Seasonal Rainfall Characteristics Research Articles

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

Seasonal Differences in Rainfall Distribution Within the Bawku Area in the Savanna Belt of Ghana

Empirical evidence suggests that temperatures are continuously rising in the savannah areas of Ghana and impacting negatively on residents’ livelihood activities. However, there is paucity of information on the wet and dry seasons’ rate of wetness or dryness in the driest belt of Ghana. Meanwhile, residents of the area are mainly rained agriculturalists. We employed gauge station rainfall and temperature data from Ghana Meteorological Agency to assess the seasonal rainfall characteristics of the Bawku area using XLSTAT and DrinC software. Results from the rainfall anomalies show persistent dryness (-0.017) in the area during the dry season and continuous wetness in the wet season (0.021). Evapotranspiration was consistently higher in the dry season at a rate of 2.6% (0.26) yearly as well as a high rate of aridity [AI] (0.00≤AI≤0.09) in the dry season and low aridity (0.56 ≤AI ≤1.13) during the wet season. Following the reduction in the amount of rainfall, we can conclude that Bawku area is continuously drying amidst the changing climate. It is recommended that the ministry of agriculture should prioritise the construction of mechanised dams or wells and expand irrigation projects in the area to reduce the climate change effects on the livelihood of the residents especially in the dry season.

EVALUATION OF THE CORDEX-SEA MODELS PERFORMANCE IN SIMULATING CHARACTERISTICS OF WET SEASON IN INDONESIA

Indonesia's climate is known to be challenging to adequately simulate by climate models because of the complexity of the weather system and sea-land distribution. Model evaluation is essential to measure confidence in the model results. This study evaluates the performance of the CORDEX-SEA model in simulating monthly rainfall patterns and the characteristics of seasonal rainfall, i.e., pattern, timing, length, and intensity, in Indonesia during 1986-2005. The performance of weighted (WMME) and unweighted ensemble methods are also calculated. Corrected CHIRPS data with similar seasonal patterns with point observation data is used as reference data to evaluate models. Percentage of the agreement of seasonal patterns between models and observation, FAR, and POD values were used to assess the model's ability to simulate seasonal patterns. WMME has the best seasonal patterns agreement with observation, 67% of all grids. The best model performance is shown by monsoonal patterns, with a POD value of 83% by WMME. Otherwise, all models could not describe an anti-monsoonal pattern, with a small POD (0-33%) and a high FAR (60-100%). In simulating the wet season on climatological, annual, and annual mean scales, both MMEs have similar performance and are better than individual models, with WMME performing best. However, on an annual scale, the yearly wet season produced by all models tends to approach its climatology value, making it less reliable in extreme years. Most models have higher daily and monthly rainfall than observation. In conclusion, the weighted ensemble method describes Indonesia's rainy season well, thus providing a reasonable basis for further research in climate projection analysis.

Performance evaluation of different gridded precipitation and CMIP6 model products with gauge observations for assessing rainfall variability under the historical and future climate change scenario over a semi-arid catchment, India

This study evaluates performance of a series of freely available gridded precipitation datasets that have been developed from the satellite (CHIRPS and TRMM TMPA), rain-gauged interpolation (APHRODITE and IMD-gridded), and Coupled Model Intercomparison Project Phase 6 (CMIP6)-General Circulation Models (GCMs) with the gauged observations (n = 49) for assessing their ability in capturing annual and seasonal rainfall characteristics over Central Godavari River basin in India. A rank matrix was developed for the purpose of comparing the performance of gridded datasets with that of gauged observations. This matrix was generated on the basis of the goodness-of-fit (GOF) statistics. The results reveal that APHRODITE dataset (r = 0.91–0.94; NSE = 0.68 to 0.87) has performed the best across all three zones, followed by the IMD-gridded (r = 0.93–0.95; NSE = 0.57 to 0.77) and CHIRPS datasets (r = 0.89–0.92; NSE = 0.38 to 0.60). However, TRMM TMPA dataset show the poorest accuracy with the gauged observation for all three zones. Additionally, projections from the CMIP6-GCMs reveal that the western part of the basin will receive more rainfall as compared to the eastern and central parts of the basin in future (2050s). This is opposite from prevailing rainfall variability pattern in the basin. The Zone 1 and Zone 2 of the basin are found to be comparatively more susceptible to the climate change under Shared Socio-Economic Pathway (SSP)245 and SSP585 scenarios than Zone 3. These results will aid in the future development of actionable agricultural water management plans for the basin, especially for rainfed agriculture, by assisting in the selection of the most effective gridded precipitation and CMIP6-GCM datasets.

