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The substantial challenge posed by inherent hydrological forecasting uncertainty has critical implications for the optimization of real-time reservoir operations. In response, this study introduces a stochastic framework explicitly devised to comprehensively quantify the ramifications of hydrological forecasting uncertainty, notably its temporal correlations, on the outcomes of real-time reservoir optimization and risk assessment. Furthermore, this framework seeks to delineate the pivotal influence of incorporating or neglecting these temporal dynamics on the eventual results, while concurrently elucidating the underlying mechanisms governing these discernible influences. The framework adopts a comprehensive approach to simulating hydrological forecast uncertainty through ensemble forecasts and scenario trees, employing three methods (two Monte Carlo sampling-based methods and one Gaussian copula method) to generate inflow forecast ensembles. To improve the adaptability to uncertainties in inflow forecasts, the framework incorporates a transformation of the generated ensembles into scenario trees, serving as input for a stochastic optimization model that derives the final optimal decision based on optimizing the expected value of the objective function for all scenarios. Additionally, a parallel differential evolution algorithm is proposed to solve the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China, where several numerical experiments are conducted to explore the effect of forecast uncertainty level and temporal correlation on real-time reservoir optimal operation. Results show that the temporal correlation of inflows must be considered in both inflow stochastic simulation and reservoir stochastic optimization to avoid overestimating or underestimating operational risk, potentially leading to operation failures. By examining the risk simulation surface, reservoir operators can evaluate the robustness of operational decisions and make more reliable final decisions. [ABSTRACT FROM AUTHOR]

The complex topology of river networks and the numerous factors influencing streamflow make it challenging to forecast streamflow in large river basins. Improving the accuracy of streamflow forecasting in these basins is of great importance for water resource planning and management. In this paper, considering the spatial variability of impacts of human activities on streamflow, a streamflow forecasting method with a hybrid physical process-mathematical statistic is proposed to obtain better results by realizing the complementary advantages of conventional methods and the streamflow into Hongze Lake in China is forecasted by the proposed method. Firstly, the physical process-based Soil and Water Assessment Tool (SWAT) is used to forecast the streamflow of tributaries with few water storage projects such as reservoirs in the catchment area. Secondly, considering the correlation between mainstream streamflow and tributaries streamflow as well as the correlation between mainstream streamflow and its previous streamflow, streamflow forecasting models based on the vine copula function are constructed to forecast the mainstream streamflow and its confidence intervals, where there are many water storage projects in the catchment area. Then, the total streamflow into Hongze Lake and its confidence intervals are obtained by coupling the outputs of the above two parts. Finally, statistical indicators are chosen to evaluate the forecasting effects in terms of the credibility of deterministic forecasting as well as the reliability and acuity of probabilistic forecasting. The results demonstrate that the proposed method outperforms existing SWAT and long and short-term memory (LSTM) neural network methods in terms of forecasting performance. Consequently, it presents an effective alternative for addressing complex hydrological forecasting tasks in large river basins. [ABSTRACT FROM AUTHOR]

Real-time error correction is an effective measure to improve forecast accuracy. This paper develops a real-time error correction model based on machine learning (ML) ensemble method, including the establishment of base learners, heterogeneity test, and Stacking combination. Then the Copula-based Bayesian processor of forecast (BPF) is adopted for probabilistic forecasts, which quantitatively describes the uncertainty of the correction results. Finally, the model performance is comprehensively evaluated from accuracy and stability, with deterministic metrics used to evaluate the correction effect and uncertain metrics for assessing probabilistic forecasts. The proposed model is applied to the multireservoir system in the Huai River Basin, and the results reveal the following: (1) Unlike the single ML algorithm with the performance of oscillations, the Stacking ensemble method can aggregate the advantages of multiple learners, showing robust correction effect and high adaptability across all data samples. (2) The forecasting error is significantly reduced by the Stacking method, with the average Nash–Sutcliffe efficiency coefficient ( NSE ) value increasing above 0.9, which is 4.93% higher than that of the autoregressive (AR) method. The greater superiority is also shown in the remaining evaluation metric values. Moreover, as the lead time increases, the performance of the stacking method tends to have a slower decline trend than the AR method. (3) The changes in the structure of the Stacking method have a relatively small influence on the forecast uncertainty, with all the Containing ratio ( CR ) values over 80% for different samples. The flexible combination of ML algorithms in the ensemble method will not add additional uncertainty factors and ensure the stability of the correction performance. The framework for real-time error correction and its uncertainty assessment has overall optimal correction performance with less observed data and lower computational cost required, which is promising for further improving the accuracy and reliability of real-time flood forecasting. [ABSTRACT FROM AUTHOR]

