Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

     

巢湖流域土地利用变化对雨洪调蓄能力的影响

为探究土地利用结构变化对雨洪调蓄能力的影响,以1980—2020年巢湖流域的土地利用数据和土壤数据为基础,采用GIS和SCS模型相结合的方法,分析了不同时期不同降雨尺度下巢湖流域径流产生及分布情况。结果表明：1980—2020年巢湖流域土地利用变化主要表现为建设用地的增加及耕地和林地的减少;流域土地利用综合指数持续增长,其中高强度土地利用区面积从127 km²增长到了1 624 km²。1980—2020年渗透性差的地区从1 706.0 km²增加到了2 398.1 km²,以城市区域扩张为主。2020年相较于1980年,在不同土地利用条件下,相同降雨量时径流量明显增大,且主要在巢湖流域的合肥地区,而在增加降雨量时,径流量的增加幅度逐渐减小,在不同降雨量条件下(日降雨量为50、100、250 mm)径流量分别增大了6.11%、2.61%和0.96%。因此可以得出土地利用变化是导致径流量变化的主要因素,降雨量是次要因素。研究结果可为巢湖流域城市规划及城市雨洪风险管理提供参考。     

统计降尺度方法及其评价指标比较研究

针对目前气候变化对水资源影响研究中关注的问题,以汉江白河上游为研究对象,比较研究统计降尺度方法及其评价指标。以美国环境预报中心/美国国家大气研究中心全球再分析资料、CGCM3和HadCM3的A2情景为大尺度气候背景资料,应用SSVM和SDSM统计降尺度方法对大尺度气候因子进行尺度降解,得到降水情景序列后作为水文模型的输入,通过模拟径流比较分析统计降尺度方法的优劣。研究结果表明,由不同统计降尺度方法得到的降水作为水文模型输入,模拟径流的结果相差很大;对广泛应用于统计降尺度方法的降水模拟评价指标和径流模拟结果进行比较,发现所采用的降水评价指标侧重于考虑降水的统计分布特征,不能完整地描述降水过程特性。分析认为,径流模拟结果应该作为气候变化对径流影响研究中统计降尺度方法评价的重要参考。     

气候变化影响下黄河上游梯级水库群未来发电量预测

为分析气候变化影响下黄河上游大型水库入库来水过程及梯级发电量的时程变化规律,以黄河上游龙羊峡刘家峡梯级水库群为例,采用Mann-Kendall 突变检验方法对唐乃亥和小川水文序列进行突变识别,在此基础上构建了考虑融雪过程的HBV 水文模型,利用统计降尺度方法对CanESM2 和GFDL_ESM2G 两种气候模式3 种气候变化情景(RCP2. 6、RCP4. 5 和RCP8. 5) 下的降水、气温数据进行空间降尺度处理,并将其驱动水文模型预测未来入库来水过程,构建黄河上游梯级联合发电调度模型分析气候变化对未来发电调度过程的影响。结果表明:黄河上游径流序列突变年份集中于20 世纪80 年代,且2000 年之后径流量显著减少;气候变化将导致未来(2021—2050年)汛期6—9 月径流增加,非汛期径流显著减少;随着时间推移,不同气候变化情景下,龙羊峡和刘家峡两库的梯级发电量变化规律不同,RCP8.5 气候变化情景下,气候模式不确定性对其影响最大。     

Spatio-Temporal Variation of Water Availability in a River Basin under CORDEX Simulated Future Projections

In the present study, spatio-temporal variability of hydrological components under climate change is analysed over Wainganga River basin, India. In order to address the climate change projection, hydrological modelling is carried out using a macro scale, semi-distributed three (3)-Layer Variable Infiltration Capacity (VIC-3 L) model. The high-resolution (0.5^o?×?0.5^o) meteorological variables are divided into multiple periods to calibrate and validate the VIC-3 L model. The future projections (2020–2094) of the water balance components are achieved using the high resolution hydrological variables from the COordinated Regional Downscaling EXperiment (CORDEX) dataset under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The uncertainty associated with the multi-model projections are evaluated using Reliability Ensemble Averaging (REA) and the bias correction is accomplished with non-parametric quantile mapping. A probabilistic based areal drought index is also computed for different scenarios using Standardized Precipitation Evapotranspiration Index (SPEI). From the results, it is observed that amount of rainfall, evapotranspiration, and runoff has increased over the basin with no change in the spatial pattern. However, temporal variability is noticed with an increasing trend for rainfall and runoff in the non-monsoon season than the monsoon. Streamflow is expected to increase significantly, especially for medium to low flows (those occurring between 0.2 and 0.9 probability of exceedance in a Flow Duration Curve). In addition, the area under the drought condition has decreased under the projected climate scenarios.     

