基于ATAK的MSWEP数据空间降尺度及对降水融合的影响研究

全球性降水数据为获取大范围降水空间分布提供了新途径,但其空间分辨率不高一直是制约其应用于流域或区域尺度上的重要因素之一,因此研究全球性降水数据的空间降尺度方法具有重要的理论和实用价值。本文采用从区域到区域的Kriging(Area to Area Kriging, ATAK)和反距离权重(Inverse Distance Weighted, IDW)两种方法,不考虑地面雨量资料及影响雨量的有关辅助信息,在汉江流域将全球性降水数据MSWEP的空间分辨率由0.1°×0.1°提高至0.02°×0.02°。结果发现ATAK降尺度得到的月雨量场虽然在统计精度上与IDW无明显差异,但提高了对月降水量局部空间变异特征的描述能力,在一定程度上克服了IDW的平滑效应。进一步以ATAK、IDW降尺度处理后的MSWEP数据以及不作空间降尺度处理的原始MSWEP数据为背景场,采用GWR方法分别与雨量站网降水数据融合,发现3种情况下得到的月降水融合数据在空间基本格局上相同,精度统计结果也较为接近,但雨量场的空间连续性及细节特征仍有一定差异。在地表雨量站网密度较高的情况下,背景场差异对MSWEP和站点降水融合结果...     

Hourly Analyses of Hydrological and Water Quality Simulations Using the ESWAT Model

Detailed analyses of hydrological and water quality variables are very important to study the dynamic processes in a river basin. In this study, we have further modified the Enhanced Soil and Water Assessment Tool (ESWAT) model by incorporating hourly evapotranspiration and overland flow routing modules. Results from comparison of the performances by two ESWAT versions indicate that the modified version performed better than the original model. The modified ESWAT model has reasonably reproduced observed time series runoff and most commonly collected water quality data. In addition, input data availability at required spatial and temporal resolutions is the major bottleneck in implementing many detailed hydrological models. In this paper, we have also developed a robust methodology to successfully disaggregate daily rainfall data into hourly datasets. Furthermore, we have assessed the implications of such daily rainfall disaggregation schemes on subsequent simulation of hydrological and water quality variables at river basin level. The outcomes suggest that the multivariate rainfall disaggregation scheme better reproduced observed rainfall and runoff data.     

Converting Daily Rainfall Data to Sub-daily—Introducing the MIMD Method

One of the most important analysis in many hydrological and agricultural studies is to convert the daily rainfall data into sub-daily (hourly) because in many rainfall stations, only the daily rainfall data are available and for a comprehensive rainfall analysis, these data should be converted to sub-daily. Many experimental and analytical methods are available for this conversion but one of the simplest yet accurate ones has been proposed by the Indian Meteorological Department (IMD). Since the IMD method has shown low accuracy in some regions, in this study, the IMD method is modified to a single parameter equation, called Modified Indian Meteorological Department (MIMD) in order to improve the accuracy of the conversion. For this reason, the parameter is calibrated so that the maximum correlation between observed and estimated values is achieved. Five stations in different regions with different climatic conditions were selected so that the daily and sub-daily rainfall data were available in each of them. Then, the parameter of the MIMD method was derived for each station. The results were compared with both observed data and IMD method and it was shown that the mean correlation coefficient of MIMD and IMD methods were 0.9 and 0.73 respectively for 12-h rainfall depth which indicated that the accuracy of the MIMD method in estimation of sub-daily rainfall depths was significantly increased. Moreover, the results showed that the accuracy of the MIMD method decreases as rainfall duration decreases.

     

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。     

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.     

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...     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

     

图神经网络驱动的流域洪水预报技术

本研究提出了一个图指导的时空关联预报模型（GSCPM，graph-guided spatiotemporal correlation prediction model），针对性地解决流域洪水预报中的时空关系建模和滞后影响问题。该模型通过多个长短期记忆网络（LSTM）编码每个监测点历史属性的时间关联特征，随后利用图卷积神经网络（GCN）挖掘监测点间的地理空间依赖。此外，提出了雨量滞后特征、泄洪量滞后特征和上游水位滞后特征用以挖掘变量滞后效应。本文在现实流域数据集上进行了广泛的实验，通过跟LSTM、RNN 等模型的比较，证明了GSCPM 模型的优越性，适合在流域洪水预报中推广使用。     

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.     

Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change

Climate Change Impacts Assessment using Statistical Downscaling is observed to be characterized by uncertainties resulting from multiple downscaling methods, which may perform similar during training, but differs in projections when applied to GCM outputs of future scenarios. The common wisdom in statistical downscaling, for selection of downscaling algorithms, is to select the model with the best overall system performance measure for observed period (training and testing). However, this does not guarantee that such selection will work best for any rainfall states, viz., low rainfall, or extreme rainfall. In the present study, for Assam and Meghalaya meteorological subdivision, India, three downscaling methods, Linear Regression (LR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used for simulating rainfall with reanalysis data and similar training and testing performances are obtained for observed period. When the developed relationships are applied to GCM output for future (21st century), differences are observed in downscaled projections for extreme rainfalls. ANN shows decrease in extreme rainfall, SVM shows increase in extreme rainfall and LR shows first decrease and then increase in extreme rainfall. Such results motivate further investigation, which reveals that, although, the overall performances of training and testing for all the transfer functions are similar, there are significant differences, when the performance measures are computed separately for low, medium, and high rainfall states. To model such uncertainty resulting from multiple downscaling methods, different transfer functions (LR, ANN and SVM) are used for different rainfall states (viz., low, medium and high), where they perform best. The rainfall states are predicted from large scale climate variables using Classification and Regression Tree (CART). As muti-model averaging (with equal weights or performance based weights) is commonly used in climatic sciences, the resulting output are also compared with the average of multiple downscaling model output. Rainfall is projected for Assam and Meghalaya meteorological subdivision, using this logic, with multiple GCMs. GCM uncertainty, resulting from the use of multiple GCMs, is further modeled using reliability ensemble averaging. The resultant Cumulative Distribution Function (CDF) of projected rainfall shows an increasing trend of rainfall in Assam and Meghalaya meteorological subdivision.     

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.

     

云南省水资源-经济-社会-水生态协调度评价

为科学评价云南省2006～2016年水资源-经济-社会-水生态系统协调度水平,引入阴-阳对优化（YYPO）算法、投影寻踪（PP）和正态云模型（CM）,构建了YYPO-PP-CM水资源-经济-社会-水生态协调度评价模型。从水资源、经济、社会、水生态系统中遴选出20个指标来构建水资源-经济-社会-水生态系统协调度评价指标体系和等级标准,采用云模型正向发生器来计算各分级评价指标隶属度;选取了8个标准测试函数对YYPO算法的优化性能进行仿真验证,并与粒子群优化（PSO）算法、布谷鸟搜索（CS）算法等4种传统优化算法的性能进行对比。基于PP基本原理,利用云南省2006～2016年水资源-经济-社会-水生态指标数据构造投影寻踪优化目标函数,通过YYPO-PP给出各评价指标权重,同时,根据隶属度矩阵和权重矩阵来计算水资源-经济-社会-水生态协调度评价的分级确定度,并进行评价分析,最后将评价结果与投影寻踪法、模糊评价法的结果进行比较。结果表明：① YYPO算法的寻优精度优于PSO、CS等传统优化算法,具有较好的开发、探索平衡能力和全局极值寻优能力。② YYPO-PP-CM模型将云南省2006～2007年水资源-经济-社会-水生态协调度评价为“极不协调”,2008～2009年为“不协调”,2010年为“基本协调”,2011～2014年为“较协调”,2015～2016年为“协调”,表明近10 a来云南省水资源-经济-社会-水生态系统协调度水平持续提升。③ 评价结果与投影寻踪法、模糊评价法的评价结果基本一致。④ YYPO-PP-CM模型兼具客观性、模糊性和随机性,既能客观确定评价指标权重,反映水资源-经济-社会-水生态协调度评价分级的定性概念,又能反映隶属程度的不确定性,具有良好的应用价值。     

变化环境下青藏高原陆地水储量演变格局及归因

基于1981—2015年全球陆面数据同化系统GLDAS和归一化植被指数GIMMS NDVI3g等多源数据,采用Theil-Sen Median斜率估计和Mann-Kendall趋势检验法探究青藏高原多年陆地水储量(TWS)及其各组分时空演变特征,采用水量平衡法、相关分析法和Hurst指数法识别水循环过程对TWS变化的影响机制。结果表明：受气候变化影响,1981—2015年青藏高原降水及冰川积雪消融增加,TWS以0.7 mm/a的速率增加,青藏高原北部TWS增加趋势极其显著,南部呈减少特征;青藏高原绝大部分地区TWS以0～200 cm的土壤含水量为主,不同深度土壤含水量具有显著空间异质性;青藏高原北部及东部降水主要通过蒸散发过程损失,南部地区径流系数较大。     