Precipitation is considered one of the most important forcing data in scientific investigations involving agriculture, water management, and climate variability. Knowledge of precipitation variation during rainy seasons is the key to the understanding of the precipitation variability under the effect of climate change. This study evaluated some rainy season features (e.g., onset, retreat, and rainy-season precipitation) over the Yellow River basin (YRB), China, and the influence of the El Niño-Southern Oscillation (ENSO). The multi-scale moving t-test was applied to capture the onset and retreat of rainy season. The possible linkage between ENSO-induced precipitation and monsoon and atmospheric circulation was also explored. From the analysis, four conclusions can be drawn: (1) Rainy season began the earliest (latest) and ended the latest (earliest) in the southern (northern) YRB, with rainy-season precipitation increasing from northwest to southeast. (2) Rainy-season precipitation showed strong correlation to SST in Niño regions. Precipitation can reach up to 20% above the average precipitation for decaying central Pacific warming (CPW) and down to 35% below the average precipitation for developing eastern Pacific warming (EPW) in most areas of the YRB. (3) Developing El Niño showed the strongest dry signals among developing ENSO and ENSO Modoki phases. Decaying El Niño and El Niño Modoki indicated overall increasing precipitation, with La Niña and La Niña Modoki during the corresponding period showing the opposite tendency. (4) Different performances of ENSO-induced precipitation attributed to the combined influence of the monsoon from the India Ocean and the atmospheric circulation in the Western North Pacific (WNP). Stronger anti-cyclone and monsoon are related to increasing rainy-season precipitation. These findings can improve the predictability of rainy season features and ENSO-induced precipitation. [ABSTRACT FROM AUTHOR]

As an essential part of the hydrological cycle, precipitation directly contributes to surface runoff and river runoff formation. Simulation on precipitation variables can effectively solve the adverse effects on hydrological assessment in some areas with insufficient or even no runoff observation. With the widespread use of various weather generators, the traditional stochastic hydrological simulation methods tend to be gradually replaced. To compare these two approaches mentioned above, this paper utilizes the precipitation records from 1958 to 2011 at nine meteorological stations within Huaihe River System in Henan Province to evaluate a stochastic hydrological simulation method, SARIMA model, and two types of weather generators, WeaGETS and LARS-WG, through the comparison of statistical characteristics regarding precipitation variables, such as mean, mean square error, extreme value and coefficient of variation. The results show that (1) on the annual scale, SARIMA has a better performance to reproduce the mean and mean square error as well as the extreme precipitation events than weather generators; (2) regarding the monthly-scale precipitation simulation, SARIMA is good at reproducing the statistical properties of monthly precipitation at the average level, while WeaGETS and LARS-WG work better in simulating monthly precipitation extremes; (3) compared with weather generators, SARIMA is highly constrained by the observed records, and among these two weather generators, WeaGETS scores higher on monthly precipitation simulation under the same sample length conditions. In conclusion, the traditional hydrological simulation method, SARIMA, and weather generators, WeaGETS and LARS-WG, have both benefits and drawbacks. The appropriate choice depends on different research backgrounds and purposes. [ABSTRACT FROM AUTHOR]

