scholarly journals Assessment of Meteorological Drought with Application of Standardized Precipitation Evapotranspiration Index (SPEI) for Tripura, Northeast India

2021 ◽  
pp. 126-135
Author(s):  
Aribam Priya Mahanta Sharma ◽  
D. Jhajharia ◽  
G. S. Yurembam ◽  
S. Gupta
Keyword(s):  
Meteorological Drought ◽  
Northeast India ◽  
Spatial And Temporal Variation ◽  
Monthly Precipitation ◽  
Standardized Precipitation Evapotranspiration Index ◽  
Geospatial Technique ◽  
Scarce Water ◽  
Drought Mitigation ◽  
Resources Planning ◽  
Monthly Precipitation Data

Drought is one of the major water-related natural hazards. Understanding the spatial and temporal variation of rainfall is of great importance in water resources planning and management as it is related with food security and management of scarce water resource, which becomes critical in case of drought events. The advent of GIS to produce spatially interpolated drought map helps the water managers to undertake appropriate measures in drought relief and prioritization of drought mitigation works. Limitation of literature on Tripura suggests that study of drought over Tripura could help in strengthening of mitigation planes and rationalization of disaster management policies. Hence, the present study is focused to investigate the drought persistence and severity in the Tripura state of India during the period 1980-2013, using Standardized Precipitation Evapotranspiration Index (SPEI). Three time scale i.e., 3, 6 and 12 month time scales were opted for the study. Gridded monthly precipitation data distributed over the four districts of Tripura was used for drought analysis. Significant drought events were detected over the study area during the selected period. Annual analysis of SPI time series showed that the study area received the intense drought during the year 1985. Geospatial technique was used to generate the SPEI drought map for the year 1985.

scholarly journals Using GIS tool for presenting spatial distribution of drought

2020 ◽  
Vol 18 (1) ◽  
pp. 77-84
Author(s):  
Milan Gocic ◽  
Danilo Misic ◽  
Slavisa Trajkovic ◽  
Mladen Milanovic
Keyword(s):  
Drought Index ◽  
National Level ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Monthly Precipitation ◽  
Gis Tools ◽  
Variability Index ◽  
Spatial Presentation ◽  
Water Surplus ◽  
Monthly Precipitation Data

By using GIS tools, it is possible to improve the preview of hydrological processes such as evapotranspiration, precipitation, flood and drought. In order to quantify drought, different type of drought indicators have been developed such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI) or Water Surplus Variability Index (WSVI). In this paper the precipitation-based SPI indicator was applied to the monthly precipitation data from Serbia during the period 1948-2012. The data were processed in the QuantumGIS software package. For the purpose of application in the monitoring of drought at the national level, a spatial presentation of meteorological drought was obtained.

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

scholarly journals Spatial and temporal variation of precipitation characteristics in the semiarid region of Xi'an, northwest China

2021 ◽  
Author(s):  
Hao Han ◽  
Jingming Hou ◽  
Rengui Jiang ◽  
Jiahui Gong ◽  
Ganggang Bai ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Arid Regions ◽  
Resource Planning ◽  
Northwest China ◽  
Annual Precipitation ◽  
Spatial And Temporal Variation ◽  
Semiarid Region ◽  
Monthly Precipitation ◽  
Xi’An City ◽  
Semi Arid

Abstract Precipitation variations mostly affect the water resource planning in semi-arid regions of northwest China. The objective of this study is to quantitatively explore the spatial and temporal variations of precipitation in different time scales in Xi'an city area. The Mann–Kendall test and wavelet analysis methods were applied to analyze the precipitation variability. In terms of temporal variation of precipitation, the results indicated that the annual precipitation exhibited a significant decreasing trend during 1951–2018. Except for summer precipitation representing a slightly increasing trend, the other seasonal precipitations had a similar decreasing trend to annual precipitation throughout 1951–2018. The monthly precipitation had different change trends, showing the precipitation from June to September could account for 58.4% of the total annual precipitation. In addition, it was clear that annual precipitation had a significant periodic change, with the periods of 6, 13, 19, and 27 years. For the spatial variation of precipitation during 1961–2018, the results showed that annual and seasonal precipitation exhibited obvious spatial differences, indicating an increasing spatial trend from north to south. Thus, understanding the precipitation variation in Xi'an city can provide a theoretical foundation of future water resources management for other cities in semi-arid regions of northwest China.

