Standardized Precipitation Index Time Series Research Articles

Analysis of SPI index trend variations in the United Kingdom - A cluster-based and bayesian ensemble algorithms approach

Study regionUnited Kingdom (UK). Study focusA regional investigation of the Standard Precipitation Index (SPI) trends and abrupt changes in the UK has been carried out. The K-means algorithm was employed to partition the study area into six homogeneous regions, each distinguished by specific SPI characteristics. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were used to evaluate the overall trends for each cluster and SPI time scale, as well as to identify abrupt changes in trend and seasonality along the SPI time series, respectively. New hydrological insights for the regionThe seasonal MK test revealed statistically significant increasing SPI trends for all clusters, except for the southeastern area of the UK, where decreasing, but not statistically significant, SPI trends were observed. Moreover, despite a scenario suggesting an increasingly humid UK, the BEAST analysis allowed the detection of decreasing abrupt changes in trends, resulting in sudden changes from wet to dry conditions, that cannot be identified using the MK test. Alongside these, the BEAST analysis has also revealed positive abrupt changes in trends across all UK, as well as positive or negative variations in seasonality, which are followed by longer or shorter wet or dry periods, respectively. Overall, the study approach provides a detailed picture of the SPI trends and abrupt changes, in light of the impact of climate change on the different areas of the UK.

Agricultural drought response to meteorological drought over different agro-climatic zones of the Ganga River basin

Abstract Drought directly impacts the agricultural ecosystem, thus causing significant threat to regional and global food security. Investigating the occurrence and propagation patterns of drought events is crucial for its better understanding and mitigation. The study investigates different agro-climatic regions of the Ganga River basin from 2001 to 2020 to quantify meteorological drought using the Standardized Precipitation Index (SPI). Additionally, the assessment of agricultural drought was conducted using the Standardized Soil Moisture Index (SSMI) and Normalized Difference Vegetation Index (NDVI). For understanding the propagation dynamics of drought, Pearson Correlation Coefficient (PCC)-based approach was employed to compute the propagation time between meteorological and agricultural drought types. Stronger correlations were observed between SPI and SSMI compared to SPI and NDVI anomaly, highlighting the direct connection between precipitation and soil moisture. The results of the present study show that the time for propagation ranges within 1–11 months across the Ganga basin as inferred from the maximum PCC values between the SPI and SSMI time series. The propagation rate from meteorological drought to agricultural drought varied from 29.03 to 73.33% among different agro-climatic regions. The insights gained from this analysis on propagation time and rate can inform policymakers in formulating appropriate measure.

Evaluation of the Drought Trend Alongside of Change Point: A Study of the Purulia District in West Bengal, India

Since the drought is a significant issue in India, therefore, the scientists should pay close attention in order to manage it. Purulia District experienced the drought on numerous occasions in the past ten years, but the accurate and definite conclusions have not arrived yet. Hence, the Standardized Precipitation Index (SPI) for the 34 years (1979–2014) was utilised in this research together with the change point to evaluate the annual and seasonal drought in the Purulia District. Theil Sen's slope and the Mann-Kendal (MK) test were both employed to determine the trend and its’ magnitude. A trend-free pre-whitening technique was used to remove the influence of lag-1 correlation data from the series. The 1988-89 was marked as the best probable single change (shift) point in the time series of SPI. By calculating the percentage deviation from the SPI's mean trend, the magnitude of the change was calculated. The percentage change of Sen's slope and MK test was high (+ve) at the eastern parts of Purulia, whereas it was observed as the smallest at the southern parts of the region. Thus, it could be concluded that the drought had begun to spread to the study region's eastern sections after the change point. As Purulia is dependent on agricultural crop production, this research would significantly contribute to agricultural planning following the local level management of crop water and rain-water harvesting. Therefore, this research is extremely important for the drought risk management on a regional scale.

