全球范围内12个典型区域提取(total runoff)

代码内容包括：

空间插值griddata函数的使用
用mask矩阵提取全球范围的12个典型区域提取
替换xticklabels

% 【摘要】读取.grd文件；对不同分辨率数据网格插值；对不重合区域取公共交集区,
%         比较全球12个典型区域的total runoff的值（CLM45 .VS. CLM50）与UNH-GRDC；
cd 'D:\数据\UNH-GRDC\World Runoff Data\runoff_grd\'
%（1）读 .grd 文件的 unh_grdc 月平均值
clc; clear; close all
flistobs = dir('D:\数据\UNH-GRDC\World Runoff Data\runoff_grd\*.grd');
for i =2:13                  % 留意读取的文件序号filename = flistobs(i).name;delimiterIn = ' ';       % 设置定界符，从grd文件复制headerlinesIn = 6;       % 设置需要跳过的行数，跳过6行，从第7行开始datastrct = importdata(filename,delimiterIn,headerlinesIn);rof = datastrct.data;rof = flip(rof);         % 数据可能是倒置的，可以通过pcolor查看rof(rof == -9999) = NaN; % 替换缺省值为 Nan rofcmp_mon{i-1} = rof;    % 以cell格式存储
end%（2）读 clm 模拟的多年月平均值（CLM45 or CLM50）
load('D:\数据\UNH-GRDC\World Runoff Data\runoff_grd\simres_clm45.mat')%（3）对 unh-grdc 数据进行空间插值计算（0.5*0.5转到1.25*0.9375）
fpath = 'D:\Models_Output\Clm45Sp\lnd\hist\';
flistsim = dir([fpath,'*.nc']);
fname = flistsim(1).name;
xx = ncread([fpath,fname],'lon');
yy = ncread([fpath,fname],'lat');
for m = 1:length(xx)if  xx(m) >= 180xx(m) = xx(m) - 360;elsecontinueend
end
xx = [xx(145:end);xx(1:144)];  % 将经度后半段(>=180)拼接到前面
[lonsim,latsim] = meshgrid(xx,yy);x = -180:0.5:179.5;
y = -55.5:0.5:82.5;
[lonobs,latobs] = meshgrid(x,y);
for k = 1:12ttmp = rofcmp_mon{k};cmprofup_mon{k} = griddata(lonobs(:),latobs(:),ttmp(:),lonsim,latsim,'nearest');
end%（4）确定全局情况下两者共同交集网格（利用Nan 与 非Nan 的计算结果为 Nan 确定交集区域）
commask = cmprofup_mon{1} - rofsim_mon{1};   % comaree中非nan的网格就是两者共有网格
commask(~isnan(commask)) = 1;
for m = 1:12tmpcmp = cmprofup_mon{m};cmprofup_mon{m} = tmpcmp.*commask;tmpsim = rofsim_mon{m};rofsim_mon{m} = tmpsim.*commask;
end%（5）划定各个典型区域坐标范围
% West_Siberia(50-70N，60-90E)  East_Siberia(50-70N,90-140E)  Canada(50-70N,90-170W)
% Amazon_Basin(10S-0，70-50W)   Congo(10S-5N,10-30E)          India(10-30N,70-90E)
% South_Africa(35-10S,10-40E)   Eastern_China(25-40N,110-125E)
% Central_Europ(40-50N,0-40E)   Sahara+Arabla(10-30N,0-50E)   Western_US(30-50N,130-110W)
% Australia(40-10S,110-160E)
reginal_name = ...
