Adjustment of CLIGEN parameters to generate precipitation change scenarios in southeastern Australia

Global climate model predictions are often downscaled with stochastic weather generators to produce suitable climate change scenarios for impact analysis. Proportional adjustment to generated daily precipitation and direct adjustment to parameter values for weather generators have been used for assessing the impact of climate change on runoff and soil loss. Little is known of how these parameter values should be realistically adjusted, the amount of adjustment, and whether the adjustments are correlated among different parameters. Rainfall in southeastern Australia has significantly increased since the late 1940s. Rainfall records in Sydney show a similar trend. Long term daily and 6-min intensity data from Sydney have made it possible to examine how CLIGEN parameter values have changed in relation to the underlying significant increase in rainfall. This study shows that for Sydney, most of the increase in rainfall is a result of the increase in wet day precipitation. The increase in the standard deviation of wet-day precipitation is greater than that in the mean, implying a greater rainfall variability during wetter periods. The wet-following-wet transition probability, and maximum 30-min rainfall intensity are all positively and significantly correlated with the change in wet-day precipitation. The change in peak intensity is about half the change in rainfall. No significant relationship can be established between the changes in mean monthly rainfall and those in the skewness coefficient for wet day precipitation and wet following dry transition probability for the site. Simultaneous adjustment of all these parameters is needed for generation of precipitation change scenarios for the region. Using simple proportional adjustment to generated precipitation sequences would lead to maximum impacts on runoff and soil loss predicted with WEPP, while attributing precipitation change equally to the change in wet day precipitation and the number of wet days would under-estimate the magnitude of the impacts considerably for the site.     

WGDWS天气发生器在中国五大气候区的适用性

为了检验基于干湿期的天气发生器(Weather Generator based on Dry and Wet Spells,WGDWS)在中国不同气候区的应用效果,该研究利用中国五大主要气候区16个站点57 a的逐日天气数据,通过对比生成与实测气象要素统计值,及比较WGDWS与随机天气模拟器(Daily Weather...     

Evaluation of CLIGEN for storm generation on the semiarid Loess Plateau in China

The Stochastic generation of storm patterns is often necessary for driving process-based hydrological and ecological models. CLIGEN is the only weather generator being able to generate internal storm patterns. Its goodness needs to be evaluated for its proper application. This paper aims to find the advantages and limitations of CLIGEN on semiarid areas and provide references for custom-built weather generators for the Loess Plateau. The daily rainfall time series (1957–2002) and breakpoint rainfall data (more than 20 years) on six stations on the Loess Plateau were used to estimate input parameters for CLIGEN and to compare with CLIGEN-generated 50 years of storm data. Precipitation occurrence (wet day and dry day sequence) is well-simulated without significant difference across months and sites. Errors of monthly average number of wet days range from − 0.67 to 1.08 days, standard deviations range from − 1.19 to 0.76 days, and the distributions of continuous number of wet and dry days on the semiarid Loess Plateau are adequately simulated. Daily rainfall amount is not simulated as well as precipitation occurrence. The relative errors of average daily rainfall range from − 12.93% to 8.64% and those of standard deviations range from − 21.35% to 27.46%. During the rain seasons (May–September), among 30 month–location combinations, all the Mann–Whitney tests for the means passed, 47% for squared ranks tests rejected the null hypothesis of equality of standard deviations, and 73% for K–S test suggested that the generated and measured distributions of daily rainfall were different at 0.01 level of significance Three variables to describe internal storm patterns in CLIGEN are storm duration, relative peak intensity, and time to peak. Storm duration was not well-reproduced because none of squared ranks tests and K–S test passed at the significance level of 0.01. The frequency of short duration storms (< 300 min) was over-predicted while frequency of long duration storms (400–1200 min) was significantly under-predicted. The distribution of maximum 5 min rainfall intensity (ip₅) was well-simulated for four sites out of the six because all tests passed. However, generated maximum ip₅ for all six sites are around 190 mm/h, which are much larger than the measured (70 to 150 mm/h). Ip₃₀ is simulated better than ip₅, suggesting that CLIGEN can reliably generate rainfall erosivity. Time to peak was well-simulated because all the tests passed with P values significantly greater than the significance level of P = 0.01. Improvement for CLIGEN has to be made in terms of the daily rainfall simulation in rainfall-concentrated seasons and storm pattern generation in order to generate reliable rainfall time series on the Loess plateau.     