Optimal Probability Distribution and Applicable Minimum Time-Scale for Daily Standardized Precipitation Index Time Series in South Korea

The Standardized Precipitation Index (SPI) is a standardized measure of the variability of precipitation and is widely used for drought assessment around the world. In general, the probability distribution used to calculate the SPI in many studies is Gamma. In addition, a monthly time-scale is applied to calculate the SPI to assess drought based on atmospheric moisture supply over the medium-to-long term. However, probability distributions other than Gamma are applied in various regions, and the need for a daily time-scale is emerging as concerns about fresh drought increase. There are two main innovations of our work. The first is that we investigate the optimal probability distribution of daily SPIs rather than monthly SPIs, and the second is that we address the issue of determining the minimum time-scale that can be applied when applying a daily time-scale. In this study, we investigate the optimal probability distribution and the minimum-applicable time-scale for calculating the daily SPI using daily precipitation time series observed over 42 years at 56 sites in South Korea. Six candidate probability distributions (Gumbel, Gamma, GEV, Log-logistic, Log-normal, and Weibull) and ten time-scales (5 day, 10 day, 15 day, 21 day, 30 day, 60 day, 90 day, 180 day, 270 day, and 365 day) were applied to calculate the daily SPI. A chi-square test and AIC were applied to investigate the appropriate probability distribution for each time-scale, and the normality of the daily SPI time series derived from each probability distribution were compared. The Weibull distribution was suitable for calculating the daily SPI for short time-scales of 30 days or less, while the GEV distribution was suitable for longer time-scales of 270 days or more. However, overall, Gamma was found to be the best probability distribution. While there were some regional variations, the minimum time-scales that could be applied per season were as follows: 15 days for spring and summer, 21 days for fall, and 30 days for winter. It is shown that the minimum time-scale depends on how many zero values are included in the moving cumulative-precipitation time series, and it is shown that it is appropriate to have less than about 2.5%. Finally, the applicability of the GEV distribution is investigated.

Assessment of drought trends in the Upper Ewaso Ng'iro River Basin using the SPI and SPEI

Abstract Drought assessment is necessary for creating adaptation and resilience measures for the livelihoods of the affected communities. This study assessed drought trends in Kenya's Upper Ewaso Ng'iro River Basin (UENB) from 1981 to 2020. A Standardized Precipitation Index (SPI), a precipitation-based index, and a Standardized Precipitation Evapotranspiration Index (SPEI), a multivariate index that considers the difference between precipitation and potential evapotranspiration (PET), were used to evaluate drought severity and frequency over varying timescales. Monthly rainfall and temperature data for 10 stations within the basin were analyzed to calculate the SPI and SPEI time series values for 3, 6, and 12 months. The results demonstrate an alarming increase in the severity and frequency of drought events in the UENB since 1999. Additionally, the study reveals that the SPI and SPEI indices differ in identifying temporal and spatial drought characteristics, with longer timescales showing improved accuracy. Notably, the SPEI identifies more extensive and severe drought periods in the region compared to the SPI. The research findings are crucial in guiding policy decisions related to SDGs as they provide valuable information on drought trends necessary for implementing effective drought adaptation and resilience measures and promoting sustainable development in the UENB.

Propagation from meteorological to hydrological drought in the Horn of Africa using both standardized and threshold-based indices

