Combination of Hargreaves method and linear regression as a new method to estimate solar radiation in Perlis, Northern Malaysia

The best way to obtain the solar radiation data of a particular place of interest (POI) is to measure at the specific site continuously and accurately over the long term. However, due to financial, maintenance, calibration requirement of the measuring equipment or institutional limitations, these data are absent, incomplete or inaccessible in most areas of the world. Based on meteorological data from Chuping Station, Perlis which is at Northern Malaysia, there were several missing data of solar radiation for the year 2007 and 2008. This paper presents a new method to estimate the solar radiation which is a combination of Hargreaves method and linear regression. Normally, both regression coefficients, a and b of the linear regression are found based on the measured data, but using the proposed method, both regression coefficients based on the Hargreaves method with the correlated parameter, x is the difference of daily temperature. This paper also presents the basic knowledge of Hargreaves method before the proposed method is implemented. As validation, those solar radiation data that are measured by Chuping Station for the year 2006 and by Electrical Energy and Industrial Electronic System (EEIES) Cluster Station for the month of March–June 2011 and their estimated solar radiation data are compared and analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), Nash–Sutcliffe equation (NSE) and percentage error (e). The statistical analysis of the average monthly measured solar radiation data for the past 26 years (1979–2006) is compared with the estimated solar radiation data for 3 years (2006–2008). The proposed method result shows that the value of CRM is closer to zero which indicates that the proposed method is perfectly estimated, the values of RMSE are low value, this indicates that the method performs well, the value of NSE is closer to 1 which indicates that the estimated solar radiation match perfectly with the measured data taken for the past 26 years, the value of e is closer to zero which indicates that the proposed method is acceptable and applicable.     

Equations for estimating global solar radiation in data sparse regions

The knowledge of the amount of solar radiation in an area/region is very essential in the field of Solar Energy Physics. In this work two equations are put forward for estimating global solar radiation from common climate variables in data sparse regions. The first is the Hargreaves equation, R_s=0.16R_aT_d^0.5 where R_a is the extraterrestrial solar radiation and T_d is the temperature difference (maximum minus minimum), while the second is the Angstrom equation, R_s=R_a(0.28+0.39n/N) where n and N are the measured sunshine hours and the maximum daylight duration respectively. The global solar radiation estimated by the two equations for three sites, Owerri (5°28′N, 7°2′E), Umudike (5°29′N, 7°33′E) and Ilorin (8°32′N, 4°46′E), located in different climate zones of in Nigeria, West Africa, are in agreement with those of earlier workers and that from Photovoltaic Geographic Information System (PVGIS) project. The implication of this in solar photovoltaic applications has been stressed.     

Regeneration for All Generations: The Queen Elizabeth Olympic Park

Kathryn Firth , Chief of Design at the London Legacy Development Corporation (LLDC), and Manisha Patel , a director at PRP Architects, describe how the London 2012 bid's promise to promote wellbeing and social inclusion in the Stratford area of East London is now being borne out in urban design and housing that meet ‘the evolving needs and desires of people throughout their lifetimes’. A key scheme is PRP Architects' design for multi-generation houses at Chobham Manor, on the eastern side of the Queen Elizabeth Olympic Park.     

基于生态用水的Hargreaves模型修正

生态需水量核算问题中,国内外常用的几种潜在蒸散量计算模型具有一定的限制性,为此,结合东北地区特点,以哈尔滨市为例,选取时间序列指标数据,对比分析常用计算模型的适用性及不足,提出修正并优化Hargreaves(HM) 模型的方法并进行参数修定．修正后HM模型的计算结果与PM模型的相关系数达0.826,相对均方差为0.119,相关性较好,可满足哈尔滨市蒸散量的计算．     

用Hargreaves法与Penman-Monteith法计算ET_0——以太湖流域的应用为例

为了在太湖流域找到一种适合资料缺乏地区的蒸散发计算方法,利用太湖以西宜兴市梅林试验区的气象监测资料,以PM(Penman-Monteith)公式为标准,评价Hargreaves方法计算潜在蒸散发量(ET0,即参考蒸散发量)的效果。结果表明:在太湖流域应用Hargreaves方法计算的ET0与PM方法的计算结果吻合较好。Hargreaves法在日、旬、月尺度上结果与PM法结果均保持较好的变化趋势及峰值的一致性,误差随计算时间段的延长而减小。在阴雨天较多的夏季采用Hargreaves方法计算ET0,结果会明显偏高。两种方法具有很好的相关性,可以此为依据建立修正方程。经过线性回归方程修正的Hargreaves法计算结果平均相对误差明显下降,并且以日ET0结果改进最多。     

