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Increasing studies have highlighted that the remarkable inherent uncertainty in design flood hydrograph (DFH) can potentially undermine flood management decisions. In order to quantitatively trace the propagation of DFH uncertainty in reservoir flood control system, we propose a novel methodological framework including three corn parts. First, a copula-based DFH estimation model integrating Bayes’ theorem is presented to estimate DFH under model parameter uncertainty. Second, we perform an optimal reservoir operation model for flood control (OROMFC) with uncertain DFH as input variable to derive reservoir flood control operations (i.e. output variable). Third, an information theory-based model is designed to trace the DFH uncertainty propagation in reservoir flood control system. A reservoir flood control system in the Han River basin in China is selected as case study. Related results indicate that uncertainty in reservoir flood control operations reduces in comparison with the remarkable uncertainty in DFH due to the performance of the OROMFC. Specifically, uncertainty in reservoir flood control operations in periods close to peak flow is much smaller compared with that in other periods. The phenomenon further highlights the importance of reservoir flood control operations during periods prior to peak flow. Additionally, we find that uncertainty in flood peak of DFH is the dominant factor affecting reservoir flood control operations compared with that in flood volume of DFH. Moreover, we explore the impact of reservoir flood control capacity on reservoir flood operations in the context of DFH uncertainty. An interesting linear expression is found and fitted for identifying design flood events inducing reservoir overtopping under specific reservoir flood control capacity.

Data Availability Statement: Data and code used in this study are available at https://doi.org/10.4211/hs.48c6a00bb6c449afbe33b67250cd1ae7 . Copyright © 2022 The Authors. The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi-category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory-based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi-category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, National Centers for Environmental Prediction, and United Kingdom Meteorological Office were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (a) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (b) the proposed approach is resistant to the extreme bias; (c) the proposed approach needs a careful choice of bin width; and (d) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts. National Natural Science Foundation of China. Grant Numbers: 51779030, 52079015; Beijing Science and Technology Planning Project. Grant Number: Z191100006919002; University of Glasgow CoSS Strategic Research Fund. Grant Number: PO20028963; China Scholarship Council. Grant Number: 201906060080.

Input error is one of the main sources of uncertainty in hydrological models. It mainly comes from the uncertainty of precipitation data, which is caused by inaccurate measurement at the point scale and imperfect representation at the regional scale. The structural error of the hydrological model is dependent on the input, and the uncertainty interaction between the model input and structural will increase the cumulative error of the hydrological process. Therefore, the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes by setting nine input levels of rain gauge stations using three hydrological models (i.e., HyMod, XAJ and HBV). The variance decomposition method based on subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty. The results show that different rain gauge station input levels and hydrological models dynamically affected the hydrological simulation due to an uneven spatiotemporal distribution of precipitation. Moreover, the simulation accuracy was poor at low flow but better at high flow. Increasing the number of rainfall stations input under a certain threshold could significantly improve the hydrological simulation accuracy. In addition, the contributions of the uncertainties of the rain gauge station input levels and its interaction with the hydrological model to runoff were significantly enhanced in the flood season, but the contribution of the hydrological model uncertainty was still dominant. The results of this study can provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment.

The mismatch between the spatiotemporal distribution patterns of water resources and recent regional socioeconomic development threatens social stability and sustainable development, particularly in river basins affected by water scarcity. Therefore, it is necessary to adapt a water resource regulation model to address these new challenges. Based on the theory of Maslow's hierarchy of needs and the Gini coefficient, in this study, a water resource equilibrium regulation model is constructed from the demand side and applied to the Yellow River Basin (YRB). Different from fairness, equilibrium is a stable state under the influence of diversity of interests and other factors. The results show that the model realizes the spatial and temporal equilibrium regulation of water resources. The water supply assurance rates of ecological and agricultural water users increase significantly to 94.1% and 74.3%, respectively. In particular, in dry years, the rigid demands of water users can be well met, and irreversible losses can be avoided. Compared with Scheme 1987, the proportion of water supply for upstream provinces increases to 42.34%, which will contribute to the balanced development of the basin. This model has the potential to be used in a wide range of applications, providing not only theoretical and technical support for the adjustment of Scheme 1987 in the YRB but also a reference for other governments and water resource management institutions around the world.