Abstract. There have been numerous drought propagation studies in data-rich countries, but not much has been done for data-poor regions (such as the Horn of Africa, HOA). In this study, we characterize meteorological, soil moisture and hydrological drought and the propagation from one to the other for 318 catchments in the HOA to improve understanding of the spatial variability in the drought hazard. We calculate the standardized precipitation index (SPI), standardized soil moisture index (SSMI) and standardized streamflow index (SSI). In addition, we use the variable threshold method to calculate the duration of drought below a predefined percentile threshold for precipitation, soil moisture and discharge. The relationship between meteorological and soil moisture drought is investigated by finding the SPI accumulation period that has the highest correlation between SPI and SSMI, and the relationship between meteorological and hydrological drought is analysed by the SPI accumulation period that has the highest correlation between SPI and SSI time series. Additionally, we calculated these relationships with the ratio between the threshold-based meteorological-drought duration and soil moisture drought duration and the relation between threshold-based meteorological-drought duration and streamflow drought duration. Finally, we investigate the influence of climate and catchment characteristics on these propagation metrics. The results show that (1) the propagation from SPI to SSMI and the mean drought duration ratio of meteorological to soil moisture drought (P / SM) are mainly influenced by soil properties and vegetation, with the short accumulation periods (1 to 4 months) of SPI in catchments with arable land, high mean annual precipitation, and low sand and silt content, while longer accumulations (5 to 7 months) are in catchments with low mean annual upstream precipitation and shrub vegetation; (2) the propagation from SPI to SSI and precipitation-to-streamflow duration ratio are highly influenced by the climate and catchment control, i.e. geology, elevation and land cover, with the short accumulation times in catchments with high annual precipitation, volcanic permeable geology and cropland and the longer accumulations in catchments with low annual precipitation, sedimentary rocks and shrubland; and (3) the influence of mean annual upstream precipitation is more important for the propagation from SPI to SSI than from SPI to SSMI. Additionally, precipitation accumulation periods of approximately 1 to 4 months in wet western areas of the HOA and of approximately 5 to 7 months in the dryland regions are found. This can guide forecasting and management efforts as different drought metrics are thus of importance in different regions.

Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model

Long-term vegetation time series (TS) forecasting based on climatic data is one of the most challenging topics, capable of assisting in advanced estimation and management for scientific decision-making in different applications. However, these vegetation TS from remote sensing (i.e. normalized difference vegetation index (NDVI) and climatic data (standardized precipitation index (SPI)) are generally non-stationary in the real world. For this reason, traditional TS forecasting algorithms are incapable of extracting sufficient sequence data features, resulting in poor forecasting accuracy. In this paper, a novel parallel hybrid forecasting framework (Veg-W2TCN) is proposed combining multi-resolution analysis wavelet transform (MRA-WT) and temporal convolution network (TCN) model for vegetation change forecasting. In the first step, the NDVI and SPI TSs are divided into multiple groups using land cover information. For effective feature extraction, each group of data is decomposed using MRA-WT into trend and seasonal. Then, the obtained trend/seasonal from NDVI and SPI are concatenated. Multivariate TSs are obtained for each land cover. Then, each component is trained individually using the TCN model. In this step, the grid search algorithm is exploited to select the optimal TCN hyperparameters. Finally, a sum is used to integrate the different results. The proposed method is tested in the Mediterranean area. Veg-W2TCN proved its effectiveness for non-stationary TS forecasting. In fact, around 83% of pixels have a RMSE lower than 0,1 using Veg-W2TCN, compared to 74%, 72%, 60% and 59% of pixels are presented in TCN, long short-term memory (LSTM), multiple linear regression (MLR) and gated recurrent unit (GRU).

SPI-Based Drought Classification in Italy: Influence of Different Probability Distribution Functions

Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.

Impacts of land-use changes and landholding fragmentation on crop water demand and drought in Wadi El-Farigh, New Delta project, Egypt

Despite Egypt’s limited water resources, more lands in the western desert of the Nile Delta are currently being reclaimed to increase the agricultural area to feed its fast-growing population and provide jobs for new generations. These reclaimed lands are prone to drought since they are located in arid regions with limited water resources for irrigation. Moreover, a high rate of landholdings fragmentation was observed with the migration of large numbers of new farmers to the newly reclaimed areas. Hence, the current work uses remote sensing data to investigate the impacts of landholdings fragmentation and land-use changes on irrigation water demand and drought propagation in two selected sites in Wadi El-Farigh area, one of the newly reclaimed valleys in the western desert of Egypt. To achieve that goal, high-resolution satellite images were used to quantify the land-use changes and patterns. The Surface Energy Balance System (SEBS) was used to estimate the actual evapotranspiration (ETa) from 2010 to 2020. Standardized Precipitation Index (SPI) and Evaporative Stress Index (ESI) were used to monitor drought. Results indicated a large transformation of seasonal herbaceous crops to tree-fruit crops in the two study sites. However, landholding fragmentation caused a reduction in the total cultivated area and thus the accumulated water consumption over one study site. Although the accumulated ETa decreased following the decline in the total cultivated area, the ETa rate per hectare increased by 18.4% and 19.6% for trees and herbaceous crops, respectively, reflecting the negative impact of climate change. ESI and SPI time series confirms that the area became extremely dry, particularly after 2017. In conclusion, land use and climate change increased crop water demand, while landholding fragmentation led to cropland loss. Hence, more policies for land protection and adaptation strategies to climate change are essentially needed.