流域有众多小水电站的锦江水库降雨径流预报

根据降雨径流相关原理,结合锦江水库流域地理环境、水文、气象、上游小型水电站众多特点,将该流域划分为8个单元区,并在各单元区建立一个虚拟水库,代替众多小水电站调节水量的水库降雨径流预报模型,应用于锦江水库水文预报系统。2008年至今共6年的入库流量短期预报实践证明,该模型预报精度高,在水库防洪发电、水库调度应用中效果优良。     

Remote Sensing and GIS Innovation with Hydrologic Modelling for Hydroelectric Power Plant (HPP) in Poorly Gauged Basins

Within the last two decades, modelling of rainfall–runoff has become an important topic in water resources assessment due to increasing water demand and energy, particularly in the determination of hydropower potential. In addition to remote sensing (RS) and geographical information systems (GIS), with the development on satellite technologies, it becomes possible to asses rapid and economic solutions to determine a practical rainfall–runoff relation, particularly poorly gauged or ungauged basins. In this paper, Solakli Watershed which is located in Eastern Black Sea Region of Turkey is selected as the study area. To determine the hydroelectric water potential in a poorly gauged basin, basin boundary and area, minimum maximum and mean elevation, slope information of the basin have been derived from the digital elevation model (DEM) using remote sensing (RS) and geographical information systems (GIS) techniques. IRS P5 stereo satellite data with 2.5-m spatial resolution has been used for deriving the DEM. This DEM is used to produce the flow direction and flow accumulation maps of the basin. Afterward, synthetic drainage network is obtained with the analysis of these maps. Using topographical data such as area, mean basin elevation and limited point observations of rainfall data; a regression model was derived for the whole watershed. This regression model was validated on a sub-basin with satisfactory results using mean areal rainfall which was calculated isohyetal map produced by kriging method. Suggested hydropower station points are also determined.     

基于SWAT模型的秦淮河流域气候变化水文响应研究

为了解气候变化对水文水资源的影响机理,以秦淮河流域为研究区构建SWAT模型,使用SWAT-CUP对模型进行参数敏感性分析、率定及验证,并采用任意假设法设计未来气候情景,分析温度及降雨变化对流域径流及实际蒸散发量的影响。结果表明:模型在月径流模拟中具有较高的精度,适用于秦淮河流域气候变化下的水文响应研究;气温降低或降雨量上升都会引起流域径流量增加,反之则减少;实际蒸散发量与降雨量正相关,而实际蒸散发量对气温变化的响应不明显;平水年径流量对降雨量变化的响应较强,枯水年径流量对温度变化的响应较强;枯水年实际蒸散发量对降雨量变化的响应较强。     

Assessment of flood regulation service based on source–sink landscape analysis in urbanized watershed

Facing climate change and rapid urbanization, urban flooding has exposed human and properties to increasing disaster risks. The attention from researchers and decision-makers to understand the key role of flood regulation service (FRS) in flood management has arisen. However, the mechanism of FRS supply–demand is little known from landscape scale. The FRS assessment methodology considering interacts between source, sink, and flow landscape was proposed in this study. The spatial distributions of surface runoff generation, runoff reduction capacity, and flood inundation were mapped using one-dimensional rainfall–runoff method SCS-CN and two-dimensional flood propagation model CADDIES. Four 3-hour designed rainfall scenarios ranging from nuisance to extreme events (3a, 11a, 56a, and 100a) were simulated. The Liuyang River Watershed in Changsha Municipality, China was selected for case study. The results showed that, the differences of runoff reduction coefficient and runoff generation volume between vegetation and built-up landscape have shortened. The peak flood depth, extent of flood inundation, and peak flood velocity have increased continuously with the growing rainfall intensity. The number of source–sink mismatch catchment was the highest under 56 and 100a, and the most of source-sink match catchments were observed under 3a. Under four rainfall scenarios, the changes of source–sink relationships were witnessed and the potentials of flow zone in source–sink mismatch catchments have increased. The FRS management framework concerning supply–demand connections has been proposed based on source–sink–flow analysis. These findings could provide a scientific basis for sustainable urban flood management and disaster risk mitigation.     

