130
Keywords Spatial–temporal · Trends · Precipitation · Drought · Satellite-based
precipitation
Introduction
Detailed temporal and spatial trend analysis of precipitation is essential to supply
useful and reliable information to water managers. This analysis provides detail on
how much rain falls, and also its intensity, temporality, and variability. This information can be applied in agricultural planning, flood frequency analysis, flood hazard, hydrological modeling, and water resource assessment (Gallego et al. 2011).
Precipitation can be measured by gauge observations, weather radar observation,
and remotely sensed observation (Ashouri et al. 2016). From these, gauge stations
are subject to major limitations, including sparse network observations, complex
terrains, interrupted/limited data monitoring records, instrumental error, and, especially in the case of developing countries, the lack of equipment and limited funds
(Vu et al. 2018).
An alternate way to obtain an estimation of precipitation in areas where rain
gauge data are scarce or uncertain is radar observations. Hydrological studies based
on radar data solve some of the problems associated with gauge measurements by
discriminating different forms of precipitation such as hail, snow, and rainfall and
by determining the appropriate relationship between radar reflectivity and rain rate,
storm, and rainfall. They are however available only in limited land regions (Fuka
et al. 2014).
Satellite remote sensing and reanalysis techniques have gained more attention
recently, because of their ability to provide continuous and high-resolution precipitation estimates at a quasi-global scale, and by not being limited by topography
(Gao et al. 2012). Reanalysis data are an important source of high quality data for
analyses of precipitation of the current situation and past conditions (Chen and Han
2016). The reanalysis dataset is obtained by combining advanced forecast models
and data assimilation systems to create global datasets of the atmosphere–land surface and ocean. The climate forecast system reanalysis and reforecast (CFSR) products are available hourly and with a resolution of up to 20 s, horizontal of 0.5°
latitude × 0.5° longitude (Saha et al. 2010). The National Prediction Center (NCEP),
The National Center for Atmospheric Research (NCAR), and the National Oceanic
and Atmospheric Administration/Climate Diagnostics Center (NOAA/CDC) developed data from the numerical model NCEP/NCAR Reanalysis. These are available
at the levels of 2.5° latitude by 2.5° longitude (278.3 km by 278.3 km) (Kistler et al.
2001). The Japanese Meteorological Agency (JMA) also has its reanalysis databases: the JRA-25 (Japanese 25-year Reanalysis)—JCDAS (JMA Climate Data
Assimilation System) and the JRA-55 (Japanese 55-year Reanalysis) (Kobayashi
et al. 2015).
In the state of Durango, Mexico, an arid to semi-arid climate predominates and
drought events are common. This climate contributes to the scarcity of water
D. A. Martinez-Cruz et al.
Keywords Spatial–temporal · Trends · Precipitation · Drought · Satellite-based
precipitation
Introduction
Detailed temporal and spatial trend analysis of precipitation is essential to supply
useful and reliable information to water managers. This analysis provides detail on
how much rain falls, and also its intensity, temporality, and variability. This information can be applied in agricultural planning, flood frequency analysis, flood hazard, hydrological modeling, and water resource assessment (Gallego et al. 2011).
Precipitation can be measured by gauge observations, weather radar observation,
and remotely sensed observation (Ashouri et al. 2016). From these, gauge stations
are subject to major limitations, including sparse network observations, complex
terrains, interrupted/limited data monitoring records, instrumental error, and, especially in the case of developing countries, the lack of equipment and limited funds
(Vu et al. 2018).
An alternate way to obtain an estimation of precipitation in areas where rain
gauge data are scarce or uncertain is radar observations. Hydrological studies based
on radar data solve some of the problems associated with gauge measurements by
discriminating different forms of precipitation such as hail, snow, and rainfall and
by determining the appropriate relationship between radar reflectivity and rain rate,
storm, and rainfall. They are however available only in limited land regions (Fuka
et al. 2014).
Satellite remote sensing and reanalysis techniques have gained more attention
recently, because of their ability to provide continuous and high-resolution precipitation estimates at a quasi-global scale, and by not being limited by topography
(Gao et al. 2012). Reanalysis data are an important source of high quality data for
analyses of precipitation of the current situation and past conditions (Chen and Han
2016). The reanalysis dataset is obtained by combining advanced forecast models
and data assimilation systems to create global datasets of the atmosphere–land surface and ocean. The climate forecast system reanalysis and reforecast (CFSR) products are available hourly and with a resolution of up to 20 s, horizontal of 0.5°
latitude × 0.5° longitude (Saha et al. 2010). The National Prediction Center (NCEP),
The National Center for Atmospheric Research (NCAR), and the National Oceanic
and Atmospheric Administration/Climate Diagnostics Center (NOAA/CDC) developed data from the numerical model NCEP/NCAR Reanalysis. These are available
at the levels of 2.5° latitude by 2.5° longitude (278.3 km by 278.3 km) (Kistler et al.
2001). The Japanese Meteorological Agency (JMA) also has its reanalysis databases: the JRA-25 (Japanese 25-year Reanalysis)—JCDAS (JMA Climate Data
Assimilation System) and the JRA-55 (Japanese 55-year Reanalysis) (Kobayashi
et al. 2015).
In the state of Durango, Mexico, an arid to semi-arid climate predominates and
drought events are common. This climate contributes to the scarcity of water
D. A. Martinez-Cruz et al.