Multifractal Properties of Meteorological Drought at Different Time Scales in a Tropical Location

2020 ◽  
pp. 2150007
Author(s):  
Samuel Toluwalope Ogunjo
Keyword(s):  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Temporal Scale ◽  
Fluctuation Analysis ◽  
Monthly Precipitation ◽  
Scaling Exponent ◽  
Economic Activities ◽  
Temporal Scales ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Range Correlations

Tropical countries, like Nigeria, depend on rainfall for agriculture, power generation, transportation and other economic activities. Drought will hinder the performance of these activities, hence, it poses a significant threat to the economy. Understanding fluctuations and structures in droughts will help in forecasting, planning and mitigating its impact on livelihoods. In this study, the multifractal properties of drought at four temporal scales were investigated over different locations across Nigeria. Drought was computed using the standardized precipitation index from monthly precipitation data from 1980 to 2010. Using multifractal detrended fluctuation analysis, meteorological drought was found to have multifractal properties at 1-, 6-, 12- and 24-month temporal scale. The generalized Hurst exponent of drought at different time-scale showed dependence on scaling exponent. Long-range correlations were found to be main source of multifractality at all temporal scales. The multifractal strength increases with increasing temporal scale except for a few locations. The range of spectrum width were found to be 0.306–0.464 and 0.596–0.993 at 1- and 24-month temporal scale, respectively. No significant trend was found in the degree of multifractality across different climatic zones of Nigeria.

INDEXING OF METEOROLOGICAL DROUGHT

2019 ◽  
pp. 71-77
Author(s):  
K. Kaverina ◽  
I. Kostyrko ◽  
R. Oliynyk
Keyword(s):  
Hydrological Cycle ◽  
Regional Climate ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Time Duration ◽  
Comprehensive Overview ◽  
Response Strategies ◽  
Operational Information ◽  
Drought Mitigation ◽  
Effects Of Drought

Drought is a serious extreme climatic event that negatively affects the physical environment and water systems causing disruptions in the hydrological cycle of the region. This is a stochastic natural hazard caused by persistent rainfall scarcity. The life cycle of the drought begins with a meteorological phenomenon, and further influence is realized throughout the hydrological cycle. Adequate measures are needed to respond and mitigate the various effects of drought. Developing and implementing drought mitigation and response strategies requires understanding the various indicators used to study drought. Drought characteristics are an important condition that allows both retrospective analysis and forward planning – risk assessment. Thus, it is necessary to objectively identify the events of drought by time, duration, scale and severity of manifestation. This can be implemented with integrated indicators that involve the main characteristics of the drought. More than 150 drought indices have now been proposed, some of which are perceived as operational information used to characterize drought through maps at regional and national levels. By quantifying the level of severity and announcing the onset and end of drought, drought indices are now assisting in a variety of operations, including early warning, drought monitoring and contingency planning. Despite their diversity and continuous development, it is important to provide a comprehensive overview of the available weather indicators, which highlights their differences and studies their trends and which can be used in a special given manner regional climate.

scholarly journals Assessment of different methods for estimation of missing data in precipitation studies

2016 ◽  
Vol 48 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Mohammad-Taghi Sattari ◽  
Ali Rezazadeh-Joudi ◽  
Andrew Kusiak
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Regression Method ◽  
Linear Regression Method ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Multiple Regression Method ◽  
Multiple Imputation Method ◽  
Estimation Of Missing Data ◽  
Monthly Precipitation Data

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit, and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.

scholarly journals Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

2020 ◽  
Author(s):  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Meteorological Parameters ◽  
Global Climate ◽  
Data Series ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Analysis Method ◽  
Distinctive Features ◽  
Monthly Precipitation Data

Abstract The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

scholarly journals COMPORTAMENTO ESPACIAL DA PRECIPITAÇÃO PLUVIAL MENSAL PROVÁVEL DA MESOREGIÃO OESTE DO ESTADO DO PARANÁ

Irriga ◽  
2006 ◽  
Vol 11 (2) ◽  
pp. 150-168 ◽  
Author(s):  
Antonio Luiz Baú ◽  
Benedito Martins Gomes ◽  
Manoel Moisés Ferreira de Queiroz ◽  
Miguel Angel Uribe Opazo ◽  
Silvio César Sampaio
Keyword(s):  
Probability Distribution ◽  
Precipitation Variability ◽  
Spatial Behavior ◽  
Distribution Model ◽  
Monthly Precipitation ◽  
Precipitation Behavior ◽  
Classical Statistics ◽  
Map Generation ◽  
Standard Difference ◽  
Monthly Precipitation Data

