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。     

An Evaluation of CMIP5 GCM Simulations over the Athabasca River Basin,Canada

Long‐term hydrological forecasting, water resources management and other climate change impacts or adaptation analysis studies on large continental river basins, for example, the Athabasca River Basin (ARB) in Canada, desire a reliable climatic projection. This usually relies on general circulation models (GCMs) in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). However, there is a lack of a systematic evaluation of CMIP5 GCM performances over the ARB that vary with multiple factors, for example, statistical metrics, temporal scales and spatial locations, challenging the reliability of water‐related or other studies over the ARB. For this gap to be filled, six CMIP5 GCMs, namely, IPSL‐CM5A‐LR, IPSL‐CM5A‐MR, MIROC‐ESM‐CHEM, MIROC5, GFDL‐ESM2G and GFDL‐ESM2M, and their ensemble mean are selected according to data availabilities of representative climate variables: Tmin, Tmax and Prec (TTP). Accuracies of the selected CMIP5 GCMs in reproducing TTP over the ARB are evaluated comprehensively. The ensemble mean cannot outperform any GCM in all cases in the ARB, although its overall accuracy seems to be higher in consideration of all cases. These accuracies vary with TTP, locations, metrics and scales. For instance, ESM2G shows the highest accuracies in reproducing monthly/seasonal variability and magnitudes of grid‐averaged TTP and inter‐annual variability of grid‐averaged annual means of Tmax; CM5A‐LR in multi‐year‐averaged spatial variability of TTP and magnitudes of spatially distributed multi‐year‐averaged Tmax; while the ensemble mean only in some aspects, for example, intraseasonal variability and magnitudes of TTP and inter‐annual variability and magnitudes of grid‐averaged annual means of TTP. GCMs should be systematically integrated according to accuracy variations. Multiple statistical metrics are recommended in GCM evaluations. These findings facilitate water resources systems analyses and other related studies in the ARB. Copyright © 2017 John Wiley & Sons, Ltd.     

Heatwaves and storms contribute to degraded water quality conditions in the nearshore of Lake Ontario

As extreme climatic events, such as heatwaves and storms, become more frequent in response to changing climates, understanding the role climatic events play on water quality is essential. Here, we use water quality monitoring data collected from the nearshore of Lake Ontario between 2000 and 2018 to ask: i) which sites in the nearshore of Lake Ontario have statistically extreme water quality conditions?; ii) do water quality conditions differ in extreme versus non-extreme climate years?; and iii) what are the significant antecedent extreme weather drivers of water quality in the nearshore of Lake Ontario? Three sites with the highest chlorophyll a concentrations and eutrophic conditions, two of which are in Areas of Concern, exhibited the strongest responses to climate extremes. Antecedent weather conditions explained 87.2% of the variation in extreme chlorophyll a concentrations. In particular, warmer temperatures and heatwaves corresponded with statistical extremes in chlorophyll a concentrations. Precipitation accounted for 35.5% of the variation in extreme conditions of turbidity, including storm events the day prior to sampling. When considering site-specific extreme conditions, antecedent weather conditions explained 66.8% of the variation in turbidity. We illustrate the strong role that heatwaves and storm events play on spatial and temporal patterns in extreme water quality conditions, highlighting the importance of incorporating climate change adaptation plans into ecosystem management strategies to preserve water quality in the highly important and iconic nearshore regions of the Laurentian Great Lakes.     

非平稳时间序列极值统计模型及其在气候-水文变化研究中的应用综述

伴随全球气候变暖和平均海平面持续上升,极端气象事件出现的频率增加、强度增大,气候变化已经成为导致水文极值非平稳性的一个重要原因。本文总结了气候-水文变化研究中常用非平稳时间序列极值统计模型的结构以及统计推断方法,从降雨极值变化、洪水极值变化等方面分析了非平稳时间序列极值统计模型在气候-水文变化研究中的典型应用案例。国内外研究表明:非平稳时间序列极值统计模型能体现水文极值随时间或协变量的变化情势,非平稳情况下水文极值重现期和风险的概念和计算方法与传统平稳时间序列的频率分析相比存在显著差异。最后对需要进一步研究的问题进行了展望。     

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.     

