Rainfall Variability Research Articles

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Deciphering of the morpho-physiological traits of two native grasses from Argentina with contrasting drought resistance strategies

Context Semiarid environments, such as the Patagonian shrublands, are characterised by having shrubby patches surrounded by grasses with different ecophysiological strategies to tolerate long dry periods. Aims We hypothesised that coexisting grasses of the Patagonian rangeland, already classified as drought-escaping or drought-tolerant, have different traits according to the season and the annual rainfall events. Methods Two dominant native grasses were selected: Pappostipa speciosa (evergreen) and Poa ligularis (deciduous). Samples were collected in the four seasons for the term of 1 year. Rainfall events and soil water content of each season were determined. Spring was the wettest season and autumn the driest. Physiological (relative water content, pigments, hormones), biochemical (polyphenols, antioxidant activity) and morphological traits were measured in the four seasons. Key results P. speciosa was characterised by keeping evergreen leaves with high production of polyphenols as secondary metabolites with high antioxidant capacity during the dry autumn and winter seasons. P. ligularis was characterised by high contents of gibberellin (GA1), auxin (IAA), total phenols, total flavonoids and tartaric acid esters, and high antioxidant capacity in roots during the autumn dry season. In addition, P. ligularis leaves had higher content of carotenoids and polyphenols than P. speciosa during the summer dry season. Conclusions The major adaptive strategy to tolerate dry periods is the high activity of the secondary metabolism, mainly in leaves in P. speciosa (a drought-tolerant grass) and in roots in P. ligularis (a drought-escaping grass). Implications Rainfall variations during a year can affect the phenological growth stages and the metabolism of two native grasses from Argentina characterised by different drought resistance mechanisms.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia

Ethiopia gets its agricultural water primarily from rainfall. This study was intended to investigate current climate variability and trends across space and time. Daily gridded temperature and rainfall data from 1993 to 2022 in the Hulbarag district, Silte Zone of Ethiopia was obtained from the Ethiopian National Metrological Institute and the Climate Hazard Group Infrared Precipitation with Station. The trends and variability of temperature and rainfall were analyzed using the Mann-Kendall trend test, Sen's slope, coefficient of variation, precipitation concentration index, and rainfall anomaly index. The results indicated that annual, spring, and summer rainfall revealed statistically significant decreasing trends at Hulbarag and Sankura stations, with the magnitude of −13.4, −11.6, and −10.6 mm per year and −6.8, −3.6 and −10.9 mm per year respectively. Conversely, autumn and winter season rainfall showed statistically significant increasing trends at Hulbarag and Sankura stations, with the magnitude of 5.1 and 5.5mm per year and 3.4 and 1.84mm per year consecutively. Between 43 % and 47 % of the observation periods had negative anomalies. The average yearly temperature, average yearly minimum temperature, and average yearly maximum temperatures at Sankura and Fonko stations all displayed statistically significant increasing trends, with a magnitude of 0.091 °C, 0.009 °C and 0.051 °C per year and 0.03 °C,0.01 °C and 0.0022 °C per year successively. It is advisable to develop a farming system that is climate-resilient by improving the adaptive capacity of wheat and maize-growing farmers by expanding the availability of early maturing seeds, changing crop calendars, and enhancing proactive and credible climate information services.

Spatiotemporal climate variability and extremes in Middle Awash Afar region Ethiopia: implications to pastoralists and agro-pastoralists food security

Purpose This study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia. Design/methodology/approach The study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity. Findings The result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods. Practical implications The analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers. Originality/value The research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.

Assessment of the Impact of Magnesium and Nitrogen Fertilization on Two Species of Grasses Used as Horse Feed

The aim of this study was to determine the effect of nitrogen (three doses of N) and magnesium (two doses of Mg) fertilization on the yield and quality of fodder obtained from two old and extensive grass species Festulolium braunii cv. ‘Felopa’ and Lolium multiflorum cv. ‘Tur’ in field cultivation under dry conditions. F. braunii was better adapted to cultivation on light, dry soils than L. multiflorum; in such conditions, it produces higher yields of dry matter and protein, characterized by a higher concentration of nutrients. F. braunii fertilized with doses of 120 and 180 kg N∙ha−1 yielded higher than that fertilized with a dose of 60 kg N∙ha−1, and L. multiflorum produced similar yields after applying doses of 60, 120 and 180 kg N∙ha−1. For tested grass pasture, a single N application after the start of vegetation in two forms (fast- and slow-acting) appears to be adequate. Resignation from splitting the nitrogen dose due to variable rainfall distribution that can occur after the first cut during the dry summer is beneficial due to a reduction in the losses of nutrients and environmental burdens.

Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance

AbstractThe advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Coupled effects of heavy rainfall and falling water‐level exhibit the most significant influences on reservoir bank failures

AbstractMass failures are often induced or activated by the variation of hydrological factors within a reservoir area. Clarifying the relationships between the failure volume and different hydrological factors may help to identify both the dominant factors influencing reservoir bank failures and the most hazardous combinations of factors inducing such events. This study examines the influence of different hydrological factors on bank failures using model experiments incorporating variations in rainfall, wave and water level. The results indicate that the coupled effects of heavy rainfall and falling water‐level had the most significant impact on bank failure. The cumulative failure mass of the reservoir bank peaked at 177.2 × 103 cm3 under the coupled effects of rainfall and falling water‐level. In comparison, the cumulative mass failures on the reservoir bank under rainfall superimposed onto waves and rainfall superimposed onto water level rise were 162.4 × 103 and 129.2 × 103 cm3, respectively. The impact of heavy rainfall significantly contributed the mass failures on soil reservoir bank. The analysis reveals that the highest cumulative failure, 106.3 × 103 cm3, occurred during periods of heavy rainfall. Compared with heavy rainfall, the cumulative failure volumes in the reservoir experienced a reduction of 46% and 55% under solely declining and solely rising reservoir water levels, respectively. The early phases of changing hydrological conditions have a significant effect on the stability of the soil reservoir bank. Mass failures on the bank predominantly occurred during the initial three events of the five simulation experiments performed for each landform, which could account for up to 87% of the total volume. Hence, it is advisable to avoid lowering reservoir water levels during periods of sustained heavy rainfall in order to mitigate the risk of bank failure.

Innovative polygon trend analysis for the monthly rainfall in the Mae Klong River Basin, Thailand

ABSTRACT Historical fluctuations in hydroclimatic variables can reveal important information about water resource changes in any basin. The innovative polygon trend analysis (IPTA) method was used to analyze 45-year rainfall data from eight monitoring stations in the Mae Klong River Basin, Thailand. The IPTA graphics were developed for the mean monthly rainfall and the standard deviation of the monthly rainfall for each station. In addition, the IPTA results were compared with the Mann–Kendall (MK) test and the Sen's slope method. The IPTA graphics showed no regular polygon for any rainfall station, which indicated variability of rainfall over the years. The IPTA method showed increasing trends for the month of April while decreasing trends in the month of October for the majority of the stations (7 of 8). A good agreement for detection of trends was found between the IPTA method and the MK test. Both the methods found trends (increasing/decreasing) for 69 months (72%) and showed no trend for 27 months (28%) out of a total of 96 months. The findings of this study could benefit water supply and management, drought monitoring, and agricultural production activities in the Mae Klong River Basin in the future.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Rainfall variability and welfare of agricultural households: Evidence from rural Niger

AbstractVariable weather continues to be the major source of vulnerability to chronic hunger and poverty in many developing countries due to the strong dependency of livelihood strategies on rainfed farming. Quantifying the effect of climatic parameters on agricultural households is, therefore, necessary to help policymakers understand the benefits of climate policies, improve the allocation of the scarce resources dedicated to adaptation and prioritize among adaptation strategies. This article investigates the empirical relationship between the welfare of rural households and rainfall variability in the semi‐arid tropics, using household panel data and high‐resolution remotely sensed rainfall data from Niger. We find that a standard deviation increase in rainfall variability is associated with a reduction of real food consumption by 11.13%. Results also indicate that a standard deviation increase in rainfall variability reduces expenditures for cereal‐based products, animal‐based products and processed foods by 11.96%, 21.31%, and 16.23%, respectively. Our results are consistent across a battery of robustness checks. Finally, we find geographical‐based differences in terms of the effect and that access to cereal banks cushions the negative effect of rainfall variability. Policy interventions aiming at improving the well‐being of rural households should therefore emphasize improving climate adaptation strategies.

