Large-scale Climate Indices Research Articles

Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates.

Spatial and Temporal Variation of Droughts in the Mongolian Plateau during 1959–2018 Based on the Gridded Self-Calibrating Palmer Drought Severity Index

Drought monitoring is challenging, but it is required for improving agricultural production, protecting the ecological environment, and reducing economic losses in drought-prone regions such as the Mongolian Plateau (MP). This study is a systematic analysis of the spatiotemporal changes in the characteristics of drought events (drought duration, severity, intensity, frequency, peak, and starting season) at the sub-regional scale between 1959 and 2018 based on the run theory and using the gridded self-calibrating Palmer Drought Severity Index (scPDSI) dataset. Principal component analysis and Varimax rotation and the Mann–Kendall trend and Sen’s slope were used for the sub-regional division and drought trend analysis, respectively. In addition, wavelet analysis was employed to analyze drought periodicity and determine the influence of large-scale climate indices on regional drought variation. The study results indicate clear differences in the spatial patterns of drought characteristics in the MP. The northern part suffered from droughts with longer duration and higher severity, whereas more drought events with shorter duration and less severity occurred in the southern part. Most of the MP experienced a relatively wet trend in 1996–2018 compared to the period of 1959–1995. The frequency of spring drought events showed an increasing trend in 1996–2018, unlike in 1959–1995. Some drought events simultaneously affected two or several sub-regions. The wavelet analysis results indicated that the drought periodicity in the MP was 10–64 months. The Arctic Oscillation (Pacific Decadal Oscillation) was significantly correlated with drought in the southern (northern) part.

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

Space–Time Variability of Summer Hydroclimate in the U.S. Prairie Pothole Region

Abstract The Prairie Pothole Region (PPR) experiences considerable space–time variability in temperature and precipitation, and this variability is expected to increase. The PPR is sensitive to this variability—it plays a large role in the water availability of the region. Thousands of wetlands in the region, sometimes containing ponds, provide habitats and breeding grounds for various species. Many wildlife management decisions are planned and executed on subseasonal-to-seasonal time scales and would benefit from knowledge of seasonal conditions at longer lead times. Therefore, it is important to understand potential driving mechanisms and teleconnections behind space–time climate variability in the PPR. We performed principal component analysis on summer precipitation of the southeastern PPR (SEPPR) to determine the leading principal components (PCs) of variability. These PCs were used to establish teleconnections to large-scale climate variables and indices. They were also used to determine potential mechanisms driving the precipitation variability. There were teleconnections to Pacific and Atlantic Ocean sea surface temperatures (SST) resembling the Pacific decadal oscillation and El Niño–Southern Oscillation, low 500-hPa heights over the western United States, and the Palmer drought severity index over the SEPPR. A large-scale low pressure region over the northwestern United States and a pattern like the Great Plains low-level jet, observed in the 500- and 850-hPa heights and winds, are a potential mechanism of the precipitation variability by increasing precipitation during wet PC1 years. These findings can inform management actions to maintain and restore wildlife habitat and the resources used for those actions in the PPR.

COMBINED TEMPERATURE-PRECIPITATION MODES AND THEIR RELATIONSHIP WITH LARGE-SCALE CLIMATE INDICES IN PARANÁ, SOUTHERN BRAZIL (1980-2014)

In recent decades the Northeast of Brazil experienced several episodes of intense droughts while other regions were affected by heavy rainfall events that caused severe flooding. The variability of temperature and precipitation in Brazil are associated with large-scale climatic indices, such as the El Nino Southern Oscillation (ENSO), the Multidecadal Atlantic Oscillation (AMO) and the Tropical North Atlantic (TNA). In this study, quantiles 25 and 75 of temperature and precipitation were used to determine the climatic trends in terms of number of days for the different modes (warm and dry, warm and humid, cold and dry or cold and wet). Subsequently, correlation analyzes were carried out with nine different climatic indices that influence the regional climate of southern Brazil. Our results highlighted the absence of a dominant mode throughout the seasons and over the years. We also found spatio-temporal trends in this region. In addition, except for the warm-dry mode where 8 out of 10 stations were correlated with the Nino1 + 2 index, there were few correlations between the modes and the different climate indices used in this research. Despite the increasing temperature trends, and a complex and heterogeneous variations in precipitation regime, our results did not indicate any significant changes in the modes nor their relationship with the climate indices.

