scholarly journals Development of a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for Drought Monitoring in a Changing Climate

2021 ◽  
Author(s):  
Javad Bazrafshan ◽  
Majid Cheraghalizadeh ◽  
Kokab Shahgholian
Keyword(s):  
Vegetation Cover ◽  
Additive Models ◽  
Drought Indices ◽  
Climate Indices ◽  
Monthly Precipitation ◽  
Standardized Precipitation Evapotranspiration Index ◽  
Drought Period ◽  
Changing Climate ◽  
Drought Characteristics

Abstract In a changing climate, drought indices as well as drought definitions need to be revisited, because some statistical properties, such as long-term mean, of climate series may change over time. The study aims to develop a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for reliable and robust quantification of drought characteristics in a changing environment. The proposed indicator is based on a non-stationary log-logistic probability distribution, assuming the location parameter of the distribution is a multivariable function of time and climate indices, as covariates. The optimal non-stationary model was obtained using a forward selection method in the framework of Generalized Additive Models in Location, Scale and Shape (GAMLSS) algorithm. The Non-stationary and Stationary forms of SPEI (i.e. NSPEI and SSPEI) were calculated using the monthly precipitation and temperature data of 32 weather stations in Iran for the common period of 1964–2014. The results showed that almost at all the stations studied, the non-stationary log-logistic distributions outperformed the stationary one. Both drought indicators SSPEI and NSPEI significantly differed in terms of spatial and temporal variations of drought characteristics. While SSPEI identified the long-term and continuous drought/wet events, NSPEI revealed the short-term and frequent drought/wet periods at almost all the stations of interest. Finally, it was revealed that NSPEI, compared to SSPEI, was a more reliable and robust indicator of drought duration and drought termination in vegetation cover during the severest drought period (the 2008 drought), and therefore, was suggested as a suitable drought index to quantify drought impact on vegetation cover in Iran.

scholarly journals Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5

2021 ◽  
Vol 13 (4) ◽  
pp. 2066
Author(s):  
Jin Hyuck Kim ◽  
Jang Hyun Sung ◽  
Eun-Sung Chung ◽  
Sang Ug Kim ◽  
Minwoo Son ◽  
...  
Keyword(s):  
General Circulation ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Coupled Model ◽  
Meteorological Drought ◽  
General Circulation Models ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Drought Period ◽  
Drought Characteristics

Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.

scholarly journals Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

2013 ◽  
Vol 10 (11) ◽  
pp. 13333-13361 ◽  
Author(s):  
S. K. Sigaroodi ◽  
Q. Chen ◽  
S. Ebrahimi ◽  
A. Nazari ◽  
B. Choobin
Keyword(s):  
Proper Time ◽  
Time Lag ◽  
Precipitation Forecast ◽  
Climate Indices ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Ann Model ◽  
Drought Disaster ◽  
Artificial Neural Network Ann

Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the south of Iran and is continuously suffering from drought disaster, as a case to investigated the relationships between climatic indices and precipitation. Cross correlation in combination with stepwise regression technique were used to determine the best variables among 40 indices and identify the proper time-lag between dependent and independent variables for each month. The monthly precipitation was predicted using Artificial Neural Network (ANN) and multi- regression stepwise methods, and results were compared with observed rainfall data. According to R2, root mean square error (RMSE) and Nash–Sutcliffe factors, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Prediction accuracy was higher in the dry season (June to October) than in the other seasons. The highest and lowest accuracy of the ANN model were in September and March, respectively. Based on this research, the monthly precipitation anomalies in the Maharloo Basin in north of Persian Gulf can be forecast about ten months earlier using NOAA (National Oceanic and Atmospheric Administration) climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and Nino, which will support drought-risk alleviation in the region.

scholarly journals A Non-Stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

2021 ◽  
Author(s):  
Soumyashree Dixit ◽  
K V Jayakumar
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Climate Indices ◽  
Copula Model ◽  
Reconnaissance Drought Index ◽  
Drought Characteristics ◽  
Recurrence Intervals

Abstract Under the variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. So in the present study, two indices namely, Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are developed by fitting non-stationary gamma (for precipitation series) and lognormal (for initial values,δ0) distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. This includes various large scale climate indices namely Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared based on the Akaika Information Criterion (AIC). Additionally, the drought characteristics are evaluated using Run theory analysis for both stationary and non-stationary drought indices. The study also concentrated on the trivariate copula as well as the Pairwise Copula Construction (PCC) models to estimate the drought recurrence intervals. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. The area taken for the study is the Upper and Lower sub basins of the Godavari River basin. This study shows that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

scholarly journals Hubungan Indeks Osilasi Selatan dan Indeks Curah Hujan terhadap Kejadian Kekeringan di Kabupaten Indramayu, Jawa Barat, Indonesia

2017 ◽  
Vol 1 (1) ◽  
pp. 64
Author(s):  
BOEDI TJAHJONO ◽  
BABA BARUS ◽  
NINA WIDIANA DAROJATI
Keyword(s):  
Time Scale ◽  
Southern Oscillation ◽  
Research Area ◽  
Extreme Drought ◽  
Drought Indices ◽  
Short Term ◽  
Drought Period ◽  
Strong Relation