Multi-Objective Differential Evolution for Design of Cascade Hydropower Reservoir Systems

After Paris Agreement and obligation made by various countries to decrease greenhouse gases, generation of clean energy with low carbon was taken into consideration. Hydropower plant is considered as a clean, cheap and renewable energy source for generating electrical energy. Through the construction of the multipurpose dams and their optimal planning and management, we may decrease the potential losses sustained by aquatic ecosystem in addition to supplying the energy and fulfilling the industrial, agricultural and drinking water demands. In the present study, a multi-objective optimization model was proposed for determination of design parameters in cascade hydropower multi-purpose reservoir systems. Considering the significant number of constraints and decision variables and non-convex form of the objective functions and constraints, particularly in multi-reservoir systems, a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting differential evolution (NSDE) was developed to solve the problem and reduce the computational costs. Karkheh River basin was selected as a case study in order to make an assessment on the capabilities and strength of the model. This basin is capable of generating hydropower energy and agricultural development with high environmental considerations due to Hurolazim International Wetland. Based on the results, we may supply various demands such as environmental demands of the aquatic ecosystem with high reliability as well as generating firm hydropower energy through optimal design of cascade hydropower reservoirs.     

基于SDSM的疏勒河流域气候变化统计降尺度研究

疏勒河流域属于气候变化敏感区和生态脆弱区,开展该流域未来气候变化研究,对于水资源合理利用及生态环境保护具有重要意义。为预估该流域的未来气候变化,采用SDSM(statistical downscaling model)模型,根据6个地面气象站41年(1961—2001年)的观测数据、NCEP数据和Had CM3模式模拟数据开展未来气温和降水降尺度研究。结果表明:SDSM对气温的月值模拟精度较高,各站月平均气温纳什效率系数均在0.98以上;SDSM对降水的月值模拟值较实测值整体偏高,模拟效果最好的托勒站月累计降水的纳什效率系数达到0.6。SDSM能较好地模拟气温的年际变化,模拟的年际变化趋势与实测值相差不大;但SDSM对降水的年际变化模拟较差,一些站点的变化趋势方向相反,趋势模拟最好的站点为托勒站和瓜州站。根据SDSM预估结果,与1961—2001年平均值相比,2020—2039年各站点的平均气温均有所升高,A2情景下升幅为(0.8~1.9)℃,B2情景下升幅为(1~2)℃;降水在A2和B2情景下差别不大,其中托勒站减少约54 mm,马鬃山站增加6 mm。研究发现,除托勒站外,疏勒河流域与预报变量相关性最高的预报因子并不在站点所在网格,而是其东侧网格,其原因有待进一步研究。     

基于SWAT模型的浏阳河流域径流对土地利用和气候变化的协同响应

气候和土地利用同时作用于流域径流,影响着流域水资源的量和质。以浏阳河流域为例,基于SWAT模型和情景分析方法定量评估未来流域内土地利用和气候变化对径流的作用。首先采用元胞自动机-马尔科夫(CA-Markov)模型模拟浏阳河流域2020和2050年的土地利用空间格局,其次在World Clim数据库中获得未来流域内气候变化数据,最后采用SWAT模型定量评估未来不同情境下土地利用和气候变化对径流的影响。研究结果表明:未来浏阳河流域林地比例下降、城市建设用地和耕地比例增加;气候呈暖干趋势; 2020和2050年,土地利用变化时,浏阳河榔梨站模拟径流将分别减少2. 42和0. 96 m~3/s;气候变化时,榔梨站模拟径流将分别减少3. 02和1. 13 m~3;土地利用和气候变化综合影响下,榔梨站模拟径流将分别减少8. 54和4. 27 m~3/s;说明浏阳河流域径流的变化对气候响应更加敏感,土地利用和气候变化对径流的影响呈非线性协同作用。     

基于自组织进化规划的径流式水电站厂内经济运行算法

针对径流式水电站基本无调节能力的运行特点，提出其发电量最大优化调度模型，并采用罚函数法与自组织进化规划法相结合进行模型求解．选择某径流式水电站进行模拟计算，说明该方法具有求得整体最优解的能力，算法可行．     

白盆珠水库流域下垫面变化的洪水响应研究

基于栅格分布式水文模型并结合GIS技术,进行东江流域白盆珠水库子流域下垫面变化的洪水响应分析.根据该流域现状土地利用情景分析方法构建9种流域下垫面情景,选取大、中、小3场典型洪水过程,定量分析下垫面变化造成的洪水响应.结果表明:林地及其空间分布在截留降雨、消减洪峰和延缓洪峰滞时方面效果明显,故保护林地,特别是沿河道及流...     

