Rainfall Districts Research Articles

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India

The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall. The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.

Analysis of Dry-Spells in the Western Maize-Growing Areas of South Africa

Crop yield in rainfed agriculture is directly influenced by rainfall patterns, which vary from one growing season to another. The failure or success of such crops can depend on the amount and distribution of the rainfall and, particularly, on the occurrence of dry- and wet-spells during the growing season. The aim of this study was to investigate the initial and conditional probabilities of dry-spell pentads using the Markov chain model in the western maize-growing region of South Africa, as well as to determine the direction and magnitude of dry-spell trends using the Mann–Kendal monotonic trend test and Sen’s slope estimator. The results revealed that all the rainfall districts are affected by dry-spells during the mid-January-to-end-of-February period. This finding is significant because maize is usually planted during late November to late December in this region, and dry-spells may coincide with the flowering stage of the maize crop. When dry-spells occur during the flowering stage of maize, they significantly affect yield. The Mann–Kendal analysis revealed that most of the districts (7 out of 11 districts) have a decreasing trend in dry-spell occurrences except for districts 86, 87, 91 and 93. However, the decreasing trend is statistically insignificant in all the rainfall districts, and, thus, this reveals that there is no change or there is a minor change in dry-spell occurrence across all the districts. Furthermore, Sen’s slope estimator signalled a decrease in dry-spell magnitude or occurrence over the study period. Information from this study will inform farmers of the various districts regarding changes in their particular risk profile for dry-spells.

First report of Neofusicoccum australe causing dieback of honeybush in the Western Cape, South Africa.

Honeybush (Cyclopia spp.) is an indigenous, leguminous member of the Cape fynbos biome growing in the coastal winter rainfall districts of the Western and Eastern Cape Provinces of South Africa (Joubert et al. 2011). Honeybush is used for the production of herbal teas and is harvested from wild-growing and cultivated plantations (du Toit et al. 1998). Very little is known regarding diseases caused by pathogens on this indigenous plant. Only one report of twig dieback on honeybush caused by several Diaporthe Nitschke species have been reported in South Africa (Smit et al. 2021). Several honeybush producers reported poor growth and dieback in their C. subternata plantations in the Western Cape Province, South Africa. Symptoms included twig dieback, branch dieback, death of branches as well as death of entire plants. In April 2008, branches from 8-year-old cultivated plants with dieback symptoms were collected in Stellenbosch. Fungal isolations were carried out from affected material as described by Van Niekerk et al. (2004) which consistently revealed the presence of a Botryosphaeriaceae species. Two isolates were grown on water agar with sterile pine needles and incubated at 25˚C using a 12-hour day/night cycle and near-ultraviolet light. Pycnidia formed after two weeks. Morphological characteristics similar to Neofusicoccum australe (Slippers, Crous & Wingfield) Crous, Slippers & Phillips were observed (Phillips et al. 2013). Conidia were hyaline, aseptate, fusiform with subtruncate bases (16.8-)18.8-22.1(-24.6) × (4.8-)5.3-6.1(-6.4) µm (n=50). Conidiogenous cells were holoblastic, hyaline and subcylindrical to flask-shaped tapering to the apex (11-15 × 2 µm) (n=10). Colonies on potato dextrose agar were light primrose turning olivaceous grey after 7 days with a light-yellow pigment diffusing into the medium. Mycelia was moderately dense with an appressed centre mat. The identity of the isolates was further confirmed by sequencing the ribosomal RNA Internal Transcribed Spacer (ITS) and the elongation factor 1-alpha (EF-1α) gene regions using primer pairs ITS4-ITS5 (White et al. 1990) and EF1-728F-EF1-986R (Alves et al. 2008), respectively. Sequences had a 100% similarity to N. australe ex-type CMW6837 isolate (accessions AY339262 and AY339270) (Slippers et al. 2004). Two isolates (STEU6554 and STEU6557) were deposited in the culture collection at the Department of Plant Pathology at Stellenbosch University and the sequences were submitted to GenBank with accession numbers ON745603, ON745604, ON746573 and ON746574. Pathogenicity tests using the two N. australe isolates were conducted by inoculating two shoots each of three field-grown C. subternata plants with a 4mm colonised potato dextrose agar (PDA) mycelium plug of each isolate on wounds made by a 4mm cork borer (Van Niekerk et al. 2004). A third shoot was inoculated with a uncolonized PDA plug as the negative control. After 12 weeks, brown-black lesions that were significantly longer (average 55.2 mm) than the uncolonized agar plug control (16.1 mm) were observed. Lesions were observed in all three plants. Neofusicoccum australe was re-isolated (van Niekerk et al. 2004) from all inoculated shoots confirming Koch's postulates. The economic impact and damages caused by N. australe as well as its incidence and severity on honeybush in South Africa is unknown. However, the pathogen caused dieback of entire branches and death of plants indicating that it could be an important pathogen of honeybush. Additionally, N. australe is one of the most important disease-causing Botryosphaeriaceae pathogens on a wide range of economical fruit and vine crops globally (Mojeremane et al. 2020). This is the first report of N. australe as a known pathogen causing decline and dieback of C. subternata in South Africa.

