Showing papers by "Faisal Hossain published in 2016"

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

Abstract: The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 125% SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

Assessment of the weather research and forecasting model generalized parameterization schemes for advancement of precipitation forecasting in monsoon-driven river basins

Abstract: Some of the world’s largest and flood-prone river basins experience a seasonal flood regime driven by the monsoon weather system. Highly populated river basins with extensive rain-fed agricultural productivity such as the Ganges, Indus, Brahmaputra, Irrawaddy, and Mekong are examples of monsoon-driven river basins. It is therefore appropriate to investigate how precipitation forecasts from numerical models can advance flood forecasting in these basins. In this study, the Weather Research and Forecasting model was used to evaluate downscaling of coarse-resolution global precipitation forecasts from a numerical weather prediction model. Sensitivity studies were conducted using the TOPSIS analysis to identify the likely best set of microphysics and cumulus parameterization schemes, and spatial resolution from a total set of 15 combinations. This identified best set can pinpoint specific parameterizations needing further development to advance flood forecasting in monsoon-dominated regimes. It was found that the Betts-Miller-Janjic cumulus parameterization scheme with WRF Single-Moment 5-class, WRF Single-Moment 6-class, and Thompson microphysics schemes exhibited the most skill in the Ganges-Brahmaputra-Meghna basins. Finer spatial resolution (3 km) without cumulus parameterization schemes did not yield significant improvements. The short-listed set of the likely best microphysics-cumulus parameterization configurations was found to also hold true for the Indus basin. The lesson learned from this study is that a common set of model parameterization and spatial resolution exists for monsoon-driven seasonal flood regimes at least in South Asian river basins.

Benchmarking wide swath altimetry‐based river discharge estimation algorithms for the Ganges river system

Abstract: The objective of this study is to compare the effectiveness of three algorithms that estimate discharge from remotely sensed observables (river width, water surface height, and water surface slope) in anticipation of the forthcoming NASA/CNES Surface Water and Ocean Topography (SWOT) mission. SWOT promises to provide these measurements simultaneously, and the river discharge algorithms included here are designed to work with these data. Two algorithms were built around Manning's equation, the Metropolis Manning (MetroMan) method, and the Mean Flow and Geomorphology (MFG) method, and one approach uses hydraulic geometry to estimate discharge, the at-many-stations hydraulic geometry (AMHG) method. A well-calibrated and ground-truthed hydrodynamic model of the Ganges river system (HEC-RAS) was used as reference for three rivers from the Ganges River Delta: the main stem of Ganges, the Arial-Khan, and the Mohananda Rivers. The high seasonal variability of these rivers due to the Monsoon presented a unique opportunity to thoroughly assess the discharge algorithms in light of typical monsoon regime rivers. It was found that the MFG method provides the most accurate discharge estimations in most cases, with an average relative root-mean-squared error (RRMSE) across all three reaches of 35.5%. It is followed closely by the Metropolis Manning algorithm, with an average RRMSE of 51.5%. However, the MFG method's reliance on knowledge of prior river discharge limits its application on ungauged rivers. In terms of input data requirement at ungauged regions with no prior records, the Metropolis Manning algorithm provides a more practical alternative over a region that is lacking in historical observations as the algorithm requires less ancillary data. The AMHG algorithm, while requiring the least prior river data, provided the least accurate discharge measurements with an average wet and dry season RRMSE of 79.8% and 119.1%, respectively, across all rivers studied. This poor performance is directly traced to poor estimation of AMHG via a remotely sensed proxy, and results improve commensurate with MFG and MetroMan when prior AMHG information is given to the method. Therefore, we cannot recommend use of AMHG without inclusion of this prior information, at least for the studied rivers. The dry season discharge (within-bank flow) was captured well by all methods, while the wet season (floodplain flow) appeared more challenging. The picture that emerges from this study is that a multialgorithm approach may be appropriate during flood inundation periods in Ganges Delta.