日降雨对降雨侵蚀力年雨量简易算法的影响分析

使用重庆市沙坪坝气象站1951—2010年日降雨数据构建降雨侵蚀力年雨量简易算法,分析年雨量简易算法预测精度,确定了年雨量算法预测精度最高时对应的日雨量,并进一步探讨了年降雨侵蚀力和降雨参数的时间变化特征。结果表明:(1)年雨量与年降雨侵蚀力呈指数关系。日雨量≥25mm的年雨量与年降雨侵蚀力的关系最为密切;采用日雨量≥25mm的年雨量算法预测年降雨侵蚀力的精度均优于其他日雨量对应算法。(2)1951—2010年年降雨侵蚀力与年降雨参数随时间变化趋势不显著。对于某一变化趋势时段,日雨量≥25mm的年雨量与时间的相关系数和年降雨侵蚀力与时间的相关系数数值相近。可使用日雨量≥25mm的年雨量作为替代指标分析水土流失对气候变化的响应。该文研究结果可为提高降雨侵蚀力简易算法预测精度,深入理解降雨侵蚀力对气候变化的响应以及区域水土流失防治提供参考。     

CLIGEN降水要素在黄土塬区的适应性评估

 CLIGEN是目前较全面产生降水要素(降水量、历时、达到最大降水强度的时间与降水总历时的比率、最大降水强度与平均降水强度的比率)的天气发生器,其生成降水要素的质量直接影响水文和农业响应模型的输出结果。利用黄土高原长武1957—2001年的日气象观测数据、王东沟流域1988—2001年的降水要素数据和CLIGEN生成的100年日气象数据,对CLIGEN模型产生日、月、年降水量的均值和方差、概率分布、降水极端值和降水历时、强度进行评估。结果表明:CLIGEN对日、月和年降水量均值的模拟效果较好,相对误差都不大于1.0%;对标准差的模拟结果偏低,相对误差的绝对值小于6.6%;没有模拟出日降水量的概率分布,但是较好地模拟出了月和年降水量的概率分布;对日、月和年最大降水量的模拟误差较大,表明CLIGEN对极值的模拟精度有待提高。CLIGEN很好地模拟出连续降水的频率,但是连续干旱天数在20d以内的累积频率的平均相对误差为8.9%;CLIGEN产生的最大降水强度与平均降水强度的比率高于实测数据;相对于实测数据,CLIGEN模拟的降水历时和降水量具有相同的趋势,对小降水量或短历时的模拟结果偏高,对大降水量或长历时的模拟结果偏低。     

香溪河流域降雨侵蚀力特征及简易算法初步研究

降雨侵蚀力表示降雨引起土壤侵蚀的潜在能力,对土壤侵蚀定量预报及评价研究有重要意义。利用三峡库区香溪河流域兴山气象站1990—2009年20 a的逐日降雨量资料,采用日降雨侵蚀力模型估算了研究区的降雨侵蚀力,分析了降雨侵蚀力的年内、年际演变特征,并以此为基准值建立了降雨侵蚀力简易算法模型。结果表明:香溪河流域年内降雨侵蚀力R主要集中在5—8月,占全年的71%,峰值与侵蚀性降雨峰值一致,均出现在7月;R值年际变化较大,变异系数达到0.36,多年平均降雨侵蚀力为4 361.55（MJ·mm）/（hm²·h）,R值与年降雨量和年侵蚀性降雨量年际变化趋势基本一致,但也存在少数异常年份,多年降雨侵蚀力年际变化趋势系数为0.106,呈增加趋势;简易算法模型决定系数均在0.9以上,相对误差较小,均能满足要求,可应用于研究流域,但降雨侵蚀力精确值未知,模型参数有待进一步优化。     

黄土高原不同地貌类型区降雨侵蚀力时空特征研究

通用土壤流失方程USLE是迄今为止较为成熟,应用较广的土壤侵蚀预报模型,区域降雨侵蚀力R及其分布特征是将USLE应用于较大地区的关键.以日降雨量计算侵蚀力模型为基础,建立了黄土高原月降雨量计算降雨侵蚀力模型.用黄土高原235个气象站点1971-2000年30 a的月降雨量数据,计算得各站点的时间序列月降雨侵蚀力和年降雨侵蚀力,通过Kriging空间插值方法生成降雨侵蚀力时空分布栅格图像,并分析了不同地貌类型区降雨侵蚀力的时空特征.黄土高原降雨侵蚀力空间分布从东南到西北呈梯度递减趋势,范围在300～7 500,平均不到3 000,不同地貌类型区从大到小依次为土石山区、丘陵沟壑区(延安)、高塬沟壑区、丘陵沟壑区(榆林)、丘陵区(陇西);降雨侵蚀力年内分布主要集中于7,8两月,年际变化上存在一个2.7 a的波动周期,波动范围在多年平均值的1倍以上,不同地区相差较大.     

