Seasonal Rainfall Patterns Research Articles

The influence of tropical seasonality on breeding phenology, growth, survival and movement of a large reptile (Varanus komodoensis)

AbstractTerrestrial animals in global wet-dry tropical ecosystems experience year-round high temperatures but concentrated summer rainfall. This highly seasonal precipitation is expected to have major evolutionary and ecological consequences for animals. We considered how strong seasonal patterns of rainfall might influence the environment and biological attributes of the world’s largest predatory lizard, the Komodo dragon (Varanus komodoensis). We predicted that annual variation in rainfall or temperature may influence the seasonal phenology of vegetation, and in turn, the phenology of Komodo dragon reproduction and hatchling emergence. Similarly, seasonal environmental differences may affect Komodo dragon rates of somatic growth, body condition, survival and daily movement. Our results indicated evidence of strong seasonal variation in mean monthly rainfall and leaf area index for open deciduous forest and closed dense forest. These environmental variables were significantly correlated with female Komodo dragon nesting activity and hatchling emergence. Neither Komodo dragon somatic growth rate nor body condition exhibited seasonal differences. There was, however, increased daily movement rates of Komodo dragons during the dry season. This suggests dry season associated life-history activities or thermoregulatory efficiency might explain increased daily movement. Ultimately, the Komodo dragon showed differential sensitivities in its biology to seasonal environmental variation.

Empirical analysis of rice and maize production under climate change in China.

The past few decades of extreme drought and flooding caused by changing climate conditions have significantly affected agricultural production globally. This study focuses on two vital crops in China-maize and rice-and provides a comprehensive analysis of how these crops are affected by climate change-induced factors over the periods 1978Q1-2015Q4. Four key findings were obtained. First, using a nonparametric approach to estimate actual and observed trends of climatic variables, the results show a significant positive trend in average temperature from February to October. On the other hand, seasonal temperatureincreases during spring, summer, and autumn. Second, the results show no significant change in the monthly, seasonal, and annual rainfall patterns when examined over the study period. Third, using an autoregressive distributed lag (ARDL) model, we find that while temperature and rainfall do not significantly support rice production in the long and short run, they play a substantial role in maize production in China. Finally, we find no significant difference in the results for rice when the quantile regression (QR) technique that controls for distributional asymmetry effects is employed. However, the impact of temperature on maize decreases at higher quantiles. Given the outcomes of our study, we arguethat an advanced irrigation system is crucial and must be encouraged to minimize the effects of climate change on crop production.

Vulnerable Neotropical migratory songbird demonstrates flexibility in space use in response to rainfall change

Abstract Behavioral flexibility of individuals is essential if organisms are to ultimately adapt to climate change. As environmental conditions, such as precipitation patterns become increasingly variable, fine-scale spatiotemporal flexibility in space use may allow for individuals to track resources during periods of adverse or atypical conditions. Individual behavioral flexibility is observable over short timeframes and can therefore be used to assess resilience of a species to projected shifts in climate. The goal of our study was to determine if and how individuals modified their space and habitat use in response to rainfall-driven changes in resources throughout a period of atypical seasonal rainfall patterns. We used radio telemetry to estimate home ranges of nonbreeding Swainson’s Warblers (Limnothlypis swainsonii) in 2 time frames (bi-seasonally and bi-weekly) in Jamaica during dry and wet periods. We measured habitat structure and food (leaf litter arthropod) availability within each home range to determine possible predictors of space use change. Individuals modified the area and/or location of their home ranges with changes in precipitation, and those occupying more open habitats had greater changes in home range area as seasonal rainfall increased. As food increased following rain, individuals constricted their home ranges (bi-weekly) or shifted spatially (bi-seasonally) to a novel area with greater food availability. This suggests individuals are able to rapidly respond to how their environment changes, presumably adjusting to trade-offs between home range size and resource availability. This flexibility may be a key behavioral component in enduring long-term increasingly unpredictable environmental variability and may have population-level consequences. These responses are, however, mediated by habitat, suggesting the ability to respond to variable or poor conditions is not homogeneous across a population.

