Fig 1
Colorado River Mid-term Modeling System (CRMMS) configuration of the Upper Colorado River Basin, where dots represent approximate forecast locations, numbered from 1 to 12 and described in the top left table. Triangles represent reservoirs (only Lake Powell and Lake Mead are shown).
Contexts in source publication
Context 1
... provided monthly historical unregulated streamflow for the input sites used in CRMMS. As described in Baker (2019), there are 12 Upper CRB forecast locations in CRMMS (Fig. 1). Monthly flows were provided for 1980 to 2019. Unregulated flows were back calculated to simulate flows as if there were no reservoir regulation or depletion upstream of the forecast point, except for three transbasin diversions that are explicitly modeled in CRMMS (see Lukas et al. 2020 for details). Unregulated flows differ from ...
Context 2
... 2018;Milly and Dunne 2020;Hoerling et al. 2019), it is useful to look specifically at the relationship between climate and streamflow in the Upper CRB. Fig. 2 shows the relationship between the 5-year mean precipitation and temperature versus the naturalized streamflow at Lees Ferry, which is just downstream of the outflow for Lake Powell (Fig. 1). Naturalized streamflow is back calculated to have the effect of both upstream reservoir regulation and human induced depletions removed from the observed streamflow record. As expected, the 5-year average annual precipitation has the strongest correlation with 5-year average streamflow (r ¼ 0.95). Although this is a very strong ...
Context 3
... streamflow ensembles are generated for all 12 forecast points, results are shown for the most downstream point, the Lake Powell unregulated inflows (forecast location 12 in Fig. 1). The evaluation on the 5-year average is designated "1-5", i.e., for each trace, the monthly streamflows over the 60 months from the run date are averaged before they are evaluated. Evaluation metrics are also calculated on several additional multi-year and individual year forecasts. Multi-years include the aforementioned, 1-5, as ...
Context 4
... 3-and 4-year leads) WeighESP has lower (better) median RMSE values than ESP. For example, at a 49-month lead time the median RMSE improves by ∼1.1 m (3.5 ft; Table 5). Results using blocks from only 2000 to 2017 show similar, slightly better results, with the median RMSEs performing marginally better for WeighESP for leads >13 months (Fig. 9). Fig. 10 reveals similar results for the Lake Mead EOCY pool elevations for the 1981-2017 projection blocks. Again, lead month 1 shows low RMSE values, and ESP modestly outperforms WeighESP for 1-, 13-, and 25-month leads. However, for lead times of 37-and 49-months, WeighESP outperforms ESP, yielding reductions in hindcast median RMSE of ...
Context 5
... However, for lead times of 37-and 49-months, WeighESP outperforms ESP, yielding reductions in hindcast median RMSE of -10.8% and -5.8%, respectively (Table 6). Results for 5-year projection blocks from the more recent period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) are shown in Fig. 11, which show slightly different results. Here, WeighESP is similar or outperforms ESP at earlier leads (13-and 25-months) but performs worse at longer leads. The differences between the results for Lake Powell and Mead could have to do with the fact that the two reservoirs operate in coordination with one another: Lake Mead inflows are ...
Similar publications
The main objective of most hydropower systems is to pursue the efficient use of water resources and maximize economic benefits. At the same time the protection of ecological environment should not be neglected. In this study, a coordination model of power generation and ecological flow for the operation of cascade hydropower system was first establ...
Compared to conventional operating rules (CORs) for multiple reservoirs in a basin, joint flood control operation can reduce flood loss in downstream cities. The CORs adopted for most reservoirs ignore among–basin coordination, leading to incomplete consideration of the problem of the compensation effect between the reservoir flood control storage...
Stochastic Dynamic Programming (SDP) has been used to solve reservoir management problems in different parts of the world; specifically in Mexico, it has been used to obtain operating policies that optimize a given objective function. By simulating the operation of the system with a comprehensive model, the behavior of such policies can be accurate...
Reservoirs are susceptible to interference from inter-basin water transfer projects intended to relieve serious water shortages. The Central Route of the South-to-North Water Division Project in China has altered the hydrological conditions and water storage status of the terminal reservoir, the Miyun Reservoir, thereby affecting the flood control...