{['West Siberia(50-70N，60-90E)'] ['East Siberia(50-70N,90-140E)']...['Canada(50-70N,90-170W)']       ['Amazon Basin(10S-0，70-50W)']...['Congo(10S-5N,10-30E)']         ['India(10-30N,70-90E)']...['South Africa(35-10S,10-40E)']  ['Eastern China(25-40N,110-125E)']...['Central Europ(40-50N,0-40E)']  ['Sahara-Arabla(10-30N,0-50E)']...['Western US(30-50N,130-110W)']  ['Australia(40-10S,110-160E)']};
dlon = 1.25;
dlat = 0.9375;%（5-1）划定各个区域范围
West_Siberia_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
West_Siberia_lon = [floor((60+180)/dlon):ceil((90+180)/dlon)];East_Siberia_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
East_Siberia_lon = [floor((90+180)/dlon):ceil((140+180)/dlon)];Canada_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
Canada_lon = [floor((180-170)/dlon):ceil((180-90)/dlon)];Amazon_Basin_lat = [floor((90-10)/dlat):ceil((90-0)/dlat)];
Amazon_Basin_lon = [floor((180-70)/dlon):ceil((180-50)/dlon)];Congo_lat = [floor((90-10)/dlat):ceil((90+5)/dlat)];
Congo_lon = [floor((10+180)/dlon):ceil((30+180)/dlon)];India_lat = [floor((90+10)/dlat):ceil((90+30)/dlat)];
India_lon = [floor((70+180)/dlon):ceil((90+180)/dlon)];South_Africa_lat = [floor((90-35)/dlat):ceil((90-10)/dlat)];
South_Africa_lon = [floor((10+180)/dlon):ceil((40+180)/dlon)];Eastern_China_lat = [floor((90+25)/dlat):ceil((90+40)/dlat)];
Eastern_China_lon = [floor((110+180)/dlon):ceil((125+180)/dlon)];Central_Europ_lat = [floor((40+90)/dlat):ceil((50+90)/dlat)];
Central_Europ_lon = [floor((180+0)/dlon):ceil((180+40)/dlon)];Sahara_Arabla_lat = [floor((90+10)/dlat):ceil((90+30)/dlat)];
Sahara_Arabla_lon = [floor((180+0)/dlon):ceil((180+50)/dlon)];Western_US_lat = [floor((90+30)/dlat):ceil((90+50)/dlat)];
Western_US_lon = [floor((180-130)/dlon):ceil((180-110)/dlon)];Australia_lat = [floor((90-40)/dlat):ceil((90-10)/dlat)];
Australia_lon = [floor((110+180)/dlon):ceil((180+160)/dlon)];%（5-2）提取各个区域网格
for i = 1:12  tmpobs = cmprofup_mon{i}; % 逐月提取观测值West_Siberia_obs{i} = tmpobs(West_Siberia_lat, West_Siberia_lon);East_Siberia_obs{i} = tmpobs(East_Siberia_lat, East_Siberia_lon);Canada_obs{i}       = tmpobs(Canada_lat,Canada_lon);Amazon_Basin_obs{i} = tmpobs(Amazon_Basin_lat,Amazon_Basin_lon);Congo_obs{i}        = tmpobs(Congo_lat,Congo_lon);India_obs{i}        = tmpobs(India_lat,India_lon);South_Africa_obs{i} = tmpobs(South_Africa_lat, South_Africa_lon);Eastern_China_obs{i}= tmpobs(Eastern_China_lat,Eastern_China_lon);Central_Europ_obs{i}= tmpobs(Central_Europ_lat,Central_Europ_lon);Sahara_Arabla_obs{i}= tmpobs(Sahara_Arabla_lat,Sahara_Arabla_lon);Western_US_obs{i}   = tmpobs(Western_US_lat,Western_US_lon);Australia_obs{i}    = tmpobs(Australia_lat,Australia_lon);tmpsim = rofsim_mon{i};   %  逐月提取模拟值West_Siberia_sim{i} = tmpsim(West_Siberia_lat, West_Siberia_lon);East_Siberia_sim{i} = tmpsim(East_Siberia_lat, East_Siberia_lon);Canada_sim{i}       = tmpsim(Canada_lat,Canada_lon);Amazon_Basin_sim{i} = tmpsim(Amazon_Basin_lat,Amazon_Basin_lon);Congo_sim{i}        = tmpsim(Congo_lat,Congo_lon);India_sim{i}        = tmpsim(India_lat,India_lon);South_Africa_sim{i} = tmpsim(South_Africa_lat, South_Africa_lon);Eastern_China_sim{i}= tmpsim(Eastern_China_lat,Eastern_China_lon);Central_Europ_sim{i}= tmpsim(Central_Europ_lat,Central_Europ_lon);Sahara_Arabla_sim{i}= tmpsim(Sahara_Arabla_lat,Sahara_Arabla_lon);Western_US_sim{i}   = tmpsim(Western_US_lat,Western_US_lon);Australia_sim{i}    = tmpsim(Australia_lat,Australia_lon);