District-Level Seasonal Rainfall Characteristics over Andhra Pradesh and its Global Teleconnections in Changing Climate

Investigating the trends and changes in rainfall over vulnerable regions is of huge importance in this global warming era. The present study intensively investigates the rainfall over the Indian state, Andhra Pradesh (AP), and its 13 districts using a high-resolution (0.25°×0.25°) gridded rainfall analysis dataset from India Meteorological Department (IMD) for the study period of 118 years (1901-2018). For this, normality, homogeneity, persistence, and change-point tests are performed and changes in the district-level rainfall in the present global warming period (1991-2018) as compared to the pre-global warming period (1901-1990) is also analyzed.
 The results suggest that the long-term average annual rainfall over AP is 882 mm and most of the rainfall is contributed by the monsoon (55.7%) and the post-monsoon rainfall (32.8%). The coefficient of variation is low (high) during monsoon (winter). The coastal region receives more rainfall than the inland districts. The post-monsoon rainfall over AP is more consistent than in other seasons, and the persistence is only during the southwest monsoon season. The southwest monsoon and post-monsoon rainfall have increased (by about 10%) over most of the districts in the recent period. The Nino3.4 region SST (South Oscillation Index; SOI) has a significant negative (positive) relationship with southwest summer monsoon rainfall in most of the districts. The relationship of Nino 3.4 SST and DMI is strikingly similar for post-monsoon and has significantly weakened in recent decades. This study is useful for proper planning and mitigation measures for the agricultural and water resources sector at the district level over AP in this global warming era.

Microphysical characteristics of seasonal rainfall observed by a Parsivel disdrometer in the Tianshan Mountains, China

Raindrop size distribution (DSD) is critical for understanding rainfall microphysical processes. Correlated DSD relationships, including the radar reflectivity factor-rainfall rate (Z–R), shape factor-slope parameter (μ–∧), and the rainfall kinetic energy-R (mass-weighted average diameter) (KE–R (Dm)) are significant in many fields such as quantitative precipitation estimation, parameterization schemes in weather forecast models, and assessing rainfall kinetic energy; however, the collective scientific understanding of DSD in arid regions remains insufficient. Here, 2020–2021 DSD data from the Tianshan Mountains, China, were collected to assess various corresponding characteristics for a typical arid region in China across different seasons, rainfall types, and rainfall rate classes. Rainfall in the fall (summer) had the most (least) small raindrops (diameter < 1 mm), the least (most) mid-size raindrops (1 ≤ diameter ≤ 3 mm), and large raindrops (diameter > 3 mm). Mean Dm was the largest (smallest) in the summer (fall) at 1.059 mm (0.876 mm), while the mean generalized intercept parameter was the largest (smallest) in the fall (winter) at 3.738 (3.505). For stratiform rainfall, fall (summer) had the most (least) small raindrops, as well as the least (most) mid-size and large raindrops, whereas, for convective rainfall, summer displayed fewer small and mid-size raindrops compared to spring. Regarding the rainfall rate classes, the concentration of mid- and large-size raindrops in spring changed from a minimum at C1 (0.1 ≤ R < 0.5 mm·h−1; notably comparable to fall rates) to C4 (2 ≤ R < 5 mm·h−1; comparable to summer), before peaking beyond summer rates at C5 (R ≥ 5 mm·h−1). The localized Z-R, μ–∧, KEtime–R, and KEmm–Dm relationships also exhibited marked seasonal characteristics, which ultimately impact estimates of regional radar quantitative precipitation and rainfall kinetic energy, as well as efforts aimed at improving model parameterization schemes. Additionally, the more abundant water vapor, the more frequent cold rain process, and the stronger warm–dry atmospheric vertical environment in summer appeared as the most important influencing factors leading to the fewest small-, and greatest mid- and large-size raindrops.

Machine-learning-based water quality management of river with serial impoundments in the Republic of Korea

