•We incorporate ecological requirement into multipurpose reservoir operating rule curves.•We explore how multipurpose reservoir operating rule curves adapt to future climate change.•Ecological supply ...water operation will alleviate negative effect of dam on river ecosystem.
Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves excluding ecological requirement are generally derived from observed or synthetic flows and have rarely been determined by future flows under climate change. This paper develops an integrated adaptive optimization model (IAOM) for derivation of multipurpose reservoir operating rule curves including ecological operating rule curve under future climate change. Steps in the proposed IAOM include: (1) weather generator module to generate feasible future climate conditions, (2) VIC model as the hydrological simulation module to generate streamflows from those future weather conditions, and (3) multipurpose reservoir optimization module to determine the optimal reservoir operations to deal with climate change. China’s Danjiangkou reservoir in Han River basin is selected for a case study. The results demonstrate that the IAOM provides optimal multipurpose reservoir operating rule curves that reflect the hydrologic characteristics of future climate change. Ecological supply water operation will alleviate negative effect of dam on river ecosystem without reducing conservation benefits and flood control standard. Therefore, they can consult with reservoir administrators if it is useful results for operations.
► We compare two statistical downscaling methods (SD) combined with two GCMs and two hydrological models. ► We find runoff simulations vary greatly driven by rainfall simulations from same climate ...scenario and different SD. ► SDSM performs better than SSVM in simulating rainfall, but simulated runoff driven by SDSM’ simulation worse than by SSVM’s. ► We verified Nash–Sutcliffe efficiency can be used as a key indicator to evaluate the downscaling rainfall’s performance.
In this study a rigorous evaluation and comparison of the difference in water balance simulations resulted from using different downscaling techniques, GCMs and hydrological models is performed in upper Hanjiang basin in China. The study consists of the following steps: (1) the NCEP/NCAR reanalysis data for the period 1961–2000 are used to calibrate and validate the statistical downscaling techniques, i.e. SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model); (2) the A2 emission scenarios from CGCM3 and HadCM3 for the same period are used as input to the statistical downscaling models; and (3) the downscaled local scale climate scenarios are then used as the input to the Xin-anjiang and HBV hydrological models. The results show that: (1) for the same GCM, the simulated runoffs vary greatly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models; (2) although most widely used statistics in the literature for evaluation of statistical downscaling methods show SDSM has better performance than SSVM in downscaling rainfall except the Nash–Sutcliffe efficiency (NSC) and root mean square error-observations standard deviation ratio (RSR), the runoff simulation efficiency driven by SDSM rainfall is far lower than by SSVM; and (3) by comparing different statistics in rainfall and runoff simulation, it can be concluded that NSC and RSR between simulated and observed rainfall can be used as key statistics to evaluate the statistical downscaling models’ performance when downscaled precipitation scenarios are used as input for hydrological models.
The hydrological cycle, affected by climate change and rapid urbanization in recent decades, has been altered to some extent and further poses great challenges to three key factors of water resources ...allocation (i.e., efficiency, equity and sustainability). However, previous studies usually focused on one or two aspects without considering their underlying interconnections, which are insufficient for interaction cognition between hydrology and social systems. This study aims at reinforcing water management by considering all factors simultaneously. The efficiency represents the total economic interests of domesticity, industry and agriculture sectors, and the Gini coefficient is introduced to measure the allocation equity. A multi-objective water resources allocation model was developed for efficiency and equity optimization, with sustainability (the river ecological flow) as a constraint. The Non-dominated sorting genetic algorithm II (NSGA-II) was employed to derive the Pareto front of such a water resources allocation system, which enabled decision-makers to make a scientific and practical policy in water resources planning and management. The proposed model was demonstrated in the middle and lower Han River basin, China. The results indicate that the Pareto front can reflect the conflicting relationship of efficiency and equity in water resources allocation, and the best alternative chosen by cost performance method may provide rich information as references in integrated water resources planning and management.
Weather extremes have widespread harmful impacts on ecosystems and human communities with more deaths and economic losses from flash floods than any other severe weather-related hazards. Flash floods ...attributed to storm runoff extremes are projected to become more frequent and damaging globally due to a warming climate and anthropogenic changes, but previous studies have not examined the response of these storm runoff extremes to naturally and anthropogenically driven changes in surface temperature and atmospheric moisture content. Here we show that storm runoff extremes increase in most regions at rates higher than suggested by Clausius-Clapeyron scaling, which are systematically close to or exceed those of precipitation extremes over most regions of the globe, accompanied by large spatial and decadal variability. These results suggest that current projected response of storm runoff extremes to climate and anthropogenic changes may be underestimated, posing large threats for ecosystem and community resilience under future warming conditions.
•A coupled 2D hydrodynamic inundation model is proposed.•Future urban inundation is projected under environmental changes.•Hydrological response of low impact development is explored.•A ...cost-effectiveness methodology is outlined to optimize LID proposal.