Vegetation degradation is the key cause of land desertification in arid areas. Water stress is one of the most critical factors leading to vegetation degradation. The water needed for vegetation growth is inseparable from the water cycle processes. It is a new scope to reveal the vegetation water demand mechanisms from the water cycle processes. Water cycle processes in arid inland basins can be conceptually separated as RFA (runoff formation area) and RCA (runoff consumption area). In this study, both the water demand mechanisms of natural vegetation and farmland were discovered by creatively constructing the vegetation water demand route model. The TRB (Tarim River Basin), a typical arid inland basin system that RFA is separated from RCA, is considered as the study area. The tendency and relevance of water cycle factors and NDVI were detected. The dominant factors of vegetation growth were identified. According to the interaction causality of water cycle factors and vegetation, the PLS-SEM (partial least squares structural equation models) were constructed in RFA and RCA. Results displayed that SMroot (root-zone soil moisture), groundwater and precipitation were the dominant water sources for natural vegetation in RFA. The water demand for natural vegetation in RCA mainly came from SMroot and that for farmland mainly came from SMsurf (surface soil moisture). New findings showed that blue and green water circulations were more active in RFA than in RCA. Natural vegetation had better adaptability and resilience to water shortages compared with farmland. The higher effect of vegetation on AET (actual evapotranspiration) denoted the better growth status. It is contributed to the rational utilization of water resources in arid basins.

Hydrological modeling has provided key insights into the mechanisms of model state, such as the watershed partitioning level and optimization algorithm, and their impacts on the hydrological process, but the uncertainty of this impact is poorly understood. To this end, in this study, the effects of the watershed partitioning level and optimization algorithm for hydrological simulation uncertainty were assessed based on the semi-distributed TOPMODEL model, i.e., six watershed partitioning levels and three intelligent global optimization algorithms were used in the source region of the Yellow River. Meanwhile, the uncertainty contribution of the individual and interaction of the watershed partitioning levels and optimization algorithms on the hydrological process were dynamically evaluated using the variance decomposition method based on subsampling. Results showed that the impacts of the watershed partitioning level and optimization algorithm on the runoff simulation were particularly obvious for different characteristic periods. In the flood period, the optimization algorithm was the dominant factor affecting the runoff simulation uncertainty, with the proportion of up to 0.50, whereas the contribution of the watershed partitioning level was only 0.22. In the non-flood period, they contributed substantially to the uncertainty of the runoff simulation, accounting for about 0.30. Moreover, the interactions between the watershed partitioning level and optimization algorithm had a strong influence throughout the year, especially in the non-flood period, which may be because the hydrological model amplifies the output error and increases the interaction effect. Generally, the results shed important insight into reducing the uncertainty of the runoff simulation in future research.

Hydrologic characteristics including extreme-flow events in many rivers around the world have been altered due to the climate change and human activities. Most available research concerns about the extreme event trend, frequency and duration in a single river rather than the synchronicity or coincidence probability among different tributaries. Accurately knowing the coincidence probability of hydrologic extreme-flow events is vital for better water resources allocation and project design. Joint distribution constructed by the copulas function is a widely used method to conduct this issue. However, studies conducted by the copulas function are mostly carried out in a bivariate environment and ignore the nonstationary. These may not comprehensively reflect the hydrologic characteristics under the changing environment. This paper analyzes the related hydrologic low-flow changes considering the nonstationary under the changing environment in the Wei River Basin, China, where the climate tends to be drier. These analyses are obtained by using trivariate copulas function and Kendall’s return period method to derive the joint distribution of hydrologic low-flow in a seasonal time scale. The results mainly show that the coincidence probabilities of low-flow among three tributaries of the Wei River in four seasons after the change point are basically higher than those before the change point. It may indicate that the low-flow negative impacts in the whole Wei River Basin in four seasons may be intensified (which needs to be paid more attention) under the changing environment.

