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 Monthly drought prediction based on ensemble models

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9853
Author(s):  
Muhammad Haroon Shaukat ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Ahmad Al-Dousari ◽  
Muhammad Ismail ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Standardized Precipitation Index ◽  
Climate Indices ◽  
Climate Information ◽  
Study Region ◽  
Ensemble Models ◽  
Percentage Occurrence ◽  
Weather Pattern ◽  
Drought Prediction ◽  
The Times

Drought is a natural hazard, which is a result of a prolonged shortage of precipitation, high temperature and change in the weather pattern. Drought harms society, the economy and the natural environment, but it is difficult to identify and characterize. Many areas of Pakistan have suffered severe droughts during the last three decades due to changes in the weather pattern. A drought analysis with the incorporation of climate information has not yet been undertaken in this study region. Here, we propose an ensemble approach for monthly drought prediction and to define and examine wet/dry events. Initially, the drought events were identified by the short term Standardized Precipitation Index (SPI-3). Drought is predicted based on three ensemble models i.e., Equal Ensemble Drought Prediction (EEDP), Weighted Ensemble Drought Prediction (WEDP) and the Conditional Ensemble Drought Prediction (CEDP) model. Besides, two weighting procedures are used for distributing weights in the WEDP model, such as Traditional Weighting (TW) and the Weighted Bootstrap Resampling (WBR) procedure. Four copula families (i.e., Frank, Clayton, Gumbel and Joe) are used to explain the dependency relation between climate indices and precipitation in the CEDP model. Among all four copula families, the Joe copula has been found suitable for most of the times. The CEDP model provides better results in terms of accuracy and uncertainty as compared to other ensemble models for all meteorological stations. The performance of the CEDP model indicates that the climate indices are correlated with a weather pattern of four meteorological stations. Moreover, the percentage occurrence of extreme drought events that have appeared in the Multan, Bahawalpur, Barkhan and Khanpur are 1.44%, 0.57%, 2.59% and 1.71%, respectively, whereas the percentage occurrence of extremely wet events are 2.3%, 1.72%, 0.86% and 2.86%, respectively. The understanding of drought pattern by including climate information can contribute to the knowledge of future agriculture and water resource management.

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 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 A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

2018 ◽  
Vol 22 (1) ◽  
pp. 529-546
Author(s):  
Zhenchen Liu ◽  
Guihua Lu ◽  
Hai He ◽  
Zhiyong Wu ◽  
Jian He
Keyword(s):  
Prediction Model ◽  
Water Resource ◽  
Water Resource Management ◽  
Standardized Precipitation Index ◽  
Mitigation Measures ◽  
Drought Severity ◽  
Calibration Model ◽  
Seasonal Drought ◽  
Drought Prediction ◽  
Regional Drought

Abstract. Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial–temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric–oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric–oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

scholarly journals A conceptual prediction model of seasonal drought processes using atmospheric and oceanic Standardized Anomalies: application in four recent severe drought events in China

2017 ◽  
Author(s):  
Zhenchen Liu ◽  
Guihua Lu ◽  
Hai He ◽  
Zhiyong Wu ◽  
Jian He
Keyword(s):  
Prediction Model ◽  
Water Resource Management ◽  
Large Scale ◽  
Mitigation Measures ◽  
Drought Severity ◽  
Severe Drought ◽  
Climate Forecast ◽  
Simple Method ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. Reliable drought prediction is fundamental for end water managers to develop and implement drought mitigation measures. Considering the idea that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we develop a conceptual prediction model of seasonal drought processes based on atmospheric/oceanic Standardized Anomalies (SA). Empirical Orthogonal Function (EOF) analysis was firstly applied to drought-related SA of 200 hPa/500 hPa geo-potential height (HGT) and sea surface temperature (SST), respectively. Subsequently, SA-based predictors were built based on the spatial configuration of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric/oceanic SA-based predictors and 3-month SPI (SPI3), calibrated by the simple method of stepwise regression. It is forced by seasonal climate forecast models like the NCEP Climate Forecast System Version 2 (CFSv2). It can make seamless drought prediction for operational use after being calibrated year-by-year. Model application during four recent severe drought events in China indicates its good performance at predicting seasonal drought development, despite its weakness in predicting drought severity. Therefore, it can provide some valuable information and is a worthy reference for seasonal water resource management.

scholarly journals Estimating scenarios for domestic water demand under drought conditions in England and Wales