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

In the context of global warming, the increasing frequency of drought events has caused negative impacts on agricultural productivity and societal activities. However, the drought occurrences have not been well predicted by any single model, and precipitation may show nonstationary behavior. In this study, 60 years of monthly precipitation data from 1960 to 2019 for the Ningxia Hui Autonomous Region were analyzed. The standard precipitation index (SPI) was used to classify drought events. This study combined the strengths of autoregressive integrated moving average (ARIMA) and complementary ensemble empirical mode decomposition (CEEMD) to predict drought. First, based on the precipitation dataset, the SPI at timescales of 1, 3, 6, 9, 12, and 24 months was calculated. Then, each of these SPI time series was predicted using the ARIMA model and the CEEMD–ARIMA combined model. Finally, the models′ performance was compared using statistical metrics, namely, root-mean-square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency (KGE), Willmott index (WI), and Nash–Sutcliffe efficiency (NSE). The results show that the following: (1) Compared with the ARIMA forecast value, the prediction results of the CEEMD–ARIMA model were in good agreement with the SPI values, indicating that the combined model outperformed the single model. (2) Two different models obtained the lowest accuracy for the SPI1 prediction and the highest accuracy for the SPI24 prediction. (3) The CEEMD–ARIMA model achieved higher prediction accuracy than the ARIMA model at each time scale. The most precise model during the test phase was the CEEMD–ARIMA model at SPI24 at Xiji Station, with error measures of MAE = 0.076, RMSE = 0.100, NSE = 0.994, KGE = 0.993, and WI = 0.999. Such findings will be essential for government to make decisions.

Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China

Abstract Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions.

Evaluating performance of 20 global and quasi-global precipitation products in representing drought events in Ethiopia I: Visual and correlation analysis

Increased availability and access to satellite observation and the advanced blending techniques for generating reanalysis data have improved accuracy of precipitation products for data poor regions. However, all data products should be evaluated for their performance before being used for specific applications in different regions or locations. In this study, we evaluated 20 precipitation products for their drought monitoring performance over Ethiopia. These datasets are produced by different state-of-the-art techniques, including: 1) gauge-interpolated, 2) satellite-estimate, 3) reanalysis and 4) merged from multi-sources. The Ethiopian gauge-satellite gridded precipitation dataset and gauge records from 126 stations were used for ground truthing. Drought indices were generated for all data products using the Standardized Precipitation Index (SPI) method at 3- and 12-month time scales. We evaluated accuracy of the data products in representing occurrence and spatial and temporal patterns of the 3- and 12-month droughts using visual and correlation analysis techniques. The data were visually compared against reference data in representing four selected major drought episodes in the country (1984, 2002, 2009 and 2015). The Spearman's correlation was used to quantify relationships between the 20 precipitation products and two reference datasets using SPI values generated at 3- and 12-month time scales. Results showed considerable discrepancies and poor performance for most datasets. The ability to represent the spatial pattern and severity of major drought events varied between drought years. Although the correlation result for areal average SPI time series of the 3- and 12-month droughts for all data products showed statistically significant correlation with the reference data, there is discrepancy between the data products across space. Overall, only three out of the 20 products (CHIRPS, FLDAS and GPCC) performed relatively better. Our results provide important information to guide the choice of precipitation products for drought research, and operational drought risk management as well as useful feedback to data developers to further improve their products.

