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Faisal Hossain
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Xiaodong Chen and Faisal Hossain
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Hisham Eldardiry and Faisal Hossain

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Transboundary river basins are experiencing extensive dam development that challenges future water management, especially for downstream nations. Thus, adapting the operation of existing reservoirs is indispensable to cope with alterations in flow regime. We proposed a Forecast-Based Adaptive Reservoir Operation (FARO) framework to evaluate the use of long-term climate forecasts in improving real-time reservoir operations. The FARO approach was applied to the High Aswan Dam (HAD) in the Nile River basin. Monthly precipitation and temperature forecasts at up to 12 months of lead time are used from a suite of eight North American Multi-Model Ensemble (NMME) models. The value of NMME-based forecasts to reservoir operations was compared with perfect and climatology-based forecasts over an optimization horizon of 10 years from 1993 to 2002. Our results indicated that the forecast horizon for HAD operation ranges between 5 and 12 months lead time at low- and high-demand scenarios, respectively, beyond which the forecast information no longer improves the release decision. The forecast value to HAD operation is more pronounced in the months following the flooding season (October–December). During these months, the skill of streamflow forecasts using NMME forcings outperforms the climatology-based forecasts. When considering the operation of upstream Grand Ethiopian Renaissance Dam (GERD), using streamflow forecasts minimally helps to maintain current target objectives of HAD operation and therefore result in higher operation costs as opposed to current conditions without GERD. Our study underlined the importance of deriving a new adaptive operating policy for HAD to improve the value of available forecasts while considering GERD filling and operation phases.

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Hisham Eldardiry and Faisal Hossain

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Challenges to manage and secure a sustainable water supply are expected to become more acute in Egypt as the lowermost riparian country of the Nile basin with the construction of new transboundary water infrastructures in Ethiopia and Sudan. To understand the impact of such transboundary water projects on Egypt, it is first necessary to develop a modeling tool that can simulate potential flow and reservoir scenarios inside Egypt without requiring in situ hydrologic or transboundary dam data that are typically unavailable. This study presents the water management value of a modeling framework to predict the current and future reservoir operating rules in the lower Nile basin using satellite Earth observations and hydrologic models. The platform comprises the Variable Infiltration Capacity (VIC) hydrologic model driven by high spatial and temporal resolution of satellite observations. Reservoir storage change is estimated using altimeter and visible imagery of lake area for Lake Nasser and then applied to infer reservoir operation for High Aswan Dam (HAD). The modeling framework based on satellite observations yielded a simulated streamflow at the outlet for Blue Nile basin (BNB) with a Nash–Sutcliffe efficiency of 0.68 with a correlation and RMSE of 0.94 and 1095 m3 s−1, respectively. Storage and outflow discharge of HAD were estimated for the period of 1998–2002 within 1.4% accuracy (0.076 km3 month−1) when compared with published reports. Because BNB controls the lion’s share of the variability to HAD inflow inside Egypt, the proposed modeling framework is appropriate for policy-makers to understand the implications of transboundary projects on the future water security of Egypt.

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Xiaodong Chen and Faisal Hossain

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Extreme precipitation events bring huge societal and economic loss around the world every year, and they have undergone spatially heterogeneous changes in the past half-century. They are fundamental to probable maximum precipitation (PMP) estimation in engineering practice, making it important to understand how extreme storm magnitudes are related to key meteorological conditions. However, there is currently a lack of information that can potentially inform the engineering profession on the controlling factors for PMP estimation. In this study, the authors present a statistical analysis of the relationship between extreme 3-day precipitation and atmospheric instability, moisture availability, and large-scale convergence over the continental United States (CONUS). The analysis is conducted using the North America Regional Reanalysis (NARR) and ECMWF ERA-Interim reanalysis data and a high-resolution regional climate simulation. While extreme 3-day precipitation events across the CONUS are mostly related to vertical velocity and moisture availability, those in the southwestern U.S. mountain regions are also controlled by atmospheric instability. Vertical velocity and relative humidity have domainwide impacts, while no significant relationship is found between extreme precipitation and air temperature. Such patterns are stable over different seasons and extreme precipitation events of various durations between 1 and 3 days. These analyses can directly help in configuring the numerical models for PMP estimation at a given location for a given storm.