Modeling and analysis of rainfall processes in the context of climate change for Mekong,Chi, and Mun River Basins (Thailand)

This paper provides a detailed characterization of the observed daily rainfall series available for the Mekong, Chi, and Mun River Basins in the context of climate change; and describes the linkage between climate simulations given by Global Circulation Models (GCMs) and the local rainfall characteristics using the popular Statistical Downscaling Model (SDSM). Observed daily rainfall records at 11 stations in the study area for the 1961–2007 period were considered. Results of characterizing the available rainfall data for the 1961–1990 and 1991–2007 periods show different trends of rainfall characteristics for different locations in the study area. However, a consistent increase in the annual maximum number of consecutive dry days (CDD) was observed in the Chi catchment area, the eastern part of the Mun watershed, and the western portion of the Mekong River Basin. In addition, decrease in the annual maximum daily rainfall (AMDR) was found in most locations of the study area, except for the central part of the Chi and Mun River Basins. Moreover, it has been shown in this paper that the SDSM could adequately describe the basic statistical and physical characteristics of the observed rainfall processes for the calibration (1961–1975) and validation (1976–1990) periods. This statistical downscaling method was then used to project future rainfall characteristics for the 1961–2099 period using the climate simulations given by the UK HadCM3 (HadCM3) model under A2 and B2 scenarios (HadCM3A2 and HadCM3B2), and by the Canadian GCM3 (CGCM3) model under A2 and A1B scenarios (CGCM3A2 and CGCM3A1B). In general, the projected trends of rainfall characteristics by both HadCM3 and CGCM3 were found to be consistent with the observed historical trends. However, there was a large difference in the projection results given by these two models. This would indicate the presence of high uncertainty in climate simulations provided by different GCMs. In addition, the climate change impacts on the flood and drought problems in the study area were shown using the CDD and AMDR indices of 100-year return period.     

Developing a Robust Multi-Attribute Decision-Making Framework to Evaluate Performance of Water System Design and Planning under Climate Change

In theory, emergence of robustness concept has pushed decision-makers toward designing alternatives, such as resistant against the potential fluctuations fueled by uncertain surrounding environment. This study promotes an objective-based multi-attributes decision-making framework that takes into account the uncertainties associated with the impacts of the climate change on water resources systems. To capture the uncertainties of climate change, Monte Carlo approach has been used to generate a series of ensembles. These generated ensembles represent the stochastic behavior of the hydro-climatic variables under climate change. This framework represents the inherent uncertainties associated with hydro-climatic simulations. Next, a coupled TOPSIS/Entropy multi-attribute decision-making framework has been formed to prioritize the feasible alternatives using system performance measures. The main objective of this framework is to minimize the risk of deceptive and subjective assessments during decision-making process. Karkheh River basin has been selected as a case study to demonstrate the implication of this framework. Using a set of system performance attributes, the performance of two hydropower systems has been estimated during the baseline period and under the future climate change conditions. According to the conducted frequency analysis, the alternative in which both hydropower projects would go under construction emerged as the robust solution (i.e., there was a 99.9% chance that it outperforms other solutions). The results indicate that the construction of these hydropower systems leads to the increase of Karkheh River basin robustness in the future.

     

Impact of Climate Change on the Hydrological Regime and Water Resources in the Basin of Siatista

This paper examines an assessment of the impact of climate change on hydrological regimes and water resources in the basin of Siatista, a sub-basin of the Aliakmon river basin, located in Northern Greece. Initially all acquired hydrometeorological data of the study area, as well as the hydrometric data at the outlet of the basin, were analyzed and processed. A monthly conceptual water balance model was then calibrated using historical hydrometeorological data for determining changes in streamflow runoff under two different equilibrium scenarios (UKHI, CCC) referring to the years 2020, 2050 and 2100. It was found that by applying the two scenarios there will be a reduction of the mean winter runoff values, a serious reduction of summer runoff, an increase of maximum annual runoff and a decrease of minimum annual runoff values, an increase of potential and actual evapotranspiration, leading to a decrease of soil moisture, a reduction of snow accumulation and melting due to temperature increases, resulting in a decrease of spring runoff values and a shifting of the wet period towards December, resulting in severe prolongation of the dry period.     

