多模式下泾河上游流域未来降水变化预估

利用站点实测资料、GCMs 月数据对 GCMs 进行秩评分评估排序, 从 21 种 GCMs 模式优选出的 6 种 GCM模式的日数据、6 种 GCM 集成的气候模式、站点实测资料和 NCEP 再分析资料构建统计降尺度模型 SDSM, 预估泾河上游流域的未来降水变化。结果表明: 构建的降尺度模型对降水模拟较为可靠, 率定期各模式决定系数 R2 为 0.228~ 0.324, 标准误差为 0.354~ 0.450, 率定期和验证期模拟月均降水与实测值年内分布相近。在降尺度性能评价中集成模式表现最好。在 RCP 4.5 情景下, 泾河上游流域未来降水大多数模式和集成模式呈增加趋势, 到 2030 年泾河上游流域降水量将增加 4.8% , 且当地的春季雨量会增加, 夏季雨量会减少。     

Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee,India

Intensification and frequency of hydrologic events are attributed to climate change and are expected to increase in coming future. Intensity-Duration-Frequency (IDF) curves quantify the extreme precipitation and are used extensively to assess the return periods of rainfall events. It is expected that climate change will modify the occurrence of extreme rainfall events. Thus a need of updating IDF curves arises under the climate change scenario. This paper aims at updating the IDF curves for a typical Indian town using an ensemble of five General Circulation Models (GCMs) for all the Representative Concentration Pathways (RCP) scenarios. Sub-daily maximum intensities (15-, 30-, 45-, 60-, 120-, and 180 min) were obtained from the observed records. Equidistance quantile method was used to study the relationships between the historical and projected GCM data, and the historical GCM and observed sub-daily data. This relationship was used to obtain projected sub-daily intensities. The IDF curves were developed using observed and projected data. Analysis of the curves indicated increase in precipitation intensities for all the RCP scenarios. It was also found that intensities of all return periods increases with intensifying RCP scenarios. The variation in the intensities across the GCMs was attributed to the driving forces considered in a particular GCM.     

A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling

Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.

     

Statistical Downscaling of River Runoff in a Semi Arid Catchment

Linear and non-linear statistical ‘downscaling’ study is applied to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in west Iran. This study aims to investigate and evaluate the more promising downscaling techniques, and provides a through inter comparison study using Karkheh catchment as an experimental site in a semi arid region for the years of 2040 to 2069. A hybrid conceptual hydrological model was used in conjunction with modeled outcomes from a General Circulation Model (GCM), HadCM3, along with two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. The results show that the choice of a downscaling algorithm having a significant impact on the streamflow estimations for a semi-arid catchment, which are mainly, influenced, respectively, by atmospheric precipitation and temperature projections. According to the SDSM and ANN projections, daily temperature will increase up to +0.58 0C (+3.90 %) and +0.48 0C (+3.48 %), and daily precipitation will decrease up to ?0.1 mm (?2.56 %) and ?0.4 mm (?2.82 %) respectively. Moreover streamflow changes corresponding to downscaled future projections presented a reduction in mean annual flow of ?3.7 m^3/s and ?9.47 m^3/s using SDSM and ANN outputs respectively. The results suggest a significant reduction of streamflow in both downscaling projections, particularly in winter. The discussion considers the performance of each statistical method for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.     

应用光滑支持向量机预测汉江流域降水变化

统计学降尺度方法是国内外研究全球气候模型尺度降解的热点问题。研究和探讨了基于光滑支持向量机的统计学降尺度方法;建立大尺度气候观测资料和实测降水之间的统计关系;模拟和预测汉江流域降水变化,并同传统的多元线性回归分析方法相比较。结果表明,基于光滑支持向量机的统计学降尺度方法的模拟精度不仅高于多元线性回归分析方法,而且明显优于CGCM2气候模型的输出降水结果。     