ERA5-Derived Precipitation: Insights from Historical Rainfall Networks in Southern Africa

Abstract Patterns of freshwater availability—its variability and distribution—are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region. Significance Statement High-resolution gridded datasets are invaluable tools for gaining improved understanding of historical rainfall variations under the influence of climate change. In addition, these datasets provide consistent information for purposes such as water resources management. Quantification of dataset biases provides important guidance for robust decision-making as well as for the development of future climate scenarios. However, rainfall is an especially challenging quantity to assess. With the increasing incidence of drought and flood, methods that independently validate this high-resolution gridded data are needed to ensure high-quality knowledge support. This study demonstrates an approach using convergent streams of evidence to assess the European Centre for Medium-Range Weather Forecasts gridded rainfall dataset with the purpose of better understanding the evolving characteristics of rainfall in southern Africa.

Australian rainfall variability—Why is the eastern seaboard of Australia different to the rest of Australia and also internally inhomogeneous

AbstractThe Eastern Seaboard of Australia (ESA) has been identified as being different to the rest of Australia in terms of rainfall patterns. However, rainfall patterns within the ESA itself are not well understood. This study identifies Australian rainfall districts that exhibit similar (and different) temporal variation, with the primary aim being to determine if the ESA can be considered a homogenous region with respect to rainfall. The results (a) confirm that the ESA is different to the rest of Australia and (b) show that the ESA is not homogenous and that East Coast Lows (ECLs) are linked to this inhomogeneity. For winter (JJA) three separate divisions within the ESA are identified and at least two clear divisions are identified in other seasons. ECL sub‐types contribute to this inhomogeneity because each ECL sub‐type is associated with different spatio‐temporal rainfall patterns across the ESA. This is an important insight for natural resources management and emergency services within the heavily populated ESA because it demonstrates that existing climate related risks are different across the ESA. Further, the way climate risks change in the future is also likely to be inconsistent across the ESA. Future climate related risks will depend on how ECL behaviour (e.g., intensity, frequency, location, duration, path and geographical extent) changes. This reinforces the need for locally relevant and practically useful climate science information and adaptation strategies—as opposed to the State‐ or country‐wide information and adaptation approaches that are commonly used.

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

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.

Prediction of Flash Flood using Rainfall by MLP Classifier

Flood are one of the unfavorable natural disasters. A flood can result in a huge loss of human lives and properties. It can also affect agricultural lands and destroy cultivated crops and trees. The flood can occur as a result of surface-runoff formed from melting snow, long-drawn-out rains, and derisory drainage of rainwater or collapse of dams. Today people have destroyed the rivers and lakes and have turned the natural water storage pools to buildings and construction lands. Flash floods can develop quickly within a few hours when compared with a regular flood. Research in prediction of flood has improved to reduce the loss of human life, property damages, and various problems related to the flood. Machine learning methods are widely used in building an efficient prediction model for weather forecasting. This advancement of the prediction system provides cost-effective solutions and better performance. In this paper, a prediction model is constructed using rainfall data to predict the occurrence of floods due to rainfall. The model predicts whether “flood may happen or not” based on the rainfall range for particular locations. Indian district rainfall data is used to build the prediction model. The dataset is trained with various algorithms like Linear Regression, K- Nearest Neighbor, Support Vector Machine, and Multilayer Perceptron. Among this, MLP algorithm performed efficiently with the highest accuracy of 97.40%. The MLP flash flood prediction model can be useful for the climate scientist to predict the flood during a heavy downpour with the highest accuracy.

Ecological vulnerability indicators to drought: Case of communal farmers in Eastern Cape, South Africa.