Analysis of Linear Scaling Method in Downscaling Precipitation and Temperature

Climate change is one of the greatest challenges in the 21^st century that may influence the long haul and the momentary changeability of water resources. The vacillations of precipitation and temperature will influence the runoff and water accessibility where it tends to be a major issue when the interest for consumable water will increase. Statistical downscaling model (SDSM) was utilized in the weather parameters forecasting process in every 30 years range (2011-2040, 2041-2070, and 2071-2100) by considering Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The Linear Scaling (LS) method was carried out to treat the gaps between ground/ observed data and raw/ simulated results after SDSM. After the LS method was executed to raw/ simulated data after SDSM, the error decrease reaches over 13% for rainfall data. The Concordance Correlation Coefficient (CCC) value clarifies the correlation of rainfall amount among observed and corrected data for all three (3) RCPs categories. There are very enormous contrasts in rainfall amount during the wet season where CCC-values recorded are 0.22 and beneath (low correlation). The findings demonstrated that the rainfall amount during the dry season will contrast for all RCPs with the CCC-values are between 0.44-0.53 (moderate correlation). RCP8.5 is the pathway with the the most elevated ozone-depleting substance emanations and demonstrated that the climate change impact is going on and turn out to be more awful step by step.

     

Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches

Skill of a time-varying downscaling approach, namely Time-Varying Downscaling Model (TVDM), against time-invariant Statistical Downscaling Model (SDSM) approach for the assessment of precipitation extremes in the future is explored. The downscaled precipitation is also compared with a Regional Climate Model (RCM) product obtained from Coordinated Regional Climate Downscaling Experiment (CORDEX). The potential of downscaling the extreme events is assessed considering Bhadra basin in India as the study area through different models (SDSM, TVDM and RCM) during historical period (calibration: 1951–2005, testing: 2006–2012). Next, the changes in precipitation extremes during future period (2006–2035) have been assessed with respect to the observed baseline period (1971–2000), for different Representative Concentration Pathway (RCP) scenarios. All the models indicate an increasing trend in the precipitation, for the monsoon months and maximum increase is noticed using RCP8.5. The annual precipitation during the future period (RCP8.5) is likely to increase by 7.6% (TVDM) and 4.2% (SDSM) in the study basin. An increase in magnitude and number of extreme events during the future period is also noticed. Such events are expected to be doubled in number in the first quarter of the year (January–March). Moreover, the time-invariant relationship (in SDSM) between causal-target variables is needed to be switched with time-varying (TVDM). This study proves that the time-varying property in TVDM is more beneficial since its performance is better than SDSM and RCM outputs in identifying the extreme events during model calibration and testing periods. Thus, the TVDM is a better tool for assessing the extreme events.

     

Impact of climate change on hydropower generation in Rio Jubones Basin,Ecuador

This study attempted to use the soil and water assessment tool(SWAT), integrated with geographic information systems(GIS), for assessment of climate change impacts on hydropower generation. This methodology of climate change impact modeling was developed and demonstrated through application to a hydropower plant in the Rio Jubones Basin in Ecuador. ArcSWAT 2012 was used to develop a model for simulating the river flow. The model parameters were calibrated and validated on a monthly scale with respect to the hydro-meteorological inputs observed from 1985 to 1991 and from 1992 to 1998, respectively. Statistical analyses produced Nash-Sutcliffe efficiencies(NSEs) of 0.66 and 0.61 for model calibration and validation, respectively, which were considered acceptable. Numerical simulation with the model indicated that climate change could alter the seasonal flow regime of the basin, and the hydropower potential could change due to the changing climate in the future.Scenario analysis indicates that, though the hydropower generation will increase in the wet season, the plant will face a significant power shortage during the dry season, up to 13.14% from the reference scenario, as a consequence of a 17% reduction of streamflow under an assumption of a 2.9℃ increase in temperature and a 15% decrease in rainfall. Overall, this study showed that hydrological processes are realistically modeled with SWAT and the model can be a useful tool for predicting the impact of climate change.