Euiam Lake in the Republic of Korea This study establishes a framework to prioritize total phosphorus (TP) management strategies based on machine learning (ML). A comparative analysis is conducted to evaluate the performance of four ML methods: random forest (RF), extreme gradient boosting (XGBoost), deep neural network (DNN), and long short-term memory (LSTM). The LSTM-based model is selected as the optimal predictive model of TP concentration in Euiam Lake (E_TP) on seasons (May to October) with high rainfall and inflow from two upstream dams (Chuncheon Dam and Soyanggang Dam). We also perform a gradient-based analysis to figure out the most influential factors on E_TP using the LSTM model. The top four priority factors are TP concentrations and suspended solids concentrations in the upstream dams. This application of the gradient-based analysis enables the predictive model to discuss quantitative reductions in the priorities. Based on these numerical results, we anticipate that the proposed framework can enhance the feasibility of management practices for achieving the water quality management goal of the study region. This study demonstrates that a robust predictive model can be developed for a serial impoundment system with distinct seasonal characteristics of rainfall, temperature, and water quality, thereby facilitating the selection of management priorities. Based on the predictive model results, we conclude that it is the key for managing the target TP concentration to prioritize the incoming TP concentrations and determine the quantitative • Water quality management strategy is established using RF, XGBoost, DNN, and LSTM. • The LSTM model outperforms the other ML methods when predicting the TP concentration in Euiam Lake. • The LSTM gradient-based analysis determines TP management priorities and their quantitative reduction.

Spatio‐temporal rainfall variability and trend analysis in the Tekeze‐Atbara river basin, northwestern Ethiopia

AbstractSpatial and temporal rainfall variability analysis at a basin scale is essential to understand water availability for sustainable water resource management. This study investigated the spatial distribution and temporal rainfall trend in the Tekeze river basin using daily rainfall data collected from 43 meteorological stations with records from 1960 to 2019. Coefficient of variation, seasonality index (SI) and trend index were used to evaluate monthly, seasonal and annual rainfall variability in the basin. The non‐parametric Mann‐Kendall's test was used to understand any rainfall trends. Results showed that there is moderate annual rainfall variability in the basin. Rainfall variability is higher in the Tsedey, Bega and Belg than kiremt season. SI results showed that majority of the Tekeze river basin is categorized as seasonal, most of the rain occurring in less than 3 months. Overall, Mann‐Kendall's test result revealed that there is no significant annual rainfall trend in the basin except for four stations. The seasonal rainfall characteristics and short duration of rain in the basin indicate that there is a need to implement water harvesting measures during the wet season for use in the dry periods.

Spatial and Temporal Analysis of Daily, Monthly, and Seasonal Rainfall Characteristics across Northern Cyprus

AbstractIn this study, rainfall time series data of 36 years collected at 33 meteorological stations were analyzed to introduce spatial and temporal characteristics of daily, monthly, and seasonal ...

Spatiotemporal monsoon characteristics and maize yields in West Africa

To assess the vulnerability of rainfed agriculture in West Africa (WA) to climate change, a detailed understanding of the relationship between food crop yields and seasonal rainfall characteristics is required. The highly seasonal rainfall in the region is expected to change characteristics such as seasonal timing, duration, intensity, and intermittency. The food crop yield response to changes in these characteristics needs greater understanding. We follow a data-driven approach based on historical yield and climate data. Such an approach complements model-based approaches. Previous data-driven studies use spatially and temporally averaged precipitation measures, which do not describe the high degree of spatial and temporal variability of the West African Monsoon (WAM), the primary source of water for agriculture in the region. This has led previous studies to find small or insignificant dependence of crop yields on precipitation amount. Here, we develop metrics that characterize important temporal features and variability in growing season precipitation, including total precipitation, onset and duration of the WAM, and number of non-precipitating days. For each temporal precipitation metric, we apply several unique spatial aggregation functions that allow us to assess how different patterns of high-resolution spatial variability are related to country-level maize yields. We develop correlation analyses between spatiotemporal precipitation metrics and detrended country-level maize yields based on findings that non-climatic factors, such as agricultural policy reform and increased investment, have driven the region’s long-term increase in maize yields. Results show that that the variability in the number of days without rain during the monsoon season and the lower bounds to the spatial rain pattern and end to the monsoon season are most strongly associated with maize yields. Our findings highlight the importance of considering spatial and temporal variability in precipitation when evaluating impacts on crop yields, providing a possible explanation for weak connections found in previous studies.

Management of crop planting in the dry season 2020, an adaptation to the impact of drought for supporting the food security

One of the impacts of climate change is the different of seasonal rainfall characteristics from year to year, thus the conditions for planting schedules and potential planting areas will also differ from year to year. At the beginning of the Dry Season 2020, the competent authority published that 30% of the season forecast zone in Indonesia was predicted to experience Below-Normal rainfall. This paper aims to analyze areas that have the potential to experience drought and recommend crop management in paddy fields in the Dry Season 2020. The methods are to identify the rainfall condition at rice field area, overlay the rainfall condition with the cropping calendar information, recommend planting management, and balance the needs and fulfillment of rice. The results of the analysis showed that the area of rice, maize, and soybean plants in paddy fields that had the potential to experience drought were 1,087,937 hectares, 1,996,156 hectares, and 786,849 hectares. The potential area of maize and soybean to be converted into rice plants are 1,448,110 hectares, and 102,868 hectares. The potential for rice production during the period July 2020 to January 2021 is estimated at around 25.55 million tons of rice. It is predicted that the conversion of maize and soybeans to become rice plants through fulfilling their water needs will result in an increase in rice production to 33.10 million tons, and produce a supplement in the balance of rice availability to their needs during the July-December 2020 period.

