scholarly journals GPCC Drought Index – a new, combined, and gridded global drought index

2014 ◽  
Vol 7 (1) ◽  
pp. 243-270
Author(s):  
M. Ziese ◽  
U. Schneider ◽  
A. Meyer-Christoffer ◽  
K. Schamm ◽  
J. Vido ◽  
...  
Keyword(s):  
Drought Index ◽  
Standardized Precipitation Index ◽  
Regular Grid ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Precipitation Anomalies ◽  
The Standardized Precipitation Index ◽  
Global Precipitation ◽  
Climate Prediction Center

Abstract. The Global Precipitation Climatology Centre Drought Index (GPCC-DI) provides estimations of precipitation anomalies with respect to long term statistics. It is a combination of the Standardized Precipitation Index with adaptations from Deutscher Wetterdienst (SPI-DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation data were taken from the Global Precipitation Climatology Centre (GPCC) and temperature data from NOAA's Climate Prediction Center (CPC). The GPCC-DI is available with several averaging periods of 1, 3, 6, 9, 12, 24 and 48 months for different applications. Since spring 2013, the GPCC-DI is calculated operationally and available back to January 2013. Typically it is released at the 10th day of the following month, depending on the availability of the input data. It is calculated on a~regular grid with 1° spatial resolution. All averaging periods are integrated into one netCDF-file for each month. This dataset can be referenced by the DOI:10.5676/DWD_GPCC/DI_M_100 and is available free of charge from the GPCC website ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_di_doi_download.html.

scholarly journals The GPCC Drought Index – a new, combined and gridded global drought index

2014 ◽  
Vol 6 (2) ◽  
pp. 285-295 ◽  
Author(s):  
M. Ziese ◽  
U. Schneider ◽  
A. Meyer-Christoffer ◽  
K. Schamm ◽  
J. Vido ◽  
...  
Keyword(s):  
Drought Index ◽  
Standardized Precipitation Index ◽  
Regular Grid ◽  
Data Set ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
The Standardized Precipitation Index ◽  
Global Precipitation ◽  
Climate Prediction Center

Abstract. The Global Precipitation Climatology Centre Drought Index (GPCC-DI) provides estimations of water supply anomalies with respect to long-term statistics. It is a combination of the Standardized Precipitation Index with adaptations from Deutscher Wetterdienst (SPI-DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation data were taken from the Global Precipitation Climatology Centre (GPCC) and temperature data from NOAA's Climate Prediction Center (CPC). The GPCC-DI is available with several accumulation periods of 1, 3, 6, 9, 12, 24 and 48 months for different applications. It is issued monthly starting in January 2013. Typically, it is released on the 10th day of the following month, depending on the availability of the input data. It is calculated on a regular grid with 1° spatial resolution. All accumulation periods are integrated into one netCDF file for each month. This data set is referenced by the doi:10.5676/DWD_GPCC/DI_M_100 and is available free of charge from the GPCC website ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_di_doi_download.html.

scholarly journals A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data

2021 ◽  
Author(s):  
Vempi Satriya Adi Hendrawan ◽  
Wonsik Kim ◽  
Yoshiya Touge ◽  
Shi Ke ◽  
Daisuke Komori
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Input Data ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Global Scale ◽  
Data Set ◽  
Drought Impact ◽  
The Standardized Precipitation Index ◽  
Global Precipitation

Abstract Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the Standardized Precipitation Index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981-2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23, 8, 30, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5 – 12-months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.

DROUGHT OCCURRENCE UNDER LITHUANIAN CLIMATIC CONDITIONS

2015 ◽  
Author(s):  
Laima TAPARAUSKIENĖ ◽  
Veronika LUKŠEVIČIŪTĖ
Keyword(s):  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Vegetation Period ◽  
Climatic Conditions ◽  
Precipitation Record ◽  
Calculation Step ◽  
Drought Conditions ◽  
The Standardized Precipitation Index ◽  
June July

This study provides the analysis of drought conditions of vegetation period in 1982-2014 year in two Lithuanian regions: Kaunas and Telšiai. To identify drought conditions the Standardized Precipitation Index (SPI) was applied. SPI was calculated using the long-term precipitation record of 1982–2014 with in-situ meteorological data. Calculation step of SPI was taken 1 month considering only vegetation period (May, June, July, August, September). The purpose of investigation was to evaluate the humidity/aridity of vegetation period and find out the probability of droughts occurrence under Lithuanian climatic conditions. It was found out that according SPI results droughts occurred in 14.5 % of all months in Kaunas region and in 15.8 % in Telšiai region. Wet periods in Kaunas region occurred in 15.8 %, and in Telšiai region occurrence of wet periods was – 18.8 % from all evaluated months. According SPI evaluation near normal were 69.7 % of total months during period of investigation in Kaunas and respectively – 65.5 % in Telšiai. The probability for extremely dry period under Lithuania climatic conditions are pretty low – 3.0 % in middle Lithuania and 2.4 % in western part of Lithuania.

Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC)

2021 ◽  
Author(s):  
Elke Rustemeier ◽  
Udo Schneider ◽  
Markus Ziese ◽  
Peter Finger ◽  
Andreas Becker
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Land Surface ◽  
Quality Data ◽  
Digital Object Identifier ◽  
Full Data ◽  
High Quality ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

<p><span>Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. </span><span>In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality.</span> <span>Today, the GPCC holds data from </span><span>123,000 stations, about three quarters of them having long time series.</span></p><p><span>The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.  </span><span>In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.</span></p><p><span>Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.</span></p><p><span>In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). </span></p><p><span>With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version. </span></p><p><span>A</span><span>ll gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.</span></p>

scholarly journals Using integrated meteorological and hydrological indices to assess drought characteristics in southern Taiwan

2019 ◽  
Vol 50 (3) ◽  
pp. 901-914 ◽  
Author(s):  
Hsin-Fu Yeh
Keyword(s):  
River Basin ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Assessment Method ◽  
Drought Indices ◽  
Trend Test ◽  
Integrated Analysis ◽  
Drought Assessment ◽  
Southern Taiwan ◽  
The Standardized Precipitation Index

Abstract Numerous drought index assessment methods have been developed to investigate droughts. This study proposes a more comprehensive assessment method integrating two drought indices. The Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI) are employed to establish an integrated drought assessment method to study the trends and characteristics of droughts in southern Taiwan. The overall SPI and SDI values and the spatial and temporal distributions of droughts within a given year (November to October) revealed consistent general trends. Major droughts occurred in the periods of 1979–1980, 1992–1993, 1994–1995, and 2001–2003. According to the results of the Mann–Kendall trend test and the Theil–Sen estimator analysis, the streamflow data from the Sandimen gauging station in the Ailiao River Basin showed a 30% decrease, suggesting increasing aridity between 1964 and 2003. Hence, in terms of water resources management, special attention should be given to the Ailiao River Basin. The integrated analysis showed different types of droughts occurring in different seasons, and the results are in good agreement with the climatic characteristics of southern Taiwan. This study suggests that droughts cannot be explained fully by the application of a single drought index. Integrated analysis using multiple indices is required.

scholarly journals Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016

2020 ◽  
Vol 24 (2) ◽  
pp. 919-943 ◽  
Author(s):  
Steefan Contractor ◽  
Markus G. Donat ◽  
Lisa V. Alexander ◽  
Markus Ziese ◽  
Anja Meyer-Christoffer ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
In Situ Data ◽  
Global Precipitation Climatology Centre ◽  
Historical Climatology ◽  
Global Land ◽  
Global Historical Climatology Network ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

scholarly journals Long-Term, Gridded Standardized Precipitation Index for Hawai‘i

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Matthew P. Lucas ◽  
Clay Trauernicht ◽  
Abby G. Frazier ◽  
Tomoaki Miura
Keyword(s):  
High Spatial Resolution ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatially Explicit ◽  
Historical Period ◽  
The Standardized Precipitation Index ◽  
Climatic Information

Spatially explicit, wall-to-wall rainfall data provide foundational climatic information but alone are inadequate for characterizing meteorological, hydrological, agricultural, or ecological drought. The Standardized Precipitation Index (SPI) is one of the most widely used indicators of drought and defines localized conditions of both drought and excess rainfall based on period-specific (e.g., 1-month, 6-month, 12-month) accumulated precipitation relative to multi-year averages. A 93-year (1920–2012), high-resolution (250 m) gridded dataset of monthly rainfall available for the State of Hawai‘i was used to derive gridded, monthly SPI values for 1-, 3-, 6-, 9-, 12-, 24-, 36-, 48-, and 60-month intervals. Gridded SPI data were validated against independent, station-based calculations of SPI provided by the National Weather Service. The gridded SPI product was also compared with the U.S. Drought Monitor during the overlapping period. This SPI product provides several advantages over currently available drought indices for Hawai‘i in that it has statewide coverage over a long historical period at high spatial resolution to capture fine-scale climatic gradients and monitor changes in local drought severity.

scholarly journals Information Content of Seasonal Forecasts in a Changing Climate

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Michael D. Grossberg ◽  
Irina Gladkova ◽  
Hannah Aizenman
Keyword(s):  
Information Gain ◽  
Seasonal Forecasts ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
The Usa ◽  
Precipitation Anomalies ◽  
The Mean ◽  
Climate Prediction Center

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

Building Drought Index Based on AMSR-E Data - A Case Study in Hilly Area of Central Sichuan Basin

2013 ◽  
Vol 781-784 ◽  
pp. 2292-2295
Author(s):  
Qiong Lian Chen ◽  
Jing Wen Xu ◽  
Shao Wei Shi ◽  
Xin Li ◽  
Peng Wang
Keyword(s):  
Correlation Analysis ◽  
Sichuan Basin ◽  
Drought Index ◽  
Daily Precipitation ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Ratio Method ◽  
Hilly Area ◽  
Central Sichuan Basin ◽  
The Standardized Precipitation Index

Sichuan Hilly Area is selected as the study area. This paper uses ten brightness temperature AMSR-E data during 2006-2010. It constructs 8 preferred drought index by using the polarization ratio method (Polarization Rations, PR). This paper makes Pearson correlation analysis by using the 8 preferred drought index and the soil moisture of study area. Meanwhile, Linear regression and correlation analysis with the Daily precipitation and the standardized precipitation index SPI are also made. The results show: for example, in December 2009, drought index DI74, DI92, DI96 were basically consistent with the spatial distribution. Drought degree has an increasing trend from southeast to northwest regional gradually. And with the drought conditions in hilly area of actual and daily precipitation, SPI correlation between interannual and Sichuan are proper. So the drought index is more suitable for drought study in hilly area of central Sichuan Basin.

Sign in / Sign up

Export Citation Format

Share Document