Application of GIS & RS in Rainwater Harvesting for an Arid Region

In dry areas with variable rainfall, water shortages severely affect the livelihoods of local populations. Rainwater harvesting from land sites and roof-tops is a readily available solution to this problem. This study presents a GIS & RS-based methodology to identify potential land sites and roof-top rainwater harvesting potential using easily obtainable and freely available data. Slope, land cover, soil, and drainage maps were used to identify suitable rainwater harvesting land sites, which were further analyzed for surface runoff using the curve number method. This research also incorporated obtainable socio-economic factors in identifying the suitable locations of dams considering the implementation stage. Based on the proposed criteria, 44 suitable locations for dams were identified and validated using a validation map and Google Earth images. CROPWAT model analysis revealed that paddy water demand in Batticaloa would increase threefold if all available land for paddy cultivation was utilized. Therefore, abandoned lands can be utilized by erecting dams at those identified locations. Analysis of Google Earth images also revealed that roof rainwater harvesting would be sufficient to meet the basic water demand in the area. This study demonstrates that rainwater harvesting can be an effective strategy for addressing water scarcity in dry areas.

Investigating the Morphometry and Hydrometeorological Variability of a Fragile Tropical Karstic Lake of the Yucatán Peninsula: Bacalar Lagoon

Comprehensive morphometric and hydrometeorological studies on Bacalar Lagoon, Mexico’s largest tropical karstic lake and a significant aquatic system of the Yucatán Peninsula, are lacking. This study provides a detailed analysis of its bathymetry, morphometry, and hydrometeorological characteristics. The lake’s main basin stretches more than 52.7 km in length, with widths varying from 0.18 km to 2.28 km. It has a volume of 554.4 million cubic meters, with an average depth of 8.85 m, reaching depths of up to 26 m in the north and featuring sub-lacustrine dolines in the south, with depths of 38 m, 48.5 m, and 63.6 m. The study reveals seasonal variations in surface water temperature, closely linked to air temperature (r = 0.89), and immediate responses of water levels to hydrometeorological events. Water level fluctuations also exhibit seasonal patterns that are correlated with regional aquifer conditions, with a lag of 2 months after seasonal rainfall. Interannual variability in rainfall and water levels was observed. From 2010 to 2012, rainfall consistently remained below its mean climatic value, due to a prolonged La Niña event, while the exceptionally wet conditions in 2020 were also associated with La Niña. Extreme and anomalous hydrometeorological events, such as those following tropical storm Cristobal in 2020, revealed the fragility of Bacalar Lagoon, causing a notable transformation in lake color and transparency, shifting it from its typical oligotrophic state to eutrophic conditions that lasted longer than a year. These color changes raise questions about the factors impacting ecological health in tropical karstic regions. Additional factors affecting water quality in the BL in 2020, such as deforestation, coastline changes, and urban growth, warrant further investigation. Our study can serve as a starting landmark.

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

AbstractThe current study explores the linkage and possible mechanisms of the Southern Annular Mode (SAM) with rainfall variability over southeastern South America (SESA). The findings indicate that the preceding May SAM can modulate the subsequent rainfall over the SESA region in September‐October‐November (SON), independent of the El Niño‐Southern Oscillation (ENSO) signal. Further analysis suggests that the SAM‐rainfall relationship is mainly due to the response of sea surface temperature (SST) in the subtropical and extratropical Southern Hemisphere to SAM variability via air‐sea interactions. Such SST anomalies persist to the subsequent SON and stimulate an upstream wave train extending into the study region from the South Pacific. Such wave patterns form a cyclone anomaly over SESA, indicating strong subsidence and reduced convection over the region. Meanwhile, the low‐level anomalous cyclone and anticyclone located over the subtropical South Atlantic (on the coast of the study region) are associated with easterly winds to the South (North) of the anomalous cyclone (anticyclone), which advect colder and drier air into the study region due to the cold SST over the subtropical South Atlantic and leading to increased moisture scarcity and decreased rainfall over SESA. Besides, model results based on the joint climate indices (i.e., SAM, ENSO and Indian Ocean Dipole (IOD)) revealed a satisfactory estimate of the SON rainfall compared with either single index, indicating that the SAM can be one of the precursors of the SESA rainfall, besides ENSO and IOD.

Urbanizational impact on climatic variables and geographical analysis of physical land use land cover variation of a city using Remote Sensing.