COMPORTAMENTO ESPACIAL DA PRECIPITAÇÃO PLUVIAL MENSAL PROVÁVEL DA MESOREGIÃO OESTE DO ESTADO DO PARANÁ  Antonio Luiz Baú1; Benedito Martins Gomes2; Manoel Moisés Ferreira de Queiroz2; Miguel Angel Uribe Opazo2; Silvio César Sampaio21Coordenação de Tecnologia Ambiental, Centro Federal de Educação Tecnológica do Paraná, Medianeira, PR,  [email protected] de Ciências Exatas e Tecnológicas, Universidade Estadual do Oeste do Paraná, Cascavel, PR..   1 RESUMO             O objetivo do presente trabalho foi avaliar o comportamento espacial da precipitação mensal provável, associado ao nível de 75% de probabilidade, da mesoregião Oeste do Estado do Paraná, utilizando-se os dados de 90 estações meteorológicas com séries superiores a 20 anos de registros. Para tanto foram utilizadas ferramentas de estatística clássica e geoestatística. A estatística clássica foi aplicada, quando, a partir das séries mensais das referidas estações, procedeu-se ao ajuste para o modelo probabilístico teórico denominado modelo gama de distribuição de probabilidades. Foram, analisadas 1080 séries de precipitações mensais. Por meio da geoestatística os valores de precipitação estimados pelo modelo de distribuição gama foram devidamente georeferenciados e por meio de suas técnicas foram desenvolvidos modelos experimentais de semivariogramas, possibilitando a devida interpolação dos dados e a geração dos mapas de contorno por período estudado, que representam, em síntese, a variabilidade da precipitação. A distribuição espacial da precipitação mensal provável sofreu variações de acordo com os níveis temporais pressupostos e o comportamento espacial da precipitação demonstrou semelhança em todas as séries, com exceção para a série de junho, que apresentou diferença de padrão relativamente às demais séries. Os menores valores da precipitação provável ocorreram no setor noroeste-norte da mesoregião estudada. UNITERMOS: Precipitação Pluvial Mensal; Comportamento Espacial; Variabilidade Espacial; Geoestatística.  BAU, A. L.; GOMES, B. M.; QUEIROZ, M. M. F. de; OPAZO, M. A. U.;SAMPAIO, S. C. SPATIAL BEHAVIOR OF PROBABLE MONTHLY PLUVIAL PRECIPITATION IN THE WESTERN MESOREGION OF PARANÁ STATE  2 ABSTRACT The objective of this study was to evaluate the probable monthly precipitation spatial behavior in the western mesoregion ofParanáState, considering a 75% probability and using 90 meteorological stations that had registered information series for over 20 years. Classical statistics was used to adjust the theoretical probability pattern, which was called gamma probability distribution model. 1080 series of monthly precipitation data were analyzed. Through geostatistics, precipitation scores calculated by the gamma probability distribution model were georeferenced and experimental semivariogram patterns were developed using georeference techniques. This allowed data interpolation and map generation for the studied period, showing concisely precipitation variability. Probable monthly precipitation spatial distribution varied according to supposed weather levels; and spatial precipitation behavior was similar in all series, except for the June one, which presented a standard difference in relation to the other series. Lower scores of probable precipitation occurred in the northwestern-northern area of the studied mesoregion.  KEYWORDS: Pluvial Monthly Precipitation; Spatial Behavior; Spatial Variability; Geostatistics.

scholarly journals Mapping the Spatial and Temporal Variation of Agricultural and Metrological Drought Using Geospatial Techniques, Ethiopia

2020 ◽  
Author(s):  
Abebe Senamaw ◽  
Solomon Addisu ◽  
K.V. Suryabhagavan
Keyword(s):  
Temporal Variation ◽  
Crop Yield ◽  
Meteorological Drought ◽  
Condition Index ◽  
Agricultural Drought ◽  
Spatial And Temporal Variation ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Condition ◽  
Gis And Remote Sensing

Abstract Background Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The objective of this study was to assess spatial and temporal variation of agricultural and metrological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI). To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as SPI and crop yield anomaly. The results revealed that the year 2009 and 2015 were drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r=0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. ResultThe result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56%, 35% and 8% of study area were vulnerable to very severe, severe and moderate drought condition respectively. Conclusion In conclusion, the study area is highly vulnerable to agricultural and meteorological drought. Thus besides mapping drought vulnerable areas, integrating socioeconomic data for better understand other vulnerable factors were recommended.

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