PIE-engine 教程 ——提取黄河流域/山西省1980—2018年流域降水量并对比分析

编程语言 2022-09-26 21:13:59 阅读次数: 0

这里面我们首先要上传我们自己的研究区，然后加载每一年的数据降水数据，通过系数转化，完成正常降水量的展示，我们通过对reduceregion的统计，分别算出平均降水量，分辨率设定为1000米，最后加载影像的chart折线图，用于存放坐标横轴时间和纵轴的对象（降水），这里我们首先看一下降水数据

中国区域地面气象要素驱动数据年度合成产品（1979-2018），包括近地面气温、近地面气压、近地面空气比湿、近地面全速、地面向下短波辐射、地面向下长波辐射、地面降水率共7个要素。数据为NETCDF格式，时间分辨率为年，水平空间分辨率为0.1°。可为中国区陆面过程模拟提供驱动数据。

数据集ID: 

TPDC/CMFD_01YEAR

时间范围: 1979年-2018年

范围: 全国

来源: 国家青藏高原科学数据中心

复制代码段: 

var images = pie.ImageCollection("TPDC/CMFD_01YEAR")

波段统计：

名称 类型 空间分辨率 无效值
lrad Float32 0.1度 -32767
prec Float32 0.1度 -32767
pres Float32 0.1度 -32767
shum Float32 0.1度 -32767
srad Float32 0.1度 -32767
temp Float32 0.1度 -32767
wind Float32 0.1度 -32767

属性：

date

string

影像日期

数据引用：阳坤, 何杰. (2019). 中国区域地面气象要素驱动数据集（1979-2018）. 国家青藏高原科学数据中心, DOI: 10.11888/AtmosphericPhysics.tpe.249369.file. CSTR: 18406.11.AtmosphericPhysics.tpe.249369.file.
[Yang, K., He, J. (2019). China meteorological forcing dataset (1979-2018). National Tibetan Plateau Data Center, DOI: 10.11888/AtmosphericPhysics.tpe.249369.file. CSTR: 18406.11.AtmosphericPhysics.tpe.249369.file. ] (下载引用： RIS格式 | RIS英文格式 | Bibtex格式 | Bibtex英文格式 )

文章引用：
1.He, J., Yang, K., Tang, W. Lu, H., Qin, J., Chen, Y.Y., & Li, X. (2020). The first high-resolution meteorological forcing dataset for land process studies over China. Scientific Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.( 查看 | Bibtex格式)
2.Yang, K., He, J.,Tang, W.J., Qin, J., & Cheng, C. (2010). On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agricultural and Forest Meteorology, 150(1), 38-46.( 查看 | Bibtex格式)

文中用到的函数：

reduceRegion(reducer,geometry,scale)

对特定区域的所有像素进行统计，返回结果为一个JSON对象；目前可完成最大、最小和求和统计计算。

方法参数：

- image(Image)

Image实例。

- reducer(Reducer)

统计类型，包括最大值、最小值和求和。

- geometry(Geometry)

统计区域范围。默认是影像第一个波段的范围。

- scale(Number)

统计采样比例。

返回值：Dictionary

ChartImage(ySeries,xSeries,options)

即将弃用，请使用 ui.Chart.image.*替换。
通过统计的结果在控制台绘制图表，绘制的图表可以保存。

方法参数：

- ySeries(List)

计算结果Array对象

- xSeries(List)

横坐标标注Array对象

- options(Object)

用于描述统计图的Json对象

返回值：null

代码：


//加载用户已上传的研究区边界数据(以黄河流域为例/山西省为例)
var region = pie.FeatureCollection("user/PieGeoLiu666/RiverBasin/YellowRiver_Basin")//选择用户已上传的矢量边界
    .first()
    .geometry();

//显示计算区域边界
Map.addLayer(region, { color: "0000ff", fillColor: "00000000", width: 2 }, "黄河流域");
Map.setCenter(112.254, 35.726, 3);
print(region);

//加载逐年CMFD数据
var CMFD1979 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1979").select('prec').clip(region);
var prec1979 = CMFD1979.multiply(8760);

var CMFD1980 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1980").select('prec').clip(region);
var prec1980 = CMFD1980.multiply(8760);

var CMFD1981 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1981").select('prec').clip(region);
var prec1981 = CMFD1981.multiply(8760);

var CMFD1982 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1982").select('prec').clip(region);
var prec1982 = CMFD1982.multiply(8760);

var CMFD1983 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1983").select('prec').clip(region);
var prec1983 = CMFD1983.multiply(8760);

var CMFD1984 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1984").select('prec').clip(region);
var prec1984 = CMFD1984.multiply(8760);

var CMFD1985 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1985").select('prec').clip(region);
var prec1985 = CMFD1985.multiply(8760);