Identification of changes in rainfall pattern in paddy growing areas in Sri Lanka: A case study in Anuradhapura district

It is now widely accepted that the global climate is changing with increasing greenhouse gas concentration in the atmosphere due to human activities. There is enough evidence for more negative impacts of climate change on world agricultural sector. As an agricultural country, Sri Lanka is also experiencing some inevitable consequences due to changing rainfall onset, distribution and the amount. Standardized Precipitation Index (SPI) is a drought monitoring index which is developed for identifying rainfall anomalies with respect to the past data in the same location for specific time scales. The aim of this study was to identify the changes in rainfall patterns using SPI in Anuradhapura district which is one of the major paddy growing areas in Sri Lanka. Monthly rainfall data were collected from four Meteorological Stations viz, Anuradhapura, Maha Iluppallama, Maradankadawala and Kebithigollewa for the period of 1980-2018. Five months SPI values were computed to represent Yala & Maha cultivation seasons and 12 months SPI values were computed to represent the annual rainfall anomalies. Mann Kendal Test was used to identify the trends in selected SPI time series. The results of the study revealed that, there are significant increasing trends in total annual rainfall in Anuradhapura district indicating low risk of having drought years in future. The significant increasing trend in SPI values in Yala & Maha seasons in both Anuradhapura and Maha Iluppallama stations imply that there is a high chance of receiving enough water for cultivation in both seasons in near future. Further, a significant decrease could be observed in total annual and Yala season’s SPI values in Maradankadawala indicating a high potential of having droughts. However, Kebithigollewa does not show any significant change in total annual, Yala or Maha seasons rainfall patterns. Finally, it can be concluded that, as Anuradhapura, Maha Iluppallama and Kebithigollewa areas are getting slightly wetter, the paddy cultivation in the Western and Southwestern areas of Anuradhapura district would not be severely affected due to changing rainfall patterns. However, as Maradankadawala area is getting slightly dryer with the changing rainfall patterns, strengthening water conservation techniques would be better for paddy cultivation in the Southeastern area of Anuradhapura district in order to adapt to the incoming dry periods.

Development and application of SPI generator using open source for analyzing drought at a varying time scale

The Standardized Precipitation Index (SPI) is a probability index that gives a belier representation of abnormal wetness and dryness than any other drought indices. The primary objective of the current study is to develop a comprehensive tool to compute SPI on a spatial basis and analyze spa!iotemporal variability of drought in North West Indian region during 1951-2007 using APHRODITE waler resource data at 0.25-degree resolution. This tool was developed using the python programming language, and the site-packages such as Numpy, Scipy, Ma!plo!lib, Ne!CDF, PyQt were used. The result showed Iha! the SPI time series showed significant inter-annual and multi-decadal variations. In the whole data period, three consecutive droughts have occurred only once, 1999-2002. This prolonged drought hurt the agricultural and water resources sectors over the study area. The computed SPI for the year2002 showed an extreme dry spell over the study area signifying the major drought over India in the same year with a 56% deficit of rainfall in July. The computed 12-month SPI for the year 1996 shows a wet period over the northwestern part of India, especially over Haryana signifying medium to heavy rainfall, conforming 1996 flood. The developed SPI tool, portray a realistic picture of drought scenario over the Northwest region and improve the timely identification of emerging drought conditions that can trigger appropriate responses by the decision makers.

The effects of non‐stationarity on SPI for operational drought monitoring in Europe