end%（6）比较逐月runoff数据
for i = 1:12tmpreg = West_Siberia_obs{i};West_Siberia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = West_Siberia_sim{i};West_Siberia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = East_Siberia_obs{i};East_Siberia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = East_Siberia_sim{i};East_Siberia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Canada_obs{i};Canada_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Canada_sim{i};Canada_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Amazon_Basin_obs{i};Amazon_Basin_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Amazon_Basin_sim{i};Amazon_Basin_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Congo_obs{i};Congo_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Congo_sim{i};Congo_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = India_obs{i};India_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = India_sim{i};India_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = South_Africa_obs{i};South_Africa_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = South_Africa_sim{i};South_Africa_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Eastern_China_obs{i};Eastern_China_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Eastern_China_sim{i};Eastern_China_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Central_Europ_obs{i};Central_Europ_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Central_Europ_sim{i};Central_Europ_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Sahara_Arabla_obs{i};Sahara_Arabla_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Sahara_Arabla_sim{i};Sahara_Arabla_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Western_US_obs{i};Western_US_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Western_US_sim{i};Western_US_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Australia_obs{i};Australia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Australia_sim{i};Australia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));
end%(7) 画每个区域逐月runoff图，为方便画图，将此12个区域汇总起来
regset_45sim{1}  = West_Siberia_simmon;
regset_45sim{2}  = East_Siberia_simmon;
regset_45sim{3}  = Canada_simmon;
regset_45sim{4}  = Amazon_Basin_simmon;
regset_45sim{5}  = Congo_simmon;
regset_45sim{6}  = India_simmon;
regset_45sim{7}  = South_Africa_simmon;
regset_45sim{8}  = Eastern_China_simmon;
regset_45sim{9}  = Central_Europ_simmon;
regset_45sim{10} = Sahara_Arabla_simmon;
regset_45sim{11} = Western_US_simmon;
regset_45sim{12} = Australia_simmon;regset_obs{1}  = West_Siberia_obsmon;
regset_obs{2}  = East_Siberia_obsmon;
regset_obs{3}  = Canada_obsmon;
regset_obs{4}  = Amazon_Basin_obsmon;
regset_obs{5}  = Congo_obsmon;
regset_obs{6}  = India_obsmon;
regset_obs{7}  = South_Africa_obsmon;
regset_obs{8}  = Eastern_China_obsmon;
regset_obs{9}  = Central_Europ_obsmon;
regset_obs{10} = Sahara_Arabla_obsmon;
regset_obs{11} = Western_US_obsmon;