Simulation of wheat yield using CERES-Wheat under rainfed and supplemental irrigation conditions in a semi-arid environment

Wheat-fallow rotation is the major land-use system in the rainfed cropping system of Pakistan. Crop production in rainfed cropping systems is often jeopardized due to the scare and erratic seasonal patterns of rainfall. Climate change is further threatening the extent and productivity of rainfed agriculture in Pakistan. Climatic risk reduction strategies such as supplemental irrigation (SI) can assist in sustaining the productivity of rainfed agriculture. However, little has been done to investigate the potential of SI in sustaining the productivity of the rainfed cropping system of Pakistan despite the recent water resource developments in the rainfed regions of the country. For this purpose, a four-year (2010–2014) study was conducted to assess wheat yield and water productivity under rainfed and SI using a crop modeling approach. Calibrated CERES-Wheat was evaluated for its ability to simulate soil moisture dynamics, water productivity, canopy growth, in-season biomass, phenology, grain yield, and biomass at harvest based on soil water balance. Results showed a good to excellent performance of CERES-Wheat during evaluation. For example, combined values of soil moisture content between different layers, root zone soil moisture, seasonal crop evapotranspiration, in-season biomass growth, and canopy cover showed NRMSE values ranging from 13%–89%, 5–11%, 2–17%, 12–26%, and 13–22%, respectively. The NRMSE values of rainfall productivity of biomass and grain yield and water productivity of biomass and grain yield ranged from 18%, 16%, and 17%, 6%, respectively. The model was also applied to determine favorable management practices (appropriate planting dates from 15 October to 15 December at 15-day intervals and SI of 50 mm either at planting or 30 days after planting) as their determination under actual field conditions is laborious. Simulations for the best combination of planting date and SI suggested that higher crop yield and water productivity can be achieved with planting in November with irrigation applied 30 days after planting.

Seasonal changes in metal and nutrient fluxes across the sediment-water interface in tropical mangrove creeks in the Amazon region

Mangrove creeks are considered important routes between terrestrial and adjacent coastal waters regarding the transport of dissolved material to oceans. The present study assessed if Amazonian seasonal rainfall patterns affect the pore water biogeochemistry and the intensity and direction of nutrient (NH4+ and PO43−) and metal (Fe2+ and Mn2+) exchanges from intertidal creek mudflats fringed by pristine mangroves. The results indicate that mangrove-fringed mudflats are effective in retaining iron and nutrients in solid sediment phases compared to export to coastal waters, also potentially comprising a significant manganese contributor to coastal waters. However, nutrient and metal retention are lower during the wet season, as intense rainfall periods reduce pore water salinity and promote increased reducing sediment conditions. Such conditions enhance organic matter degradation and pore water NH4+, PO43−, Fe2+ and Mn2+ concentrations just below the sediment-water interface, generating higher effluxes during this period. Our findings demonstrate that seasonal variabilities drive substantial physicochemical property and pore water biogeochemistry changes, affecting the efficiency of mudflat sediments retaining and exporting nutrients and metals.

Water and Emotion: Testing a New Approach for Monitoring Water Security Among Afar Pastoralists in Ethiopia

Ethiopia has over 12 million pastoralists that raise livestock and move their herds in search of fresh pasture and water. This way of life is especially vulnerable to climate change as drought and shifts in seasonal rainfall patterns are changing the distribution and availability of these resources in pastoralist regions. The dynamic use of water within these settings is also not well-understood or captured by conventional water sector monitoring systems, which prevents appropriate supportive interventions and policies to be delivered. This paper presents results from a study into a new approach to measuring water security that focuses on assessing the emotional response of pastoralist populations to their water security situation. Formative research involving focus groups and interviews was followed by a survey of 148 pastoralists to assess their emotional response to different water security dimensions. The results indicate that emotional response can be used to elicit valuable insights into water security and provide a powerful complement to conventional water security monitoring techniques. Using the approach, we show a strong relationship between variation in seasonal water access and reported emotional response. Negative emotions also strongly associate with the most laborious methods of collecting water such as scoop holes and hand dug wells, whereas positive emotions were associated with access to higher quantities of water. Access to equines for carrying water was associated with more positive emotional well-being indicating a route to water security improvement in this context could be through the provision of donkeys and mules for water carrying. The paper discusses the value of using an emotion-based approach to capture experiences of water security alongside more conventional objective measures, especially among populations with water use patterns that continue to be poorly understood.