var CMFD1986 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1986").select('prec').clip(region);
var prec1986 = CMFD1986.multiply(8760);

var CMFD1987 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1987").select('prec').clip(region);
var prec1987 = CMFD1987.multiply(8760);

var CMFD1988 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1988").select('prec').clip(region);
var prec1988 = CMFD1988.multiply(8760);

var CMFD1989 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1989").select('prec').clip(region);
var prec1989 = CMFD1989.multiply(8760);

var CMFD1990 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1990").select('prec').clip(region);
var prec1990 = CMFD1990.multiply(8760);

var CMFD1991 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1991").select('prec').clip(region);
var prec1991 = CMFD1991.multiply(8760);

var CMFD1992 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1992").select('prec').clip(region);
var prec1992 = CMFD1992.multiply(8760);

var CMFD1993 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1993").select('prec').clip(region);
var prec1993 = CMFD1993.multiply(8760);

var CMFD1994 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1994").select('prec').clip(region);
var prec1994 = CMFD1994.multiply(8760);

var CMFD1995 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1995").select('prec').clip(region);
var prec1995 = CMFD1995.multiply(8760);

var CMFD1996 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1996").select('prec').clip(region);
var prec1996 = CMFD1996.multiply(8760);

var CMFD1997 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1997").select('prec').clip(region);
var prec1997 = CMFD1997.multiply(8760);

var CMFD1998 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1998").select('prec').clip(region);
var prec1998 = CMFD1998.multiply(8760);

var CMFD1999 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1999").select('prec').clip(region);
var prec1999 = CMFD1999.multiply(8760);

var CMFD2000 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2000").select('prec').clip(region);
var prec2000 = CMFD2000.multiply(8760);

var CMFD2001 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2001").select('prec').clip(region);
var prec2001 = CMFD2001.multiply(8760);

var CMFD2002 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2002").select('prec').clip(region);
var prec2002 = CMFD2002.multiply(8760);

var CMFD2003 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2003").select('prec').clip(region);
var prec2003 = CMFD2003.multiply(8760);

var CMFD2004 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2004").select('prec').clip(region);
var prec2004 = CMFD2004.multiply(8760);

var CMFD2005 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2005").select('prec').clip(region);
var prec2005 = CMFD2005.multiply(8760);

var CMFD2006 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2006").select('prec').clip(region);
var prec2006 = CMFD2006.multiply(8760);

var CMFD2007 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2007").select('prec').clip(region);
var prec2007 = CMFD2007.multiply(8760);

var CMFD2008 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2008").select('prec').clip(region);
var prec2008 = CMFD2008.multiply(8760);

var CMFD2009 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2009").select('prec').clip(region);
var prec2009 = CMFD2009.multiply(8760);

var CMFD2010 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2010").select('prec').clip(region);
var prec2010 = CMFD2010.multiply(8760);

var CMFD2011 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2011").select('prec').clip(region);
var prec2011 = CMFD2011.multiply(8760);

var CMFD2012 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2012").select('prec').clip(region);
var prec2012 = CMFD2012.multiply(8760);

var CMFD2013 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2013").select('prec').clip(region);
var prec2013 = CMFD2013.multiply(8760);

var CMFD2014 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2014").select('prec').clip(region);
var prec2014 = CMFD2014.multiply(8760);

var CMFD2015 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2015").select('prec').clip(region);
var prec2015 = CMFD2015.multiply(8760);

var CMFD2016 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2016").select('prec').clip(region);
var prec2016 = CMFD2016.multiply(8760);

var CMFD2017 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2017").select('prec').clip(region);
var prec2017 = CMFD2017.multiply(8760);

var CMFD2018 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2018").select('prec').clip(region);
var prec2018 = CMFD2018.multiply(8760);

//统计计算研究区内的逐年均值
var prec1979_mean = prec1979.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1980_mean = prec1980.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1981_mean = prec1981.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1982_mean = prec1982.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1983_mean = prec1983.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1984_mean = prec1984.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1985_mean = prec1985.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1986_mean = prec1986.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1987_mean = prec1987.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1988_mean = prec1988.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1989_mean = prec1989.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1990_mean = prec1990.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1991_mean = prec1991.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1992_mean = prec1992.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1993_mean = prec1993.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1994_mean = prec1994.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1995_mean = prec1995.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1996_mean = prec1996.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1997_mean = prec1997.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1998_mean = prec1998.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1999_mean = prec1999.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2000_mean = prec2000.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2001_mean = prec2001.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2002_mean = prec2002.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2003_mean = prec2003.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2004_mean = prec2004.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2005_mean = prec2005.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2006_mean = prec2006.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2007_mean = prec2007.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2008_mean = prec2008.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2009_mean = prec2009.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2010_mean = prec2010.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2011_mean = prec2011.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2012_mean = prec2012.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2013_mean = prec2013.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2014_mean = prec2014.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2015_mean = prec2015.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2016_mean = prec2016.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2017_mean = prec2017.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2018_mean = prec2018.reduceRegion(pie.Reducer.mean(), region, 1000);