Indramayu district experiences frequent droughts that leads to many paddy fields harvest failure. Since the district is one of the national granary, this disaster needs to be addressed urgently. This study aimed to assess the level of dryness in Indramayu using Standard Precipitation Index (SPI) and its relation with the Southern Oscillation Index (SOI). The study used monthly rainfall data from 1996 to 2013 observed by 19 stations and the score of SOI that came from the Bureau of Meteorology of Australia. The method used quantitative approach using SPI and software SPI_sl_6.exe. Drought indices was measured in four different time scale which are 1, 3, and 6 month(s) (for the short term period) and the 12 months time scale (for the long term period). SPI’s assessment was classified in accordance with the classification of WMO (World Meteorological Organization) which consist of seven classes, ranging from wet extreme to dry extreme class. The results showed that the occurence of "very dry" to "dry extreme“ drought was occured mainly from February 1997 to January 1998 at most stations, while for some stations, it lasted until March 1998. The drought period was lasted from nine to ten months. In 2002 to 2003, the droughts that classified as "very dry" on a 3 and 6 months time scale lasted about five months, while the 12 months time scale was lasted about nine months. SPI value that obtained from different time scales has a strong relation with the value of SOI. The negative value of SOI tends to be followed by the negative value of SPI, and vice versa. SOI that has negative value below -7 and occured in a long period (more than three months) indicates a prolonged El Nino which occurred in 1997 and 2002/2003 when the research area was struck by "being dry" to "dry extreme" drought state.

scholarly journals Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

2014 ◽  
Vol 18 (5) ◽  
pp. 1995-2006 ◽  
Author(s):  
S. K. Sigaroodi ◽  
Q. Chen ◽  
S. Ebrahimi ◽  
A. Nazari ◽  
B. Choobin
Keyword(s):  
Time Lag ◽  
Precipitation Forecast ◽  
Climate Indices ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Ann Model ◽  
Data Set ◽  
Climatic Indices ◽  
Drought Relief

Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the north of Persian Gulf, southern Iran, and is continuously suffering from drought disaster, as a case to investigate the relationships between climatic indices and precipitation. Cross-correlation in combination with stepwise regression technique was used to determine the best variables among 40 indices and identify the proper time lag between dependent and independent variables for each month. The monthly precipitation was predicted using an artificial neural network (ANN) and multi-regression stepwise methods, and results were compared with observed rainfall data. Initial findings indicated that climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and El Niño are the main indices to forecast drought in the study area. According to R2, root mean square error (RMSE) and Nash–Sutcliffe efficiency, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Moreover, the model accuracy to forecast the rare rainfall events in dry months (June to October) was higher than the other months. From the findings it can be concluded that there is a relationship between monthly precipitation anomalies and climatic indices in the previous 10 months in Maharloo Basin. The highest and lowest accuracy of the ANN model were in September and March, respectively. However, these results are subject to some uncertainty due to a coarse data set and high system complexity. Therefore, more research is necessary to further elucidate the relationship between climatic indices and precipitation for drought relief. In this regard, consideration of other climatic and physiographic factors (e.g., wind and physiography) can be helpful.

DEVELOPMENT OF STREAMFLOW DROUGHT INDICES IN THE MORAVA RIVER BASIN

2020 ◽  
Author(s):  
Ondrej Ledvinka ◽  
◽  
Pavel Coufal ◽  
Keyword(s):  
Time Series ◽  
Kendall Test ◽  
Drought Indices ◽  
Drought Period ◽  
Left Hand ◽  
Low Flows ◽  
Current Period ◽  
Catchment Size ◽  
Morava River ◽  
The Stability

The territory of Czechia currently suffers from a long-lasting drought period which has been a subject of many studies, including the hydrological ones. Previous works indicated that the basin of the Morava River, a left-hand tributary of the Danube, is very prone to the occurrence of dry spells. It also applies to the development of various hydrological time series that often show decreases in the amount of available water. The purpose of this contribution is to extend the results of studies performed earlier and, using the most updated daily time series of discharge, to look at the situation of the so-called streamflow drought within the basin. 46 water-gauging stations representing the rivers of diverse catchment size were selected where no or a very weak anthropogenic influences are expected and the stability and sensitivity of profiles allow for the proper measurement of low flows. The selected series had to cover the most current period 1981-2018 but they could be much longer, which was considered beneficial for the next determination of the development direction. Various series of drought indices were derived from the original discharge series. Specifically, 7-, 15- and 30-day low flows together with deficit volumes and their durations were tested for trends using the modifications of the Mann– Kendall test that account for short-term and long-term persistence. In order to better reflect the drivers of streamflow drought, the indices were considered for summer and winter seasons separately as well. The places with the situation critical to the future water resources management were highlighted where substantial changes in river regime occur probably due to climate factors. Finally, the current drought episode that started in 2014 was put into a wider context, making use of the information obtained by the analyses.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Short- and long-term effects of ponderosa pine fuel treatments intersected by the Egley Fire Complex, Oregon, USA