An analysis of 50 years of seasonal rainfall and temperature pattern data in the Sylhet region of Bangladesh

Climate variability, particularly due to rainfall and temperature, has a significant impact on the country's economy and has garnered significant global attention. The magnitude of climatic factor fluctuations varies according to geographic locations. Meteorological parameter analysis is critical in countries where rainfed agriculture predominates. The current study examined the monthly variations in rainfall and temperature (maximum and minimum) over a 50-year period (1967 to 2017) in Sylhet region of Bangladesh. The non-parametric Mann Kendall test was used to detect the trend, and the Sen's slope estimation method was used to identify the trend value. A thorough analysis of the data (1967 to 2017) reveals that the monthly rainfall occurs during the monsoon season (June to September). In terms of temperature, the maximum temperature was found from June to September, indicating the monsoon season. Mann Kendall trend analysis shows that the seasonal monthly rainfall and minimum temperature trends are not statistically significant, with Sen's slope values of <0.001 and 0.034, respectively. The maximum temperature trend is significant, with a p value of 0.042. As a result, the study concludes that Sylhet's climate is changing.

Spatial and Temporal Assessment of Rainfall Variability using GIS Approach in Trichy District of Tamil Nadu

Precipitation is one of the transportation components in hydrological cycle. The magnitude of precipitation swings with time and space. Majorly India receives precipitation in the form of rainfall. Precipitation plays a key role in the rainfed agriculture. The present study deals with the rainfall characteristics of Tiruchirappalli district, Tamil Nadu. Seasonal rainfall data from eighteen rain gauge stations (1971-2012) have been taken for analysis of seasonal and annual rainfall pattern of Tiruchirappalli district. Mean rainfall of the district is about 696 mm. The highest rainfall of 1247 mm recorded in the year 2005 and the lowest precipitation of 227 mm recorded in the year 1976. About 48 percent and 35 percent of the rainfall received in North East and South West Monsoon, respectively. Spatial rainfall distribution was studied with the help of Kriging interpolation technique and respective maps were prepared with Geographical Information System. The percentage departure of annual rainfall is classified under the category of excess, normal and large excess category. South East and central part of Tiruchirappalli receives moderate to low rainfall. North East parts of Tiruchirappalli district such as pullambadi, Lalgudi and nearby areas received maximum rainfall during North East Monsoon and South West Monsoon. In winter season Manapparai and Vaiyampatti region received more rainfall while in summer season Thottiam and Mayanur area received more rainfall. The two major highlighted crops in Trichy district are Banana and Onion. Tiruchirappalli district is one of the Banana growing belts in Tamil Nadu. Spatial distribution of rainfall maps will be helpful to form a crop plan for different crops to increase the agricultural productivity of Tiruchirappalli district and to ensure the food security.

Impact of climate variability on the yield of staple grain crops in Wudil local government area, Kano State, Nigeria

This study is centered on the impacts of climate variability on some cereals crop yields (sorghum, millet and maize) in Wudil local government area, Kano State. Climatic data were used that covers 1997–2017. Seasonal data were collected from Kano State Agricultural and Rural Development Authority where rainfall and crop yields data was collected. Geography Department, Kano State University of Science and Technology, Wudil, was where rainfall and temperature data were collected. Atmospheric humidity was derived by using 6.11 x 100 (7.5 x TC/ 237.7 +TC), were TC = Temperature in Degree Celsius. And also Evapotranspiration data was derived using 0.0023 (Tavg + 17.8) (Tmax _ Tmin) 0.5 Ra. Where Tavg, Tmax and Tmin are respectively mean, average, maximum and minimum temperature and Ra represents extraterrestrial radiation. Regression analysis was used to determine the impacts of climatic elements (rainfall, temperature, humidity and evapotranspiration) as an independent variables on crop yields (sorghum, millet and maize) as dependent variables; furthermore, standardize coefficient index were determined in order to know which climatic variable has more effects on the crop yields in the study area. The results obtained reveals that all the climatic elements were highly variable as no years with the same annual total except temperature which shows little annual variation over the study period. Climatic elements were found to have moderate impacts on the crop yields based on the regression coefficient of determination. However, rainfall was found to be the most influential variable on crop yields followed by atmospheric humidity, temperature and evapotranspiration based on standardize coefficients indices. In the situation of changing climate and weather variability and changing seasonal rainfall pattern in present and future, it is important to enhance the efficiency of rainfall and soil water use in crop cultivations. Adaptation of water saving/conserving technologies and identification of crops species and species-specific traits that improves crop water use efficiency of crops under rain-fed, irrigated and dry land farming system is recommended.

