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Journal ArticleDOI: 10.1080/07900627.2020.1745159

Forecast-informed reservoir operations to guide hydropower and agriculture allocations in the Blue Nile basin, Ethiopia

04 Mar 2021-International Journal of Water Resources Development (Routledge)-Vol. 37, Iss: 2, pp 208-233
Abstract: Predictive hydroclimate information, coupled with reservoir system models, offers the potential to mitigate climate variability risks. Prior methodologies rely on sub-seasonal, dynamic/synthetic fo...

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Topics: Hydropower (52%)
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10 results found


Open access
Hannah Cloke, Florian Pappenberger1Institutions (1)
01 May 2010-
Abstract: Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

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Topics: Flood forecasting (68%), Flood myth (56%)

45 Citations


Open accessJournal ArticleDOI: 10.3390/W12082237
08 Aug 2020-Water
Abstract: Due to renewed interest in hydropower dams in the face of climate change, it is important to assess dam operations and management in combination with downstream impacts on rivers in (semi-)arid environments. In this study, the impacts of the Tekeze hydropower dam on downstream hydrology and river morphology were investigated, including impacts under normal and extreme reservoir operation conditions. Field observations, in-depth interviews, repeat terrestrial photographs, multi-year high-resolution satellite images, daily reservoir water levels and data on hourly to daily energy production were collected and studied. The results show that high flows (Q5) have declined (with factor 5), low flows (Q95) have increased (with factor 27), seasonal flow patterns have smoothened, river beds have incised (up to 4 m) and locally aggraded near tributary confluences. The active river bed has narrowed by 31%, which was accelerated by the gradual emergence of Tamarix nilotica and fruit plantations. A new post-dam equilibrium had been reached until it was disrupted by the 2018 emergency release, caused by reservoir management and above-normal reservoir inflow, and causing extensive erosion and agricultural losses downstream. Increased floodplain occupation for irrigated agriculture consequently provides an additional argument for reservoir operation optimization to avoid future risks for riparian communities.

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Topics: River morphology (55%), Tributary (53%), Hydropower (53%) ... show more

7 Citations


Open access
01 Dec 2012-
Abstract: Abstract. Users of hydrologic predictions need reliable, quantitative forecast information, including estimates of uncertainty, for lead times ranging from less than an hour during flash flooding events to more than a year for long-term water management. To meet this need, operational agencies are developing hydrological ensemble forecast techniques to account for sources of uncertainty such as future precipitation, initial hydrological conditions, and hydrological model limitations including uncertain model parameters. Research advances in areas such as hydrologic modeling, data assimilation, ensemble prediction, and forecast verification need to be incorporated into operational forecasting systems to assure that the state-of-the-art products are reaching the forecast user community. The Hydrologic Ensemble Prediction EXperiment (HEPEX) has been formed to develop and demonstrate new hydrologic forecasting technologies, and to facilitate the implementation of beneficial technologies into the operational environment.

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3 Citations


Journal ArticleDOI: 10.1016/J.JHYDROL.2021.126794
Yuxue Guo1, Yue-Ping Xu1, Jingkai Xie1, Hao Chen1  +2 moreInstitutions (1)
Abstract: Stochastic nature of streamflow poses significant challenges in attaining a reliable forecasting model. In general, variational mode decomposition (VMD) can improve the forecast performance but easily expose the sub-signals to boundary effect. Furthermore, one model is not able to adapt all properties of different sub-series. Accordingly, we have two aims in this study. One is to propose an adaptive weight combined forecasting model to improve the middle and long-term streamflow forecast skill. It adapts the boundary effect in such a way that its inputs come from decomposition during calibration sets, and outputs are extracted from decomposition during the entire series. The other one is to link system performance improvement, i.e., the forecast skill, to the forecast value to address the gap in methodologies appropriate for data-limited locations (only hydrological time series collected). Four artificial intelligence-based models coupled with adaptive appendant and parameter optimization are developed as hybrid adaptive (HA) for forecasting each sub-signal decomposed by VMD in the proposed forecast model. The multi-objective grey wolf optimization (MOGWO) algorithm is then employed to combine individual forecasts based on performance-based weighting strategies and provide the Pareto-optimal ensemble forecasts. The proposed model is applied to forecast streamflow 1 to 6 months ahead of two stations in the Yellow River, China, and the results show that the ensemble forecasts can increase the values of Nash-Sutcliffe efficiency coefficient by 0.10 ~ 7.96% and reducing the values of root mean squared error by 1.08 ~ 32.11% compared to the HA model. The relationship between the forecast skill and its value can be strongly affected by decision-makers priorities, but the relative improvement in hydropower generation obtained by the compromised forecasts going from 0.02% to 3.39% indicates that improved forecasts are potentially valuable for informing strategic decisions.

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Topics: Forecast skill (67%)

1 Citations


Open accessPosted ContentDOI: 10.5194/HESS-25-5951-2021
Yuxue Guo1, Xinting Yu1, Yue-Ping Xu1, Hao Chen1  +2 moreInstitutions (1)
Abstract: . Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

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1 Citations


References
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90 results found


Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

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26,349 Citations


Journal ArticleDOI: 10.1007/BF00122574
Amos Tversky1, Daniel Kahneman2Institutions (2)
Abstract: We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting functions. A review of the experimental evidence and the results of a new experiment confirm a distinctive fourfold pattern of risk attitudes: risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability. Expected utility theory reigned for several decades as the dominant normative and descriptive model of decision making under uncertainty, but it has come under serious question in recent years. There is now general agreement that the theory does not provide an adequate description of individual choice: a substantial body of evidence shows that decision makers systematically violate its basic tenets. Many alternative models have been proposed in response to this empirical challenge (for reviews, see Camerer, 1989; Fishburn, 1988; Machina, 1987). Some time ago we presented a model of choice, called prospect theory, which explained the major violations of expected utility theory in choices between risky prospects with a small number of outcomes (Kahneman and Tversky, 1979; Tversky and Kahneman, 1986). The key elements of this theory are 1) a value function that is concave for gains, convex for losses, and steeper for losses than for gains,

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Topics: Prospect theory (74%), Cumulative prospect theory (72%), Loss aversion (66%) ... show more

12,066 Citations


Journal ArticleDOI: 10.1177/001316446002000116
Abstract: more stodgy and less exciting application of computers to psychological problems. Let me warn you about how I am going to talk today. I have not conducted a survey of available computer programs for factor analytic computations, nor have I done an analysis of the problems of the application of computers to factor analysis in any way that could be considered scientific. I am saying that I shall ask you to listen to my opinions about the applications of computers to factor

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8,718 Citations


Open accessJournal ArticleDOI: 10.1002/JOC.3711
Ian Harris1, Philip Jones1, Philip Jones2, Timothy J. Osborn1  +1 moreInstitutions (2)
Abstract: This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society

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4,840 Citations


Open accessJournal ArticleDOI: 10.1038/SDATA.2015.66
Chris Funk1, Pete Peterson2, Martin Landsfeld2, Diego Pedreros1  +7 moreInstitutions (3)
08 Dec 2015-Scientific Data
Abstract: The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

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Topics: PERSIANN (60%), Precipitation (50%)

1,762 Citations