A modeling approach to simulate impact of climate change in lake water quality: Phytoplankton growth rate assessment

Global climate change induced by increased concentrations of greenhouse gases (especially CO₂) is expected to include changes in precipitation, wind speed, incoming solar radiation, and air temperature. These major climate variables directly influence water quality in lakes by altering changes in flow and water temperature balance. High concentration of nutrient enrichment and expected variability of climate can lead to periodic phytoplankton blooms and an alteration of the neutral trophic balance. As a result, dissolved oxygen levels, with low concentrations, can fluctuate widely and algal productivity may reach critical levels. In this work, we will present: 1) recent results of GCMs climate scenarios downscaling project that was held at the University of Derby, UK.; 2) current/future comparative results of a new mathematical lake eutrophication model (LEM) in which output of phytoplankton growth rate and dissolved oxygen will be presented for Suwa lake in Japan as a case study. The model parameters were calibrated for the period of 1973–1983 and validated for the period of 1983–1993. Meteorologic, hydrologic, and lake water quality data of 1990 were selected for the assessment analysis. Statistical relationships between seven daily meteorological time series and three airflow indices were used as a means for downscaling daily outputs of Hadley Centre Climate Model (HadCM2SUL) to the station sub-grid scale.     

Spatial Pattern Characterization and Multivariate Hydrological Frequency Analysis of Extreme Precipitation in the Pearl River Basin, China

Consecutive extreme rainfall events, especially those having unfavourable spatio-temporal patterns, always trigger large floods. This paper aims to examine, through the multivariate hydrological frequency analysis, the probability of the synchronous occurrence of rainfall extremes in the Pearl River basin. The copula method together with the stationarity and independence tests, which are crucial to the valid use of statistical methods in regional frequency analyses, were applied in the study. The obtained results indicate that: (1) major precipitation events of the annual maximum 1-, 3-, 5- and 7-day rainfall recorded at 42 stations are the flat looking series and variables are independent, (2) the marginal distribution of all extreme rainfall variables in four homogeneous hydrologic regions fits the log-normal probability distribution and most of their joint distribution fits the Gumbel-Hougaard distribution, (3) on that basis the contour maps of the joint distribution of annual maximum 1-, 3-, 5- and 7-day rainfall between different regions are drawn and the probability of the synchronous occurrence of the extreme rainfalls in different regions are estimated. These findings have great practical value for the regional water resources and flood risk management and are important in exploration of the spatial patterns of rainfall extremes in the Pearl River basin in order to reveal the underlying linkages between precipitation and floods from a broader geographical perspective.     

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.

     

Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble

Water managers are faced with a changing climate in the decision-making process while adaptation and mitigation strategies need to be developed. The climate change impact towards the end of the century, however, is highly uncertain and coping with this is a great challenge for decision makers. Over the recent years, combined efforts of hydrologists and climatologists have led to many climate change impact studies on water resources. However, most studies only use a limited ensemble size and/or focus on only one contributing source and hence possibly underestimate the total uncertainty.

For two Belgian catchments, we simulated daily flow with five different lumped conceptual hydrological models and ten different parameter sets each, forced by the output of 24 global climate models covering four different emission scenarios, combined with 9 different downscaling methods over reference (1961–1990) and future (2071–2100) periods, resulting in a large multi-model ensemble with 41,850 members. Results show that both low and peak flows would become more extreme in the future, and these changes are stronger with increased radiative forcing. The most important uncertainty sources in low-flow projections are the global climate models (explaining 27–36% of the total variance) and the hydrological model structure (34–42%). For peak flow projections, these are global climate models (32–39%) and statistical downscaling methods (21–26%). Also, interaction effects account for a significant part of the uncertainty (24–38%). The results of this study illustrate that one might end up with biased results and overly confident conclusions when only focusing on some of the uncertainty sources in multi-model ensembles.