Global warming has caused severe regional water security risks, and from the theory of the hydrological cycle, the daily extreme temperature could also bring an impact on the streamflow volume, which could be even more important than the average temperature. Therefore, based on the level of the maximum or minimum temperature warming scenarios, a variety of meteorological datasets were selected to assess the asymmetric effect of increased daily extreme temperature on the streamflow from a multi-scale perspective by using the Variable Infiltration Capacity (VIC) model. Model simulations indicate that the streamflow experiences more significant changes in response to the maximum temperature than in response to the minimum temperature, and the relationships of streamflow with both the maximum and minimum temperatures show an upwards parabolic response function, but the response function varies with the type of warming. Additionally, the seasonal and monthly duration curves results show that the increases in both the maximum and minimum temperatures demonstrate a similar response that leads the proportion of the flood period streamflow to be increased ( Tmin : 0.16–0.53%/°C; Tmax : 0.11–0.51%/°C). When the minimum temperature increases, the higher the original temperature in the region, the greater the proportion of the flood period streamflow increases. However, when the maximum temperature increases, the opposite effect occurs.

Drought disasters restrict economic and social development. Understanding the evolution of the spatiotemporal characteristics of drought plays a vital role in basin risk response and management. Complex empirical orthogonal function analysis is widely used in meteorological research and is an important tool for studying the spatial-temporal distribution structure of arid fields. This research aims to take the Yellow River Basin (YRB) as an example, separate the temporal and spatial distribution structure of drought based on the CEOF method, and explore the abrupt changes, trends, and periodic characteristics of different temporal and spatial distribution patterns. In addition, the cross-wavelet analysis reveals the lagging correlation relationship between the distribution pattern of the arid field in the basin and the Southern Oscillation factor. Results indicated that: (1) there are three typical spatial and temporal distribution patterns (full basin type, southeast-northwest reversed type and east-west reversed type) of the interannual scale drought in the YRB; (2) interannual drought evolution have obvious spatial propagation characteristics. (3) There are correlations between the three types and the Southern Oscillation on the temporal-frequency scale, and there is a time lag of 1-2a with the Southern Oscillation factor.

Compared to the usual environment, the potential momentary emotional benefits of exposure to street-level urban green spaces (UGS) in the unusual environment have not received much academic attention. This study applies an online randomized control trial (RCT) with 299 potential tourists who have never visited Xi’an and proposes a regression model with mixed effects to scrutinize the momentary emotional effects of three scales (i.e., small, medium and large) and street types (i.e., traffic lanes, commercial pedestrian streets and culture and leisure walking streets). The results identify the possibility of causality between street-level UGS and tourists’ momentary emotional experiences and indicate that tourists have better momentary emotional experiences when urban streets are intervened with large-scale green vegetation. The positive magnitude of the effect varies in all three types of streets and scales of intervention, while the walking streets with typical cultural attractions, have a larger impact relative to those with daily commute elements. These research results can provide guidance for UGS planning and the green design of walking streets in tourism.