Satellite Gravimetric Estimation of Groundwater Storage Variations Over Indus Basin in Pakistan

Abstract: Like other agrarian countries, Pakistan is now heavily dependent on its groundwater resources to meet the irrigated agricultural water demand. Groundwater has emerged as a major source with more than 60% contribution in total water supplies. In the absence of groundwater regulation, the uneven and overexploitation of groundwater resource in Indus Basin has caused several problems of water table decline, groundwater mining, and deterioration of groundwater quality. This study evaluates the potential of Gravity Recovery and Climate Experiment Satellite (GRACE)-based estimation of changes in groundwater storage (GWS) as a cost-effective approach for groundwater monitoring and policy recommendations for sustainable water management in the Indus basin. The GRACE monthly gravity anomalies from 2003 to 2010 were analyzed as total water storage (TWS) variations. The variable infiltration capacity hydrological model-generated soil moisture and surface runoff were used for the separation of TWS into GWS anomalies. The GRACE-based GWS anomalies are found to favorably agree with trends inferred from in situ piezometric data. A general depletion trend is observed in Upper Indus Plain (UIP) where groundwater is found to be declining at a mean rate of about 13.5 mm per year in equivalent height of water during 2003–2010. A total loss of about 11.82 km3 per year fresh groundwater stock is inferred for UIP. Based on TWS variations and ground knowledge, the two southern river plains, Bari and Rechna are found to be under threat of extensive groundwater depletion. GRACE TWS data were also able to pick up signals from the large-scale flooding events observed in 2010 and 2014. These flooding events played a significant role in the replenishment of the groundwater system in Indus Basin. Our study indicates that the GRACE-based estimation of GWS changes is skillful enough to provide monthly updates on the trend of the GWS changes for resource managers and policy makers of Indus basin.

Advancing river modelling in ungauged basins using satellite remote sensing: the case of the Ganges–Brahmaputra–Meghna basin

Abstract: River modelling is the process of simulating the water flow dynamics of a stream network against time-varying boundary conditions. Such river models are often an important component of any flood forecasting system that forecasts river levels in flood-prone regions. However, large river basins such as the Ganges, Brahmaputra, and Meghna (GBM), Indus, Irrawaddy, Salween, Mekong, and Niger in the developing world are mostly ungauged as they lack the necessary and routine in situ measurements of river bed depth/slope, bathymetry (river cross section), flood plain mapping, and boundary condition flows for setting up of a river model. For such basins, proxy approaches relying primarily on remote-sensing data from space platforms may be the only way to overcome the lack of in situ data. In this study, we share our experience in setting up the one-dimensional River Analysis System model of the Hydrologic Engineering Center over the stream network of the GBM basin. Good-quality in situ measurements of rive...

Water sustainability of large cities in the United States from the perspectives of population increase, anthropogenic activities, and climate change

Abstract: Balancing water demand and supply with depleting sources and increasing demand needs a multi-dimensional approach given the pace at which the world is urbanizing. This study selected the contiguous United States (CONUS), 42 specific cities and their river basins to determine: Which basins and cities are more susceptible to increased water shortage? Population, water use, hydrologic model and climate model data from CMIP5 were used. Representative Concentration Pathways scenarios: RCP2.6, RCP4.5, RCP6, and RCP8.5 represented different climate change conditions. Period 1 (1950–2004) showed that more areas are affected by monthly runoff and streamflow than annual averages. In some cases, significant decreasing trends in water availability were observed during the summer (June–July–August) and spring (March–April–May) seasons. The second period (2005–2049) indicated an annual increasing trend (more water available) with higher intensity for the RCP6 scenario. Summer and spring showed areas of decreasing trend (less water available) for RCP4.5 and RCP6. Period 3 (2050–2099) exhibited a decreasing trend for the RCP2.6 (Western and Central CONUS, Great Lakes, and FL), RCP4.5 (Southwest CONUS), RCP6 (Western United States), and Central CONUS (RCP8.5). The Mississippi River has a mixed sensitivity to future climate change. The Central Valley of California, Los Angeles, Phoenix, and Tucson can face further challenges as the Colorado River becomes depleted. Seawater desalination and inter-basin water transfer can be considered in future and present policies and structural developments. The West, Southeastern Coast, and FL may consider desalination, while the West and Central CONUS can use the Mississippi for inter-basin transfer.

Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls

Abstract: Growing population and increased demand for water is causing an increase in dam and reservoir construction in developing nations. When rivers cross international boundaries, the downstream stakeholders often have little knowledge of upstream reservoir operation practices. Satellite remote sensing in the form of radar altimetry and multisensor precipitation products can be used as a practical way to provide downstream stakeholders with the fundamentally elusive upstream information on reservoir outflow needed to make important and proactive water management decisions. This study uses a mass balance approach of three hydrologic controls to estimate reservoir outflow from satellite data at monthly and annual time scales: precipitation-induced inflow, evaporation, and reservoir storage change. Furthermore, this study explores the importance of each of these hydrologic controls to the accuracy of outflow estimation. The hydrologic controls found to be unimportant could potentially be neglected from similar future studies. Two reservoirs were examined in contrasting regions of the world, the Hungry Horse Reservoir in a mountainous region in northwest U.S. and the Kaptai Reservoir in a low-lying, forested region of Bangladesh. It was found that this mass balance method estimated the annual outflow of both reservoirs with reasonable skill. The estimation of monthly outflow from both reservoirs was however less accurate. The Kaptai basin exhibited a shift in basin behavior resulting in variable accuracy across the 9 year study period. Monthly outflow estimation from Hungry Horse Reservoir was compounded by snow accumulation and melt processes, reflected by relatively low accuracy in summer and fall, when snow processes control runoff. Furthermore, it was found that the important hydrologic controls for reservoir outflow estimation at the monthly time scale differs between the two reservoirs, with precipitation-induced inflow being the most important control for the Kaptai Reservoir and storage change being the most important for Hungry Horse Reservoir.

Revisiting extreme storms of the past 100 years for future safety of large water management infrastructures

Abstract: Historical extreme storm events are widely used to make Probable Maximum Precipitation (PMP) estimates, which form the cornerstone of large water management infrastructure safety. Past studies suggest that extreme precipitation processes can be sensitive to land surface feedback and the planetary warming trend, which makes the future safety of large infrastructures questionable given the projected changes in land cover and temperature in the coming decades. In this study, a numerical modeling framework was employed to reconstruct 10 extreme storms over CONUS that occurred during the past 100 years, which are used by the engineering profession for PMP estimation for large infrastructures such as dams. Results show that the correlation in daily rainfall for such reconstruction can range between 0.4 and 0.7, while the correlation for maximum 3-day accumulation (a standard period used in infrastructure design) is always above 0.5 for post-1948 storms. This suggests that current numerical modeling and reanalysis data allow us to reconstruct big storms after 1948 with acceptable accuracy. For storms prior to 1948, however, reconstruction of storms shows inconsistency with observations. Our study indicates that numerical modeling and data may not have advanced to a sufficient level to understand how such old storms (pre-1948) may behave in future warming and land cover conditions. However, the infrastructure community can certainly rely on the use of model reconstructed extreme storms of the 1948-present period to reassess safety of our large water infrastructures under assumed changes in temperature and land cover.

Are General Circulation Models Ready for Operational Streamflow Forecasting for Water Management in the Ganges and Brahmaputra River Basins

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 o...

Land Use and Land Cover Impact on Probable Maximum Flood and Sedimentation for Artificial Reservoirs: Case Study in the Western United States

Abstract: Unanticipated peak inflows that can exceed the inflow design flood (IDF) for spillways and result in possible storage loss in reservoirs from increased sedimentation rates lead to a greater risk for downstream floods. Probable maximum precipitation (PMP) and probable maximum flood (PMF) are mostly used to determine IDF. Any possible change of PMP and PMF resulting from future land use and land cover (LULC) change therefore requires a methodical investigation. However, the consequential sediment yield resulting from altered precipitation and flow patterns into the reservoir has not been addressed in literature. Thus, this study aims to determine the combined impact of a modified PMP on PMF and sediment yield for an artificial reservoir. The Owyhee Dam of the Owyhee River watershed (ORW) in Oregon is selected as a case study area for understanding the impact of LULC change on PMF and sedimentation rates. Variable infiltration capacity (VIC) is used for simulating streamflow (PMF) and the revised uni...