One major threat to cities at present is the increased inundation hazards owing to changes in climate and accelerated human activity. Future evolution of urban inundation is still an unsolved issue, given large uncertainties in future environmental conditions within urbanized areas. Developing model techniques and urban inundation projections are essential for inundation management. In this paper, we proposed a 2D hydrodynamic inundation model by coupling SWMM and LISFLOOD-FP models, and revealed how future urban inundation would evolve for different storms, sea level rise and subsidence scenarios based on the developed model. The Shiqiao Creek District (SCD) in Dongguan City was used as the case study. The model ability was validated against the June 13th, 2008 inundation event, which occurred in SCD, and proved capable of simulating dynamic urban inundation. Scenario analyses revealed a high degree of consistency in the inundation patterns among different storms, with larger magnitudes corresponding to greater return periods. Inundations across SCD generally vary as a function of storm intensity, but for lowlands or regions without drainage facilities inundations tend to aggravate over time. In riverfronts, inundations would exacerbate with sea level rise or subsidence; however, the inland inundations are seemingly insensitive to both factors. For the combined scenario of 100-yr storm, 0.5m subsidence and 0.7m sea level rise, the riverside inundations would occur much in advance, whilst catastrophic inundations sweep across SCD. Furthermore, the optimal low-impact development found for this case study includes 0.2km2 of permeable pavements, 0.1km2 of rain barrels and 0.7km2 of green roofs.
According to the Chinese Flood Control Act, reservoir water levels generally are not allowed to exceed the flood limited water level (FLWL) during flood season in order to offer adequate storage for ...flood prevention. However, the operation rules based on the current FLWL have neglected meteorological and real-time flood forecasting information and give too much priority to low probability floods. For floodwater utilization, dynamic control of reservoir FLWL is a valuable and effective methodology to compromise between flood control and conservation for reservoir operation during the flood season. The dynamic control bound is a fundamental key element for implementing reservoir FLWL dynamic control operation. In this paper, a dynamic control operation model that considers inflow uncertainty, i.e. the inflow forecasting error and uncertainty of the flood hydrograph shape is proposed and developed. The model consists of three modules: the first one is a pre-release module, which is used to estimate the upper boundary of dynamic control bound on basis of inflow forecasting results; the second one is a refill operation module, which is used to retain recession flood, and the third one is a risk analysis module, which is used to assess flood risk. The acceptable flood control operation risk constraints and quantificational analysis methods are given, and the dynamic control bound of reservoir FLWL is estimated by using Monte Carlo simulation. The China’s three gorges reservoir (TGR) is selected as a case study. A multiple-input single-output linear systematic model is chosen for inflow forecasting of the TGR, and the future inflows are derived from gauged records by assuming that the inflow forecasting error follows a normal distribution. The application results show that the dynamic control of reservoir FLWL can effectively increase hydropower generation and the floodwater utilization rate without increasing flood control risk.
•The long-term complementary operation for hydro-PV hybrid power plant is necessary.•Considering the uncertainty in the streamflow and PV output improves operations.•Considering the independent ...streamflow and PV output yields the best results.
The complementary operation of hydro-photovoltaic (PV) hybrid power plants has become important in the modern power systems. However, the strong variations involved in the streamflow and PV generation lead to uncertainties in the complementary operations. To improve the long-term complementary operation performance of a large-scale hydro-PV hybrid power plant, this study developed long-term stochastic optimization methods that simultaneously consider the uncertainty of the streamflow and PV power output. A multi-objective optimization model was established to maximize the total energy production and guaranteed rate. The model was then solved using stochastic dynamic programming to obtain the operation decisions (carryover storage), which are functions of the initial storage, streamflow, and PV output. Finally, four schemes for deriving the operation rules were constructed: (1) stochastic optimization of the hydropower station directly plus PV output, (2) stochastic optimization of the hydro-PV hybrid power plant considering stochastic streamflow and deterministic PV output, (3) stochastic optimization of the hydro-PV hybrid power plant considering independent stochastic streamflow and PV output, and (4) stochastic optimization of the hydro-PV hybrid power plant considering correlative stochastic streamflow and PV output. The proposed methods were applied to the Longyangxia hydro-PV hybrid power plant in Qinghai province, China, which is the largest hydro-PV plant in the world. The performance of the four schemes was compared across calibration and validation periods. The results show that: (1) the complementarity of hydropower and PV power in long-term operations is highly necessary, (2) considering the uncertainty of stochastic streamflow and PV output simultaneously improves the efficiency of complementary operations, and (3) stochastic optimization of the hydro-PV hybrid power plant considering the independent stochastic streamflow and PV output gives the best performance in this particular case study. Specifically, compared to conventional operations, the total generation and total guaranteed rate could be increased by 3.18% and 10.63%, respectively, in the calibration period, and by 6.66% and 22.92%, respectively, in the validation period. Thus, the proposed stochastic optimization method is helpful in improving the long-term complementary operation of large-scale hydro-PV hybrid power plants.
