scholarly journals A hybrid downscaling approach for the estimation of climate change effects on droughts using a geo-information tool. Case study: Thessaly, Central Greece

2016 ◽  
Vol 8 (1) ◽  
pp. 728-746 ◽  
Author(s):  
John Tzabiras ◽  
Athanasios Loukas ◽  
Lampros Vasiliades
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Standardized Precipitation Index ◽  
Historical Period ◽  
Monthly Precipitation ◽  
Max Planck ◽  
Climate Change Effects ◽  
Geostatistical Approach ◽  
The Standardized Precipitation Index ◽  
Stochastic Time Series

AbstractMultiple linear regression is used to downscale large-scale outputs from CGCM2 (second generation CGCM of Canadian centre for climate monitoring and analysis) and ECHAM5 (developed at the Max Planck Institute for Meteorology), statistically to regional precipitation over the Thessaly region, Greece. Mean monthly precipitation data for the historical period Oct.1960-Sep.2002 derived from 79 rain gauges were spatially interpolated using a geostatistical approach over the region of Thessaly, which was divided into 128 grid cells of 10 km × 10 km. The methodology is based on multiple regression of large scale GCM predictant variables with observed precipitation and the application of a stochastic time series model for precipitation residuals simulation (white noise). The methodology was developed for historical period (Oct.1960–Sep.1990) and validated against observed monthly precipitation for period (Oct.1990–Sep.2002). The downscaled proposed methodology was used to calculate the standardized precipitation index (SPI) at various timescales (3-month, 6-month, 9-month, 12-month, 24-month) in order to estimate climate change effects on droughts. Various evaluation statistics were calculated in order to validate the process and the results showed that the method is efficient in SPI reproduction but the level of uncertainty is quite high due to its stochastic component.

scholarly journals Climate change effects on drought severity

2008 ◽  
Vol 17 ◽  
pp. 23-29 ◽  
Author(s):  
A. Loukas ◽  
L. Vasiliades ◽  
J. Tzabiras
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Geographical Information ◽  
Multiple Time Scales ◽  
Drought Severity ◽  
Multiple Time ◽  
Monthly Precipitation ◽  
Climate Change Effects

Abstract. This paper evaluates climate change effects on drought severity in the region of Thessaly, Greece. The Standardized Precipitation Index (SPI) has been used for estimation of drought severity. A geographical information system is applied for the division of Thessaly region to twelve hydrological homogeneous areas based on their geomorphology. Mean monthly precipitation values from 50 precipitation stations of Thessaly for the hydrological period October 1960–September 1990 were used for the estimation of mean areal precipitation. These precipitation timeseries have been used for the estimation of Standardized Precipitation Index (SPI) for multiple time scales (1-, 3-, 6-, 9-, and 12-months) for each sub-basin or area. The outputs of Global Circulation Model CGCM2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. The GCM outputs were downscaled to the region of Thessaly using a statistical methodology to estimate precipitation time series for two future periods 2020–2050 and 2070–2100. A method has been proposed for the estimation of annual cumulative drought severity-time scale-frequency curves. These curves integrate the drought severity and frequency for various types of drought. The SPI timeseries and annual weighted cumulative drought severity were estimated and compared with the respective timeseries and values of the historical period 1960–1990. The results showed that the annual drought severity is increased for all hydrological areas and SPI time scales, with the socioeconomic scenario SRES A2 being the most extreme.

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 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 Early warning of drought in Europe using the monthly ensemble system from ECMWF

2015 ◽  
Vol 19 (7) ◽  
pp. 3273-3286 ◽  
Author(s):  
C. Lavaysse ◽  
J. Vogt ◽  
F. Pappenberger
Keyword(s):  
Large Scale ◽  
Standardized Precipitation Index ◽  
Seasonal Forecast ◽  
Cumulative Distribution ◽  
False Alarms ◽  
Negative Effects ◽  
European Continent ◽  
Probabilistic Forecasts ◽  
Extended Range ◽  
The Standardized Precipitation Index

Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI-1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40 % of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.

scholarly journals Drought and climate change assessment using Standardized Precipitation Index (SPI) for Sarawak River Basin

2019 ◽  
Vol 11 (4) ◽  
pp. 956-965 ◽  
Author(s):  
C. H. J. Bong ◽  
J. Richard
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Recent Decade ◽  
Kendall Test ◽  
Precipitation Index ◽  
Irrigated Agriculture ◽  
Drought Event ◽  
Monthly Precipitation