Markovian descriptors based stochastic analysis of large-scale climate indices

Markovian descriptors based stochastic analysis of large-scale climate indices

A study on copula-based bivariate and trivariate drought assessment in Godavari River basin and the teleconnection of drought with large-scale climate indices

A study on copula-based bivariate and trivariate drought assessment in Godavari River basin and the teleconnection of drought with large-scale climate indices

Influence of environmental variation on spatial distribution and habitat-use in a benthic foraging marine predator.

The highly dynamic nature of the marine environment can have a substantial influence on the foraging behaviour and spatial distribution of marine predators, particularly in pelagic marine systems. However, knowledge of the susceptibility of benthic marine predators to environmental variability is limited. This study investigated the influence of local-scale environmental conditions and large-scale climate indices on the spatial distribution and habitat use in the benthic foraging Australian fur seal (Arctocephalus pusillus doriferus; AUFS). Female AUFS provisioning pups were instrumented with GPS or ARGOS platform terminal transmitter tags during the austral winters of 2001–2019 at Kanowna Island, south-eastern Australia. Individuals were most susceptible to changes in the Southern Oscillation Index that measures the strength of the El Niño Southern Oscillation, with larger foraging ranges, greater distances travelled and more dispersed movement associated with 1-yr lagged La Niña-like conditions. Additionally, the total distance travelled was negatively correlated with the current year sea surface temperature and 1-yr lagged Indian Ocean Dipole, and positively correlated with 1-yr lagged chlorophyll-a concentration. These results suggest that environmental variation may influence the spatial distribution and availability of prey, even within benthic marine systems.

Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites

Despite the existence of several stochastic streamflow generators, not much attention has been given to representing the impacts of large-scale climate indices on seasonal to interannual streamflow variability. By merging a formal predictor selection scheme with vine copulas, we propose a generic approach to explicitly incorporate large-scale climate indices in ensemble streamflow generation at single and multiple sites and in both short-term prediction and long-term projection modes. The proposed framework is applied at three headwater streams in the Oldman River Basin in southern Alberta, Canada. The results demonstrate higher skills than existing models both in terms of representing intra- and inter-annual variability, as well as accuracy and predictability of streamflow, particularly during high flow seasons. The proposed algorithm presents a globally relevant scheme for the stochastic streamflow generation, where the impacts of large-scale climate indices on streamflow variability across time and space are significant.

Spatiotemporal characteristics of regional extreme precipitation in Yangtze River basin

The frequency of extreme precipitation is increasing globally and poses a significant threat to human and natural systems. At the regional scale, these changes are becoming increasingly uncertain. Therefore, it is critical to understand the spatiotemporal evolution of extreme precipitation on a regional scale. In this study, we applied the connected component labelling algorithm to define regional extreme precipitation events (REPEs). This approach considers spatial coverage and its correlation with precipitation. We further investigated the spatiotemporal changes in extreme precipitation in the Yangtze River basin using the empirical orthogonal function and examined their potential relationships with large-scale climate indices, including the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). The results show that REPEs in the study area exhibited an increasing trend in both frequency and spatial coverage since 1970. In addition, the integrated effects of PDO and ENSO have a significant impact on REPEs at various scales in summer and at large scales in winter. We also analysed the differences in these events between the current year and the following year for PDO and ENSO combinations and found that the position of REPEs change with different PDO and ENSO combinations in both summer and winter season. The spatiotemporal characteristics of REPEs in the study area reveal the corresponding connections with the East Asian continent–ocean 500-hPa pressure field. Based on our analysis and current predictions of these ocean–atmospheric oscillations, we suggest creating more REPE scenarios for upcoming decades, which may benefit forecasting and water resource management.