regset_obs{12} = Australia_obsmon;%% 重复上述计算，导入 clm50 的数据
%（2）读 clm 模拟的多年月平均值（CLM45 or CLM50）
load('D:\数据\UNH-GRDC\World Runoff Data\runoff_grd\simres_clm50.mat')%（3）对 unh-grdc 数据进行空间插值计算（0.5*0.5转到1.25*0.9375）
fpath = 'D:\Models_Output\Clm50Sp\lnd\hist\';
flistsim = dir([fpath,'*.nc']);
fname = flistsim(1).name;
xx = ncread([fpath,fname],'lon');
yy = ncread([fpath,fname],'lat');
for m = 1:length(xx)if  xx(m) >= 180xx(m) = xx(m) - 360;elsecontinueend
end
xx = [xx(145:end);xx(1:144)];  % 将经度后半段(>=180)拼接到前面
[lonsim,latsim] = meshgrid(xx,yy);x = -180:0.5:179.5;
y = -55.5:0.5:82.5;
[lonobs,latobs] = meshgrid(x,y);
for k = 1:12ttmp = rofcmp_mon{k};cmprofup_mon{k} = griddata(lonobs(:),latobs(:),ttmp(:),lonsim,latsim,'nearest');
end%（4）确定全局情况下两者共同交集网格（利用Nan 与 非Nan 的计算结果为 Nan 确定交集区域）
commask = cmprofup_mon{1} - rofsim_mon{1};   % comaree中非nan的网格就是两者共有网格
commask(~isnan(commask)) = 1;
for m = 1:12tmpcmp = cmprofup_mon{m};cmprofup_mon{m} = tmpcmp.*commask;tmpsim = rofsim_mon{m};rofsim_mon{m} = tmpsim.*commask;
end%（5）划定各个典型区域坐标范围
% West_Siberia(50-70N，60-90E)  East_Siberia(50-70N,90-140E)  Canada(50-70N,90-170W)
% Amazon_Basin(10S-0，70-50W)   Congo(10S-5N,10-30E)          India(10-30N,70-90E)
% South_Africa(35-10S,10-40E)   Eastern_China(25-40N,110-125E)
% Central_Europ(40-50N,0-40E)   Sahara+Arabla(10-30N,0-50E)   Western_US(30-50N,130-110W)
% Australia(40-10S,110-160E)
reginal_name = ...
{['West Siberia(50-70N，60-90E)'] ['East Siberia(50-70N,90-140E)']...['Canada(50-70N,90-170W)']       ['Amazon Basin(10S-0，70-50W)']...['Congo(10S-5N,10-30E)']         ['India(10-30N,70-90E)']...['South Africa(35-10S,10-40E)']  ['Eastern China(25-40N,110-125E)']...['Central Europ(40-50N,0-40E)']  ['Sahara-Arabla(10-30N,0-50E)']...['Western US(30-50N,130-110W)']  ['Australia(40-10S,110-160E)']};
dlon = 1.25;
dlat = 0.9375;%（5-1）划定各个区域范围
West_Siberia_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
West_Siberia_lon = [floor((60+180)/dlon):ceil((90+180)/dlon)];East_Siberia_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
East_Siberia_lon = [floor((90+180)/dlon):ceil((140+180)/dlon)];Canada_lat = [floor((50+90)/dlat):ceil((70+90)/dlat)];
Canada_lon = [floor((180-170)/dlon):ceil((180-90)/dlon)];Amazon_Basin_lat = [floor((90-10)/dlat):ceil((90-0)/dlat)];
Amazon_Basin_lon = [floor((180-70)/dlon):ceil((180-50)/dlon)];Congo_lat = [floor((90-10)/dlat):ceil((90+5)/dlat)];
Congo_lon = [floor((10+180)/dlon):ceil((30+180)/dlon)];India_lat = [floor((90+10)/dlat):ceil((90+30)/dlat)];
India_lon = [floor((70+180)/dlon):ceil((90+180)/dlon)];South_Africa_lat = [floor((90-35)/dlat):ceil((90-10)/dlat)];
South_Africa_lon = [floor((10+180)/dlon):ceil((40+180)/dlon)];Eastern_China_lat = [floor((90+25)/dlat):ceil((90+40)/dlat)];
Eastern_China_lon = [floor((110+180)/dlon):ceil((125+180)/dlon)];Central_Europ_lat = [floor((40+90)/dlat):ceil((50+90)/dlat)];
Central_Europ_lon = [floor((180+0)/dlon):ceil((180+40)/dlon)];Sahara_Arabla_lat = [floor((90+10)/dlat):ceil((90+30)/dlat)];
Sahara_Arabla_lon = [floor((180+0)/dlon):ceil((180+50)/dlon)];Western_US_lat = [floor((90+30)/dlat):ceil((90+50)/dlat)];
Western_US_lon = [floor((180-130)/dlon):ceil((180-110)/dlon)];Australia_lat = [floor((90-40)/dlat):ceil((90-10)/dlat)];
