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 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 Spatiotemporal Evolution of Droughts and Their Teleconnections with Large-Scale Climate Indices over Guizhou Province in Southwest China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2104
Author(s):  
Liying Xiao ◽  
Xi Chen ◽  
Runrun Zhang ◽  
Zhicai Zhang
Keyword(s):  
Large Scale ◽  
Southwest China ◽  
Standardized Precipitation Index ◽  
Extreme Drought ◽  
Guizhou Province ◽  
Drought Severity ◽  
Climate Indices ◽  
Spatiotemporal Evolution ◽  
Study Region ◽  
Periodicity Analysis

The spatiotemporal evolution of meteorological droughts in Guizhou Province, Southwest China is analyzed based on a new set of the Standardized Precipitation Index series that mainly includes drought events that occurred from 1961 to 2004 at 81 meteorological stations. The cluster analysis shows that the study region can be classified into six homogeneous sub-regions where the drought characteristics and their temporal evolutions are quite different. The trend test and periodicity analysis indicate that Guizhou Province experienced a drier trend, which was most significant in the western parts of the region. It was found that the intensified drought severity was not always coincident with the drier trend but relied on the occurrence of extreme drought events. The trends of drier climate and drought severity were highly coincident with the temporal evolution of the drought periodicities, which were shortened from 1–4 years to less than one year. The shortened drought periodicity was found to be associated principally with a shift of the large-scale dominant climate indices from the North Atlantic Oscillation to the Indian Ocean Dipole after the late 1970s, and variations of the extreme drought events were mostly related to NINO34 in the study region.

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 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.

The influence of global climate and local hydrological features over streamflow extremes.  Case of study in a tropical Andean basin

2021 ◽  
Author(s):  
Alex Avilés ◽  
Juan Contreras ◽  
Daniel Mendoza ◽  
Jheimy Pacheco
Keyword(s):  
Global Climate ◽  
Standardized Precipitation Index ◽  
Additive Models ◽  
Mitigation Strategies ◽  
Climate Indices ◽  
Local Climate ◽  
Climate Conditions ◽  
Lulc Changes ◽  
Daily Streamflow ◽  
Floods And Droughts

<p>Hydrological extremes such as floods and droughts are the most common and threatening natural disasters worldwide. Particularly, tropical Andean headwaters systems are prone to hazards due to their complex climate conditions. However, little is known about the underlying mechanisms triggering such extremes events. In this study, the Generalized Additive Models for Location, Scale and Shape (GAMLSS) were used for investigating the relations between the Annual- Peak-Flows (APF) and Annual-Low-Flows (ALF), respecting to climate and land use/land cover (LULC) changes. Thirty years of daily streamflow data-sets taken from two Andean catchments of southern Ecuador are used for the experimental research. Global climate indices (CI), describing the large-scale climate variability were used as hypothetical drivers explaining the extreme’s variations on streamflow measures. Additionally, the Antecedent-Cumulative-Precipitation (AP) and the Standardized-Precipitation-Index (SPI), and LULC percentages were also included as possible direct drivers – synthetizing local climate conditions and localized hydrological changes. The results indicate that AP and SPI clearly explain the extreme streamflow variability. Nonetheless, global variables play a significant role underneath the local climate. For instance, ENSO and CAR exert influence over the APF, while ENSO, TSA, PDO and AMO control ALF. Furthermore, it was found that LULC changes strongly influence both extremes; although this is particularly important for relative more disturbed catchments. These results provide valuable insights for future forecasting of floods and droughts based on precipitation and climate indices, and for the development of mitigation strategies for mountain catchments.</p>

Quantifying Teleconnection pathways leading to Low Rainfall anomalies during Boreal Summer in Indonesian Borneo

2021 ◽  
Author(s):  
Timothy Lam ◽  
Marlene Kretschmer ◽  
Samantha Adams ◽  
Alberto Arribas ◽  
Rachel Prudden ◽  
...  
Keyword(s):  
Causal Inference ◽  
Climate Model ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Boreal Summer ◽  
Climate Indices ◽  
Sea Surface Temperatures ◽  
Study Region ◽  
Surface Temperatures ◽  
Rainfall Anomalies