Estimation of ecological drought vulnerability indicators is the important step for drought mitigation management. This article identified and estimated ecological drought vulnerability indicators among communal farmers in the Eastern Cape province of South Africa, using an ecological vulnerability index based on a household survey of 121 communal farmers. The results identified overgrazing, soil erosion, land degradation, surface and groundwater supply, and land use management as the main ecological vulnerability variables. The results showed that climate is not necessarily linked to ecological vulnerability. High rainfall districts in this study showed higher ecological vulnerability to drought because of poor planning and management of water supply, poor grazing practices and land management that leads to serious land degradation. The identification and analysis of ecological vulnerability indicators to drought would aid in reconsidering priorities for the government to implement appropriate policy measures in response to drought and suggest strategies to reduce drought vulnerability. Such policies and strategies will strengthen climate change adaptation and ensure ecological and climate sustainability that comply with the Millennium Development Goals set out by the United Nations in 2000 and the subsequent 2030 development agenda for the Sustainable Development Goals.

A PRELIMINARY ETHNOBOTANICAL SURVEY OF SEHORE DISTRICT OF MADHYA PRADESH

The present communication deals with the ethnobotanical study of Sehore. Sehore district with an area of 6579 km2 lying between the North Latitudes 22 0 33’ 30’’ & 22 0 40’ 25” and East Longitudes 780 26’ 00” and 78 0 02’ 00”. The normal rainfall of district is 1217.7 mm. The study revealed with 41 taxa belonging to 40 genera and 30 angiospermic families that are used by the tribal people to cure various human diseases.

Irrigation, Conservation Agriculture and Crop Yield Response in a Selected District in Zimbabwe

Zimbabwe’s low-potential zones can be identified with a soaring prevalence of scarcity, shortage, deficiency and lack; depressed rural proceeds, earnings and revenues; low farm yields; and food unavailability. These factors combine to collectively contribute to cause complications, frustrations, hitches and snags to efforts to earn a decent living, income, occupation and employment in the rural areas. Not much is recognized about the effects of farm skills, knowledge, expertise, machinery, tools and equipment on crop output, efficiency, yield and production, and food availability in the semi-arid, low-rainfall and parched segments of land situated in between the higher rainfall districts of Zimbabwe. This research investigated the interaction between selected farm technologies (water harvesting, conservation agriculture, fertilizer/ manure application, and irrigation) and farm yield, production and food availability among households in Ward 11 of Makonde District in Zimbabwe. The data collection method used entailed questionnaire interviews of 60 households chosen through stratified random sampling. Data analysis was conducted in SPSS. Hypothesis testing was conducted by means of the independent samples t-test and one-way between groups analysis of variance. Utilization of conservation agriculture led to significantly greater maize yields. The t-test to determine the effect of utilizing irrigation technology on yields showed that there was a significant difference between mean yields of those practicing irrigation and conservation agriculture, and mean yields of non-adopters. It is thus recommended that development factors like capital, income, wealth and assets for agricultural growth and expansion in low-rainfall districts such as Makonde need to be directed and concentrated towards farm technologies like irrigation and conservation agriculture.

Long-term rainfall trend and drought analysis for Bundelkhand region of India

In this study, an effort has been made to characterise the spatial and temporal variability of precipitation in 13 districts of the Bundelkhand region spreading in Uttar Pradesh and Madhya Pradesh of India in the during 1901–2002. The Bundelkhand region receives an average annual rainfall of 1,071.7 mm. The highest annual rainfall of 1,190.6 mm has been recorded in Sagar district, whereas the lowest annual rainfall of 902 mm has been recorded in Jalaun district. On the basis of rainfall records, the Bundelkhand region can be classified into two groups, namely, districts with high rainfall, namely, Panna, Tikamgarh, Lalitpur and Sagar receiving an average annual rainfall of 1,138 mm and low rainfall districts, namely, Jhansi, Jalaun, Hamirpur, Banda, Mahoba, Chitrakut, Datia, Chhatarpur and Damoh receiving an annual average rainfall of 1,011 mm. Standardized Precipitation Index indicates that 5 drought years were observed during 1901–1950 and 12 drought years were noticed during 1951–2000. This indicated a rising trend in the occurrence of drought in the region. Also indicates that the first half of the century was slightly wetter that the second half.