Abstract Severe droughts in the year 1998 and 2014 in Sarawak due to the strong El Niño has impacted the water supply and irrigated agriculture. In this study, the Standardized Precipitation Index (SPI) was used for drought identification and monitoring in Sarawak River Basin. Using monthly precipitation data between the year 1975 and 2016 for 15 rainfall stations in the basin, the drought index values were obtained for the time scale of three, six and nine months. Rainfall trend for the years in study was also assessed using the Mann–Kendall test and Sen's slope estimator and compared with the drought index. Findings showed that generally there was a decreasing trend for the SPI values for the three time scales, indicating a higher tendency of increased drought event throughout the basin. Furthermore, it was observed that there was an increase in the numbers of dry months in the recent decade for most of the rainfall stations as compared to the previous 30 to 40 years, which could be due to climate change. Findings from this study are valuable for the planning and formulating of drought strategies to reduce and mitigate the adverse effects of drought.

scholarly journals Drought cycle tracking in Hungary using Standardized Precipitation Index (SPI)

2019 ◽  
pp. 97-101 ◽  
Author(s):  
Safwan A. Mohammed ◽  
Endre Harsányi
Keyword(s):  
Climate Change ◽  
Eastern Europe ◽  
Natural Hazard ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Rainfall Data ◽  
Trend Test ◽  
Economic Sectors ◽  
Similar Vein ◽  
The Standardized Precipitation Index

 Drought is one of the natural hazard risks which badly affects both agricultural and socio-economic sectors. Hungary, which is located in Eastern Europe has been suffering from different drought cycles; therefore, the aim of this study is to analyse the rainfall data obtained from ten metrological stations (Békéscsaba, Budapest, Debrecen, Győr, Kékestető, Miskolc, Pápa, Pécs, Szeged, Siófok, Szolnok) between 1985 and 2016, by using the Standardized Precipitation Index (SPI). The results showed that 2011 was recorded as the worst drought cycle of the studied period, where the SPI ranged between -0.22 (extreme drought) in Siófok, and 0.15 (no drought) in Miskolc. In a similar vein, the study highlighted the year 2010 to be the best hydrological year, when the SPI reached 0.73 (mildly wet) on average. Interestingly, the Mann-Kendall trend test for the drought cycle showed no positive trends in the study area. Finally, more investigation should be conducted into the climate change spatial drought cycle in Europe.

scholarly journals Hydraulic diversity stabilizes productivity in a large-scale subtropical tree biodiversity experiment

2021 ◽  
Author(s):  
Florian Schnabel ◽  
Xiaojuan Liu ◽  
Matthias Kunz ◽  
Kathryn E. Barry ◽  
Franca J. Bongers ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Richness ◽  
Tree Species ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Forest Productivity ◽  
Climate Mitigation ◽  
Climate Change Effects ◽  
Tree Species Richness

AbstractExtreme climatic events threaten forests and their climate mitigation potential globally. Understanding the drivers promoting ecosystems stability is therefore considered crucial to mitigate adverse climate change effects on forests. Here, we use structural equation models to explain how tree species richness, asynchronous species dynamics and diversity in hydraulic traits affect the stability of forest productivity along an experimentally manipulated biodiversity gradient ranging from 1 to 24 tree species. Tree species richness improved stability by increasing species asynchrony. That is at higher species richness, inter-annual variation in productivity among tree species buffered the community against stress-related productivity declines. This effect was mediated by the diversity of species’ hydraulic traits in relation to drought tolerance and stomatal control, but not the community-weighted means of these traits. Our results demonstrate important mechanisms by which tree species richness stabilizes forest productivity, thus emphasizing the importance of hydraulically diverse, mixed-species forests to adapt to climate change.

scholarly journals Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

2021 ◽  
Vol 13 (23) ◽  
pp. 4730
Author(s):  
Malak Henchiri ◽  
Tertsea Igbawua ◽  
Tehseen Javed ◽  
Yun Bai ◽  
Sha Zhang ◽  
...  
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Joint Probability ◽  
Meteorological Drought ◽  
Socioeconomic Impacts ◽  
The North ◽  
The Standardized Precipitation Index ◽  
The North Atlantic Oscillation

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria.

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.

'Paris rules' will drive global climate progress

2016 ◽  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Global Climate ◽  
National Level ◽  
Implementation Process ◽  
Peer Pressure ◽  
Content Type ◽  
Climate Change Effects ◽  
Growth Prospects ◽  
The Impact

Significance COP22 has been dubbed "the COP of action, adaptation and Africa". It is a key opportunity to build confidence in the system of global cooperation adopted at the Paris Climate Conference. The Paris meeting ushered in a new framework for cooperation on climate change based on voluntary emissions reductions targets that will be jointly reviewed every five years. Negotiators gathering in Marrakech for COP22 face the task of making the Paris Agreement work -- and delivering results on a sufficiently large scale. Impacts Cooperation under the Paris framework will help reduce climate change effects, though overshooting of the 2 degree target is inevitable. The Paris deal's reliance on peer pressure and self-policing will risk national-level backsliding during the implementation process. Actions taken in the next ten years will determine the impact of climate change on global growth prospects for the whole of this century.

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