Establishing influence of morphological aspects on microclimatic conditions through GIS-assisted mathematical modeling and field observations
Establishing influence of morphological aspects on microclimatic conditions through GIS-assisted mathematical modeling and field observations
复制标题
通过GIS辅助的数学模拟和野外观测建立形态特征对小气候条件的影响
DOI：
10.1007/s10668-021-01320-4
复制
发表时间：
2021-03-14
影响因子：
4.9
通讯作者：
Kumar, Rakesh
中科院分区：
文献类型：
--
作者：
Bherwani, Hemant;Anjum, Saima;Kumar, Rakesh
一个地区的局部气候受城市化程度的影响很大。小气候参数，如风速（W），相对湿度（RH）和温度（T）的影响，由于自然表面和土地利用的修改。随着人们对城市微气候研究的兴趣日益增加，诸如节能、环境可持续性和具有热舒适性的城市设计等因素被广泛研究。本研究试图通过使用Python和ArcGIS求解小气候控制方程来模拟纳格布尔市城市景观中的T、W和RH。在夏季和冬季进行了大量的现场测量，以验证模型数据。结果显示T和RH的建模和监测数据之间存在统计学正相关和显著相关性（p < 0.001，CI 95%），R-2范围分别为92.5-97.7%和82.2- 88.7%。然而，该模型在冬季和夏季分别低估了平均4-7%的T，而RH总体上平均高估了2%。W值与实测值之间的相关性中等，两季的平均变化范围为0.02-0.1 m/s。整体而言，模型数据与地面数据显著相关，模型很好地捕捉了地表点之间的变化，表明Python和ArcGIS可用于测量小气候参数，为可持续城市设计奠定基础。评价格林菲尔德和布朗菲尔德项目的城市小气候参数可以帮助景观设计师、规划师有效地控制温度和风条件，改善城市地区的室外热条件。
Local climate in an area is significantly affected by the extent of urbanization. The microclimatic parameters such as wind speed (W), relative humidity (RH) and temperature (T) are affected due to modifications in natural surfaces and land use. With increasing interest in urban microclimate research, factors such as energy conversation, environmental sustainability, and urban design with thermal comfort are researched extensively. The current study attempts to model T, W and RH in an urban landscape of Nagpur City by solving the microclimate governing equations using python and ArcGIS. Numeral field measurements are carried out during summer and winter seasons for validation of modeled data. The results show a statistically positive and significant correlation between modeled and monitored data (p < 0.001 at CI 95%) for T and RH with R-2 ranging from 92.5-97.7% and 82.2-88.7%, respectively. However, the model underpredicts T by an average of 4-7% in winter and summer, respectively, while RH is overpredicted by an average of 2% overall. W shows moderate correlation between modeled and monitored data with an average variation of 0.02-0.1 m/s for two seasons. Holistically, the modeled data are significantly correlated with ground data, and variations between surface points are captured well by the model, indicating that python and ArcGIS can be used for the measurement of microclimate parameters, forming the basis of sustainable urban design. Evaluating the urban microclimate parameters for both greenfield and brownfield projects can assist the landscape designers, planners to effectively control the temperature and wind conditions and improve the outdoor thermal conditions in an urban area.