A time-varying drought identification and frequency analyzation method: A case study of Jinsha River Basin

The traditional drought identification indices are usually based on the assumption of stationarity, which should no longer act as a fundamental and central theory of hydrological studies in global warming context. This study develops a time-varying framework for drought identification, characterization, and frequency analyzation: firstly, a nonstationary Standardized Precipitation Index (mnSPI) is developed to modify the time-varying linear relationships with both distribution parameters and clarify the explicit expressions incorporating large-scale climate indices as covariates as well. Then time-varying copulas are employed to evaluate the potential risk of drought events. As one of the most sensitive and vulnerable regions in response to climate change, the Jinsha River Basin (JRB) is selected as the study area to test the framework. Our results indicate that comparing with the traditional SPI, the mnSPI is superior in disclosing the trend of temporal and spatial changes of JRB over 1969–2018. In general, drought events have experienced a trend of first slowing down and then aggravating, while severely arid areas have shifted from the northwest to the central and eastern regions, and then to the southwest in JRB. Besides, it is likewise important to consider nonstationarity when carrying out the drought monitoring and early warning system (M&EW). Still more work needs to be done in dynamically identifying droughts involving teleconnection of climate factors.

Ecological considerations for species distribution modelling of euphausiids in the Northeast Pacific Ocean

Euphausiids are a keystone species in marine food webs of the Northeast Pacific Ocean, important for forage fish, commercial fish species and marine predators such as seabirds and whales. Ecosystem-based fisheries management relies on realistic lower-trophic-level information for forecasting under future changing climate scenarios. We synthesised information from quantitative modelling studies analysing the relationship between euphausiids and their environment for two species of euphausiids that dominate assemblages in this region — Euphausia pacifica and Thysanoessa spinifera. Studies analysed suggest that variables reflecting the physical and biological environment in situ, features reflecting the geomorphic marine landscape, and large-scale climate indices all significantly affect euphausiid biomass and distribution. Temperature was the most tested predictor variable in the euphausiid models reviewed; however, it was significant in fewer models than other variables tested. We review and compare model structures, predictor variable selection and temporal lag phases to develop recommendations for species distribution modelling of euphausiids in the Northeast Pacific Ocean. We believe the results from this review will be applicable globally across regions with similar climates where euphausiids are numerous and can be adapted for different species and environments.

Benchmarking of drought and climate indices for agricultural drought monitoring in Argentina

Site-specific studies are required to identify suitable drought indices (DIs) for assessing and predicting drought-related impacts. This study presents a benchmark of eight DIs and 19 large-scale climate indices (CIs) to monitor agricultural drought in Argentina. First, the link between the CIs and DIs was investigated at the departmental-administrative level and at different temporal scales. Then, the effectiveness of the DIs in explaining the variability of crop yields, understood as impacts of agricultural droughts, was evaluated using statistical regression models. Soybeans were used as the reference crop. Additionally, the performances of DIs and CIs in explaining the variability of crop yields were compared. The CIs located in the Pacific Ocean (El Niño 3.4 and El Niño 4) were found to have the best correlations with the DIs (R values up to 0.49). These relationships were stronger with longer temporal aggregations and during the wet and hot seasons (summer), showing a significant role in the triggering of droughts in Argentina. The DIs that best corelated with CIs were those that included temperature in their calculations (STCI, SVHI, and SPEI). The impacts of droughts on soybean production were better explained using DIs than with CIs (up to 89% vs 8% of variability explained) as predictors of the statistical models. SVHI-6 and SPEI-6, depending on the area of interest, were, during the phenological period of crop growth (summer), the most effective DIs in explaining annual variations in soybean yields. The results may be of interest in water resource management, drought risk management, and the Argentinean soybean production sector. Furthermore, they provide a foundation for future studies aimed at forecasting agricultural droughts and their impacts.