Citations
... The following equations (Eqs. (1)- (6)) were used to validate the model's fitting ability: determination coefficient (R 2 ) [26], root mean square errors (RMSE) [27], mean absolute errors (MAE) [28], mean absolute percentage errors were (MAPE) [29], nash efficiency coefficient (NSE) [30], and willmott index (WI) [31]. The specific formula is: ...
Auto-ignition temperature (AIT) is one of the crucial exponents in the design of fire and explosion safety measures. Therefore, in this study, quantitative structure-property relationship approach was used to predict the AIT of ternary hybrid liquids based on molecular structure information. The optimal molecular descriptors were calculated and filtered using Mordred software. Twelve mixing rules were proposed for calculating molecular descriptors of mixtures. A prediction model for the AIT value of binary liquid mixtures was developed, validated and evaluated using a back propagation neural network (BPNN) and a one-dimensional convolutional neural network (1DCNN). The relative contribution and positive and negative correlations between individual molecular descriptors and AIT in the model were interpreted using the shapley additive explanations method. The results show that BPNN and 1DCNN models using mixing rule 1 have the best fitting ability, stability and prediction ability. The determination coefficient of the BPNN and 1DCNN models in the training set were 0.996 and 0.992, the root mean square errors were 3.613 °C and 5.284 °C, the mean absolute errors were 2.483 °C and 4.144 °C, the nash efficiency coefficient was 0.996 and 0.992, respectively, the willmott index was 0.999 and 0.998. and the values of the top three molecular descriptors of relative contribution, SssCH2, SsOH and SsCH3, were negatively correlated with the AIT values. The BPNN and 1DCNN models provide an accurate and reliable method for predicting ternary mixing liquid AIT.
... More precisely, an accurate streamflow forecasting can be integrated into global water, agricultural, and industry program, and a continuous progress in predicting and forecasting streamflow at several time steps has attracted a great deal of attention over the world, and the use of measured and available weather and hydrological variables, i.e., precipitation (P), air temperature (Ta), evapotranspiration (EP) among others, has considerably advanced in recent years [6]. In a recently conducted investigation for the Colorado River basin in the United States (USA), Towler et al. [7] demonstrated that streamflow is highly sensitive to the variation of Ta and P. Several kinds of approaches can be used for accurately predicting streamflow, ranging from physically based, conceptual, statistical, and stochastic, to data-driven artificial intelligence (AI) models [8,9]. During the last few years, the application of AI models for streamflow has significantly increased and there has been an explosion in the number vector regression (SVR) hybridized using Salp swarm algorithm (SVR-SSA) for forecasting monthly streamflow in Baitarani river basin, Odisha, India. ...
In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction
and estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the M5
model tree (M5Tree), artificial neural networks (ANN), radial basis function neural networks
(RBFNN), and multivariate adaptive regression spline (MARS) using data of two stations from Sweden.
The accuracy of the methods is assessed based on root mean square errors (RMSE), mean absolute
errors (MAE), mean absolute percentage errors (MAPE), and Nash Sutcliffe Efficiency (NSE)
and the methods are graphically compared using time variation and scatter graphs. The benchmark
results show that the RM5Tree offers better accuracy in predicting daily streamflow compared to
other four models by respectively improving the accuracy of M5Tree with respect to RMSE, MAE,
MAPE, and NSE by 26.5, 17.9, 5.9, and 10.9%. The RM5Tree also acts better than the M5Tree, ANN,
RBFNN, and MARS in estimating streamflow of downstream station using only upstream data.