//配置折线图的样式
var line_options = {
    title: '1979-2018年黄河流域年降水量(mm)',
    legend: ['降水量(mm)'],
    xAxisName: "年份",
    yAxisName: "年降水量(mm)",
    chartType: "line"
};
var images = [prec1979_mean, prec1980_mean, prec1981_mean, prec1982_mean, prec1983_mean, prec1984_mean,
    prec1985_mean, prec1986_mean, prec1987_mean, prec1988_mean, prec1989_mean, prec1990_mean,
    prec1991_mean, prec1992_mean, prec1993_mean, prec1994_mean, prec1995_mean, prec1996_mean,
    prec1997_mean, prec1998_mean, prec1999_mean, prec2000_mean, prec2001_mean, prec2002_mean,
    prec2003_mean, prec2004_mean, prec2005_mean, prec2006_mean, prec2007_mean, prec2008_mean,
    prec2009_mean, prec2010_mean, prec2011_mean, prec2012_mean, prec2013_mean, prec2014_mean,
    prec2015_mean, prec2016_mean, prec2017_mean, prec2018_mean];

var xSeries = [1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995,
    1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
    2013, 2014, 2015, 2016, 2017, 2018];

//绘制折线图
ChartImage(images, xSeries, line_options);

 同样我们加载山西省的数据来尝试一下，当然你也可以画一个自己的研究区完成指定区域的降水数据的统计，当然这里加载新的研究区会有卡顿现象，所以大家要注意研究加载较大的时候，我们注意等待，大概2分钟山西省的降水量也能出来啦：

 山西省的代码：

//加载山西省边界
var region = pie.FeatureCollection('user/xg346049806/shanxibianjie').first().geometry();;


//显示计算区域边界
Map.addLayer(region, { color: "0000ff", fillColor: "00000000", width: 2 }, "黄河流域");
Map.setCenter(112.254, 35.726, 3);
print(region);

//加载逐年CMFD数据
var CMFD1979 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1979").select('prec').clip(region);
var prec1979 = CMFD1979.multiply(8760);

var CMFD1980 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1980").select('prec').clip(region);
var prec1980 = CMFD1980.multiply(8760);

var CMFD1981 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1981").select('prec').clip(region);
var prec1981 = CMFD1981.multiply(8760);

var CMFD1982 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1982").select('prec').clip(region);
var prec1982 = CMFD1982.multiply(8760);

var CMFD1983 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1983").select('prec').clip(region);
var prec1983 = CMFD1983.multiply(8760);

var CMFD1984 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1984").select('prec').clip(region);
var prec1984 = CMFD1984.multiply(8760);

var CMFD1985 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1985").select('prec').clip(region);
var prec1985 = CMFD1985.multiply(8760);

var CMFD1986 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1986").select('prec').clip(region);
var prec1986 = CMFD1986.multiply(8760);

var CMFD1987 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1987").select('prec').clip(region);
var prec1987 = CMFD1987.multiply(8760);

var CMFD1988 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1988").select('prec').clip(region);
var prec1988 = CMFD1988.multiply(8760);

var CMFD1989 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1989").select('prec').clip(region);
var prec1989 = CMFD1989.multiply(8760);

var CMFD1990 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1990").select('prec').clip(region);
var prec1990 = CMFD1990.multiply(8760);

var CMFD1991 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1991").select('prec').clip(region);
var prec1991 = CMFD1991.multiply(8760);

var CMFD1992 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1992").select('prec').clip(region);
var prec1992 = CMFD1992.multiply(8760);

var CMFD1993 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1993").select('prec').clip(region);
var prec1993 = CMFD1993.multiply(8760);

var CMFD1994 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1994").select('prec').clip(region);
var prec1994 = CMFD1994.multiply(8760);

var CMFD1995 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1995").select('prec').clip(region);
var prec1995 = CMFD1995.multiply(8760);

var CMFD1996 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1996").select('prec').clip(region);
var prec1996 = CMFD1996.multiply(8760);

var CMFD1997 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1997").select('prec').clip(region);
var prec1997 = CMFD1997.multiply(8760);

var CMFD1998 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1998").select('prec').clip(region);
var prec1998 = CMFD1998.multiply(8760);

var CMFD1999 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_1999").select('prec').clip(region);
var prec1999 = CMFD1999.multiply(8760);

var CMFD2000 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2000").select('prec').clip(region);
var prec2000 = CMFD2000.multiply(8760);