满足机械收割农艺条件下稻田排水暗管布局DRAINMOD模型模拟

在长江中下游稻麦轮作区,水稻秋收期阴雨连绵现象时有发生,收割机械因农田土壤过湿而无法及时下田收割。如果建设暗管排水系统,则可及时降低地下水埋深,保证机械收割的顺利进行。该文以江苏省扬州市江都区昭关灌区为例,以地下水埋深降至60~80 cm作为适于一般机械收割的田间排水要求,运用田间水文模型-DRAINMOD模拟了满足1~5 d机械下田条件的暗管排水布局,并分析了相应的田间水文效应以及模型主要输入参数的敏感性。根据研究区1954-2016年逐日气象数据(包含降雨、气温、湿度、风速和日照时数等)的模拟结果显示:考虑大型机械收割要求(地下水埋深大于80 cm),当暗管埋深为90~150 cm时,满足98%、95%和90%保证率的最大暗管间距分别为7.42~18.74 m、13.01~26.20 m和15.27~28.72 m;满足小型机械收割要求(地下水埋深大于60 cm)的暗管布置间距则可更大,满足98%、95%和90%保证率的最大暗管间距分别为10.36~19.59 m、18.17~30.90 m和22.88~33.02 m。多年平均机械收割天数对侧向饱和导水率、不透水层深度、土壤蒸发蒸腾量、潜水上升通量和土壤可排空体积5类参数最为敏感。研究成果可为类似水稻种植区基于机械收割要求的农田暗管排水系统设计提供理论依据。     

黑龙江省降雨侵蚀力空间分布规律

利用黑龙江省16个国家级气象站,1960-2000年日降雨量资料,分析黑龙江省侵蚀性降雨和降雨侵蚀力的空间分布规律。在16个气象站中,日降雨量达到侵蚀性标准（≥12mm／d）的降雨时间为9～15d／a,最大值同最小值之间相差近0．7倍;日降雨量达到侵蚀性标准的年降雨量为192～387mm,最大值同最小值之间相差l倍。16个气象站年降雨侵蚀力多年平均值为794～2144MJ·mm／（hm^2·h·a）,最大值同最小值之间相差近2倍。降雨侵蚀力空间分布从西北到中南部逐渐升高,东部低于中部,年降雨侵蚀力空间分布基本与年降雨量空间分布相似。年内降雨侵蚀力分布主要集中在6—9月,7月份下半月或8月份上半月达到最高值,6—9月降雨侵蚀力占全年比率为88％～95％,其中西部比东部略高。     

递推关系概化前期产流条件改进SCS模型

降雨径流的精准模拟和预测是开展水资源管理和水土环境质量评价的重要依据之一,但现有SCS模型不能有效表征前期降雨蓄存和消耗对产流的影响,进而限制了其径流预测精度。该文基于潜在初损和有效降雨影响系数形成日有效影响雨量的递推关系,将前期产流条件概化成前期日降雨量对降雨初损的影响函数,从而构建了改进SCS模型。其中潜在初损量明确了产流前流域的最大降雨蓄存潜力和日降雨量的有效影响阈值,而前期有效降雨影响系数则表示了在蒸发蒸腾或渗漏过程作用下前期有效日降雨量的动态消耗。在小区、田间、流域3种排水面积下的模型应用结果表明,改进SCS模型能更准确地预报产流的变化,验证期的确定系数R2和纳什系数NSE比SCS原模型分别提高了27.0%~30.9%和1.0%~78.3%。前期有效降雨影响系数的稳定性较好,两模型的曲线数的拟合值比较一致。该改进SCS模型为更准确预测蒸发蒸腾或渗漏较为剧烈地区的径流提供参考。     

Rainfall erosivity estimation over the Tibetan plateau based on high spatial-temporal resolution rainfall records