Lake water resources operation and water quality management come up with higher challenges due to climate change. The frequency and intensity of extreme hydrological events are increasing under global warming, which may directly lead to more uncertainty and complexity for hydrodynamic and water-quality conditions in large shallow lake. However, studies about effects of climate change on lake hydrodynamic and water-quality conditions are not enough. Thus, a coupled model is es-tablished to investigate the potential responses of lake water level, flow field and pollutant migra-tion to the changing climatic factors. The results imply that water flow capacity and self-purification in the Hongze Lake can be improved by west, northwest, north, south and southeast winds indi-cating wind filed change has a great effect on the hydrodynamic and water-quality conditions in large shallow lake. It is further observed that both hydrodynamics and water quality are more sensitive to rainfall change than to temperature change; compared to the effect from temperature and rainfall, the effect from wind field appear to be more pronounced. Moreover, the results verify the feasibility of coupling basin hydrological model with lake hydrodynamic and water quality model. To the best of knowledge, the coupled model should not be used until independent calibra-tions and verifications for hydrodynamics and water quality modeling, the hydrological model and the coupled model. [ABSTRACT FROM AUTHOR]

The joint optimal operation of cascade reservoir system can greatly improve the utilization of water resources. However, the complex high-dimensional and non-linear features and calculated costs often hinder the refined operation and management of reservoirs. Recently, the local parallel computing has become an effective way to alleviate the "curse of dimensionality". Current local parallel computing has hardware limitations, which is difficult to adapt to large-scale computing. This study proposes a novel parallel dynamic programming algorithm based on Spark (PDPoS) via cloud computing. The simulation experiments are carried out for a comparative analysis of the solution efficiency, influence factors and stability of cloud computing. The results are as follows: (1) The efficiency of the cloud-based PDPoS is related to some factors; the number of CPU cores is the main influencing factor, followed by the operator, and the architecture has the least influence. (2) The runtime variance of cloud computing is 2.03, indicating cloud computing has high stability. (3) Under the same configuration (i.e., CPU and memory), the runtime of cloud computing is 41.5% ~ 110.3% longer than that of physical machines. However, cloud computing has rich resources, good scalability, and good portability of online operations, which is an attractive alternative for optimal operation of large-scale reservoir system. [ABSTRACT FROM AUTHOR]

Dynamic control of flood-limited water level (FLWL) is an effective method to realize floodwater utilization, which can alleviate the contradiction between flood control and conservation during flood seasons. In this study, a method of identifying upper bound of FLWL is presented simultaneously considering pre-release for water supply in rainless period and pre-release for flood control in the early period of flood. A risk analysis framework considering continuous rainless-day forecast error and flood forecast error is established. An improved truncated Gaussian distribution to describe rainless-day forecast error is proposed, combined with normal distribution describing flood forecast error to derive the distribution of excess storage determined by the method of pre-release based on forecasts. Then risk assessment and decision making are conducted. The Xianghongdian Reservoir is selected for a case study. The results indicate that the allowable excess storage can be determined under a given acceptable risk by using this analytical method. Moreover, the excess storage decreases with the safety margin reserved for the downstream increasing under the same acceptable risk. [ABSTRACT FROM AUTHOR]

The "curse of dimensionality" is a major problem in dynamic programming (DP) algorithms for large-scale hydropower systems. This study proposes a parallel DP algorithm based on Spark (PDPoS) to alleviate the "curse of dimensionality". Parallel computing experiments are formulated by varying the number of reservoirs, the number of discrete water levels and the number of CPU cores to analyze the quality and efficiency of PDPoS. The methodologies were applied to a cascade reservoir system made up of eight reservoirs in the Yuanshui River Basin in China. The results are as follows. (1) The number of discrete water levels is the dominant factor in the solution quality, while the number of reservoirs is the dominant factor in the solving efficiency. (2) The runtime of PDPoS is markedly affected by the calculational scale (determined by the number of reservoirs and discrete water levels), and the relationship between the number of CPU cores and the runtime is triphasic with increasing calculational scale. (3) The larger the calculational scale is, the better the parallel performance (i.e., the parallel speedup and parallel efficiency). The proposed PDPoS method has strong generality, high parallel performance, and high practical value. [ABSTRACT FROM AUTHOR]

The accurate and efficient identification of effective reservoirs plays an important role in the real-time flood control operation of multireservoir systems. The redundancy of the initial criteria system affects the efficiency of identifying effective reservoirs. In this paper, a criteria reduction model based on the equivalence relation of rough set theory is proposed to solve this problem. Correspondingly, the decision rules are derived for identifying effective reservoirs. In addition, the reduction result of rough set theory is compared with the results of correlation analysis-principal component analysis (CA-PCA), vague set theory and back-propagation (BP) neural network methods. The proposed methodology is applied to the flood control system in the upper and middle reaches of the Huaihe River basin in China. The results show that the dimensionality of the initial criteria system is reduced and that the reduction result is reasonable. The proposed reduction model is an effective tool to reduce the initial criteria system for effective reservoir identification in real-time flood control operation. [ABSTRACT FROM AUTHOR]