乌鲁木齐参考作物蒸散量计算方法的比较

以Penman-Monteith方法为标准,利用Hargreaves方法,Priestley-Taylor方法和FAO-17 Penman方法计算乌鲁木齐的参考作物蒸散量ET0(Reference Crop Evapotranspiration),对计算结果分别作了对比分析,并对不同的方法进行相应的修正。结果表明:(1)乌鲁木齐的ET0季节分布极不均匀,表现出夏季、春季、秋季、冬季依次减小的趋势;(2)总的来说,PT和HG方法的估算值比PM的标准值要偏低,F17方法的估算值比PM方法的标准值要偏高,造成不同方法的估算偏差的主要原因是由于各自选用了不同的辐射项和动力项所致;(3)在气象资料缺乏、精度要求不高的时候,PT方法能够用来计算乌鲁木齐的ET0,如果精度要求较高,可以使用修正后的公式;(4)修正后的HG公式计算结果最接近PM方法的标准计算结果,如果使用HG方法估算乌鲁木齐的ET0,必须先进行修正;(5)F17方法采用了与PM方法不同的风速修正方案,修正前后的误差都较大,不适用于乌鲁木齐ET0的计算。上述方法在其它地区的适用性有待进一步检验。     

西安地区Priestley-Taylor和Hargreaves方法应用比较

以世界粮农组织推荐的Penman-Monteith方法作为计算参考作物蒸发蒸腾量的标准,对Priestley-Taylor和Hargreaves方法在西安地区的适用性问题进行了探讨,分别建立了这两个公式的回归关系式,并且对这两种方法进行修正。结果表明:Hargreaves方法在西安地区有着较好的准确度,相对误差较小。而Priestley—Taylor方法得到的结果虽然与Penman—Monteith方法计算结果呈相同的变化趋势,相关系数R2=0.94,但计算值有较大的差异;经过修正后相对误差明显减小。因此,在气象资料有限的条件下,可以直接用Hargreaves方法来计算参考作物蒸发蒸腾量;而Priestley-Taylor方法必须经过修正后才能使用。     

熵与开放式新景观--哈格里夫斯的景观设计

哈格里夫斯的景观设计之路经过了三个“顿悟”的过程，通过体验自然、学习现代艺术和对景观的动态美的认识，发现一种符合时代进程的景观设计语言，创作了许多诗意的开放式新景观。     

大地艺术对现代风景园林设计的影响

大地艺术是20世纪现代艺术的一个重要艺术流派，许多大地艺术作品与景观和生态问题密切相关，大地艺术关注自然因素，时间因素，对艺术创作的重要性和工业废弃地问题，从而开拓了风景园林师的思维，影响到风景园林师对工业废弃地的处理方式，并丰富了风景园林师的形式语言，从而对现代风景园林设计产生了重要的影响。     

滇池流域多年ET_0及其影响因素变化趋势分析

分析滇池流域多年参考蒸腾蒸发量(Reference Evapotranspiration,ET_0)及其影响因素的变化趋势,为该流域水资源合理配置和作物需水规律研究提供一定的理论基础。采用联合国粮食及农业组织(FAO)推荐的彭曼-蒙蒂斯公式(Penman-Monteith)以及经验公式法Hargreaves(Har)和IrmarkAllen(Irm-All)计算了滇池流域昆明气象站近38年(1980—2017)的ET_0,研究多年ET_0的变化趋势和气象因素对其的影响。三种方法计算得出的年际平均ET_0整体变化均呈上升趋势,且Hargreaves公式计算的结果最为明显。采用线性趋势法、Mann-Kendall法和累积距平法对计算得出的ET_0进行趋势检验分析,表明彭曼-蒙蒂斯公式和Hargreaves公式计算的结果在2008年左右发生突变,IrmarkAllen公式在2012年和2016年分别发生了突变。对影响ET_0的气象要素变化趋势进行分析,相对湿度和平均最低气温多年变化呈下降趋势,日照时数、风速和平均最高气温均表现为上升趋势。通过相关分析,彭曼-蒙蒂斯公式与Hargreaves公式计算时对其影响最大的气象因素是日最高气温,影响Irmark-Allen公式计算结果的气象因素是平均气温。