scholarly journals Changes in the frequency and severity of hydrological droughts over Ethiopia from 1960 to 2013

2016 ◽  
Vol 42 (1) ◽  
pp. 145 ◽  
Author(s):  
A. M. El Kenawy ◽  
M. F. McCabe ◽  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno ◽  
S. M. Robaa
Keyword(s):  
Standardized Precipitation Index ◽  
Principal Component ◽  
Research Unit ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Policy Makers ◽  
Local Character ◽  
Afar Region ◽  
The Standardized Precipitation Index ◽  
Evidence For Policy

Here we present an analysis of drought occurrence and variability in Ethiopia, based on the monthly precipitation data from the Climate Research Unit (CRU-v3.22) over the period from 1960 to 2013. The drought events were characterized by means of the Standardized Precipitation Index (SPI) applied to precipitation data at a temporal scale of 12 months. At the national scale, the results reveal a statistically significant decrease in the severity of droughts over the 54-year period, a pattern that is mostly attributed to a statistically significant decrease in the frequency of high intensity drought episodes (i.e., extreme and very extreme droughts), compared to moderate droughts. To assess the general patterns of drought evolution, a principal component analysis (PCA) was applied to the SPI series. PCA results indicate a high spatial heterogeneity in the SPI variations over the investigated period, with ten different spatially well-defined regions identified. These PCA components accounted for 72.9% of the total variance of drought in the region. These regions also showed considerable differences in the temporal variability of drought, as most of the regions exhibited an increase in wetness conditions in recent decades. In contrast, the regions that receive less than 400 mm of annual precipitation showed a declining  trend, with the largest changes occurring over Afar region. Generally, the highly elevated regions over the central Ethiopian Highlands showed the weakest changes, compared to the lowlands. This study confirms the local character of drought evolution over Ethiopia, providing evidence for policy makers to adopt appropriate local policies to cope with the risks of drought. Over Ethiopia, the detailed spatial assessment of drought evolution is required for a better understanding of the possible impacts of recurrent drought on agriculture, food production, soil degradation, human settlements and migrations, as well as energy production and water resources management across Ethiopia.

scholarly journals Assessing the changes in drought conditions during summer in the Republic of Moldova based on RegCM simulations

2013 ◽  
Vol 2 (3) ◽  
pp. 63 ◽  
Author(s):  
Vera Potop ◽  
Constanta Boroneant ◽  
Mihaela Caian
Keyword(s):  
Standardized Precipitation Index ◽  
Horizontal Resolution ◽  
Precipitation Index ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Republic Of Moldova ◽  
Drought Conditions ◽  
The Republic ◽  
The Standardized Precipitation Index ◽  
Monthly Precipitation Data

We assess the changes in drought conditions during summer in the Republic of Moldova based on the Standardized Precipitation Index (SPI) calculated from monthly precipitation data simulated by the regional climatic model RegCM3. The RegCM simulations were conducted at a horizontal resolution of 10 km in the framework of EU-FP6 project -CECILIA. The domain was centered over Romania at 46°N, 25°E and included the Republic of Moldova.

Observed Changes of Drought/Wetness Episodes in the Middle and Lower Reaches of the Yangtze River, China

2012 ◽  
Vol 212-213 ◽  
pp. 765-771
Author(s):  
Yi Wei Zhang ◽  
Wei Guang Wang
Keyword(s):  
Rainy Season ◽  
Yangtze River ◽  
Standardized Precipitation Index ◽  
Aridity Index ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
The Yangtze River ◽  
Dry Conditions ◽  
The Standardized Precipitation Index ◽  
Monthly Precipitation Data

Monthly precipitation data of 76 meteorology stations over the middle and lower reaches of Yangtze river for 1961–2010 were analyzed by using the standardized precipitation index (SPI) and aridity index (I) for the rainy season (April–September) and winter (December– February). Trends of the number of wet and dry months were tested with Mann-Kendall technique. The results showed that: (1) The middle and lower reaches of the Yangtze River as a whole has become wetter during the rainy season and winter. (2) Major parts of the study area are characterized by increasing frequencies of severe and moderate wet months in the rainy season. (3) The study tries to explore the spatial and temporal changes in the wet and dry conditions across the middle and lower reaches of the Yangtze River by using SPI and I, and get the complete picture of the change of wet and dry.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

scholarly journals Application of TRMM Precipitation Data to Evaluate Drought and Its Effects on Water Resources Instability

2019 ◽  
Vol 9 (24) ◽  
pp. 5377
Author(s):  
Ata Amini ◽  
Abdolnabi Abdeh Kolahchi ◽  
Nadhir Al-Ansari ◽  
Mehdi Karami Moghadam ◽  
Thamer Mohammad
Keyword(s):  
Water Resources ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Discharge Data ◽  
Satellite Precipitation ◽  
Discharge Rates ◽  
Trmm Precipitation

The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations’ data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges.

scholarly journals Spatial and temporal variability of precipitation and drought in Portugal

2012 ◽  
Vol 12 (5) ◽  
pp. 1493-1501 ◽  
Author(s):  
D. S. Martins ◽  
T. Raziei ◽  
A. A. Paulo ◽  
L. S. Pereira
Keyword(s):  
Principal Component Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Temperature ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Precipitation Regimes

Abstract. The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI), was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI) and the modified PDSI for Mediterranean conditions (MedPDSI) were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

scholarly journals Computation of the Standardized Precipitation Index (SPI) and Its Use to Assess Drought Occurrences in Cameroon over Recent Decades