Australia_lon = [floor((110+180)/dlon):ceil((180+160)/dlon)];%（5-2）提取各个区域网格
for i = 1:12  tmpobs = cmprofup_mon{i}; % 逐月提取观测值West_Siberia_obs{i} = tmpobs(West_Siberia_lat, West_Siberia_lon);East_Siberia_obs{i} = tmpobs(East_Siberia_lat, East_Siberia_lon);Canada_obs{i}       = tmpobs(Canada_lat,Canada_lon);Amazon_Basin_obs{i} = tmpobs(Amazon_Basin_lat,Amazon_Basin_lon);Congo_obs{i}        = tmpobs(Congo_lat,Congo_lon);India_obs{i}        = tmpobs(India_lat,India_lon);South_Africa_obs{i} = tmpobs(South_Africa_lat, South_Africa_lon);Eastern_China_obs{i}= tmpobs(Eastern_China_lat,Eastern_China_lon);Central_Europ_obs{i}= tmpobs(Central_Europ_lat,Central_Europ_lon);Sahara_Arabla_obs{i}= tmpobs(Sahara_Arabla_lat,Sahara_Arabla_lon);Western_US_obs{i}   = tmpobs(Western_US_lat,Western_US_lon);Australia_obs{i}    = tmpobs(Australia_lat,Australia_lon);tmpsim = rofsim_mon{i};   %  逐月提取模拟值West_Siberia_sim{i} = tmpsim(West_Siberia_lat, West_Siberia_lon);East_Siberia_sim{i} = tmpsim(East_Siberia_lat, East_Siberia_lon);Canada_sim{i}       = tmpsim(Canada_lat,Canada_lon);Amazon_Basin_sim{i} = tmpsim(Amazon_Basin_lat,Amazon_Basin_lon);Congo_sim{i}        = tmpsim(Congo_lat,Congo_lon);India_sim{i}        = tmpsim(India_lat,India_lon);South_Africa_sim{i} = tmpsim(South_Africa_lat, South_Africa_lon);Eastern_China_sim{i}= tmpsim(Eastern_China_lat,Eastern_China_lon);Central_Europ_sim{i}= tmpsim(Central_Europ_lat,Central_Europ_lon);Sahara_Arabla_sim{i}= tmpsim(Sahara_Arabla_lat,Sahara_Arabla_lon);Western_US_sim{i}   = tmpsim(Western_US_lat,Western_US_lon);Australia_sim{i}    = tmpsim(Australia_lat,Australia_lon);
end%（6）比较逐月runoff数据
for i = 1:12tmpreg = West_Siberia_obs{i};West_Siberia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = West_Siberia_sim{i};West_Siberia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = East_Siberia_obs{i};East_Siberia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = East_Siberia_sim{i};East_Siberia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Canada_obs{i};Canada_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Canada_sim{i};Canada_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Amazon_Basin_obs{i};Amazon_Basin_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Amazon_Basin_sim{i};Amazon_Basin_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Congo_obs{i};Congo_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Congo_sim{i};Congo_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = India_obs{i};India_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = India_sim{i};India_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = South_Africa_obs{i};South_Africa_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = South_Africa_sim{i};South_Africa_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Eastern_China_obs{i};Eastern_China_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Eastern_China_sim{i};Eastern_China_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Central_Europ_obs{i};Central_Europ_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Central_Europ_sim{i};Central_Europ_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Sahara_Arabla_obs{i};Sahara_Arabla_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Sahara_Arabla_sim{i};Sahara_Arabla_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Western_US_obs{i};Western_US_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Western_US_sim{i};Western_US_simmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Australia_obs{i};Australia_obsmon(i) = mean(tmpreg(~isnan(tmpreg)));tmpreg = Australia_sim{i};Australia_simmon(i) = mean(tmpreg(~isnan(tmpreg)));