The Tibetan Plateau (TP) in China has been experiencing severe water erosion because of climate warming. The rapid development of weather station network provides an opportunity to improve our understanding of rainfall erosivity in the TP. In this study, 1-min precipitation data obtained from 1226 weather stations during 2018–2019 were used to estimate rainfall erosivity, and subsequently the spatial-temporal patterns of rainfall erosivity in the TP were identified. The mean annual erosive rainfall was 295 mm, which accounted for 53% of the annual rainfall. An average of 14 erosive events occurred yearly per weather station, with the erosive events in the wet season being more likely to extend beyond midnight. In these cases, the precipitation amounts of the erosive events were found to be higher than those of the daily precipitations, which may result in implicit bias as the daily precipitation data were used for estimating the rainfall erosivity. The mean annual rainfall erosivity in the TP was 528 MJ mm·ha^?1·h^?1, with a broader range of 0–3402 MJ mm·ha^?1·h^?1, indicating a significant spatial variability. Regions with the highest mean annual rainfall erosivity were located in the forest zones, followed by steppe and desert zones. Finally, the precipitation phase records obtained from 140 weather stations showed that snowfall events slightly impacted the accuracy of rainfall erosivity calculation, but attention should be paid to the erosion process of snowmelt in the inner part of the TP. These results can be used as the reference data for soil erosion prediction in normal precipitation years.     

Applying one-dimensional deterministic chemical fate models on a regional scale

A deterministic one-dimensional chemical transport model was applied to soil and weather data from the northeastern United States. The model used a mobile-immobile capacity flow model. Simulations using 20 years of historical rainfall data from each of 13 weather stations applied to a single soil showed that predictions of leaching varied considerably from year to year. The model was then applied to 2270 soil-weather combinations to develop indices for atrazine-leaching potential. Issues relating to quality of soil data and temporal variability of rainfall are discussed. Simulated atrazine leaching was related to the amount and distribution of rainfall, modified by soil organic matter levels.     

贵州省降雨侵蚀力时空分布规律分析

降水是导致土壤侵蚀的主要动力因素,降雨侵蚀力反映了降雨对土壤侵蚀的潜在能力。贵州省是我国典型的生态环境脆弱区之一,水土流失十分严重。以全省19个气象台站1951—2001年逐日降雨资料,利用日降雨侵蚀力模型,估算了贵州省降雨侵蚀力,分析了其时空分异规律。结果显示近50a来贵州省降雨侵蚀力呈增加趋势,即由降雨引起的土壤水蚀潜在能力增加。降雨侵蚀力年内分配主要集中在夏季,占年均降雨侵蚀力的68.48%。在空间分布上,降雨侵蚀力由南向北递减,并且在西南部和东南边缘形成侵蚀力高值中心,在西北部形成低值中心。根据年降雨侵蚀力的季节分配特征,可以将贵州省划分为3个类型区。     

利用日降雨量资料估算西南地区的降雨侵蚀力

降雨是影响土壤侵蚀的主要因素之一,降雨侵蚀力是各种土壤侵蚀预测预报模型的主要参数。降雨侵蚀力通常用降雨动能和某一时段最大降雨强度的乘积表示,如EI₃₀、EI₁₀或者EI₆₀等,但是这种计算方法需要长期且连续的自记雨量资料。我国大多数地区缺乏完整的自记雨量资料,使这些方法的应用受到了限制。一些不需要自记雨量过程的降雨侵蚀力计算方法已经提出,对考虑降雨季节变化的日降雨量估算降雨侵蚀力模型进行了修正,并用     

三峡库区泥石流灾害预警研究

 采用三峡库区有历史记录的40次泥石流灾害的日降雨量以及前15 d的降雨量,当日1 h及10 min最大降雨强度等降雨参数作为训练数据,在对研究区进行荒溪分类以及灾害危险区域制图的基础上,结合雨季的降雨特征监测以及实时天气预报模型,构建了基于神经网络的三峡库区泥石流灾害实时预报模型。详细阐述了应用神经网络方法建立三峡库区泥石流灾害实时预报模型的关键技术,包括确定输入层、输出层以及隐含层的神经节点,建立学习知识库以及各节点初始权重等相关指标。该模型可以预测泥石流发生的临界降雨量、警戒降雨量以及避灾降雨量等指标,因此,依据当地降雨的实时监测数据或天气预报,就可以预测泥石流的发生几率,从而减少泥石流发生的直接危害。模型采样GIS技术以及国际先进的软、硬件技术,系统性能稳定,运行结果准确性较高,且具有广泛的应用性。该模型为可视化信息系统,可以通过该系统监测荒溪类型变化,进行危险区域制图,实现荒溪科学管理,为三峡库区泥石流灾害预警及防治的管理和决策提供科学参考。     