Previous studies seldom consider humidity when examining heat‐related extremes, and none have explored the effects of humidity on concurrent extremes of high heat stress and low river streamflow. ...Here, we present the first global picture of projected changes in compound lethal heat stress (Th)‐drought hazards (CHD) across 11,637 catchments. Our observational datasets show that atmospheric conditions (e.g., energy and vapor flux) play an important role in constraining the heat extremes, and that Th (32% ± 11%) yields a higher coincidence rate of global CHD than wet‐bulb temperature (28% ± 11%), driven by lower relative humidity (RH) and thus air dryness in Th extremes. Our large model ensemble projects a 10‐fold intensification of bivariate CHD risks by 2071–2100, mainly driven by increases in heat extremes. Future declines in RH, wind, snow, and precipitation in many regions are likely to exacerbate such water and weather‐related hazards (e.g., drought and CHD).
Plain Language Summary
Water and weather‐related extremes such as droughts and heatwaves typically occur simultaneously, posing larger risks to humans and ecosystem than any individual hazard. Extreme high lethal heat stress, which is governed by both temperature and humidity, inhibits the evaporative cooling function of the human body. However, changes in lethal heat stress under past and future climate are poorly understood at the global scale, and no study has yet explored their influences on the risk of compound heat stress‐drought events. Here, we provide the first global picture of the concurrent high heat stress–low streamflow extremes and their projected changes. The fraction of lethal heat extremes accompanied by drought hazards is projected to rise markedly throughout the 21st century. The global magnitude and occurrence of compound hazards is projected to intensify by 4–10 times by 2071–2100, mainly due to the increasing severity of heat extremes. Our study reveals an increasing global risk of compounding hazards, highlighting the need to better prepare adaptation and mitigation solutions in the Anthropocene.
Key Points
We present the first global assessment of compound extremes of high lethal heat stress and low river streamflow
Lethal heat stress yields a higher coincidence rate of compound hazards than wet‐bulb temperature
Future lethal heat stress‐drought events are mainly exacerbated by changes in heat extremes
•The relation of the Budyko-type equation parameter w to climate and human factors is built.•The processes of impacts of climate change and human activities on runoff are presented.•The impact of ...climate change on runoff via altering the parameter w is investigated.
The Budyko-type equations have begun to be widely adopted to separate the contributions of climate change and human activities to the variation of runoff over long-term periods by using the multi-year averages of hydrological variables. In this study, a two-step framework based on four single-parameter Budyko-type equations is proposed to separate the impacts of climate change and human activities on runoff. First, the relationship of the parameter w in each Budyko-type equation with climatic and human factors is built to reveal the time-variation process of w by using an 11-year moving window. Second, the impacts of climate change and human activities on runoff are separated by using both the decomposition method and the sensitivity method. This separating framework is applied to analyze the variation of the runoff during 1960–2009 in the Weihe River. It is found that the parameter w in each Budyko-type equation is significantly related to both factors of climate and human activities. The results from both the decomposition method and the sensitivity method show that climate change is the main driving factor to the decline in runoff of the Weihe River, while human activities are another important factor. In general, climate change affects runoff not only by changing the hydrological inputs (precipitation and potential evaporation) but also by altering the watershed characteristics as represented by the parameter w; while the impacts of human activities on runoff are exerted mainly through the alteration of the watershed characteristics.
•Investigate the projected climate change on flood frequency and quantiles within a bivariate framework.•Develop the non-stationary most likely flood quantile estimation method.•Derive the adaptive ...flood quantiles by a time-varying moment approach.•Climate change exacerbates extreme floods in Ganjiang River basin.•Flood frequency curve varies significantly over time considering non-stationarity.
Climate change will lead to great impacts on flood frequency curve and design floods in the future. However, traditional hydrologic approaches often fail to analyze the flood characteristics within a bivariate framework under changing environment. Moreover, previous studies investigating bivariate characteristics of flood usually do not derive the adaptive flood quantiles. This study assesses the implications of climate change for future bivariate quantiles of flood peak and volume in Ganjiang River basin, China. The outputs of two global climate models (BNU-ESM and BCC-CSM1.1) are statistically downscaled by Daily bias correction (DBC) method and used as inputs of the Xinanjiang hydrological model to simulate streamflow during 1966–2099. Projections for future flood (2020–2099) under Representative Concentration Pathway (RCP) 8.5 scenario are divided into two 40-year horizons (2040s, 2080s) and a comparison is made between these time horizons and the baseline (1966–2005). Univariate flood frequency analysis indicates that there is a considerable increase in the magnitude and frequency of flood under the RCP8.5 scenario, especially for the higher return periods. The bivariate quantile curves under different levels of Joint Return Period (JRP) for historical and future periods are derived by copula functions and the most likely realizations are estimated. It is found that climate change has heavier impacts on the future joint bivariate quantiles for larger return periods. Finally the adaptive isolines and most likely flood quantiles under a JRP are derived from analyzing the merged series by non-stationary copula-based models. The results highlight that the joint probability, illustrated by the isoline of a given JRP, varies significantly over time when non-stationary models are applied. This study incorporates the impacts of climate change on bivariate flood quantiles and develops an adaptive quantile estimation approach, which may provide useful information for the references of flood risk assessment and management under changing environment.
PDF
