scholarly journals Exploration of Use of Copulas in Analysing the Relationship between Precipitation and Meteorological Drought in Beijing, China

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Linlin Fan ◽  
Hongrui Wang ◽  
Cheng Wang ◽  
Wenli Lai ◽  
Yong Zhao
Keyword(s):  
Conditional Probability ◽  
Water Resource Management ◽  
Meteorological Drought ◽  
Extreme Drought ◽  
Drought Risk ◽  
Severe Drought ◽  
Monthly Precipitation ◽  
Drought Prediction ◽  
Monthly Total Precipitation ◽  
Beijing China

Drought risk analysis is essential for regional water resource management. In this study, the probabilistic relationship between precipitation and meteorological drought in Beijing, China, was calculated under three different precipitation conditions (precipitation equal to, greater than, or less than a threshold) based on copulas. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated based on monthly total precipitation and monthly mean temperature data. The trends and variations in the SPEI were analysed using Hilbert-Huang Transform (HHT) and Mann-Kendall (MK) trend tests with a running approach. The results of the HHT and MK test indicated a significant decreasing trend in the SPEI. The copula-based conditional probability indicated that the probability of meteorological drought decreased as monthly precipitation increased and that 10 mm can be regarded as the threshold for triggering extreme drought. From a quantitative perspective, when R≤10 mm, the probabilities of moderate drought, severe drought, and extreme drought were 22.1%, 18%, and 13.6%, respectively. This conditional probability distribution not only revealed the occurrence of meteorological drought in Beijing but also provided a quantitative way to analyse the probability of drought under different precipitation conditions. Thus, the results provide a useful reference for future drought prediction.

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 Impact of Large-Scale Climate Indices on Meteorological Drought of Coastal Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Martin Addi ◽  
Kofi Asare ◽  
Samuel Kofi Fosuhene ◽  
Theophilus Ansah-Narh ◽  
Kenneth Aidoo ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Stepwise Regression ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Climate Indices ◽  
Severe Drought ◽  
Time Lags ◽  
Drought Prediction ◽  
Catchment Areas ◽  
Dry Seasons

The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.

scholarly journals Seasonal Drought Prediction for Semiarid Northeast Brazil: Verification of Six Hydro-Meteorological Forecast Products

2017 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Northeast Brazil ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeast 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 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 generalized linear model 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) and the relative operating characteristic (ROC) skill score. Forecasts of monthly precipitation had little or no skill considering RMSE. Still, the forecast of extreme events of low monthly precipitation showed skill for the rainy season (ROC skill score of 0.24 to 0.33). A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and significant skill when forecasting drought events of e.g. SPEI01 (ROC skill score of 0.53 to 0.61). Similar results were obtained for low regional reservoir storage forecasts. Regarding the skill in the forecasted months, it was greater for April, when compared to February and March (the remaining months of the rainy season). This work showed that a multimodel ensemble can forecast drought events of time scales relevant to water managers in northeast Brazil with skill. But no or little skill could be found in the forecasts of the whole range of monthly precipitation or drought indices (e.g. forecasting average years). Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeast Brazil.

scholarly journals Drought and Ecological Flows in the Lower Guadiana River Basin (Southwest Iberian Peninsula)

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 677
Author(s):  
Inmaculada Pulido-Calvo ◽  
Juan Carlos Gutiérrez-Estrada ◽  
Víctor Sanz-Fernández
Keyword(s):  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Standardized Precipitation Index ◽  
Ecological Status ◽  
Severe Drought ◽  
Monthly Precipitation ◽  
Precipitation Regime ◽  
Temporary Streams ◽  
Ecological Flows

Drought temporal characterization is a fundamental instrument in water resource management and planning of basins with dry-summer Mediterranean climate and with a significant seasonal and interannual variability of precipitation regime. This is the case for the Lower Guadiana Basin, where the river is the border between Spain and Portugal (Algarve-Baixo Alentejo-Andalucía Euroregion). For this transboundary basin, a description and evaluation of hydrological drought events was made using the Standardized Precipitation Index (SPI) with monthly precipitation time series of Spanish and Portuguese climatic stations in the study area. The results showed the occurrence of global cycles of about 25–30 years with predominance of moderate and severe drought events. It was observed that the current requirements of ecological flows in strategic water bodies were not satisfied in some months of October to April of years characterized by severe drought events occurring in the period from 1946 to 2015. Therefore, the characterization of the ecological status of the temporary streams that were predominant in this basin should be a priority in the next hydrologic plans in order to identify the relationships between actual flow regimes and habitat attributes, thereby improving environmental flows assessments, which will enable integrated water resource management.

scholarly journals The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

2021 ◽  
Vol 13 (22) ◽  
pp. 12674
Author(s):  
Mohammed Achite ◽  
Gokmen Ceribasi ◽  
Ahmet Iyad Ceyhunlu ◽  
Andrzej Wałęga ◽  
Tommaso Caloiero
Keyword(s):  
Climate Change ◽  
Risk Management ◽  
Standard Deviation ◽  
Trend Analysis ◽  
Arithmetic Mean ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Monthly Total Precipitation

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

scholarly journals Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble

2015 ◽  
Vol 16 (3) ◽  
pp. 1409-1424 ◽  
Author(s):  
Kingtse C. Mo ◽  
Bradfield Lyon
Keyword(s):  
Real Time ◽  
North American ◽  
Southern Oscillation ◽  
Value Added ◽  
Meteorological Drought ◽  
Precipitation Forecast ◽  
Monthly Precipitation ◽  
Model Ensemble ◽  
The North ◽  
Drought Prediction

