农业工程学报
農業工程學報
농업공정학보
2013年
13期
60-66
,共7页
蒸发%GIS%模型%海河流域%空间分布%插值%站点密度
蒸髮%GIS%模型%海河流域%空間分佈%插值%站點密度
증발%GIS%모형%해하류역%공간분포%삽치%참점밀도
evaporation%GIS%models%Haihe river basin%spatial distribution%interpolation%spatial stations density
流域参考作物蒸发蒸腾量(ET0)插值方法的研究对流域尺度作物耗水时空变化规律有重要意义。该文通过海河流域162个国家农业气象站3a(2003-2005年)旬值气象资料,利用Penman-Monteith公式计算了这些站点ET0,采用ArcGIS软件中常用的Spline、IDW和Ordinary Kriging(OK)法,以及近些年研究较多的线性回归Regression 插值法,对不同站点密度条件下的 ET0进行空间插值。分析了各空间插值方法在不同站点密度条件下的优劣性,并且给出了本流域内各种站点密度范围条件下计算 ET0最适宜的插值方法。结果表明以站点密度1.3个/万km2为界,当站点密度低于此密度时,推荐使用Regression插值法;当站点密度大于1.3个/万km2时,推荐使用IDW和OK插值法;当站点密度大于4.3个/万km2,以上三种插值法并无显著差别;不推荐使用Spline插值法。
流域參攷作物蒸髮蒸騰量(ET0)插值方法的研究對流域呎度作物耗水時空變化規律有重要意義。該文通過海河流域162箇國傢農業氣象站3a(2003-2005年)旬值氣象資料,利用Penman-Monteith公式計算瞭這些站點ET0,採用ArcGIS軟件中常用的Spline、IDW和Ordinary Kriging(OK)法,以及近些年研究較多的線性迴歸Regression 插值法,對不同站點密度條件下的 ET0進行空間插值。分析瞭各空間插值方法在不同站點密度條件下的優劣性,併且給齣瞭本流域內各種站點密度範圍條件下計算 ET0最適宜的插值方法。結果錶明以站點密度1.3箇/萬km2為界,噹站點密度低于此密度時,推薦使用Regression插值法;噹站點密度大于1.3箇/萬km2時,推薦使用IDW和OK插值法;噹站點密度大于4.3箇/萬km2,以上三種插值法併無顯著差彆;不推薦使用Spline插值法。
류역삼고작물증발증등량(ET0)삽치방법적연구대류역척도작물모수시공변화규률유중요의의。해문통과해하류역162개국가농업기상참3a(2003-2005년)순치기상자료,이용Penman-Monteith공식계산료저사참점ET0,채용ArcGIS연건중상용적Spline、IDW화Ordinary Kriging(OK)법,이급근사년연구교다적선성회귀Regression 삽치법,대불동참점밀도조건하적 ET0진행공간삽치。분석료각공간삽치방법재불동참점밀도조건하적우렬성,병차급출료본류역내각충참점밀도범위조건하계산 ET0최괄의적삽치방법。결과표명이참점밀도1.3개/만km2위계,당참점밀도저우차밀도시,추천사용Regression삽치법;당참점밀도대우1.3개/만km2시,추천사용IDW화OK삽치법;당참점밀도대우4.3개/만km2,이상삼충삽치법병무현저차별;불추천사용Spline삽치법。
With the intensified global climate change and increased human activity, water resources deficit and consequent imbalance between water supply and demand tends to be more serious. Research on water cycle and its spatial and temporal evolution under changing environment has attracted growing concerns. Evapotranspiration (ET) is not only an important component in the water cycle and water-heat balance, but also an important process in coupling and simulation interaction with the soil-atmosphere system and in the land-atmosphere system. ET is also an important basis for scientific assessment, management of water resources, and planning and design for agricultural water conservancy project, and thus attracted interests from the disciplines such as hydrology, water resources, agricultural irrigation, agricultural ecology, physical geography, and agro-meteorological. Research of interpolation models of reference crop evapotranspiration (ET0) is important to the temporal and spatial distribution of water resource in river basin scale. Haihe River basin located at the north China is one of the seven largest river basins in China, occupying an area of 3.2×105km2(34.9°-42.8°N, 112.0°-119.8°E). The middle and lower reach of the basin is one of important wheat production regions in China. This region in subtropical monsoon climate, semi-humid and semi-arid environment is strongly affected by human activities. In recent decades, several eco-environmental problems have become prominent under the combined impacts of climate change and intensified human perturbations. Water resources in Haihe are currently used for irrigation, aquaculture and industries. Due to very limited available water resources in the basin, water has been diverted from other basins to supply water to agriculture and to maintain the essential functions of the ecosystem. The ten-day average maximum air temperature and minimum air temperature, relative humidity, sunshine hours, wind speed were used to calculate ET0 using the Penman-Monteith equation recommended by FAO in 1998. We calculated ET0 using Penman-Monteith equation which was recommended by FAO according to weather data of 3 years (2003-2005) for 162 agricultural weather stations in the Haihe river basin. The temporal and spatial vatiations of ET0 were calculated by four interpolation models of Spline, Ordinary Kriging (OK), Inverse Distance Weighted (IDW) and Regression in ArcGIS. The results showed that when the spatial stations density is less-than 1.3 station every 10 000 km2, the Regression interpolation model was better than the other 3 interpolations;the OK and IDW model were recommended when the spatial stations density is greater-than 1.3 and less-than 4.3 station every 10 000 km2;when the spatial stations density is greater than 4.3 station every 10 000 km2, the results showed no big difference for three interpolations (OK, IDW, Regression). Spline method showed the worst results. In a word, Regression interpolation model presented higher accuracy if the spatial stations density is less-than 1.3 station every 10 000 km2;the OK and IDW interpolation model presented higher accuracy if the spatial stations density is greater than 1.3 and less than 4.3 station every 10 000 km2.