2018 ◽  
Vol 18 (6) ◽  
pp. 2100-2107 ◽  
Author(s):  
B. Anderson ◽  
D. Manouseli ◽  
M. Nagarajan
Keyword(s):  
Water Demand ◽  
Water Resource Management ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Severe Drought ◽  
Water Efficiency ◽  
Domestic Water ◽  
Domestic Water Demand ◽  
Catchment Areas ◽  
Drought Conditions

Abstract This paper presents preliminary results from the development of the IMPETUS model, a domestic water demand microsimulation model which was developed to estimate the results of a range of scenarios of domestic demand under drought conditions. The model is intended to enable water resource management practitioners to assess the likely impact of potential interventions in particular catchment areas. It has been designed to be driven by seasonal catchment level forecasts of potential hydrological droughts based on innovative climate and groundwater models. The current version of the model is driven by reconstructed historical drought data for the Colne catchment in the east of England from 1995 to 2014. This provides a framework of five drought phases (Normal, Developing, Drought, Severe Drought and Recovering) which are mapped to policy driven interventions such as increased provision of water efficiency technologies and temporary water-use bans. The model uses UK Census 2011 data to develop a synthetic household population that matches the socio-demographics of the catchment and it microsimulates (at the household level) the consequences of water efficiency interventions retrospectively (1995–2014). Demand estimates for reconstructed drought histories demonstrate that the model is able to adequately estimate end-use water consumption. Also, the potential value of the model in supporting cost-benefit analysis of specific interventions is illustrated. We conclude by discussing future directions for the work.

scholarly journals Development and Verification of the Available Number of Water Intake Days in Ungauged Local Water Source Using the SWAT Model and Flow Recession Curves

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1511
Author(s):  
Jung-Ryel Choi ◽  
Il-Moon Chung ◽  
Se-Jin Jeung ◽  
Kyung-Su Choo ◽  
Cheong-Hyeon Oh ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Intake ◽  
Water Resource Management ◽  
Assessment Tool ◽  
Swat Model ◽  
High Water ◽  
Coefficient Of Determination ◽  
Severe Drought ◽  
Regional Government

Climate change significantly affects water supply availability due to changes in the magnitude and seasonality of runoff and severe drought events. In the case of Korea, despite high water supply ratio, more populations have continued to suffer from restricted regional water supplies. Though Korea enacted the Long-Term Comprehensive Water Resources Plan, a field survey revealed that the regional government organizations limitedly utilized their drought-related data. These limitations present a need for a system that provides a more intuitive drought review, enabling a more prompt response. Thus, this study presents a rating curve for the available number of water intake days per flow, and reviews and calibrates the Soil and Water Assessment Tool (SWAT) model mediators, and found that the coefficient of determination, Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) from 2007 to 2011 were at 0.92, 0.84, and 7.2%, respectively, which were “very good” levels. The flow recession curve was proposed after calculating the daily long-term flow and extracted the flow recession trends during days without precipitation. In addition, the SWAT model’s flow data enables the quantitative evaluations of the number of available water intake days without precipitation because of the high hit rate when comparing the available number of water intake days with the limited water supply period near the study watershed. Thus, this study can improve drought response and water resource management plans.

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

scholarly journals Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 82
Author(s):  
Omolola M. Adisa ◽  
Muthoni Masinde ◽  
Joel O. Botai
Keyword(s):  
South Africa ◽  
South African ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Pearson Correlation Coefficient ◽  
Drought Duration

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

Improving Seasonal Predictions of Climate Variability and Water Availability at the Catchment Scale

2009 ◽  
Vol 10 (6) ◽  
pp. 1521-1533 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro
Keyword(s):  
United States ◽  
Water Availability ◽  
Colorado River ◽  
River Basins ◽  
Historical Record ◽  
Sea Surface ◽  
Climate Indices ◽  
Time Lags ◽  
Catchment Scale ◽  
Stressed Region

Abstract In a water-stressed region, such as the southwestern United States, it is essential to improve current seasonal hydroclimatic predictions. Typically, seasonal hydroclimatic predictions have been conditioned by standard climate indices, for example, Niño-3 and Pacific decadal oscillation (PDO). In this work, the statistically unique relationships between sea surface temperatures (SSTs) and particular basins’ hydroclimates are explored. The regions where global SSTs are most correlated with the Little Colorado River and Gunnison River basins’ hydroclimates are located throughout the year and at varying time lags. The SSTs, from these regions of highest correlation, are subsequently used as hydroclimatic predictors for the two basins. This methodology, named basin-specific climate prediction (BSCP), is further used to perform hindcasts. The hydroclimatic hindcasts obtained using BSCP are shown to be closer to the historical record, for both basins, than using the standard climate indices as predictors.

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