Dynamic transboundary water resources allocation based on inflow prediction results is an important task for water resources management in river basins. This paper takes the watershed management agency as the leader and the associated area as the follower, and proposes a two-level asymmetric Nash-Harsanyi Leader-Follower game model considering inflow forecasting errors. In the proposed model, the Monte Carlo method is used to analyze the uncertainty of various stakeholder allocation results and the response regularity to the total water resource uncertainty. The quantitative relationship between the allocation results of stakeholders and the mean and standard deviation of total water resource uncertainty is subsequently established. The Huaihe River basin in China is selected as a case study. The results show the following: (1) the water allocated to the watershed management agency and three provinces has a normal distribution when the inflow forecasting error obeys the normal distribution; (2) the sum of the mean of the water allocated to stakeholders equals the mean of the forecast water resource and the sum of the standard deviations of the water allocated to stakeholders equals the standard deviation of the forecast water resource; (3) the mean and standard deviation of the allocation results have a good linear relationship with the mean and standard deviation of forecast water resource; (4) the distribution parameters of the stakeholder allocation results can be directly derived from the distribution parameters of the forecast information, thus aiding the stakeholders in making decisions and improving the practical value of the method. [ABSTRACT FROM AUTHOR]

Given the institutional limitations of multi-stakeholders, exploring the synergistic revenue from the joint reservoir operations of a multi-stakeholder multi-reservoir system requires a synergistic revenue allocation mechanism to ensure a beneficial solution for multi stakeholders. This study established a synergistic revenue allocation model using bargaining game theory under the principles of equity, rationality, and efficiency. For the maximization the Nash product of gains in the utility of stakeholders and constraints on the feasibility of allocation plans considering all the possible formations of sub-coalitions, the analytic optimal solution of the bargaining model was derived using the first-order optimality condition. The optimal revenue allocation plan meets the conditions of the equal quasi-marginal utility function among stakeholders. The methodologies were applied to a hypothetical cascade reservoir system operated by multiple stakeholders. Compared with the revenue allocation plans obtained by a proportional rule method and the Shapley value method, the results corroborate that (1) the allocation plan of the bargaining model is jointly determined by the interval of the revenue range of each reservoir and the effectiveness of the sub-coalition constraints, indicating that the allocated synergistic revenue is positively correlated with the singleton contribution and team contribution on the total revenue of the grand coalition; (2) the difference in the plans obtained by the three methods is generally determined by the difference in equity definition; and (3) the synergistic revenue allocation plan obtained from the bargaining model is the highest homogenized among all reservoirs (stakeholders), which demonstrates that the revenue of those dominated stakeholders can be improved compared with other plans. The proposed methodologies provide new insights to guide benefit share decisions in multi-stakeholder reservoirs system. [ABSTRACT FROM AUTHOR]

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans. [ABSTRACT FROM AUTHOR]

In reservoir flood control operation, candidate alternatives are generally evaluated, ranked and selected through multi-criteria decision making (MCDM) techniques, yet stochastic uncertainties both in the criteria performance values (PVs) and criteria weights (CWs) exist in the MCDM process. This paper extends the traditional MCDM methods to stochastic environments for reservoir flood control operation. The criteria PVs and CWs are treated as stochastic variables with certain probability distributions. The stochastic multicriteria acceptability analysis (SMAA) theory is introduced and the differences between conventional MCDM models and the SMAA-2 model are discussed. Methods for quantifying stochastic uncertainties in the criteria PVs are discussed and four kinds of CWs are proposed. Moreover, we define the concept of the risk of decision making errors and propose the corresponding quantitative calculation method. A three-stage MCDM procedure is recommended to guide decision makers to solve MCDM problems under stochastic environments. We apply the proposed methodology to a case study through Monte Carlo simulation to demonstrate its effectiveness and advantage. The results show that the proposed methodology can provide significant risk information for decision makers and improve the reliability of decisions for reservoir flood control operation. [ABSTRACT FROM AUTHOR]