LARS-WG天气发生器在黄土高原的适应性研究

 利用黄土高原地区西安、兰州、太原、银川、呼和浩特和延安6个气象站1951—1990年的日序列的降雨量、最高温度、最低温度及日照时间,用LARS-WG天气发生器模拟1991—2000年的日气象资料,并用1991—2000年的实际观测值与之比较,对LARS-WG天气发生器适应性进行了检验。通过线性回归、平方根误差和平均偏差评价表明:通过长序列日气象资料,LARS-WG天气发生器能准确地模拟黄土高原6个站点的日最高温度、日最低温度的月分布和年辐射总量。年降水及其月分布值普遍高于实际降水值。     

基于聚类法筛选历史相似气象数据的玉米产量DSSAT-CERES-Maize预测

根据陕西杨凌、合阳、长武3个站点各2 a玉米试验,在对玉米生长模拟模型CERES-Maize进行调试、验证的基础上,探索在生育期内进行动态产量预测的方法并验证.研究将目标生育期内未知气象数据分别用试验地的多年历史同期数据代替,结合生育期实时数据对应生成多个完整的气象数据序列运行模型预测产量.随着生育期的推进,逐日在气象数据序列中融入目标年实测的气象数据,从播种至收获动态模拟玉米产量.此外该研究使用改进前后的K-NN算法从历史气象年份中筛选目标年的气象相似年份进而预测产量.通过对3种方法预测精度及预测效率对比,确定改进的K-NN算法最优.研究表明,玉米生育前期产量预测可靠性和准确率均较差,抽雄后预测精度迅速提高;利用改进的K-NN算法在3个站点全生育期预测产量的平均绝对相对误差的均值分别为9.9%、19.8%、17.9%,抽雄后预测产量的平均绝对相对误差在0.2%~12.6%之间,相比于使用全部历史年份数据进行全生育期产量预测,模拟所需时间从61 min缩短至25 min.对该方法中降雨因子的筛选进一步改进可提高预报精度,未来有望达到业务应用水平.     

CLIGEN非降水参数在黄土高原的适应性评估

 天气生成器(CLIGEN)可以产生以日为时间单元的天气数据,从而广泛应用于土壤侵蚀和作物生长模拟模型,其模拟结果的优劣直接影响这些模型的输出结果。利用散布黄土高原的12个标准气象站点长时间序列的气候数据评估CLIGEN模拟非降水参数(温度、太阳辐射、风速)的能力。结果表明:CLIGEN能较好的模拟日最高温度;对日最低温度与露点温度的模拟次之;模型对太阳辐射和风速的模拟较差,特别是对风速的模拟,模拟值要显著的高于实测值。CLIGEN模拟的温度日较差、第1天最高温度与第2天最低温度之差、第2天最高温度与第1天最低温度之差的均值和标准差普遍偏高,但均值的误差较小,而标准差被过高模拟;模型在产生气候数据时,没有保持逐日渐变性和连续性。CLIGEN能够较好的模拟最高温度与最低温度的季节连续性与相关性;而过高的模拟了太阳辐射的季节相关性以及温度与太阳辐射的季节互相关性;同时,模型没有模拟出各气象要素自身及其之间的逐日相关性。     

WXGEN天气发生器在长江上游地区的适用性评价

WXGEN天气发生器是SWAT分布式流域水文模型的组成模块之一,用于模型预测预报以及天气数据缺测时生成模拟天气数据。以长江上游5个典型国家气象站40 a的日观测数据为基础,分析评价了WXGEN天气发生器在该地区模拟的日和月气象参数的精度。结果表明,WXGEN天气发生器在长江上游5个典型气象站模拟精度基本一致;模拟的日天气数据误差较大;模拟的月天气数据效果好于日天气数据,WXGEN天气发生器能较好地拟合月天气数据的分布,更适合于长江上游地区月气象参数的模拟,但WXGEN模拟的月天气数据误差具有较强的季节规律性,月降雨量和气温的模拟值较实际值偏低,而太阳辐射量模拟值却较实际值偏高。     

近52年黑龙江省不同级别降水特征分析

利用黑龙江省28个气象站点1959—2010年逐日降水资料,采用气候倾向率、ArcGIS中的反距离加权插值等方法分析了黑龙江省不同级别降水日数、降水量和降水强度的气候特征及变化趋势。结果表明:近52a来,黑龙江省年平均降水量呈微弱的减少趋势,雨量的减少主要是由于夏季微雨和小雨雨量显著减少;降水强度呈显著减弱趋势,主要是源于微雨和小雨降水强度的减少;年雨日数及四季(夏季除外)的雨日数均呈增加趋势。雨日的增加主要是由于微雨、小雨及中雨日数显著增加;降水量空间分布特征总体表现为中部最多,南部其次,西部和北部相对较少。