A landscape hydrology approach to inform sustainable water resource management under a changing environment. A case study for the Kaleya River Catchment, Zambia

Study regionKaleya River Catchment in southern Zambia. Study focusThe ability of a landscape hydrology approach to detect controls on water availability in a fragmented landscape to inform interventions under a changing environment was investigated. Simple and measurable climatic and landscape pattern attributes were analysed using change detection, trend analysis and backward variable elimination with Partial Least Squares Regression (PLSR) to identify controls on seasonal river flows and how landscape components could be enhanced to augment natural river flows. New hydrological insights for the regionLandscape pattern showed increasing fragmentation, expansion of irrigated cropland and reservoirs and loss of forestland. Significant increasing trends (p < 0.05) were observed for reference evapotranspiration (ETo), one-day maximum rainfall, coefficient of variation (CV) of rainfall, maximum dry spell length, and start of rains but not annual rainfall. Increased CV of rainfall, rainfall intensity and ETo were the main climatic stressors on river flows. Increased Percentage of Landscape (PLAND) of irrigated cropland, PLAND of reservoirs, Patch Density (PD) and Largest Patch Index (LPI) of reservoirs were the main landscape pattern stressors. Only the LPI of forestland positively explained seasonal river flows. Water resource interventions in the region must adapt more to changing seasonal rainfall characteristics than to annual rainfall totals. Additionally, regeneration of larger forest patches could improve river flows. The approach can be applied in other regions.

The role of extreme rain events in Peninsular Florida’s seasonal hydroclimate variations

The role of extreme rains over Peninsular Florida (PF) in modulating the seasonal rainfall characteristics is investigated in this study. The paper is motivated on its potential implication on the seasonal predictability of the hydroclimate of PF by relatively coarse global seasonal climate models. A majority of these climate models are unable to resolve the weather events like tropical cyclones that produce such extreme rain events. Therefore, a legitimate question to ask is if this limits the model’s seasonal predictability of the hydroclimate of PF.In this paper, extreme rain events over PF within a season are defined as days with daily rain amount at or above the 95th percentile over 39 years from 1979 to 2017 at the grid resolution of the observed rainfall dataset (0.5° × 0.5°). The thresholds for extreme rain days range from 16 mmday−1 to 36 mmday−1 depending on the season and the location over PF, while the heaviest rainfall range from 58 mmday−1 to 278 mmday−1. These extreme rain events occur most often across PF in the boreal summer season followed by the fall season with the least in the boreal winter season. Our study reveals that removing the days of extreme rain events has the largest impact on the corresponding seasonal anomalies and daily rainfall distribution in the dry winter season and least in the wet summer season.The impact of El Niño and the Southern Oscillation (ENSO) on the extreme rain events was evaluated by contrasting the differences in the shape and the scale parameters of the fitted Gamma distribution on daily rainfall in winter/spring seasons during warm and cold phases. Results revealed that the warm ENSO phases make the tails of the daily rainfall distribution over PF heavier and longer relative to the cold ENSO phases in the winter and spring seasons. In essence, our study reveals that the extreme rain events that are critical for the overall seasonal distribution of rainfall over PF in the first half of the year is modulated by large-scale phenomenon (e.g., ENSO). In the latter half of the year (summer and fall), the extreme rain events are not as critical to the seasonal rainfall anomaly or the overall seasonal distribution of rainfall over PF. Therefore, resolving the extreme rain events need not be as critical for the seasonal predictability of the hydroclimate of PF.

Agro-meteorological risks to maize production in Tanzania: Sensitivity of an adapted Water Requirements Satisfaction Index (WRSI) model to rainfall

The Water Requirements Satisfaction Index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tanzania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R2 = 0.52–0.61 for maize varieties of 120–160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R2 = 0.46–0.55 and 0.44–0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications.