Assessing the response of flood risk caused by climate change and social development is very important in terms of determining high risk areas in different periods as well as making disaster mitigating plans. We establish a flood risk assessment model based on geographic information system and natural disaster risk assessment theory. In order to compare the index value in different periods and spaces, we utilize the spatial and temporal standardization method to standardized index. To avoid one-sidedness caused by using one weight calibration method only, we employ the least square method to synthesize weights determine by the Analytic Hierarchy Process (AHP) method and the Entropy weight method. We adopt the observed data of the Huaihe River basin from 1960 to 2010 to assess the changing of flood risk between period I (1960-1980) and period II (1980-2010). After pre-processing the atmosphere-ocean coupled global circulation models (AOGCM) data, including bias correction and downscaling, we use the corrected data to predict the flood risk during future period III (2010-2040). The results show that high risk areas and moderate to high risk areas during period I take up 17.68 and 33.88 % of the total area of the Huaihe River basin, respectively. During period II, the high risk areas show an increasing percent change of 1.93 % and a decreasing trend in moderate to high risk areas of 3.8 %. Compared with period II, the high risk areas and the moderate to high risk areas during period III show an increasing trend of 8.02 and 0.77 %, which is the result of the combined effects of climate change and social development. The results presented here can provide useful information for decision-makers. [ABSTRACT FROM AUTHOR]

The Gejiu tin polymetallic deposit, located in the southwest margin of the South China Belt bordering on the Yangtze Block, is hosted in Triassic carbonates. This study shows that there is an indium enrichment in the ores. Indium mainly exists in sphalerite-rich massive sulfide ores with a concentration of 198.3-1570 ppm; the indium concentration in sphalerite ranges from 493.3 up to 4781 ppm. The inferred indium reserve in the Gejiu Sn deposit is over 4000 t, indicating that the Gejiu tin deposit is a giant In-bearing Sn-polymetallic deposit, similar to the Dachang and Dulong in southwest China. Sphalerite with a high Fe value (8.05-13.27 wt%) is the predominant indium carrier mineral; meanwhile chalcopyrite plays the secondary indium carrier role. EPMA mapping result shows that indium is likely distributed homogeneously and presented as a substitution for Zn in sphalerite. [ABSTRACT FROM AUTHOR]

The contents of the platinum-group elements (PGEs: Os, Ir, Ru, Rh, Pt, Pd) in the Abulangdang ultramafic intrusion have been determined using ICP-MS after nickel sulfide fire assay preconcentration. Different samples show significant differences in absolute PGE abundance. They display a pronounced negative incline in mantle-normalized patterns which are characterized by strong enrichment in IPGEs (Os, Ir, Ru) and depleting to slight enrichment in PPGEs (Rh, Pt, Pd). The characteristics of PGE distribution in the Abulangdang rocks are due to the combined action of sulfide and non-sulfide (spinel/chromite or alloy or micro-granular aggregation of metals). In comparison with the mafic-ultramafic rocks which host Ni-Cu-PGE deposits in the Emeishan Large Igneous Province (ELIP), it is assumed that the Abulangdang ultramafic intrusion may be the product of early-stage magma activity in the ELIP. [ABSTRACT FROM AUTHOR]

SHRIMP U-Pb zircon dating gives ages of 260.6 ± 3.5 Ma and 260.7 ± 5.6 Ma for serpentinised wehrlite and plag-hornblendite in the Jinbaoshan ultramafic intrusion, respectively. The results indicate that the Jinbaoshan intrusion was emplaced at ca.260 Ma and contemporaneous with the Emeishan continental flood basalts (ECFB), similar to other maficultramafic intrusions of the Emeishan large igneous province (LIP). The new ages provide a geochronological constraint on the origin of the Jinbaoshan ultramafic intrusion. It confirms that the Jinbaoshan ultramafic intrusion belongs to the Emeishan LIP that formed at ca. 260 Ma. [ABSTRACT FROM AUTHOR]