The climate system has both non-stationary and nonlinear characteristics. The non-stationary processes such as climate change and human activities have brought severe challenges to life, agriculture and ecology in arid areas. It is of great practical significance to study the spatiotemporal variation and non-stationary characteristics of drought in arid region for adopting the countermeasures to adapt to climate change. The traditional trend change and variation analysis method was used to study the drought change characteristics, which could not effectively identify the clustering phenomenon of drought and the spatial and temporal variation of drought in different time intervals. Therefore, the method of breaks for additive seasonal and trend (BFAST) was introduced to determine the non-stationary characteristics and spatial variation of drought. In this study, a typical inland arid area was taken as the study area, and the drought was quantified by standardized precipitation evapotranspiration index (SPEI). The results show that: (1) the study area was prone to light drought and moderate drought, and the duration and intensity of drought events increased significantly after 1996; (2) The spatial distribution of drought frequency and drought intensity in different seasons were significantly different; (3) The results of BFAST analysis showed that there were four discontinuities in SPEI12 time series, which indicated that the drought in the study area had obvious non-stationary characteristics. Through the analysis of the breakpoint and drought change characteristics in the study area, the occurrence time of the breakpoint in the northern region had a certain periodicity, and the climate was in a state of aridity. The results of this study have important guiding significance for further understanding the non-stationary characteristics of drought in inland arid areas, and will promote the management of water resources and the protection of ecological environment.

Climate change and human development may lead to a serious crisis in food security in China, especially in areas with both water shortages and large grain production. Thus, the quantitative evaluation of future food security risk considering water scarcity is increasingly important. Here, we combined water scarcity and crop production data under different scenarios of representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs), incorporating demographic, food habit and water resource factors, to develop a new framework for measuring China's food security risk. The results show that the water scarcity and crop production-water crisis (CPWC) of China would both be aggravated during the 21st century. In particular, northern China might face more serious water scarcity than southern China and has a higher contribution rate to the national crop production-water crisis. Food scarcity in China might occur at some point in the 21st century under all SSP scenarios, except SSP1 (sustainability development pathway). The next 40 years could be the most critical period for ensuring China's food security. Moreover, by comparing the RCP2.6 and RCP6.0 scenarios, we also find that higher food production does not represent lower food security risk. The food security risk of the RCP26 scenario with higher food production was significantly higher than that of the RCP6.0 scenario at the same SSP because higher grain production comes from water shortage areas. From the perspective of societal development scenarios, SSP1 provided better results for both the risk of food security and water security in the 21st century. Our findings therefore provide useful information for a comprehensive understanding of long-term food security and water security of China.

Parametric Budyko method has been widely used to assess water availability under changing environment. Its single parameter (w) controlling the shape of the Budyko curve, is of critical importance to water availability assessment (WAA). Here, the water availability is defined as the ratio of evapotranspiration to precipitation in the context of Budyko hypothesis. As an unobserved variable, w is generally estimated through incorporating the long-term water-balance model and Budyko hypothesis, i.e. estimating w as a function of precipitation, runoff, and potential evapotranspiration over a long-term time series of these variables. Inevitably, the estimated value of w is subject to uncertainty resulting from hydro-meteorological records with limited size. Further, this uncertainty affects WAA in context of Budyko hypothesis. The uncertainty and its propagation issue is often overlooked in past research. In this paper, we develop a bootstrap-based algorithm to reveal aforementioned uncertainty. Moreover, a partial derivative-based sensitivity analysis is performed for understanding the propagation of w uncertainty on WAA. Three catchments located in the Yellow River Basin, China, are used as case study sites. Results indicate that there exists remarkable uncertainty in parameter w, which varies with the data length. The impact of such uncertainty on WAA increases quickly under the humid condition, thereafter reaches a peak under the mild condition, and decreases slowly under the arid condition. The reason behind this phenomenon is the largest sensitivity of WAA to parameter w under the mild climate condition. Moreover, a general analytical expression for determining the critical value of climate condition which maximizes the impact of parameter w uncertainty on WAA is derived in this paper. Results of this study can enhance the reliability and robustness of Budyko-based models.

