Python干旱指数库climate_indices

climate_indices库作者：詹姆斯·亚当斯

邮箱：monocongo@gmail.com

软件主页：https://www.drought.gov/software

github：https://github.com/monocongo/climate_indices

climate_indices库能够计算的指数如下：

SPI,Standardized Precipitation Index, utilizing both gamma and Pearson Type III distributions.

SPEI,Standardized Precipitation Evapotranspiration Index, utilizing both gamma and Pearson Type III distributions.

PET,Potential Evapotranspiration,

utilizing either Thornthwaite or Hargreaves equations.

PDSI,Palmer Drought Severity Index.

scPDSI,Self-calibrated Palmer Drought Severity Index.

PHDI,Palmer Hydrological Drought Index.

Z-Index,Palmer moisture anomaly index (Z-index).

PMDI,Palmer Modified Drought Index.

PNP,Percentage of Normal Precipitation.

本文以站点SPEI计算为例：

注：源代码作者信息暂匿名,CSDN:EWBA_GIS_RS_ER.

# 相关包的导入import numpy as npimport pandas as pdfrom climate_indices import indicesfrom climate_indices import compute# 计算SPEI的包# 路径处理和基本变量定义rootdir = r'F:/Rpython/lp36/data/SPEI_Cal/'tampa_file = rootdir + 'test/tampa.csv'outpath = rootdir + 'test/'lat = 27.96styr = 1900edyr = 2007# 气象数据读取tampa_data = pd.read_csv(tampa_file)pre_data = np.asarray(tampa_data['Pre']) # 降水数据转换为np.arraytas_data = np.asarray(tampa_data['Tas']) # 温度数据转换为np.array# 潜在蒸散发计算-桑斯维特方法pet_data = indices.pet(temperature_celsius=tas_data,latitude_degrees=lat,data_start_year=styr)# 计算SPEIspei = indices.spei(precips_mm=pre_data,                    pet_mm=pet_data, # pet_mm=tas_data是不正确的,                    scale=12, # 12个月尺度                    distribution=indices.Distribution.gamma,periodicity=compute.Periodicity.monthly,                    data_start_year=styr,calibration_year_initial=styr,calibration_year_final=edyr)spei[np.isnan(spei)] = -99 # nan转换为-99spei_df = pd.DataFrame(data=spei,columns=['SPEI_1']) # 计算结果转换为DataFrame# 计算结果写出spei_df.to_csv(outpath+'SPEI12.csv',index=False)print('Finished.')

将本文python-climate_indices库的SPEI12计算结果与FAO官网提供的spei.exe计算器的SPEI12计算结果对比：

Origin作图散点图：

Python作图散点密度图：

下期更新预告1：

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气象水文科研猫公众号后台回复：

“climate_indices库安装包和示例数据”，

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