then used to compare the two tide models of the Ocean Surface Topography Mission (OSTM)/Jason-2. The discrepancy of these two models as measured by the total discrepancy method decreases monotonically from around 11.4 to 2.2 cm with depth increasing from 50 to 1000 m. In contrast, the discrepancy measured by the current method varies from 21.6 to 2.9 cm. Though the discrepancy measured by the current method decreases with increasing depth in general, there are abrupt increases at several depth ranges. These increases are associated with large spatial variability of total discrepancy and their physical explanation is elusive. Because the total discrepancy method is consistent with the root-mean-square difference of tidal sea surface height and its interpretation is straightforward, its usage is suggested.

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Alfred J. Kalyanapu, A. K. M. Azad Hossain, Jinwoo Kim, Wondmagegn Yigzaw, Faisal Hossain, and C. K. Shum

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.

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Safat Sikder, Xiaodong Chen, Faisal Hossain, Jason B. Roberts, Franklin Robertson, C. K. Shum, and Francis J. Turk

Abstract

This study asks the question of whether GCMs are ready to be operationalized for streamflow forecasting in South Asian river basins, and if so, at what temporal scales and for which water management decisions are they likely to be relevant? The authors focused on the Ganges, Brahmaputra, and Meghna basins for which there is a gridded hydrologic model calibrated for the 2002–10 period. The North American Multimodel Ensemble (NMME) suite of eight GCM hindcasts was applied to generate precipitation forecasts for each month of the 1982–2012 (30 year) period at up to 6 months of lead time, which were then downscaled according to the bias-corrected statistical downscaling (BCSD) procedure to daily time steps. A global retrospective forcing dataset was used for this downscaling procedure. The study clearly revealed that a regionally consistent forcing for BCSD, which is currently unavailable for the region, is one of the primary conditions to realize reasonable skill in streamflow forecasting. In terms of relative RMSE (normalized by reference flow obtained from the global retrospective forcings used in downscaling), streamflow forecast uncertainty (RMSE) was found to be 38%–50% at monthly scale and 22%–35% at seasonal (3 monthly) scale. The Ganges River (regulated) experienced higher uncertainty than the Brahmaputra River (unregulated). In terms of anomaly correlation coefficient (ACC), the streamflow forecasting at seasonal (3 monthly) scale was found to have less uncertainty (>0.3) than at monthly scale (<0.25). The forecast skill in the Brahmaputra basin showed more improvement when the time horizon was aggregated from monthly to seasonal than the Ganges basin. Finally, the skill assessment for the individual seasons revealed that the flow forecasting using NMME data had less uncertainty during monsoon season (July–September) in the Brahmaputra basin and in postmonsoon season (October–December) in the Ganges basin. Overall, the study indicated that GCMs can have value for management decisions only at seasonal or annual water balance applications at best if appropriate historical forcings are used in downscaling. The take-home message of this study is that GCMs are not yet ready for prime-time operationalization for a wide variety of multiscale water management decisions for the Ganges and Brahmaputra River basins.

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A. H. M. Siddique-E-Akbor, Faisal Hossain, Safat Sikder, C. K. Shum, Steven Tseng, Yuchan Yi, F. J. Turk, and Ashutosh Limaye

Abstract

The Ganges–Brahmaputra–Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework.

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Faisal Hossain, A. H. M. Siddique-E-Akbor, Wondmagegn Yigzaw, Sardar Shah-Newaz, Monowar Hossain, Liton Chandra Mazumder, Tanvir Ahmed, C. K. Shum, Hyongki Lee, Sylvain Biancamaria, Francis J. Turk, and Ashutosh Limaye

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.

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