统计降尺度方法研究进展综述

统计降尺度方法是将大气环流模式GCMs输出的低分辨率的气象资料转换为流域尺度的主要方法之一,现已发展成为气候学中较为完善的领域。简要介绍了统计降尺度方法的基本原理,包括基本假设条件及主要步骤和关键点;重点介绍统计降尺度方法,大致分为转换函数法、天气分析技术和天气发生器这三类,并对几种方法的国内外应用进展做了阐述;对统计降尺度方法的不确定性研究做了简要介绍。指出未来研究应重点研究统计降尺度模型的适用条件及范围、提高降水模拟的精度;统计降尺度与动力降尺度两种降尺度结合的方法将是降尺度主要发展方向之一。     

Uncertainty Modeling of Statistical Downscaling to Assess Climate Change Impacts on Temperature and Precipitation

Consideration of different Statistical Downscaling (SD) models and multi-sources global climate models’ (GCMs) data can provide a better range of uncertainty for climatic and statistical indices. In this study, results of two SD models, ASD (Automated Statistical Downscaling) and SDSM (Statistical Downscaling Model), were used for uncertainty analysis of temperature and precipitation prediction under climate change impacts for two meteorological stations in Iran. Uncertainty analysis was performed based on application of two GCMs and climate scenarios (A2, A1B, A2a and B2a) for 2011–2040, 2041–2070 and 2071–2100 future time slices. A new technique based on fuzzy logic was proposed and only used to describe uncertainties associated with downscaling methods in temperature and precipitation predictions. In this technique, different membership functions were defined to fuzzify results. Based on these functions width, precipitation had higher uncertainty in comparison with the temperature which could be attributed to the complexity of temporal and local distribution of rainfall. Moreover, little width of membership functions for temperatures in both stations indicated less uncertainty in cold months, whereas the results showed more uncertainty for summer. The results of this study highlight the significance of incorporating uncertainty associated with two downscaling approaches and outputs of GCMs (CGCM3 and HadCM3) under emission scenarios A2, A1B, A2a and B2a in hydrologic modeling and future predictions.     

Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

     

RF-SVR降尺度模型在滦河流域的适用性分析

探讨了RF-SVR统计降尺度模型用于汛期极端降雨模拟的可能性.该统计降尺度模型由降雨状态分类和降雨量预测回归两部分构成,降雨状态分类过程中采用了随机森林(RF)方法,降雨量预测回归过程采用了支持向量机回归(SVR)法.选用1961-2000年的NCEP/NCAR再分析资料及滦河流域10个雨量站点的降雨观测数据进行模型率...     

Downscaling Monsoon Rainfall over River Godavari Basin under Different Climate-Change Scenarios

Evaluating the impact of climate change at river basin level has become essential for proper management of the water resources. In the present study, Godavari River basin in India is taken as study area to project the monthly monsoon precipitation using statistical downscaling. The downscaling method used is a regression based downscaling termed as fuzzy clustering with multiple regression. Among the atmospheric variables simulated by global circulation/climate model (GCM) mean sea level pressure, specific humidity and 500 hPa geopotential height are used as predictors. 1^o × 1^o gridded rainfall data over Godavari river basin are collected from India Meteorological Department (IMD). A statistical relationship is established between the predictors and predictand (monsoon rainfall) to project the monsoon rainfall for the future using the Canadian Earth System Model (CanESM2) over IMD grid points under the Representative Concentration Pathways 2.6, 4.5 and 8.5 (RCP 2.6, 4.5, 8.5) scenarios of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the spatial distribution of monsoon rainfall for the future scenarios. For 2.6 and 4.5 scenarios results show an increasing trend. For scenario 8.5 rainfall showed a mixed trend with rainfall decreasing in the first thirty years of prediction and then increasing gradually over the next sixty years.     