The high randomness, intermittency and uncontrollability of wind power make large-scale wind power generation present large challenges for integration into a power grid. In this paper, the principles of hydro-wind compensating operation are explored, aiming at improving the power quality of wind power and promoting the integration of wind power into the power grid. Based on the principles of hydro-wind compensating operation, the calculation method of the hydropower compensation capacity for wind power is derived. Then, an optimizing operation model of a hydro-thermal-wind hybrid power system based on the hydro-wind power compensating principle is proposed to minimize the carbon dioxide emissions and obtain optimal operation scheduling of the hybrid power system by taking advantage of hydro-wind compensation and the peak regulation capacity of hydropower. In this model, the baseload and nonbaseload output emission rates are adopted to estimate the carbon dioxide emissions of different power output processes and the emission reduction benefits. Moreover, three scenarios of the operation mode are proposed to demonstrate the applicability of the hydro-wind power compensating principle in practice. The Northwest Power Grid in China is selected as a case study to verify the effectiveness of the proposed model compared to historical operating data. The results indicate that the hydro-wind power compensating principle plays a crucial role in hydro-thermal-wind combined optimal operation. Additionally, the proposed model can improve the wind power generation and reduce the carbon dioxide emissions of the power grid. In particular, the carbon dioxide emissions can be reduced by approximately 1696*104 t each year in the Northwest Power Grid. This study provides an approach to schedule optimal operation plans for power grids with large-scale wind power and provides a valuable reference for the large-scale utilization of other kinds of renewable energy worldwide.

A flood hazard is one of the most common and destructive natural disasters. Flood risk reduction with non-engineering measures has become the primary goal for flood management. This paper proposes an approach to improve the flood control operation for cascade reservoirs by minimizing flood control risk. A framework of reservoir flood control operation coupled with risk assessment (RFCORA) and the corresponding RFCORA model are proposed to reduce risk. The model contains three sub-modules. A real-time reservoir flood control operation simulation module is developed, in which a flood discharge control chart is used to determine the flood magnitude, and flood release is obtained by operation rules. Entropy-weighted fuzzy comprehensive evaluation method is applied to assess the risk level in the flood risk assessment module. Finally, the flood release is updated according to the developed outflow adjustment optimization module aimed at minimizing flood control risk by combing future inflow information. In addition, another model without outflow adjustment optimization is set to be a comparative experiment. The water system of the Upper Yellow River is selected as a case study to verify the model. The results show that the framework and RFCORA model developed in this paper can decrease the time duration in the highest risk level without increasing the maximum water level and maximum outflow of reservoirs. The approach proposed in this paper based on flood control operation and flood risk assessment can be extended to flood mitigation in other water systems in similar situations.

A river basin usually provides water for multiagent. Imperfect coordination mechanisms among these agents, however, usually lead to serious water shortage, pollution, and ecological degradation. Therefore, it is vital to conduct research on allocating different kinds of water resources among multiagent including the water quantity agent, water quality agent and environment agent within a river basin. This paper proposes an optimization-simulation method that takes into consideration engineering measures, including water transfer projects, reservoirs, canal heads, and pump stations; multi-water resources such as local surface water, transferred water, groundwater, and reused sewage; and multiagent for remitting serious water problems. In the proposed optimization-simulation method, the groundwater and reused sewage are first allocated according to the water resources allocation simulation module (Module 1). Then, local surface and transferred water are allocated based on the water resources allocation optimization module (Module 2), which comprises water quantity, water quality and environment agents’ objectives based on synergism. Finally, the instream water quality is simulated according to the water quality simulation module (Module 3). Results show that, compared to the current situation, the water quantity and environment agent guarantee rates can satisfy the design requirements in the planning year of 2020. The regulated instream water quality is also improved; although, it still remains relatively poor. Water quality in the downstream is worse than that in the upstream. In addition, the water quality shows a positive correlation with the instream streamflow. These results certify the proposed optimization-simulation method can provide support for efficacious multiagent water resources allocation.