A Global Capacity Building Vision for Societal Applications of Earth Observing Systems and Data: Key Questions and Recommendations

Abstract: Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

Maximizing Hydropower Generation with Observations and Numerical Modeling of the Atmosphere

Abstract: The ongoing drought in California seems to indicate that water managers are now paying greater attention to the use of numerical models of the atmosphere for short-term (7–10 days) weather forecasts. At the time of writing this article, a bill to Congress was being formulated that essentially aims to make the rule curves for large dams more adaptive through the use of numerical models for weather forecast. The use of such models is expected to reduce wastage of impounded water for dam managers and allow more flexibility in water storage and release during periods of anomalous/ off-season big droughts or floods. The purpose of this opinion article is to shed light on the current state of hydropower generation in the United States and discuss how the use of such numerical models of the atmosphere can also maximize energy production while conserving water or protecting against floods. As a clean and renewable energy source, hydropower has been extensively exploited by human beings for over 100 years. According to the 2014 Hydropower Market Report released by the Department of Energy (DOE), there are 2,198 active hydropower plants with a total operational capacity of 79.64 gigawatts (GW) in the United States. Hydropower accounts approximately 7% of the total power generated in the United States (Uria-Martinez et al. 2015). Unfortunately, hydropower generation capacity has stagnated the last two decades since the 1990s owing to lower economic growth (Hall et al. 2003), stricter environmental regulations, a stagnant energy market, and recent breakthroughs in the shale gas and oil industries (such as fracking). Nevertheless, hydropower remains the single largest source of renewable energy because of its relatively low-cost and sustainable characteristics. Compared to other renewable energy sources, hydropower has several unique advantages (USBR 2005). For example, a dam, which is normally considered an expensive investment to build, has a long service life spanning at least 50–100 years. Hydropower remains a more stable and durable source compared to wind or solar power, which are vulnerable to changing or unpredictable weather conditions. Hydropower production is relatively easier to ramp up or scale back depending on the transient nature of power demand. There are also no greenhouse gas emissions as byproducts during hydropower generation. A few U.S. states that are gifted with abundant water resources and topography have already harnessed hydropower as a clean and reliable electricity generation source, such as Washington, Oregon, and California (Fig. 1). Collectively, these three states have the largest installed hydropower capacity, which is equivalent to half of the total installed capacity across the United States. Among these three states, Washington has the largest share with approximately 30.4% of total hydropower generation in the United States, which also amounts to 70% of total electricity generation in the state of Washington. Oregon and California contribute 13.5% and 6.3%, respectively to the national grid in hydropower sector. Compared to the Northwest’s large amount of installed capacity, the Northeast has the most hydropower facilities, which typically are aged and small-capacity. The states that rely more on locally available hydropower apparently have lower electricity price compared to other states that have limited access to hydropower generating resources (UriaMartinez et al. 2015). Because building a new power plant is expensive, the low prices are usually in the states that have extensive power facilities where owners have already paid off the capital cost. The three northwestern states (Washington, Oregon, Idaho) are clear examples of cheap electricity pricing due to abundant hydropower resources.

Earth Science Satellite Applications

Abstract: Mesoamerica is a term used sometimes in cultural context, but in this article we are using it to name the land bridge between North and South America, made up of the SouthernMexico States (Chiapas, Quintana Roo, Yucatán, Campeche y Tabasco), Guatemala, Belize, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama. With an area of approximately 755,000 square kilometers, it is one of the most heterogeneous regions of the world in terms of elevation, land forms, climate, natural ecosystems and human populations. In the general context given, the potential of Earth observation (EO) to assist the management of natural resources, biodiversity and disasters in the region is clear. In this chapter, we discuss the current state of EO applications and future perspectives related to land-use change, ecosystem dynamics and biodiversity and solid-earth hazards. We hope that this contribution can identify current and future challenges related to obtaining the biggest societal benefit of EO and suggest actions to take advantage of anticipated innovations and data availability.