A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

Finer spatiotemporal resolution rainfall data is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing methods pay less attention to the inter-day correlation and diurnal cycle, which can strongly influence the hydrological cycle. To address this problem, we present a spatiotemporal downscaling method that is capable of reproducing the inter-day correlation, the diurnal cycle, and rainfall statistics on daily and hourly scales. The large-scale datasets, which we obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NNR) and general circulation model (GCM) outputs, and local rainfall data are analyzed to assess the impacts of climate change on rainfall. Our proposed method consists of two steps: spatial downscaling and temporal downscaling. We apply spatial downscaling first to obtain the relationship between large-scale datasets and daily rainfall at a site scale using a k-nearest neighbor method (KNN). Then, we conduct an hourly downscaling of daily rainfall in the second step using a genetic algorithm-based KNN (GAKNN) with the inter-day correlation and the diurnal cycle. Furthermore, we analyzed changes in rainfall statistics for the periods 2046–2065 and 2081–2100 under the A2, A1B, and B1 scenarios of the third generation Coupled Global Climate Model (CGCM3.1) and Bergen Climate Model version 2 (BCM2.0). An application of our proposed method to the Shihmen Reservoir basin (Taiwan) has shown that it could accurately reproduce local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal method is a powerful tool for downscaling hourly rainfall data from a large-scale dataset. The understanding of future changes of rainfall characteristics through our proposed method is also expected to assist the planning and management of water resources systems.     

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.     

日雨量随机解集模式研究

全球气候模式(GCMs)预测的气候变化情景，必须经解集模式得出小尺度上未来气候变化时空分布资料，才能满足评估气候变化对资源、环境和社会经济等影响的需要。本文提出由随机天气生成器和统计参数尺度转换关系组成的随机解集模式，应用17个站32年实测日降雨资料，对随机解集模式进行了分析和验证。首先利用随机天气生成器，通过对站点和GCM尺度面平均降雨系列的模拟，确定模型参数，验证模型模拟历史降雨过程的可靠性。然后，建立模型参数从大尺度向站点转换的关系，并从历史降雨系列中抽出某一日雨量系列，假设为未来气候变化情形，对降雨系列在不同尺度间的转换关系进行了验证。在此基础上，对GCMs预测结果的时空解集方法进行了探讨。     

Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach

In the context of climate change, the uncertainty associated with Global Climate Models (GCM) and scenarios needs to be assessed for effective management practices and decision-making. The present study focuses on modelling the GCM and scenario uncertainty using Reliability Ensemble Averaging (REA) and possibility theory in projecting streamflows over Wainganga river basin. A macro scale, semi-distributed, grid-based hydrological model is used to project the streamflows from 2020 to 2094. The observed meteorological data are collected from the India Meteorological Department (IMD) and the streamflow data is obtained from Central Water Commission (CWC) Hyderabad. In REA, meteorological data are weighted based on the performance and convergence criteria (GCM uncertainty). Whereas in possibility theory, based on the projection of different GCMs and scenarios during recent past (2006–2015) possibility values are assigned. Based on the possibility values most probable experiment and weighted mean possible CDF for the future periods are obtained. The result shows that there is no significant difference in the outcomes is observed between REA and possibility theory. The uncertainty associated with GCM is more significant than the scenario uncertainty. An increasing trend in the low and medium flows is predicted in annual and monsoon period. However, flows during the non-monsoon season are projected to increase significantly. Moreover, it is observed that streamflow generation not only depends on the change in precipitation but also depends on the previous state of physical characteristics of the region.     

Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change: A Case Study in Southern Taiwan

The study compared the performances of three weather generators (WGs), including a parametric model and two non-parametric models, in producing synthetic daily rainfall time series for multiple sites. The observed daily rainfalls of six raingauges during 1979~2008 in the catchment of Tseng-Wen Reservoir in Southern Taiwan were used as the data set. The generated results reveal that the k-nearest neighbor WG with a fixed window (i.e., a non-parametric model) is the best for daily rainfall generation at each site and performs well in preserving spatial correlation of rainfall among sites. The best WG was further applied to assess the impact of climate change on rainfall temporal characteristics (i.e., annual number of wet day, annual maximum number of continuous wet days and annual maximum number of continuous dry days) by using the downscaling results of 24 GCMs under the A1B emission scenario during 2020~2039. It is found that the rainfall temporal characteristics will change in the future which may make Southern Taiwan tend to face a longer period with no rain.     

Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study

This study investigates the use of wavelet transformation (WT) as preprocessing tool in data-driven models (DDMs) for forecasting streamflow 7 days ahead. WT used are Continuous wavelet transformation (CWT), discrete wavelet transformation (DWT), and a new proposed combination of CWT and DWT, namely discrete continuous wavelet transformation (DCWT). In addition to these three different WTs, the single DDMs were used also to create four different schematic layouts. The DDMs applied were artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM). The lagged rainfall, temperature, and streamflow were incorporated as inputs into the WT-DDMs. It was found that CWT improved the forecasting accuracy of models which only included the rainfall and temperature but not the streamflow. Moreover, DWT improved the performance dramatically for the models with streamflow. Notably, DWT layout outperformed CWT layout in general while CWT layouts resulted in higher improvement to the models with rainfall and temperature only. The proposed DCWT in which CWT applied on the rainfall and temperature variables and DWT applied on the streamflow improved the forecasting ability in several models combinations when ANN was applied. Nevertheless, improvement in the forecasting accuracy was deteriorated in those with SVM while no improvement was observed with ANFIS. ANN outperformed both ANFIS and SVM while ANFIS performed better than SVM.     

Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation

Two screening methods aimed at selection of predictor variables for use in a statistical downscaling (SD) model developed for precipitation are proposed and evaluated in this study. The SD model developed in this study relies heavily on appropriate predictors chosen and accurate relationships between site-specific predictand (i.e. precipitation) and general circulation model (GCM)-scale predictors for providing future projections at different spatial and temporal scales. Methods to characterize these relationships via rigid and flexible functional forms of relationships using mixed integer nonlinear programming (MINLP) formulation with binary variables, and artificial neural network (ANN) methods respectively are developed and evaluated in this study. The proposed methods and three additional methods based on the correlations between predictors and predictand, stepwise regression (SWR) and principal component analysis (PCA) are evaluated in this study. The screening methods are evaluated by employing them in conjunction with an SD model at 22 rain gauge locations in south Florida, USA. The predictor variables that are selected by different predictor selection methods are used in a statistical downscaling model developed in this study to downscale precipitation at a monthly temporal scale. Results suggest that optimal selection of variables using MINLP and ANN provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Results from application and evaluations of screening methods indicate improved downscaling of precipitation is possible by SD models when an optimal set of predictors are used and the selection of the predictors is site-specific.     

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

     

Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks

Forecasting of intermittent stream flow is necessary for water resource planning and management at catchment scale. Forecasting of extreme events and events outside the range of training data used for artificial neural network (ANN) model development has been a major bottleneck in their generalization capabilities till date. Despite of several studies using wavelet analysis in water resource modelling, no study has yet been conducted to explore capabilities of hybrid ANN modelling techniques for extreme events outside the training range. In this study a wavelet based ANN model (WANN) is proposed for intermittent streamflow forecasting and extreme event modelling. This study is carried out in a watershed in semi arid middle region of Gujarat, India. 6 years of hydro-climatic data are used in this study. 4 years of data are used for model training, 1 year for cross-validation and remaining 1 year data are used to evaluate the effectiveness of the WANN model. Two different approaches of data arrangement are considered in this study, in one approach testing data are within the range of training dataset, whereas in another approach testing data are outside the training dataset range. Performance of four different training algorithms and different types of wavelet functions are also evaluated for WANN model development. In this study it is found that WANN model performed significantly better than standard ANN models. It is observed in this study that different wavelet functions have different role in modelling complexities of normal and extreme events. WANN model simulated peak values very well and it shows that WANN model has the potential to be applied successfully for intermittent streamflow forecasting even for the data outside the training range and for extreme events.