Appraising the Status of Fish Community Structure in the Yellow Sea Based on an Indicator-Testing Framework

Fish community structure (FCS) of the Yellow Sea (YS) is affected by multiple pressures. Quantifying the responses of indicators of FCS (IFCSs) to pressures is a key aspect of ecosystem-based fisheries management. Quantitative methodology has hitherto been rarely applied to evaluate the performance of ecological indicators in response to physical and anthropogenic pressures and management actions. In this study, we adopted a quantitative and flexible framework to quantify the performance of IFCSs in the YS as well as to identify a suite of operational IFCSs to evaluate the status of the FCS via two state-space approaches. A total of 22 IFCSs were tested for their responses to three types of pressures including anthropogenic activities (fishing), large-scale climate change, and regional environmental variables. Our results indicate that the majority of IFCSs have good performance in terms of sensitivity in their responses to pressures, but weak performance in terms of robustness. The IFCSs tend to respond stronger to fishing than to large-scale climatic indices and regional environmental indices both in terms of sensitivity and robustness. A final indicator suite of five best-performing IFCSs was identified. The five IFCSs include total catch (ToC), mean trophic level (MTL), the ratio of catch of large predatory groups to total catch (LPC/ToC), mean temperature of catch (MTC) [or alternatively catch of small pelagic groups (SPC)], and functional evenness based on thermal groups (T-J′FD), all of which show regime shift patterns consistent with climate change. Compared to a reference period (1960–1964), the status of the current FCS has been obviously changed, and the long-term trajectories of the final indicator suite is consistent with that of fishing pressure. This study demonstrates the applicability of the indicator-testing framework in appraising the status of FCS, and facilitates moving towards ecosystem-based fisheries management in the YS.

A workflow to address pitfalls and challenges in applying machine learning models to hydrology

Data-driven modeling with machine learning (ML) algorithms in hydrologic modeling and forecasting suffers a number of pitfalls and challenges, including variable selection bias, resubstitution validation, inconsistent validation processes for different algorithms and model selection using the test set. They lead to incorrect model development and biased, overly optimistic performance estimates – and thus, unreliable models. This study presents a novel model building and testing workflow that addresses common machine learning (ML) challenges and pitfalls. The presented workflow incorporates optional variable transformation and preprocessing techniques, and applies to various ML model types, variable selection algorithms and resampling techniques. We demonstrate its performance through streamflow forecasting for the Bow River Basin, Alberta, Canada, using four conventional ML algorithms (ANN, SVM, ELM and RBF networks) driven by local hydrometeorological conditions and large-scale climate indices. Using the cross-validation average out-of-sample results and a separate test set, the prediction accuracy estimate bias (the relative difference between the model performance estimated using the validation sets and a separate test set) was empirically estimated to be 5.6%, 4.4%, 2.5% and 3.0% for the Seasonal, May, June and July models, respectively. In addition, the streamflow forecasting models had an average coefficient of determination of 0.85. Preprocessing and dimensionality reduction through principal component analysis (PCA) was detrimental to prediction accuracy. Snow water equivalent from individual snow courses proved the most important predictor for Bow River streamflow, while global climate indices, including the PDO, AMO and PNA, increased the Nash–Sutcliffe efficiency by 6% to 50%. Finally, although forecasting skill decreased with increasing forecast lead time, satisfactory forecasts (NSE>0.5) could be obtained two months in advance of the spring melt, at the end of February. Extensions of this study should address the tendency of different variable selection algorithms to pick irrelevant or redundant predictors after changes in training data.

Large-scale climate indices teleconnections with hydrochemical and isotopic characteristics of a karst spring using wavelet analysis

This study investigated the large-scale El Nino–Southern Oscillation and Pacific Decadal Oscillation indices teleconnections with the spring chemographs and δ18O time series in the Sarabkalan Spring, Zagros Mountains, Iran. It was intended to document a karst system by identifying synchrony and coupling between climate indices and its precipitation, spring discharge, chemographs, and isotopes. Here, we used the continuous wavelet transform and wavelet coherence. The findings demonstrated that El Nino Southern Oscillation is more correlated with EC, Ca + Mg, K, and Cl while Pacific Decadal Oscillation is largely teleconnected with EC, Na, and K. Also, the El Nino phenomena coupled with either positive or neutral Pacific Decadal Oscillation phased appeared to cause a decrease in δ18O. It is further found that in addition to its quick pulses in the spring, intense precipitation can also substantially affect the spring discharge even up to ~ 1.5-years later. The discharge and physicochemical parameters of EC, HCO3 + SO4, HCO3, Mg, and Na are out of phase, indicating that the constituent concentrations generally decrease over wet years of high discharge. This work highlighted the necessity of a combination of hydrogeological and climatic-oceanic-atmospheric factors from the local to global scales to improve water resources planning and operations in karst water resources.