AbstractIt is a good practice to follow common guidelines in the computation of Standardized Precipitation Index (SPI) data sets as part of operational drought monitoring systems. In the European Drought Observatory (https://edo.jrc.ec.europa.eu/), reference statistics are computed following the World Meteorological Organization Guidelines on the Calculation of Climate Normals, where a definition of the reference period for monitoring applications is introduced as the most recent 30‐year period finishing in a year ending with 0. In this study, the temporal consistency of the SPI time series computed using the fifth‐generation European Centre for Medium‐range Weather Forecast reanalyses model precipitation data set is tested over Europe to quantify the effect of the transition from one baseline period (1981–2010) to another (1991–2020) and to evaluate the capability of these static baselines to reproduce the behaviour of non‐stationary SPIs (nSPIs). The results of the comparison suggest that the threshold commonly used to identify droughts (SPI = −1) is only marginally affected by the change in reference period (mean absolute deviation, MAD = 0.15 ± 0.1) for short‐term SPI, whereas larger differences (MAD up to 0.6) can be observed over certain areas (i.e., Southern Italy and Eastern Europe) for longer accumulation periods (i.e., SPI‐9 or SPI‐12). Examples show that changes in drought classification from extremely dry (SPI < −2) to moderately dry (SPI < −1) are not uncommon, which may lead to misinterpretation by users. Finally, analyses against nSPI highlight an overall good correspondence between stationary and nSPIs, even if both static baselines displayed difficulties in reliably capturing the magnitude of nSPI for the entire 10‐year period for which they should be used. In this regard, it has been demonstrated that more spatially uniform results can be achieved with 5‐year updates, with a good matching (MAD <0.25) for SPI‐1 and an acceptable matching (MAD < 0.50) for SPI‐12 over more than 80% of Europe.

Assessment of centennial (1918–2019) drought features in the Campania region by historical in situ measurements (southern Italy)

Abstract. Drought is a sustained period of below-normal water availability. It is a recurring and worldwide phenomenon, but the Mediterranean Basin is seen as a very vulnerable environment in this perspective, and understanding historical drought conditions in this area is necessary to plan mitigation strategies to further face future climate change impacts. The current research was aimed at the description of drought conditions and evolution for the Campania region (southern Italy), assessed by the analysis of an in situ measurement database which covers a centennial period from 1918 to 2019. Standardized Precipitation Index (SPI) time series were reconstructed for different accumulation timescales (from 3 to 48 months) and the modified Mann–Kendall and Sen's tests were applied to identify SPI changes over time. SPI time series were mostly affected by a negative trend, significant for a very large area of the region, particularly evident for the accumulation scales longer than 12 months. Mean drought duration (MDD), severity (MDS) and peak (MDP) were furthermore investigated for both moderate (SPI ≤ −1) and extremely severe conditions (SPI ≤ −2). The accumulation scale affected the drought features, with longer duration and larger severity associated with the larger accumulation scales. Drought characteristics spatial patterns were not congruent for the different SPI timescales: if duration and severity were larger in the southern areas, peaks appeared mostly severe in the northern areas of the region. Extremely severe events were featured by shorter durations and larger severity compared to the moderate drought events but were very less frequent (over 75 % less then) and did not appear to be focused on specific areas of the region.

Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipitation or to dry spells. This study makes use of CHIRPS precipitation data gridded at 0.05° × 0.05° resolution and extended from 1981 to 2019 to analyze spatial variabilities and trends of six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over Cameroon. They are the number of wet days (RR1), the simple daily intensity index (SDII), the annual total precipitation from days greater than the 95th percentile (R95ptot), the maximum number of consecutive wet days (CWD), the maximum number of consecutive dry days (CDD), the number of very heavy rainfall (RR20). The standard precipitation index (SPI) time series were also examined in the five agro-climatic regions of the domain. The pattern of annual precipitation was first checked over the entire domain. We obtain a well-known pattern showing a decreased precipitation northward with the highest values around the Atlantic Ocean coast. The analysis shows that all indices represent patterns approximately similar to that of annual rainfall except CDD where the spatial south-north gradient is reversed. RR20 shows the lowest spatial variability. Trend study of RR1 indicates negative values south of the domain and predominated positive values in the northern part, where CDD, on the contrary, shows a decreased trend. The highest trends are observed in the northernmost area for CWD and around the coast for SDII and R95ptot. SPI time series indicate an alternative dry and wet period and the years between 1990 and 2000 witnessed more annual wet conditions. Such a study is very important in this domain where variabilities of climatic components are very high due to climate change impact and diversified relief. The results can serve as a reference for agricultural activity, hydropower management, civil engineering, planning of economic activities and can contribute to the understanding of the climate system in Cameroon.