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Abebe Gebregiorgis and Faisal Hossain

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In this study, the authors ask the question: Can a more superior precipitation product be developed by merging individual products according to their a priori hydrologic predictability? The performance of three widely used high-resolution satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) real-time precipitation product 3B42 (3B42-RT), the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)] was evaluated in terms streamflow predictability for the entire Mississippi River basin using the Variable Infiltration Capacity (VIC) macroscale hydrologic model. A merging concept that was not based on a single universal merging formula for the whole basin but rather used a “localized” (grid box by grid box) approach for merging precipitation products was then explored. In this merging technique, the a priori (historical) hydrologic predictive skill of each product for each grid box was first identified. Prior to streamflow routing, the corresponding accuracy of the spatially distributed simulations of soil moisture and runoff were used as proxy for weights in merging the precipitation products. It was found that the merged product derived on the basis of runoff predictability outperformed its counterpart merged product derived on the basis of soil moisture simulation. Results indicate that such a grid box by grid box merging concept that leverages a priori information on predictability of individual products has the potential to yield a more superior product for streamflow prediction than what the individual products can deliver for hydrologic prediction.

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Faisal Hossain and George J. Huffman

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This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more insightful information on the quality of the satellite estimates. In addition, hydrologists can use the framework to develop a space–time error model for simulating stochastic realizations of satellite estimates for quantification of the implication on hydrologic simulation uncertainty. First, the authors conceptualize the error metrics in three general dimensions: 1) spatial (how does the error vary in space?); 2) retrieval (how “off” is each rainfall estimate from the true value over rainy areas?); and 3) temporal (how does the error vary in time?). They suggest formulations for error metrics specific to each dimension, in addition to ones that are already widely used by the community. They then investigate the behavior of these metrics as a function of spatial scale ranging from 0.04° to 1.0° for the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) geostationary infrared-based algorithm. It is observed that moving to finer space–time scales for satellite rainfall estimation requires explicitly probabilistic measures that are mathematically amenable to space–time stochastic simulation of satellite rainfall data. The probability of detection of rain as a function of ground validation rainfall magnitude is found to be most sensitive to scale followed by the correlation length for detection of rain. Conventional metrics such as the correlation coefficient, frequency bias, false alarm ratio, and equitable threat score are found to be modestly sensitive to scales smaller than 0.24° latitude/longitude. Error metrics that account for an algorithm’s ability to capture rainfall intermittency as a function of space appear useful in identifying the useful spatial scales of application for the hydrologist. It is shown that metrics evolving from the proposed conceptual framework can identify seasonal and regional differences in reliability of four global satellite rainfall products over the United States more clearly than conventional metrics. The proposed framework for building such error metrics can lay a foundation for better interaction between the data-producing community and hydrologists in shaping the new generation of satellite-based, high-resolution rainfall products, including those being developed for the planned Global Precipitation Measurement (GPM) mission.

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Wondmagegn Yigzaw, Faisal Hossain, and Alfred Kalyanapu

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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.

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Ling Tang, Faisal Hossain, and George J. Huffman

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Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time–space scales over the whole domain of the dataset. Here, “uncertainty” refers to the general concept of the “deviation” of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer.

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Abel T. Woldemichael, Faisal Hossain, and Roger Pielke Sr.

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Understanding the impact of postdam climate feedbacks, resulting from land use/land cover (LULC) variability, on modification of extreme precipitation (EP) remains a challenge for a twenty-first-century approach to dam design and operation. In this study, the Regional Atmospheric Modeling System (RAMS, version 6.0) was used, involving a number of predefined LULC scenarios to address the important question regarding dams and their impoundments: How sensitive are the hydroclimatology and terrain features of a region in modulating the postdam response of climate feedbacks to EP? The study region covered the Owyhee Dam/Reservoir on the Owyhee River watershed (ORW), located in eastern Oregon. A systematic perturbation of the relative humidity in the initial and boundary condition of the model was carried out to simulate EP. Among the different LULC scenarios used in the simulation over the ORW, irrigation expansion in the postdam era resulted in an increase in EP up to 6% in the 72-h precipitation total. The contribution of the reservoir on EP added 8% to the 72-h total when compared to the predam LULC conditions. To address the science question, a previously completed investigation on the Folsom Dam [American River watershed (ARW)] in California was compared with the ORW findings on the basis of contrasting differences in hydroclimatology and terrain features. The results indicate that the postdam LULC change scenarios impact EP of ORW (Owyhee Dam) much greater than the EP of the ARW (Folsom Dam) because of its semiarid climate and flat terrain. EP was less sensitive to LULC changes on the windward side of the mountainous terrain of ARW as compared to the leeward side of the flat terrain of ORW.

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