end%(7) 画每个区域逐月runoff图，为方便画图，将此12个区域汇总起来
regset_50sim{1}  = West_Siberia_simmon;
regset_50sim{2}  = East_Siberia_simmon;
regset_50sim{3}  = Canada_simmon;
regset_50sim{4}  = Amazon_Basin_simmon;
regset_50sim{5}  = Congo_simmon;
regset_50sim{6}  = India_simmon;
regset_50sim{7}  = South_Africa_simmon;
regset_50sim{8}  = Eastern_China_simmon;
regset_50sim{9}  = Central_Europ_simmon;
regset_50sim{10} = Sahara_Arabla_simmon;
regset_50sim{11} = Western_US_simmon;
regset_50sim{12} = Australia_simmon;regset_obs{1}  = West_Siberia_obsmon;
regset_obs{2}  = East_Siberia_obsmon;
regset_obs{3}  = Canada_obsmon;
regset_obs{4}  = Amazon_Basin_obsmon;
regset_obs{5}  = Congo_obsmon;
regset_obs{6}  = India_obsmon;
regset_obs{7}  = South_Africa_obsmon;
regset_obs{8}  = Eastern_China_obsmon;
regset_obs{9}  = Central_Europ_obsmon;
regset_obs{10} = Sahara_Arabla_obsmon;
regset_obs{11} = Western_US_obsmon;
regset_obs{12} = Australia_obsmon;%% 画图篇
figure(1)
set(gcf,'Color','w');
for i = 1:3subplot('Position',[0.1+0.25*(i-1) 0.78 0.22 0.17]);plot(regset_obs{i},'k-.*','LineWidth',1)hold onplot(regset_45sim{i},'b-','LineWidth',2)hold onplot(regset_50sim{i},'r-','LineWidth',2)set(gca,'FontSize',12,'LineWidth',1.5);title(reginal_name{i});xlim([0,13]);xticks(1:13);xticklabels({'J','F','M','A','M','J','J','A','S','O','N','D',''});if i == 1ylabel('runoff (mm/month)' )legend('GRDC','CLM45','CLM50')end
endfor i = 4:6subplot('Position',[0.1+0.25*(i-4) 0.55 0.22 0.17]);plot(regset_obs{i},'k-.*','LineWidth',1)hold onplot(regset_45sim{i},'b-','LineWidth',2)hold onplot(regset_50sim{i},'r-','LineWidth',2)set(gca,'FontSize',12,'LineWidth',1.5);title(reginal_name{i});xlim([0,13]);xticks(1:13);xticklabels({'J','F','M','A','M','J','J','A','S','O','N','D',''});if i == 4ylabel('runoff (mm/month)' )end
endfor i = 7:9subplot('Position',[0.1+0.25*(i-7) 0.32 0.22 0.17]);plot(regset_obs{i},'k-.*','LineWidth',1)hold onplot(regset_45sim{i},'b-','LineWidth',2)hold onplot(regset_50sim{i},'r-','LineWidth',2)set(gca,'FontSize',12,'LineWidth',1.5);title(reginal_name{i});xlim([0,13]);xticks(1:13);xticklabels({'J','F','M','A','M','J','J','A','S','O','N','D',''});if i == 7ylabel('runoff (mm/month)' )end
endfor i = 10:12subplot('Position',[0.1+0.25*(i-10) 0.09 0.22 0.17]);plot(regset_obs{i},'k-.*','LineWidth',1)hold onplot(regset_45sim{i},'b-','LineWidth',2)hold onplot(regset_50sim{i},'r-','LineWidth',2)set(gca,'FontSize',12,'LineWidth',1.5);title(reginal_name{i});xlim([0,13]);xticks(1:13);xticklabels({'J','F','M','A','M','J','J','A','S','O','N','D',''});if i == 10ylabel('runoff (mm/month)' )end
end

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全球范围内12个典型区域提取(total runoff)

代码内容包括：

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