<p>Climate change has serious implications foron the rise and variation of the average earth’s temperature, and intensified rainfall. In urban areas, temperatures and intensified rainfall variation are noticeably seenin the city which disrupts the normal life of a living things. Also, it affects the storage of rainfall runoff water in tanks and lakes. Green cover reduction due to the increase ofagriculture, industrialization, population explosion and urbanization have a direct impact on the climate pattern.The average temperature of a city is increasing in every decade due to a drastic reduction in the green cover.This is visible today from the abnormal melting of glaciers. The rainfall pattern is highly intensified, and the frequency of rainfall is on the a decreasing trend. This intensified rainfall creates high surface runoff during rainfall and disrupts the normal life in a city. The Standard Precipitation Index (SPI) for a city indicates a noticeable shift in its climatic pattern, particularly with consistent rainfall during the southwest monsoon. Over the span of four decades, LULC research has revealed an increase in residential areas and a decrease in green cover. This urbanization process is associated with changes in LULC, resulting in decreased vegetation and diminished storage water bodies.Variations in the temperature of a city are on an increasing trend. Urban forestry or the dense green coverof an area reduces the temperature and creates cool pockets in the city. The regression model for temperature and vegetation indicates the negative correlation of temperature with high vegetation growth. Keywords: Urbanization, Climate change, Rainfall-runoff, Temperature, Green Cover</p>

Rainfall Assessment Using Weighted Interval Type-2 Fuzzy Inference System

This study proposes an integrated model for assessing rainfall in Chennai using the Interval type-2 fuzzy reasoning technique and the Analytic Hierarchy Process (AHP). Interval Type-2 Fuzzy Inference System (IT2FIS) is employed to describe complex relationship between the rainfall variables and rainfall rate by considering individual weights of those variables through the aggregation of criteria weights using AHP. The suggested approach has an edge over its predecessors in terms of modeling the inter-personal and intra-personal uncertainty involved in rainfall rate classification. Finally, the developed rainfall model (RM) is applied on the historical dataset collected from Indian Meteorological Department (IMD). The actual data and the IT2FIS model’s performance are compared. Correlation coefficient ([Formula: see text]) and Bland–Altman Plot (BAP) have been used to evaluate the performance metrics of the proposed model. The outcome suggests that the AHP-IT2FIS model can accurately estimate rainfall over time, indicating its potential for long-term use.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Precipitation patterns and their variability in the southern region of Brazil

Climate change is a long-term transformation of climate patterns, which can be natural or anthropogenic. Signs of climate change can manifest themselves in different ways: through the occurrence of extreme events, rising ocean levels, melting glaciers, among others. Negative impacts are produced by climate change and are one of humanity's biggest concerns, since its effects reach the entire planet with different consequences. Considering the need for actions aimed at contributing to the process of mitigating climate change, the importance of knowing changes in regional climate patterns is emphasized. This study aims to analyze changes in precipitation patterns over time in the southern region of Brazil, including variability, trends, anomalies, droughts and excess rainfall. Daily precipitation data from 1961 to 2020 were used, collected at 15 meteorological stations in the region. Various statistical analyzes were carried out, such as averages, maximum and minimum values, seasonal and annual anomalies, frequency distribution, Mann-Kendall trend tests and the Normalized Precipitation Index (SPI). The results indicate that climate change trends are positive in terms of increasing rainfall volumes, altering rainfall dispersion patterns (volume). In other words, rainfall is more irregular in terms of distribution throughout the seasons. The big highlight was Florianópolis -SC, which during the summer had the highest volume of rain among the meteorological stations studied.

Vulnerability of geoheritage sites in South Africa to climate change: Examples from the Eastern Cape Province

Climate change and variability have potential to impact on all types of heritage sites globally, through increasing weathering and erosion of geological and built heritage, changes in biome and ecosystem viability, sea-level rise and coastal flooding, and increased climate hazard events. This study examines the most likely impacts of climate change on different heritage sites in South Africa, with a particular regional focus on sites of potential geoheritage interest in the Eastern Cape Province of the country. The diverse examples described in this study are: (1) Klasies River cave, which is a Middle Stone Age archaeological site; (2) different localities along the coast where faunal footprints are preserved within aeolianite that dates mainly to the last interglacial period; (3) different localities inland where rock art panels are present within caves or rock shelters; and (4) Cape St Francis, which is a coastal foreland where different phases of late Quaternary coastal dunes with archaeological interest are preserved. Results show that many of these very varied sites of geoheritage interest are not protected as National or Provincial Heritage Sites under current legislation, which is weak and inconsistently applied across the country. This means that no specific management plans, under national policy guidelines, are in place for mitigating against the impacts of present and future climate change and variability, either regionally or nationally. These climate changes include: sea-level rise and increased coastal storminess and wave attack; and changes in weathering and erosion regimes as a result of increased temperatures and likely decreased rainfall or higher rainfall variability. A better appreciation of the interconnections that exist between geology and natural and cultural heritage may lead to valuing landscape and site geoheritage more highly at the local, national and international level.