Drought modelling under climate change scenarios and its probabilistic characterization: A Review

ncrease in the water scarcity and the related rise in demand of water coupled with the threating events of climate change, ultimately witnessed drought in the recent years to occur frequently. Therefore, Drought hydrology is drawing most of the attention. Drought which is a natural hazard can be best characterized by various hydrological and climatological parameters. In order to model drought, researchers have applied various concepts starting from simplistic model to the complex ones. The suitability of different modelling approaches and their negative and positive traits are very essential to comprehend. This paper is an attempt to review various methodologies utilized in modelling of drought such as forecasting of drought, drought modelling based on probability, Global Climate Models (GCM) under climate change scenarios. It is obtained from the present study that the past three decades have witnessed a very significant improvement in the drought modelling studies. For the larger time window of drought forecasting, hybrid models which incorporates large scale climate indices are promisingly suitable. Drought characterization based on copula models for multivariate drought characterization seems to have an edge over the others. At the end some conclusive remarks are made as far as the future drought modelling and research is concerned.

Yellowfin Tuna Fishing Dynamic During Low IOD Positive and Negative at Eastern Indian Ocean; Study Case at Western Waters of Sumatera

Effect of Indian Ocean Dipole (IOD) was highly significant on term of environments parameter variabilities at the Indian Ocean. Variation in the abundance and distribution of yellowfin tuna have been well known associated with environment parameters that drive by large-scale climatic indices such us Indian Ocean Dipole. To better understanding on yellowfin tuna fishing activities at the Western waters of Sumatera, we investigated hand line fishing catch during 2016 and 2017 as the IOD negative and positive events representation. Our result showed significant different fishing variabilities between year with negative and positive indices. Mean annual yellowfin tuna catch significantly higher during positive IOD both of abundance and weight, following by mean annual catch per unit effort (CPUE). During positive IOD hand line fishing trips were significantly lower compare than negative IOD. We also evaluated the geographic distribution of hand line fishing activities, we found that fishing activities cover significant wider area during positive IOD. In the opposite, during negative IOD yellowfin tuna fishing activities became centralized. This study gave us new perception on large-scale climatic events effect on yellowfin tuna fishing activities at a narrow study area.

Annual and seasonal precipitation trends and their attributions in the Qinling Mountains, a climate transitional zone in China

In this study, annual and seasonal precipitation trends and their connection with large-scale climate indices in the Qinling Mountains (QMs) were investigated using the innovative trend analysis (ITA), based on observed monthly precipitation data between 1959 and 2016 from 32 meteorological stations. The results indicated a declining trend in annual precipitation in the QMs. Seasonally, a decreasing trend was also observed in spring and autumn, while increasing trends were observed in summer and winter. Spring and autumn precipitations significantly contributed to this observed decline in annual precipitation. More importantly, both low and high values indicated a declining trend, which would have a significant influence on ecology and livelihood in the QMs. Additionally, the results of wavelet coherence showed that annual precipitation is strongly associated with the East Asian Summer Monsoon Index (EASMI), Southern Oscillation Index (SOI), South Asian Summer Monsoon Index (SASMI), South China Sea Summer Monsoon Index (SCSMI), and Southwest Australian Circulation Index (SWACI). However, its relationship with the North Atlantic Oscillation (NAO) and West African Summer Monsoon Index (WASMI) was insignificant, and seasonally, EASMI had negative effects on the precipitation in each season. It was also observed that spring and autumn precipitations were more sensitive to SOI than SWACI, while SASMI had a strong positive relationship with winter precipitation, and SCSMI was more negatively related to autumn and winter precipitation. This study will provide scientific basis for precipitation forecast, prevention, and mitigation of flood in the future.

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

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.