var CMFD2001 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2001").select('prec').clip(region);
var prec2001 = CMFD2001.multiply(8760);

var CMFD2002 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2002").select('prec').clip(region);
var prec2002 = CMFD2002.multiply(8760);

var CMFD2003 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2003").select('prec').clip(region);
var prec2003 = CMFD2003.multiply(8760);

var CMFD2004 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2004").select('prec').clip(region);
var prec2004 = CMFD2004.multiply(8760);

var CMFD2005 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2005").select('prec').clip(region);
var prec2005 = CMFD2005.multiply(8760);

var CMFD2006 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2006").select('prec').clip(region);
var prec2006 = CMFD2006.multiply(8760);

var CMFD2007 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2007").select('prec').clip(region);
var prec2007 = CMFD2007.multiply(8760);

var CMFD2008 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2008").select('prec').clip(region);
var prec2008 = CMFD2008.multiply(8760);

var CMFD2009 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2009").select('prec').clip(region);
var prec2009 = CMFD2009.multiply(8760);

var CMFD2010 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2010").select('prec').clip(region);
var prec2010 = CMFD2010.multiply(8760);

var CMFD2011 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2011").select('prec').clip(region);
var prec2011 = CMFD2011.multiply(8760);

var CMFD2012 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2012").select('prec').clip(region);
var prec2012 = CMFD2012.multiply(8760);

var CMFD2013 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2013").select('prec').clip(region);
var prec2013 = CMFD2013.multiply(8760);

var CMFD2014 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2014").select('prec').clip(region);
var prec2014 = CMFD2014.multiply(8760);

var CMFD2015 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2015").select('prec').clip(region);
var prec2015 = CMFD2015.multiply(8760);

var CMFD2016 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2016").select('prec').clip(region);
var prec2016 = CMFD2016.multiply(8760);

var CMFD2017 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2017").select('prec').clip(region);
var prec2017 = CMFD2017.multiply(8760);

var CMFD2018 = pie.Image("TPDC/CMFD_01YEAR/CMFD_01YEAR_2018").select('prec').clip(region);
var prec2018 = CMFD2018.multiply(8760);

//统计计算研究区内的逐年均值
var prec1979_mean = prec1979.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1980_mean = prec1980.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1981_mean = prec1981.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1982_mean = prec1982.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1983_mean = prec1983.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1984_mean = prec1984.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1985_mean = prec1985.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1986_mean = prec1986.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1987_mean = prec1987.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1988_mean = prec1988.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1989_mean = prec1989.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1990_mean = prec1990.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1991_mean = prec1991.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1992_mean = prec1992.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1993_mean = prec1993.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1994_mean = prec1994.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1995_mean = prec1995.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1996_mean = prec1996.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1997_mean = prec1997.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1998_mean = prec1998.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec1999_mean = prec1999.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2000_mean = prec2000.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2001_mean = prec2001.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2002_mean = prec2002.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2003_mean = prec2003.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2004_mean = prec2004.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2005_mean = prec2005.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2006_mean = prec2006.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2007_mean = prec2007.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2008_mean = prec2008.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2009_mean = prec2009.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2010_mean = prec2010.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2011_mean = prec2011.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2012_mean = prec2012.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2013_mean = prec2013.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2014_mean = prec2014.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2015_mean = prec2015.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2016_mean = prec2016.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2017_mean = prec2017.reduceRegion(pie.Reducer.mean(), region, 1000);
var prec2018_mean = prec2018.reduceRegion(pie.Reducer.mean(), region, 1000);

//配置折线图的样式
var line_options = {
    title: '1979-2018年山西省年降水量(mm)',
    legend: ['降水量(mm)'],
    xAxisName: "年份",
    yAxisName: "年降水量(mm)",
    chartType: "line"
};
var images = [prec1979_mean, prec1980_mean, prec1981_mean, prec1982_mean, prec1983_mean, prec1984_mean,
    prec1985_mean, prec1986_mean, prec1987_mean, prec1988_mean, prec1989_mean, prec1990_mean,
    prec1991_mean, prec1992_mean, prec1993_mean, prec1994_mean, prec1995_mean, prec1996_mean,
    prec1997_mean, prec1998_mean, prec1999_mean, prec2000_mean, prec2001_mean, prec2002_mean,
    prec2003_mean, prec2004_mean, prec2005_mean, prec2006_mean, prec2007_mean, prec2008_mean,
    prec2009_mean, prec2010_mean, prec2011_mean, prec2012_mean, prec2013_mean, prec2014_mean,
    prec2015_mean, prec2016_mean, prec2017_mean, prec2018_mean];

var xSeries = [1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995,
    1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
    2013, 2014, 2015, 2016, 2017, 2018];

//绘制折线图
ChartImage(images, xSeries, line_options);

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