Spatiotemporal assessment of meteorological drought using satellite-based precipitation data over Iraq

Iraq is a semi-arid country, which suffers the impact of recurrent droughts. However, studies related to the characterization of drought and risk evaluation in Iraq are scarce due to the lack of accurate climatic datasets. The present work seeks to examine the feasibility of utilizing Precipitation Data based on Remotely Sensed information from satellites (PDRS) in Iraq to monitor droughts. Two monthly PDRS are collected, namely CHIRPS for the period 1983-2016, and TRMM for the period 1998-2017 were used to calculate the Standardized Precipitation Index (SPI) for various timescales (SPI-3, SPI-6, and SPI-12) of different climate zones in the region. The findings obtained have been checked using data from sparsely scattered ground meteorological stations (GS). Although the PDRS was found to be capable of capturing estimated precipitation by GS data at different climatic zone, the two PDRS products demonstrated different responses to GS data. While the TRMM revealed a strong correlation for the droughts estimated with GS data, the CHIRPS data showed a milder correlation with the GS data. Besides, good consistency was observed in the time series of SPIs calculated with GS and PDRS data. Overall, the TRMM was found to measure the dry classes more accurately while CHIRPS was found to be better at various dry and wet classes in Temporal Coincidence (TC) terms. In addition, the TRMM – SPI data showed a better correlation in detecting the drought characteristics for Z-I and Z-II, however, the CHIRPS revealed a stronger correlation for Z-III. The findings suggested the suitability of TRMM precipitation for drought analysis and monitoring in Iraq Zones I and II, and the use of CHIRPS precipitation data for Z-III.

Assessment of drought in SPI series using continuous wavelet analysis for Gediz Basin, Turkey

This study aims to estimate the short and long-term drought periodicities, and trend component using the continuous wavelet transforms. The applied approach introduces a new concept, the maxima line, which defines the most dominant component affecting the trend in Standardized Precipitation Index (SPI) time series. The analysis of the short and long-term drought cycles was performed with data from 15 stations located in Gediz Basin, at western part of Turkey, obtained during the recording period of 1960–2016. The change point in the SPI time series was determined graphically via the continuous wavelet power spectrum, and the obtained results were verified with Pettitt's test. The significant change points, in the northwest, east and southwest part of the basin, occurred by the beginning of 1980s, by the end of 1980s, and by the mid of 1990s, respectively. The most dominating periodicities affecting the trend were varying between 24 and 48 months for the short-term drought cycles, whereas they were varying between 96 and 162 months for the long-term drought cycles.

Pengaruh koreksi bias dan metode ensemble pada data curah hujan dari empat model luaran Regional Climate Model (RCM) CORDEX-SEA di Sumatera

Drought is a natural disaster that occurs slowly and lasts longer until the wet season occurred. Drought occurred in expected time, so that preparations and preparedness can be made in dealing with drought disasters. Therefore, we need an overview of future drought events (or projections).In this study, Standardized Precipitation Index (SPI) was used as drought index. The occurrence of drought is closely related to weather factors and occurs repeatedly. Time-series weather data is needed to know the time-series weather conditions. Problems with data that often occur can be overcome by using numerical climate modeling which is currently widely used. Regional Climate Model (RCM) is a climate model that can be used to build long-term climate data, both time-series and projection data. The results showed RCM model data required bias correction in order to reduce bias in the CORDEX RCM model data. RCM rainfall models before correction were still biased. Thus, bias correction is needed to reduce bias in models data. Time series obtained from SPI baseline data for 2000-2005 in Lampung and West Sumatra provinces showed SPI value which smaller than the projection SPI value in 2021-2030. While SPI time series with RCP 4.5 and 8.5 scenarios showed different results. SPI with RCP 8.5 scenario have more negative value so that drought occurred more often than RCP 4.5. The negative SPI index that often occured in RCP 8.5 scenario appeared to be in RCM IPSL and MPI models year 2025-2030.