     

Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5

In this study, outputs of three statistical downscaling (SD) methods including the change factor (Delta), simplified (simQP) and advanced (wetQP) quantile-perturbation-based approaches were compared based on daily rainfall series at 9 meteorological stations in the Lake Victoria basin (LVB) in Eastern Africa. The comparison was made considering phase 5 and phase 3 of the Coupled Model Inter-comparison Project, i.e. CMIP5 and CMIP3 respectively. For the CMIP5 (CMIP3) at each station, there were a total of 7 (14) GCMs, 18 (20) daily historical (control) simulations over the period 1961–2000, and 35 (49) daily future projection series of the periods 2050s and 2090s. The ensemble mean of the GCMs' Bias in reproducing rainfall extremes for return periods in the range of 1 to 40 years for the CMIP5 (CMIP3) varied from −19.05% to 3.11% (−65.85% to −4.86%). For the high greenhouse gas scenario rcp8.5 (A2) of the CMIP5 (CMIP3), the ensemble mean of the projected changes over the LVB in the 10-year rainfall intensity quantile obtained from the Delta, simQP, wetQP SD goes up to 5.8, 10 and 22.4% (11.7, 15.9 and 43.6%) in the 2050s and 8, 11.4, and 25.4% (14.2, 23.3 and 40.6%) in the 2090s. Rainfall totals of the main wet (dry) season are generally projected to increase (decrease) in both the 2050s and 2090s. Because the outputs from the three SD methods captured well the pattern of monthly rainfall totals, the difference between the projected changes of seasonal or annual rainfall totals from the Delta, simQP and wetQP was shown to be insignificant. However, the differences in the results from the Delta, simQP and wetQP methods with respect to the projections of rainfall quantiles indicate that the choice of the SD method can be made on a case by case basis in line with the objectives of the climate change impact study, e.g. the Delta does not capture well the changes in rainfall extremes, whereas the wetQP is suitable for both rainfall extremes and rainfall totals at both seasonal and annual time scales. The findings of this study also show the need to consider evaluations of the inter-GCM differences in the LVB as a data scarce region in assessing the discernible impact of climate change on rainfall extremes and/totals for decision making related to water resources management and engineering.     

Spatial Assessment of Climate Risk for Investigating Climate Adaptation Strategies by Evaluating Spatial-Temporal Variability of Extreme Precipitation

In response to the impacts of extreme precipitation on human or natural systems under climate change, the development of climate risk assessment approach is a crucial task. In this paper, a novel risk assessing approach based on a climate risk assessment framework with copula-based approaches is proposed. Firstly, extreme precipitation indices (EPIs) and their marginal distributions are estimated for historical and future periods. Next, the joint probability distributions of extreme precipitation are constructed by copula methods and tested by goodness-of-fit indices. The future joint probabilities and joint return periods (JRPs) of the EPIs are then evaluated. Finally, change rates of JRPs for future periods are estimated to assess climate risk with the quantitative data of exposure and vulnerability of a protected target. An actual application in Taiwan Island is successfully conducted for climate risk assessment with the impacts of extreme precipitation. The results indicate that most of regions in Taiwan Island might have higher potential climate risk under different scenarios in the future. The future joint probabilities of precipitation extremes might cause the high risk of landslide and flood disasters in the mountainous area, and of inundation in the plain area. In sum, the proposed climate risk assessing approach is expected to be useful for assisting decision makers to draft adaptation strategies and face high risk of the possible occurrence of natural disasters.

     

An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation

Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model.

     

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.     