Abstract Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Oscillation (ENSO) teleconnections. Knowledge of the initial drought state is critical in SPI prediction, and there are considerable differences in observed SPI values between different datasets. Root-mean-square differences between datasets can exceed typical thresholds for drought, particularly in the tropics. This is particularly problematic for precipitation products available in near–real time. Thus, in the near term, the largest advances in the global prediction of meteorological drought are obtainable from improvements in near-real-time precipitation observations for the globe. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.

scholarly journals Agricultural Drought Risk Assessment in Southwest China

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1064 ◽  
Author(s):  
Zhaoqi Zeng ◽  
Wenxiang Wu ◽  
Zhaolei Li ◽  
Yang Zhou ◽  
Yahui Guo ◽  
...  
Keyword(s):  
High Risk ◽  
Southwest China ◽  
Global Climate ◽  
Rain Gauge ◽  
Agricultural Drought ◽  
Drought Risk ◽  
Severe Drought ◽  
Monthly Precipitation ◽  
Drought Vulnerability ◽  
Agricultural Drought Risk

Drought disasters jeopardize agricultural production and are expected to become more serious in the context of global climate change. However, in China, little attention has been paid to evaluating agricultural drought risk in humid areas (such as in Southwest China), which have also been affected by severe drought in recent years. In this work, we used the Standardized Precipitation Evapotranspiration Index (SPEI), which was computed from high-quality monthly precipitation and temperature data from 92 rain-gauge stations across Southwest China, to study the drought characteristics (e.g., intensity, duration, and frequency) and their decadal variations from 1960 to 2017. Furthermore, we applied a widely accepted conceptual model that emphasizes the combined role of drought hazard (calculated by the intensity and frequency of drought) and agricultural drought vulnerability (integrated with high-resolution soil properties, climate, topography, irrigation, and gross domestic product) to conduct a spatial assessment of agricultural drought risk at a 1-km grid scale. The results revealed that drought has become more serious and frequent in Southwest China, especially since the 2000s. About 27.4% of the agricultural area has been exposed to an extremely high risk of drought, 33.5% to a high risk, 22.5% to a moderate risk, and 16.6% to a low risk. The extreme agricultural risk areas were located mainly in northeastern and southeastern Chongqing, southwestern Sichuan, northeastern and eastern Guizhou, and central and eastern Yunnan. Our findings highlighted that more attention should be paid to the agricultural drought risk in humid regions of China. Furthermore, this work could set the stage for policy makers and practitioners to take measures to reduce the agricultural drought risk in Southwest China.

scholarly journals Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2056
Author(s):  
Fangling Qin ◽  
Tianqi Ao ◽  
Ting Chen
Keyword(s):  
Marginal Distribution ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Copula Function ◽  
Distribution Functions ◽  
Joint Probability Distribution ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Copula Functions ◽  
Minjiang River

Based on the Standardized Precipitation Index (SPI) and copula function, this study analyzed the meteorological drought in the upper Minjiang River basin. The Tyson polygon method is used to divide the research area into four regions based on four meteorological stations. The monthly precipitation data of four meteorological stations from 1966 to 2016 were used for the calculation of SPI. The change trend of SPI1, SPI3 and SPI12 showed the historical dry-wet evolution phenomenon of short-term humidification and long-term aridification in the study area. The major drought events in each region are counted based on SPI3. The results show that the drought lasted the longest in Maoxian region, the occurrence of minor drought events was more frequent than the other regions. Nine distribution functions are used to fit the marginal distribution of drought duration (D), severity (S) and peak (P) estimated based on SPI3, the best marginal distribution is obtained by chi-square test. Five copula functions are used to create a bivariate joint probability distribution, the best copula function is selected through AIC, the univariate and bivariate return periods were calculated. The results of this paper will help the study area to assess the drought risk.

scholarly journals Spatio-Temporal Assessment of Meteorological Drought in Puerto Rico between 1950 and 2019

2021 ◽  
Vol 8 (1) ◽  
pp. 40
Author(s):  
Rogert Sorí ◽  
Rafael Méndez-Tejeda ◽  
Milica Stojanovic ◽  
José Carlos Fernández-Alvarez ◽  
Albenis Pérez-Alarcón ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Meteorological Drought ◽  
Extreme Drought ◽  
Eastern Coast ◽  
Severe Drought ◽  
Daily Data ◽  
Drought Conditions ◽  
Moderate Drought ◽  
Spatio Temporal ◽  
Maximum Temperatures

The phenomenon of drought is one of the most dangerous for small islands because of its impacts on freshwater availability. Thus, in this study, the spatio-temporal evolution of meteorological drought that affected the main island of Puerto Rico in the period 1950–2019 was investigated. In doing so, the Standardized Precipitation–Evapotranspiration Index (SPEI), using monthly values of minimum and maximum temperatures and precipitation derived from Daymet Version 4 daily data at a 1 km × 1 km spatial resolution, was used. At a 1 month temporal scale, the SPEI showed great temporal variability, but there was a clear tendency towards wetting in the last years of the study period. A total of 85 meteorological drought episodes were identified. The spatial analysis also revealed that major affectation by moderate drought conditions occurred across the half west and south of the island, by severe drought also in the west half of the island but also along the eastern coast, and finally the extreme drought conditions, which were less frequent, principally affected the northeast of the country. A trend analysis of the area affected by moderate, severe, and extreme drought conditions revealed a tendency to decrease, which is reflected by the prevalence of positive spatial trends of the SPEI1 across the country.

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