Efficient utilization of water resources in hydropower station operation is an important part of mitigating water and energy scarcity. Exploring efficient multi-objective optimization algorithms and studying the trade-off between water and energy have become the primary goal of multi-objective hydropower station optimal operation (MOHSOO). In this paper, a new improved multi-objective cuckoo search (IMOCS) algorithm is proposed to overcome the shortcomings of MOCS. Specifically, a population initialization strategy based on constraint transformation and the individual constraints and group constraints technique (ICGC) and a dynamic adaptive probability (DAP) are used to improve the search efficiency and the quality of solutions, respectively. A flock search strategy (FSS) is proposed to greatly speed up the convergence and improve the quality of the non-dominated solutions. In addition, the MOCS and NSGA-II are presented as a comparison to test the performance of IMOCS as well as three hybrids of MOCS combined with these strategies. An MOHSOO model of Xiaolangdi and Xixiayuan cascade hydropower stations in the lower Yellow River is built to verify the effectiveness of these algorithms together with five benchmark problems. The results show that IMOCS performs better than other algorithms in convergence speed, convergence property, and diversity of solutions. For the Xiaolangdi hydropower station, there is a strong competitive relationship between power generation and water supply from September to next February, which severely restricts the power generation of the hydropower station.

Due to climate change and human activities, drought frequency and its corresponding impact have intensified in many regions across China. Reservoirs are key in regulating streamflow and mitigating a drought's impact. Therefore, it is vital to analyze their drought mitigation ability. In this study, the drought adaptation capacity and drought resistance capacity of the reservoir are selected as two drought mitigation ability metrics to measure the effectiveness of a reservoir when facing a drought. The drought adaptation capacity represents the ability of a reservoir to prevent and mitigate damage caused by drought. It is assessed from two viewpoints, one reflecting the reservoir scale and layout reasonability, while the second reflecting the engineering operation and management. The drought resistance capacity refers to the ability of a reservoir to meet the water demand under a certain level of a drought (i.e. drought joint return period, which is defined later), and is calculated based on the reservoir's water supply and water demand. These two drought mitigation ability metrics can be applied not only for a single reservoir, but also for a group of reservoirs. This study applies these two metrics in the downstream of the Yellow River for a group of reservoirs. The results show that: 1) the ability of reservoirs in the downstream of the Yellow River for prevention and mitigation of damage caused by drought rates in the normal grade; and 2) the reservoirs can better resist the drought whose joint return period is smaller than 2 years while the drought resistance capacity is weak when the joint return period is over 6 years. Overall, the basin's drought adaptation and resistance capacity need to be improved. Management and operation of current water conservancy projects still need to be enhanced.

Hydropower plants operation may change river flow, thereby degrading the stability of river ecosystems. The primary purpose of this paper is to compute the comprehensive ecological water demand and establish ecological operation models to quantitatively analyze the interactions between power generation and degree of ecological flow satisfaction under different operation modes. The comprehensive ecological water demand takes the river base flow, ecological flow process and ecological water demands of fish habitat during the spawning period into account. The driest monthly streamflow under a 90% frequency method, range of variability approach and two-dimensional depth-averaged finite element model were adopted to obtain the ecological flow. In addition, to study the impacts of the ecological operation of hydropower plants on power generation, three optimal operation models including a maximum power generation model (Model-I), a minimum ecological change model (Model-II) and a multi-objective optimization operation model (Model-III), are established. In model-II, the average annual power generation of the cascade hydropower plants decreased by 10.61% compared with Model-I, and meanwhile the degree of ecological change reduced by 75.41%, which means, the reduction of power generation by 35.66 × 108 kWh could lead to a reduction of 11.4% on the degree of ecological degradation. Afterwards, the multi-objective problem of economic and ecological benefits in model-III was solved by the application of NSGA-II. Among them, scheme 3 is recommended for Model III, and its power generation and degree of ecological change are 311.69 × 108 kWh and 6.64%, respectively. In general, mutual restrictions and conflicts between power generation and ecological demand are inevitable, but they can be optimized through multi-objective ecological operation models to fetch the coordinated development of economy and ecology.