There is an increasing need for skillful runoff season (i.e., spring) streamflow forecasts that extend beyond a 12-month lead time for water resources management, especially under multiyear droughts and particularly in basins with highly variable streamflow, large storage capacity, proclivity to droughts, and many competing water users such as in the Colorado River Basin (CRB). Ensemble streamflow prediction (ESP) is a probabilistic prediction method widely used in hydrology, including at the National Oceanic and Atmospheric Administration (NOAA) Colorado Basin River Forecasting Center (CBRFC) to forecast flows that the Bureau of Reclamation uses in their water resources operational decision models. However, it tends toward climatology at 5-month and longer lead times, causing decreased skill, particularly in forecasts critical for management decisions. We developed a modeling approach for seasonal streamflow forecasts using a machine learning technique, random forest (RF), for runoff season flows (April 1-July 31 total) at the important gauge of Lees Ferry, Arizona, on the CRB. The model predictors include antecedent basin conditions, large-scale climate teleconnections, climate model projections of temperature and precipitation, and the mean ESP forecast from CBRFC. The RF model is fitted and validated separately for lead times spanning 0 to 18 months over the period 1983-2017. The performance of the RF model forecasts and CBRFC ESP forecasts are separately assessed against observed streamflows in a cross validation mode. Forecast performance was evaluated using metrics including relative bias, root mean square error, ranked probability skill score, and reliability. Measured by ranked probability skill score, RF outperforms a clima-tological benchmark at all lead times and outperforms CBRFC's ESP hindcasts for lead times spanning 6 to 18 months. For the 6-to 18-month lead times, the RF ensemble median had a root mean square error that was between ∼410-and ∼620-thousand acre-feet lower than that of the ESP ensemble median (i.e., RF reduced ensemble median RMSE by −9% to −12% relative to ESP). Reliability was comparable between RF and ESP. More skillful long-lead cross-validated forecasts using machine learning methods show promise for their use in real time forecasts and better informed and efficient water resources management; however, further testing in various decision models is needed to examine RF forecasts' downstream impacts on key water resources metrics like robustness, reliability, and vulnerability.
The Bureau of Reclamation (Reclamation) plays a central management role in the Colorado River Basin (CRB), with an increasing focus on meeting the needs of stakeholders during the current drought. One aspect of this role involves generating five‐year projections of reservoir operating conditions in the federal multi‐reservoir system. These projections are the basis for estimating the probability of shortage conditions, which are relied on by stakeholders, and are particularly important during drought. Currently, Ensemble Streamflow Prediction (ESP) forecasts drive Reclamation's Colorado River Mid‐term Modeling System to produce probabilistic reservoir projections to be used in risk‐based analysis and decision support for the first two years of the outlook period. The lack of significant forecast skill beyond the first year motivates interest in alternative forecasting approaches. The CRB Operational Prediction Testbed was created to provide a quantitative and consistent framework for assessing the skill of streamflow forecasts and their impact on associated reservoir system projections. Reservoir system projections are evaluated by analyzing Lakes Powell and Mead operations, including projected pool elevation and operating tiers. In an initial application of this testbed, ESP forecasts were compared to experimental streamflow forecasts to assess their skill impact on two‐year reservoir projections, which are critical information for managing drought.
Decadal (∼10‐year)‐scale flow projections in the Colorado River Basin (CRB) are increasingly important for water resources management and planning of its reservoir system. Physical models, ensemble streamflow prediction (ESP), do not have skill beyond interannual time scales. However, Global Climate Models have good skill in projecting decadal temperatures. This, combined with the sensitivity of CRB flows to temperature from recent studies, motivate the research question, can skill in decadal temperature projections be translated to operationally skillful flow projections and consequently, water resources management? To explore this, we used temperature projections from the Community Earth System Model‐Decadal Prediction Large Ensemble (CESM‐DPLE) along with past basin runoff efficiency as covariates in a Random Forest (RF) method to project ensembles of multiyear mean flow at the key aggregate gauge of Lees Ferry, Arizona. RF streamflow projections outperformed both ESP and climatology in a 1982–2017 hindcast, as measured by ranked probability skill score. The projections were disaggregated to monthly and subbasin scales to drive the Colorado River Mid‐term Modeling System (CRMMS) to generate ensembles of water management variables. The projections of pool elevations in Lakes Powell and Mead, the two largest U.S. reservoirs that are critical for water resources management in the basin, were found to reduce the hindcast median root mean square error by up to −20% and −30% at lead times of 48 and 60 months, respectively, relative to projections generated from ESP. This suggests opportunities for enhancing water resources management in the CRB and potentially elsewhere.