Forecasting seasonal rainfall characteristics and onset months over South Africa

ABSTRACTAspects of forecast skill in predicting seasonal characteristics using global climate models (GCMs) are assessed over South Africa. The GCMs output is configured to predict number of rainfall days at South African Weather Service stations exceeding pre‐defined threshold values for the austral summer seasons and to predict the rainfall totals of the onset months of the rainy seasons for eight homogeneous rainfall regions of South Africa. Using canonical correlation analysis (CCA) as statistical downscaling technique through model output statistics, the forecast skill levels of coupled ocean–atmosphere and uncoupled atmospheric models are determined through retro‐actively generated hindcasts. Both downscaled models have skill in predicting low and high number of rainfall days exceeding pre‐defined thresholds for the austral summer seasons as well as rainfall totals of onset months. In addition to the forecast verification results, CCA pattern is performed to determine the dominating atmospheric circulation systems predicted to be controlling rainfall variations for the seasons and months of interest. CCA patterns for both the GCMs indicate that usually when there are anomalously negative (positive) predicted 850 hPa geopotential heights over South Africa, there are anomalously wet (dry) conditions over most parts of South Africa. The work has paved the way for the operational production of seasonal rainfall characteristics over South Africa in real time.

Characteristics of Seasonal Rainfall and its Distribution Over Bale Highland, Southeastern Ethiopia

Agricultural productivity heavily depends on onset and cessation of rainfall and Length of Growing Period (LGP). Determining these variables is useful for early warning and preparedness. Therefore, the objective of this study was to show the onset, cessation and LGP of the seasons over Bale Highlands. Bale Highland is characterized by bi-modal rainfall types. In order to find Onset, Cessation and LGP of both Belg and Kiremt seasons, 30 years of rainfall data for periods of (1985 to 2014) in daily format from NMA (National Metrological Agency) Bale robe branch directorate was used. Additionally, for this study, 12 meteorological stations in the Bale Highlands were used. The study shows that there is high variability of onset and cessation in Belg season than kiremt season. The mean onset and mean cessation of Belg season over Bale Highland is March 28 and June 10 with mean standard deviation of 19 days, respectively. On the other hand, in kiremt season, mean onset and mean cessation were July 12 and October 31 with mean standard deviation of 16 and 12 days, respectively. During the major growing season (Kiremt) the mean LGP is 110 days with mean standard deviation of 19 days and for Belg season the mean LGP is 73 days with mean standard deviation of 26 days. The short in LGP and the variation of onset and cessation of the seasons will be considered by the concerned body to aware the local community on the onset and cessation as well as Length of growing period for both seasons. The result found more likely lead the traditional rain-fed agricultural practice in both seasons to shift to irrigation system.

Improved seasonal prediction skill of rainfall for the Primera season in Central America

ABSTRACTThis study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low‐level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.

Entropy theory for analysing water resources in northeastern region of Brazil

ABSTRACTUsing the Shannon entropy, the space–time variability of rainfall and streamflow was assessed for daily rainfall and streamflow data for a 10-year period from 189 stations in the northeastern region of Brazil. Mean values of marginal entropy were computed for all observation stations and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann-Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. The marginal entropy values of rainfall and streamflow were higher for locations and periods with the highest amounts of rainfall. The entropy values were higher where rainfall was higher. This was because the probability distributions of rainfall and the resulting streamflow were more uniform and less skewed. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.

Geographical Variability of Drought in Northern South Africa

This study focuses on the geographical variation of drought in northern South Africa (hereafter NSA). It assesses seasonal rainfall characteristics to determine drought occurrence and persistence in NSA. Seasonal rainfall data for the period 1960-2009 is used and was obtained from the South Africa Weather Service (SAWS). Rainfall stations in NSA are well distributed, forming a dense network of point-source data samples. Standardised Precipitation Indices (SPIs) are employed to detect drought occurrence and intensity at different locations. Analysis of SPIs with respect to time suggests that the severity of drought results from the accumulation of consecutive dry spells within a rainfall season and sometimes even consecutive dry rainfall seasons. It also shows the intensity and frequency of drought has increased in recent years. The trend towards worsening drought conditions has significant socioeconomic implications for the region and other areas with similar geographical settings.

Shannon information entropy for assessing space–time variability of rainfall and streamflow in semiarid region

The principle of maximum entropy can provide consistent basis to analyze water resources and geophysical processes in general. In this paper, we propose to assess the space-time variability of rainfall and streamflow in northeastern region of Brazil using the Shannon entropy. Mean values of marginal and relative entropies were computed for a 10-year period from 189 stations in the study area and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann–Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. High degree of similarity was found between rainfall and streamflow, particularly during dry season. Both rainfall and streamflow variability can satisfactorily be obtained in terms of marginal entropy as a comprehensive measure of the regional uncertainty of these hydrological events. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.