全球共享GPCP数据在长江中下游地区的适用性分析

应用GPCP(全球气候降水计划)数据与地面观测降水资料分析比较了1980年-2006年我国长江中下游地区27年年平均、季平均降水量的空间分布,并给出了1月、4月、7月、10月份两种数据的散点分布图及线性回归方程。结果表明:27年年平均、季平均两种数据具有较好的一致性,能够真实反映长江中下游流域降水的气候变化特征,其中夏季GPCP数据与台站资料差异较大,降水偏少的冬春两季,两者的一致性较高。4个月的相关性分析说明相关系数都在0.9以上,两者具有较好的相似性,7月份的相关系数为0.91低于其他3个月。在验证GPCP数据具有很好的适用性的基础上,分析研究了1980年-2006年长江中下游地区降水演变规律,线性趋势和差积曲线结果表明,20世纪80年代初到中期属丰水期,90年代属枯水期。M-K检验结果说明全流域年降水呈现下降趋势,长江流域内的各二级区年降水无显著变化;四季27年降水量的结果除汉江流域冬季呈显著上升,夏季呈显著下降,太湖流域冬季显著减少,长江中下游流域秋季显著下降外,其它季节无显著性变化。     

三峡库区水循环要素现状评价及预测

基于实测数据和统计资料建立三峡水库分布式水文模型,对三峡库区水循环现状进行评价, 以分析三峡库区范围内降水、径流等水循环要素的空间分布特征,并采用多个全球气候模式的集合平 均模拟结果,利用统计降尺度方法将气候模式与分布式水文模型藕合预估了未来气候变化条件下三峡 库区水循环要素的变化情况。研究结果表明:在现状条件下,除个别地区外三峡库区年降水量和年径 流量的空间分布相对较均匀,且在未来气候变化条件下,相对于历史多年平均值,预计:三峡库区年 平均温度将上升1. 3 qC,年蒸发量将增加2. 8 %,年降水量和年径流量将分别减少0. 8%和8. 2%,径 流量的减少幅度和蒸发量的增加幅度大于降水量的减少幅度,对库区未来的水资源综合管理提出了更 高的要求。     

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。     

雨量站网分布对雨量插值算法及径流响应的影响

为比较雨量站网密度及分布对不同空间插值算法的影响,选取6种雨量站密度的不同分布,采用4种空间插值算法对研究区2006—2014年的日降雨进行插值,并将面均雨量作为新安江模型的输入,分析和比较其降雨径流响应。结果表明:①雨量站网空间分布越均匀,降雨插值误差越小,其径流模拟的精度也越高;②在雨量站网均匀布置的情况下,各空间插值算法的插值结果差异较小;雨量站网布置不均匀时,站点数目越少各空间插值算法插值结果差异越大;③计算点雨量时,考虑空间变量的克里金法能更准确地计算日降雨的结果;计算面雨量时,不同插值算法间差异较小,建议选用计算简便的插值算法,比如泰森多边形、反距离权重法。     

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.     

Scenario-based Impact Assessment of Land Use/Cover and Climate Changes on Water Resources and Demand: A Case Study in the Srepok River Basin,Vietnam—Cambodia

This study investigates an interdisciplinary scenario analysis to assess the potential impacts of climate, land use/cover and population changes on future water availability and demand in the Srepok River basin, a trans-boundary basin. Based on the output from a high-resolution Regional Climate Model (ECHAM 4, Scenarios A2 and B2) developed by the Southeast Asia—System for Analysis, Research and Training (SEA-START) Regional Center, future rainfall was downscaled to the study area and bias correction was carried out to generate the daily rainfall series. Land use/cover change was quantified using a GIS-based logistic regression approach and future population was projected from the historical data. These changes, individually or in combination, were then input into the calibrated hydrological model (HEC-HMS) to project future hydrological variables. The results reveal that surface runoff will be increased with increased future rainfall. Land use/cover change is found to have the largest impact on increased water demand, and thus reduced future water availability. The combined scenario shows an increasing level of water stress at both the basin and sub-basin levels, especially in the dry season.     

Hydrological assessment of TRMM rainfall data over Yangtze River Basin

High-quality rainfall information is critical for accurate simulation of runoff and water cycle processes on the land surface. In situ monitoring of rainfall has a very limited utility at the regional and global scale because of the high temporal and spatial variability of rainfall. As a step toward overcoming this problem, microwave remote sensing observations can be used to retrieve the temporal and spatial rainfall coverage because of their global availability and frequency of measurement. This paper addresses the question of whether remote sensing rainfall estimates over a catchment can be used for water balance computations in the distributed hydrological model. The TRMM 3B42V6 rainfall product was introduced into the hydrological cycle simulation of the Yangtze River Basin in South China. A tool was developed to interpolate the rain gauge observations at the same temporal and spatial resolution as the TRMM data and then evaluate the precision of TRMM 3B42V6 data from 1998 to 2006. It shows that the TRMM 3B42V6 rainfall product was reliable and had good precision in application to the Yangtze River Basin. The TRMM 3B42V6 data slightly overestimated rainfall during the wet season and underestimated rainfall during the dry season in the Yangtze River Basin. Results suggest that the TRMM 3B42V6 rainfall product can be used as an alternative data source for large-scale distributed hydrological models.