Observed changes in southwest monsoon rainfall over India during 1901–2011

ABSTRACTThe trends and epochal variability of southwest monsoon over the country as a whole and four homogeneous regions are examined using monthly rainfall data (1901–2011) of 640 political districts of India. The district rainfall data is computed from station rainfall data. The same station data is used to analyse the trends in the frequency of rainfall events of different intensities for examining extreme rainfall events. The existence of the multidecadal epochal variability of rainfall is clearly established in the all‐India monsoon rainfall as well as monsoon rainfall over the four homogenous regions. However, over different homogenous regions, the phases of multidecadal variability are found to be different. Principal component analysis brings out Northeast India (NEI) rainfall as more dominant mode for all‐India rainfall. Significant decrease in southwest monsoon rainfall over NEI is observed during the post 1950 period. Decreasing trends are also observed over the monsoon core region during the post‐1950 period. Over these regions, monsoon rainfall has increased significantly during the pre‐1950 period. It has been shown that the decreasing trend in monsoon rainfall during the post 1950 period is the result of multidecadal epochal variability. Geographical regions that experienced significant changes in the frequency of days of rainfall with different intensities are also identified. Significant change/turning points are also detected in the southwest monsoon rainfall. Frequency of moderate rainfall events (5 mm ≤ daily rainfall < 100 mm) decreased significantly during the period 1951–2010 over the monsoon core region of India whereas no significant changes are observed in the frequencies of heavy (daily rainfall >100 mm) or very heavy rain (daily rainfall >150 mm) during the southwest monsoon season. Climatic shift or change point in monsoon rainfall in India is also detected by an established statistical test.

Effect of rainfall on cropping pattern in mid Himalayan region

The analysis of effect of rainfall during the last 20 years is needed to evaluate cropping pattern in the rain-fed region. In this study, trends in annual, seasonal and monthly rainfall of district of Himachal Pradesh in India over the past 20 years were examined. The annual rainfall varies from 863.3 to 1470.0 mm. During the summer season, the highest total rainfall was 1089.7 mm during 2000, whereas lowest rainfall was 287.9 mm. The average rainfall for 20 years was found to be highest in the month of June whereas the rainy days were highest in the month of August. Fifteen years yield data of four crops in the area was analyzed. The trends in wheat and rice are upward whereas barley trends were downward and maize trends were constant. Key words : Rainfall, cropping pattern, rain-fed region.

A comparison between the Gumbel-Hougaard and distorted Frank copulas for drought frequency analysis

Drought is a global phenomenon and is a common characteristic of extreme climate. It is widely considered as the world’s costliest natural disaster since their effects are especially devastating to the agricultural and social economy. Effective water resource infrastructure and management is required for the mitigation of drought, and this requires drought risk assessment. Essential characteristics of drought are severity, average intensity and duration, which are highly correlated. Copulas are multivariate uniform distribution functions with uniform marginals, which provide a flexible approach to model their relationship. For an extreme drought event to be modelled, the upper-tail dependence must be investigated. The construction of an alternative copula form is introduced through a transformation called a distortion, to allow for more versatile modelling of tail dependence. Data from a rainfall district in New South Wales is considered and the Gumbel-Hougaard copula and distorted Frank copulas are fitted to the drought characteristics. Goodness-of-fit and upper tail correlations of these copulas are analysed and discussed. Finally, conditional annual drought recurrence intervals are calculated and compared for these fitted copulas.

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Abstract Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the two-tiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

Drought Analysis Using Trivariate Copulas Conditional on Climatic States

Droughts are an inevitable consequence of meteorological variability, and the design of water resource infrastructure and management strategies to mitigate their effects requires assessment of the risk. Crucial characteristics of droughts are related to their peak intensities, durations, and severities. These variables are typically correlated and copulas provide a versatile means to model their dependence structure. In Australia, for example, drought severity is associated with the El-Niño Southern Oscillation. Data from two rainfall districts in New South Wales, one on the east and the other on the west of the Great Dividing Range, are considered. These rainfall data are categorized into three states, El-Niño, Neutral, and La-Niña, according to the prevailing Southern Oscillation Index. Gumbel-Hougaard copulas and t-copulas are fitted to the droughts in the three states. The copula parameters are estimated separately for each state, and the differences are analyzed. The goodness-of-fit of the Gumbel-Hougaard and t-copulas are compared, and the limitations of the two copula models are discussed. The times between drought events are also analyzed according to the El-Niño Southern Oscillation state they occur in. The fitted copulas are used to estimate annual recurrence intervals of at least one of the three variables, and of all three variables, exceeding critical values taking into account of the mixture of states.

On the use of Standardized Precipitation Index (SPI) for drought intensity assessment