<p>Teleconnections are sources of predictability for regional weather and climate, which can be represented by causal relationships between climate features in physically separated regions. In this study, teleconnections of low rainfall anomalies in Indonesian Borneo are analysed and quantified using causal inference theory and causal networks. Causal hypotheses are first developed based on climate model experiments in literature and then justified by means of partial regression analysis between NCEP reanalysis sea surface temperatures and climate indices (drivers) and rainfall data in Indonesian Borneo from various sources (target variable). We find that, as previous studies have highlighted, El Niño Southern Oscillation (ENSO) has a profound effect on rainfall in Indonesia Borneo, with positive Niño 3.4 index serving as a direct driver of low rainfall, also partially through reduced sea surface temperatures (SSTs) over Indonesian waters. On the other hand, while Indian Ocean Dipole (IOD) influences Indonesian Borneo rainfall through SSTs over the same area as a thermodynamic effect, its remaining effect has shifted at multidecadal timescale, opening the rooms for further research. This work informs the potential of a systematic causal approach to statistical inference as a powerful tool to verify and explore atmospheric teleconnections and enables seasonal forecasting to strengthen prevention and control of drought and fire multihazards over peatlands in the study region.</p><p>Keywords: Tropical teleconnections, Causal inference, Climate variability, Drought, Indonesia</p>

scholarly journals Hydrological Drought Assessment of Energy-Based Water Deficit Index (EWDI) at Different Geographical Regions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chanyang Sur ◽  
Dongkyun Kim ◽  
Joo-Heon Lee ◽  
Muhammad Mazhar Iqbal ◽  
Minha Choi
Keyword(s):  
Water Deficit ◽  
Standardized Precipitation Index ◽  
Matrix Analysis ◽  
Stress Index ◽  
Drought Indices ◽  
Error Matrix ◽  
Drought Assessment ◽  
Drought Prediction ◽  
Geographical Regions ◽  
Vegetation Activity

This study applied the remote sensing-based drought index, namely, the Energy-Based Water Deficit Index (EWDI), across Mongolia, Australia, and Korean Peninsula for the period between 2000 and 2010. The EWDI is estimated based on the hydrometeorological variables such as evapotranspiration, soil moisture, solar radiation, and vegetation activity which are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The estimated EWDI was compared with the Evaporative Stress Index (ESI), the Vegetation Condition Index (VCI), and the Standardized Precipitation Index (SPI). The correlation coefficients between the drought indices are as follows: 0.73–0.76 (EWDI vs ESI), 0.64–0.71 (EWDI vs VCI), 0.54–0.64 (EWDI vs SPI-3), 0.69–0.71 (ESI vs VCI), 0.55–0.62 (ESI vs SPI-3), and 0.53–0.57 (VCI vs SPI-3). The drought prediction accuracy of each index according to error matrix analysis is as follows: 83.33–94.17% (EWDI), 70.00–91.67% (ESI), 47.50–85.00% (VCI), and 61.67–88.33% (SPI-3). Based on the results, the EWDI and ESI were found to be more accurate in capturing moderate drought conditions than the SPI at different geographical regions.

scholarly journals Evaluation of a combined drought indicator and its predictive potential for agricultural droughts in Southern Spain

2019 ◽  
Author(s):  
María del Pilar Jiménez-Donaire ◽  
Ana Tarquis ◽  
Juan Vicente Giráldez
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Crop Damage ◽  
Rainfall Regime ◽  
Type I ◽  
Drought Prediction ◽  
Mediterranean Countries ◽  
Rainfall Trends ◽  
Four Levels ◽  
Study Sites

Abstract. Drought prediction is critical, especially where rainfall regime is irregular, such as in Mediterranean countries. A new combined drought indicator (CDI) is proposed that integrates rainfall, soil moisture and vegetation dynamics. Standardized precipitation index (SPI) is used for evaluating rainfall trends. A bucket-type soil moisture model is used to keep track of soil moisture and calculate anomalies, and, finally, satellite-based NDVI data is used for monitoring vegetation response. The proposed CDI has four levels, in increasing amount of severity: watch, alert, warning type I and II. This CDI was then applied over the period 2003–2013 to five study sites, representative for the main grain-growing areas of SW Spain. The performance of the CDI levels was assessed by comparison against observed data on crop damage. Observations show a good match between crop damage and CDI. Important crop drought events in 2004–2005 and 2011–2012, marked by crop damage between 70 and 95 % of the total insured area, were correctly predicted by the proposed CDI in all five areas.

scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

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