Compound extreme events in Yarlung Zangbo River Basin from 1977 to 2018

Extreme climate events threaten human health, economic development, and ecosystems. Many studies have been conducted on extreme precipitation and temperature changes in the Yarlung Zangbo River Basin (YZRB). However, little attention has been paid to compound climate extremes. In this study, the variations of wet/warm compound extreme events in summer and dry/cold compound extreme events in winter over the past 42 years in the YZRB were investigated using eight extreme climate indices that were estimated using monthly temperature and precipitation observations. The results showed that the numbers of frost days and ice days tended to decrease on the spatiotemporal scale, while the maximum values of daily maximum temperature and daily minimum temperature exhibited increasing trends. The frequency of wet/warm compound extreme events was significantly higher from 1998 to 2018 than from 1977 to 1997. Dry/cold compound extreme events became less frequent from 1998 to 2018 than from 1977 to 1997. The rate of increase of wet/warm compound extreme events was about ten times the absolute rate of decrease of dry/cold compound extreme events. With regard to the spatial pattern, the frequency of wet/warm compound extreme events increased significantly in almost all parts of the YZRB, while that of dry/cold compound extreme events decreased across the basin. This study helps to improve our understanding of the changes in compound precipitation and temperature extremes in the YZRB from a multivariable perspective.     

Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021–2040 (S) and 2081–2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future.     

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定,并用2001-2012年数据进行了验证。将RF-SVR统计降尺度模型与SVR模型降尺度效果做了对比。结果表明：RF-SVR模型模拟的滦河流域日降雨量偏差显著减小,并可以改善流域极端降雨的模拟预测效果。     

Effects of Global Warming on Precipitation Extremes: Dependence on Storm Characteristics

This study investigates the relationship between historically observed changes in extreme precipitation magnitudes and temperature (P_ex-T relationship) at multiple locations in Canada. The focus is on understanding the behavior of these relationships with regards to key storm characteristics such as its duration, season of occurrence, and location. To do so, three locations are chosen such that they have large amounts of moisture available near them whereas four locations are chosen such that they are located in the land-locked regions of Canada and subsequently have no nearby moisture source available on them. To investigate the effect of different storm durations on P_ex-T relationship, storms of durations: 5, 10, 15, 30 min, 1, 2, 6, 12, 24 h are considered. Finally, P_ex-T relationship is analyzed separately for summer and winter seasons to quantify the influence of seasons. Results indicate strong influences of storm duration, season of occurrence, and location on observed precipitation scaling rates. Drastic intensification of precipitation extremes with temperature is obtained for shorter duration precipitation events than for longer duration precipitation events, in summers than in the winters. Furthermore, in summertime, increases in the intensity of convection driven precipitation extremes is found highest at locations away from large waterbodies. On the other hand, in wintertime most drastic increases in extreme precipitation are obtained at locations near large waterbodies. These findings contribute towards increasing the current understanding of precipitation extremes in the context of rapidly increasing global temperatures.     

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

An estimation of daily suspended sediment concentration (SSC) is required for water resource and environmental management. The traditional methods for simulating daily SSC focus on modeling the SSCs themselves, whereas the cross-correlation structure between SSC and streamflow has received only minor attention. To address this issue, we propose a stochastic method to generate long-term daily SSC using multivariate copula functions that account for temporal and cross dependences in daily SSCs. We use the conditional copula method to construct daily multivariate distributions to alleviate the complications and workload of parameter estimations using high-dimensional copulas. The observed daily streamflow and SSC data are normalized using the normal quantile transform method to relax the computationally intensive model of building daily marginal distributions. Daily SSCs can thus be simulated through the multivariate conditional distribution using previous daily SSC and concurrent daily streamflow values. The proposed method is rigorously examined by application to a case study at the Pingshan station in the Jinsha River Basin, China, and compared with the bivariate copula method. The results show that the proposed method has a high degree of accuracy, in preserving the statistics and temporal correlation of daily SSC observations, and better preserves the lag-0 cross correlation compared with the bivariate copula method. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resource and environmental management, which play a critical role in accurately estimating the frequency and magnitude of extreme SSC events.

     

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.