AbstractMonthly rainfall data from June to October for 39 years were used to compute Standardized Precipitation Index (SPI) values based on two parameter gamma distribution for a low rainfall and a high rainfall districts of Andhra Pradesh state, India. Comparison of SPI with actual rainfall and rainfall deviation from the mean indicated that SPI values under‐estimate the intensity of dryness/wetness when the rainfall is very low/very high, respectively. As a result, the SPI in the worst drought years of 2002 and 2006 in the low rainfall district indicated only moderate dryness instead of extreme dryness. SPI values of the high rainfall district showed slightly better stretching in both positive and negative directions, compared to that of the low rainfall district. Further, the SPI values of longer time scales (2, 3 and 4 months) showed an extended range compared to that of 1 month, but the sensitivity in drought years has not improved significantly.Normality tests were conducted based on Shapiro‐Wilk statistic, p‐values and absolute value of the median to ascertain whether non‐normality of SPI is a possible reason. Although the results confirmed normal distribution, the scatter plot indicated deviation of the cumulative probability distribution of SPI from normal probability in the lower and upper ranges.Therefore, it is suggested that SPI as a stand alone indicator needs to be interpreted with caution to assess the intensity of drought. Further investigations should include sensitivity of SPI to the estimated shape and scale at lower and upper bounds of the gamma distribution and use of other distributions, such as Pearson III, to standardize the computational procedures, before using SPI as a substitute to the rainfall deviations from normal, for drought intensity assessment. Copyright © 2009 Royal Meteorological Society

Trivariate copulas for characterisation of droughts

Australian agriculture is at serious risk from drought, and water resource infrastructure and management can mitigate the effects. The consequences of droughts depend on their intensity, duration and severity. These variables are correlated and the dependence structure is here described by copulas. Copulas are multivariate uniform distributions which allow for the dependence structure to be modelled independently of the marginal distributions. Trivariate Gaussian and Gumbel copulas are fitted to the data from a rainfall district in NSW. We assess the goodness of fit of the data to the different forms using several criteria. The data are best described by a Gumbel copula and three parameter Weibull marginal distributions. References P. Embrechets, F. Lindskog and A. J. McNeil. Modelling dependence with copulas and applications to risk management. In: Rachev S. T., editor. Handbook of heavy tailed distributions in finance. Amsterdam, North--Holland: Elsevier, 2003. A-C Favre, S. E. Adlouni, L. Perreault, N. Thiemonge and B. Bobee. Multivariate hydrological frequency analysis using copulas. Water Resources Research , 2004, 40 , W01101. doi:10.1029/2003WR002456 C. Genest and J. MacKay. The joy of copulas: bivariate distributions with uniform marginals. The American Statistician , 1986, 40 (4), 280--283. C. Genest and A-C Favre. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering , 2006, 12 (4), 347--368. doi:10.1061/(ASCE)1084-0699(2007)12:4(347) S. Grimaldi and F. Serinaldi. Asymmetric copula in multivariate flood frequency analysis. Advances in Water Resources , 2006, 29 , 1155--1167. doi:10.1016/j.advwatres.2005.09.005 H. Joe. Multivariate models and dependence concepts . New York: Chapman and Hall, 1997. T. B. McKee, N. J. Doesken and J. Kliest. The relationship of drought frequency and duration to time scales. In Proceeding of the 8th Conference on Applied Climatology , 17--22 January 1993, Anaheim, CA, American Meteorological Society: Boston, MA, USA, 179--184. R Development Core Team. R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing. 2007. http://www.R-project.org J. T. Shiau. Fitting Drought Duration and Severity with Two-Dimensional Copulas. Water Resources Management , 2006, 20 , 795--815. doi:10.1007/s11269-005-9008-9 A. Sklar. Fonctions de repartitions a $n$ dimensions et leurs marges. Publications de l'Institut de Statistique de l'Universite de Paris , 1959, 8 , 229--231. The Australian. El Nino bad news for drought-stricken east . The Australian, 14th September. Accessed 19th September. http://www.theaustralian.news.com.au/printpage/0,5942,2041027 L. Zhang and V. P. Singh. Bivariate Flood Frequency Analysis using Copula Method. Journal of Hydrologic Engineering , 2006, 11 (2), 150--164. doi:10.1061/(ASCE)1084-0699(2006)11:2(150)

Grain yield and cadmium concentration of a range of grain legume species grown on two soil types at Merredin, Western Australia

Five grain legumes species, narrow-leafed lupin (Lupinus angustifolius L.), field pea (Pisum sativum L.), faba bean (Vicia faba L.), chickpea (Cicer arietinum L.), and yellow lupin (Lupinus luteus L.), were grown on 2 soil types, a red clay and red duplex soil, in the < 400 mm rainfall district of Western Australia. The study showed that chickpea, field pea and faba bean accumulated less cadmium (Cd) in dried shoots and grain than narrow-leafed lupin. Yellow lupin had Cd concentrations ~3 times higher in dried shoots and ~9 times higher in grain than narrow-leafed lupin. For both experiments, the ranking (lowest to highest) of mean Cd concentration (mg Cd/kg) in the grain was: chickpea (0.017) < field pea (0.024) = faba bean (0.024) < narrow-leafed lupin (0.033) < yellow lupin (0.300).