2014 ◽  
Vol 53 (10) ◽  
pp. 2310-2324 ◽  
Author(s):  
Guy Merlin Guenang ◽  
F. Mkankam Kamga
Keyword(s):  
Time Scales ◽  
Goodness Of Fit ◽  
Standardized Precipitation Index ◽  
Research Unit ◽  
Distribution Functions ◽  
Precipitation Index ◽  
Severe Drought ◽  
East Anglia ◽  
The Standardized Precipitation Index ◽  
Short Time

AbstractThe standardized precipitation index (SPI) is computed and analyzed using 55 years of precipitation data recorded in 24 observation stations in Cameroon along with University of East Anglia Climate Research Unit (CRU) spatialized data. Four statistical distribution functions (gamma, exponential, Weibull, and lognormal) are first fitted to data accumulated for various time scales, and the appropriate functions are selected on the basis of the Anderson–Darling goodness-of-fit statistic. For short time scales (up to 6 months) and for stations above 10°N, the gamma distribution is the most frequent choice; below this belt, the Weibull distribution predominates. For longer than 6-month time scales, there are no consistent patterns of fitted distributions. After calculating the SPI in the usual way, operational drought thresholds that are based on an objective method are determined at each station. These thresholds are useful in drought-response decision making. From SPI time series, episodes of severe and extreme droughts are identified at many stations during the study period. Moderate/severe drought occurrences are intra-annual in short time scales and interannual for long time scales (greater than 9 months), usually spanning many years. The SPI calculated from CRU gridded precipitation shows similar results, with some discrepancies at longer scales. Thus, the spatialized dataset can be used to extend such studies to a larger region—especially data-scarce areas.

scholarly journals Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

2021 ◽  
Author(s):  
Morteza Lotfirad ◽  
Hassan Esmaeili-Gisavandani ◽  
Arash Adib
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Model Combination ◽  
The North ◽  
Drought Prediction ◽  
Lag Times ◽  
The Standardized Precipitation Index

Abstract The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

scholarly journals Global and regional performances of SPI candidate distribution functions in observations and simulations

2020 ◽  
Author(s):  
Patrick Pieper ◽  
André Düsterhus ◽  
Johanna Baehr
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Weibull Distribution ◽  
Standardized Precipitation Index ◽  
Distribution Functions ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Data Set ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull

Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within literature. This study illuminates the long-standing dispute and proposes a solution which ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time-series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison evaluates actual against theoretical occurrence probabilities of SPI categories, and the quality of the fit of candidate distribution functions against their complexity with Akaike's Information Criterion. SPI time-series, spanning 1983–2013, are calculated from Global Precipitation Climatology Project's monthly precipitation data-set and seasonal precipitation hindcasts from the Max Planck Institute Earth System Model. We evaluate these SPI time-series over the global land area and for each continent individually during winter and summer. While focusing on an accumulation period of 3-months, we additionally test the drawn conclusions for other common accumulation periods (1-, 6-, 9-, and 12-months). Our results suggest to exercise caution when using the gamma distribution to calculate SPI; especially in simulations or their evaluation. Further, our analysis shows a distinctly improved normality for SPI time-series derived with the exponentiated Weibull distribution relative to other distributions. The use of the exponentiated Weibull distribution maximizes the normality of SPI time-series in observations and simulations both individual as well as concurrent. Its use further maximizes the normality of SPI time-series over each continent and for every investigated accumulation period. We, therefore, advocate to derive SPI with the exponentiated Weibull distribution, irrespective of the heritage of the precipitation data or the length of analyzed accumulation periods.

scholarly journals Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2218
Author(s):  
Bikram Parajuli ◽  
Xiang Zhang ◽  
Sudip Deuja ◽  
Yingbing Liu
Keyword(s):  
Spatial Pattern ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Geographical Regions ◽  
Brahmaputra Basin ◽  
Different Time Scales

Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

scholarly journals Meteorological Drought Monitoring in Northeastern China Using Multiple Indices

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 72 ◽  
Author(s):  
Fengping Li ◽  
Hongyan Li ◽  
Wenxi Lu ◽  
Guangxin Zhang ◽  
Joo-Cheol Kim
Keyword(s):  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Z Score ◽  
Predictive Values ◽  
Daily Precipitation Data ◽  
Precipitation Changes

Drought monitoring is one of the significant issues of water resources assessment. Multiple drought indices (DIs), including Percent of Normal (PN), Standardized Precipitation Index (SPI), statistical Z-Score, and Effective Drought Index (EDI) at 18 different timesteps were employed to evaluate the drought condition in Wuyuer River Basin (WRB), Northeast China. Daily precipitation data of 50 years (1960–2010) from three meteorological stations were used in this study. We found DIs with intermediate time steps (7 to 18 months) to have the highest predictive values for identifying droughts. And DIs exhibited a better similarity in the 12-month timestep. Among all the DIs, EDI exhibited the best correlation with other DIs for various timesteps. When further comparing with historical droughts, Z-Score, SPI, and EDI were found more sensitive to multi-monthly cumulative precipitation changes (r2 > 0.55) with respect to monthly precipitation changes (r2 ≤ 0.10), while EDI was more preferable when only monthly precipitation data were available. These results indicated that various indices for different timesteps should be investigated in drought monitoring in WRB, especially the intermediate timesteps should be considered.

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