Fire Ecology ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Jessie M. Dodge ◽  
Eva K. Strand ◽  
Andrew T. Hudak ◽  
Benjamin C. Bright ◽  
Darcy H. Hammond ◽  
...  
Keyword(s):  
Prescribed Fire ◽  
Vegetation Cover ◽  
Tree Density ◽  
Tree Canopy ◽  
Fuel Treatments ◽  
Dead Tree ◽  
High Severity ◽  
Short And Long Term ◽  
One Year

Abstract Background Fuel treatments are widely used to alter fuels in forested ecosystems to mitigate wildfire behavior and effects. However, few studies have examined long-term ecological effects of interacting fuel treatments (commercial harvests, pre-commercial thinnings, pile and burning, and prescribed fire) and wildfire. Using annually fitted Landsat satellite-derived Normalized Burn Ratio (NBR) curves and paired pre-fire treated and untreated field sites, we tested changes in the differenced NBR (dNBR) and years since treatment as predictors of biophysical attributes one and nine years after the 2007 Egley Fire Complex in Oregon, USA. We also assessed short- and long-term fuel treatment impacts on field-measured attributes one and nine years post fire. Results One-year post-fire burn severity (dNBR) was lower in treated than in untreated sites across the Egley Fire Complex. Annual NBR trends showed that treated sites nearly recovered to pre-fire values four years post fire, while untreated sites had a slower recovery rate. Time since treatment and dNBR significantly predicted tree canopy and understory green vegetation cover in 2008, suggesting that tree canopy and understory vegetation cover increased in areas that were treated recently pre fire. Live tree density was more affected by severity than by pre-fire treatment in either year, as was dead tree density one year post fire. In 2008, neither treatment nor severity affected percent cover of functional groups (shrub, graminoid, forb, invasive, and moss–lichen–fungi); however, by 2016, shrub, graminoid, forb, and invasive cover were higher in high-severity burn sites than in low-severity burn sites. Total fuel loads nine years post fire were higher in untreated, high-severity burn sites than any other sites. Tree canopy cover and density of trees, saplings, and seedlings were lower nine years post fire than one year post fire across treatments and severity, whereas live and dead tree basal area, understory surface cover, and fuel loads increased. Conclusions Pre-fire fuel treatments effectively lowered the occurrence of high-severity wildfire, likely due to successful pre-fire tree and sapling density and surface fuels reduction. This study also quantified the changes in vegetation and fuels from one to nine years post fire. We suggest that low-severity wildfire can meet prescribed fire management objectives of lowering surface fuel accumulations while not increasing overstory tree mortality.

Projection of Future Drought Characteristics under Multiple Drought Indices

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1238
Author(s):  
Muhammad Imran Khan ◽  
Xingye Zhu ◽  
Xiaoping Jiang ◽  
Qaisar Saddique ◽  
Muhammad Saifullah ◽  
...  
Keyword(s):  
Series Data ◽  
Natural Phenomenon ◽  
Drought Indices ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Analysis ◽  
Songhua River ◽  
Drought Intensity ◽  
Drought Characteristics ◽  
Rcp 8.5

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.

scholarly journals Evaluating the Spatiotemporal Characteristics of Agricultural Drought in Bangladesh Using Effective Drought Index

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2437 ◽  
Author(s):  
Mohammad Kamruzzaman ◽  
Syewoon Hwang ◽  
Jaepil Cho ◽  
Min-Won Jang ◽  
Hanseok Jeong
Keyword(s):  
Central Region ◽  
Drought Index ◽  
Regional Analysis ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Monthly Precipitation ◽  
Effective Drought Index ◽  
Spatiotemporal Characteristics ◽  
North Western

This study aims to assess the spatiotemporal characteristics of agricultural droughts in Bangladesh during 1981–2015 using the Effective Drought Index (EDI). Monthly precipitation data for 36 years (1980–2015) obtained from 27 metrological stations, were used in this study. The EDI performance was evaluated for four sub-regions over the country through comparisons with historical drought records identified by regional analysis. Analysis at a regional level showed that EDI could reasonably detect the drought years/events during the study period. The study also presented that the overall drought severity had increased during the past 35 years. The characteristics (severity and duration) of drought were also analyzed in terms of the spatiotemporal evolution of the frequency of drought events. It was found that the western and central regions of the country are comparatively more vulnerable to drought. Moreover, the southwestern region is more prone to extreme drought, whereas the central region is more prone to severe droughts. Besides, the central region was more prone to extra-long-term droughts, while the coastal areas in the southwestern as well as in the central and north-western regions were more prone to long-term droughts. The frequency of droughts in all categories significantly increased during the last quinquennial period (2011 to 2015). The seasonal analysis showed that the north-western areas were prone to extreme droughts during the Kharif (wet) and Rabi (dry) seasons. The central and northern regions were affected by recurring severe droughts in all cropping seasons. Further, the most significant increasing trend of the drought-affected area was observed within the central region, especially during the pre-monsoon (March–May) season. The results of this study can aid policymakers in the development of drought mitigation strategies in the future.

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