Earthen architecture in the Mesoamerican classic period: A micromorphological approach to its manufacture process

Earthen construction systems are generally inferred from stratigraphic evidence, without analytical follow-up, leading to poor or erroneous interpretations. Micromorphological analysis has shown potential to differentiate manufacturing techniques, and diagnostic features have been established for Mediterranean and Middle Eastern cases. This paper evaluates the method on construction samples from Mesoamerica, where earthen architecture emerged independently from the Old World and where the marked seasonal rainfall pattern characteristic of the tropics, and the human settlement on soils and sediments originating from relatively recent volcanic activity, create conditions that mostly differ from previously analyzed cases. In this first comparative analysis, we use 16 samples of the most representative construction elements in Mesoamerica: mud-brick, cob walls and facings from monumental architecture, obtained from seven major sites, dating to the first millennium AD, located both in coastal lowlands and in semi-arid to arid plateaus.All elements analyzed are made of mixes in plastic state, as there is no evidence of rammed earth or poured earth techniques in Mesoamerica. Therefore, the features related to the amount of water added are similar, with differences appearing in the selection and combination of raw materials, and the manufacturing processes of kneading, adding mineral and organic aggregates, and applying. The use of materials of volcanic origin gives a particular appearance to all samples. The features defined in previous research were useful for the sample analysis, showing that manufacturing process was very similar and that the method can be applied in Mesoamerica, to assist in the correct technological identification of construction systems.

Impact of wind-hydro dynamics on electricity price: A seasonal spatial econometric analysis

Hydro and wind are both commonly deployed renewable sources of electricity. Wind is intermittent and unpredictable to a large degree. Hydro depends on seasonal rainfall patterns, although storage may offer scope to spread the risk associated with dry seasons. We investigate the impact of intermittent wind generation, coupled with a given hydro capacity, on wholesale electricity prices, accounting for both spatial and seasonal effects. Results from a spatial Durbin model provide evidence of significant negative spill-over effects of wind generation on wholesale nodal prices. After robustness analysis with alternative spatial weight matrices, we provide estimates of the seasonal impact of wind generation on nodal prices using a transmission weight matrix. We evaluate seasonal nodal price effects during dry periods and wet periods in New Zealand's hydro-dominated electricity market. Results show that the price reduction associated with a 10% increase in wind penetration varies across seasons, ranging from $0.48 per MWh in winter in 2012 to $3.05 per MWh in spring. The largest price reduction effect of 3.05 is found for the wet season in spring 2012, but the variance change is 3.6 which is less than 5.24 for the dry season in summer 2012. This evidence reveals that increased wind generation reduces nodal prices, as expected, but also increases the variance in nodal prices, particularly during a dry season. Thus, while a wind-hydro system is shown as a favourable low-carbon combination during wet seasons, without backup generation the system remains susceptible to price volatility. We further suggest policy recommendations to ensure the reduction of price volatility and the continuity of electricity supply during dry periods.

Unravelling runoff processes in Andean basins in northern Ecuador through hydrological signatures

AbstractStreamflow is the runoff response integrated in space and time over a complex system involving climatic and catchment physiographic factors. In the Andes, accelerating runoff process understanding is hampered by the inability to quantify heterogeneity of surface and subsurface catchment properties. Here, we present a statistical approach based on regression models and correlation analysis that links hydrological signatures and catchment properties to unveil processes in a set of volcanic mountain catchments (latitude 0°30'N) in Ecuador. The catchments represent form and function diversity in the same hydrological unit. We found that despite of similar atmospheric‐water inputs the water yield in the north‐east region is about 5× larger than in the south‐west region and their flow regimes are asymmetric. The soil‐bedrock interface and lithology exert a first‐order control on hydrologic partitioning, and this allowed us to hypothesize two hydrological mechanisms. Firstly, in the north‐east region, the perennial streamflow is associated with seasonal rainfall patterns, and subsequent drainage processes taking place at the surface and subsurface level. The amount of streamflow is related to landform characteristics, high canopy density and root development of forest as well as water holding capacity of organic soils. From a mechanistic standpoint, the low concentration time, steep slopes and shallow infiltration limited by high‐consolidated deposits of sedimentary and volcanics suggest a lateral movement of the flow. Secondly, in the south‐west region the streamflow regime is mostly groundwater‐dependent and it becomes seasonally enhanced by rainfall. Larger seasonal variations of precipitation and temperature result into enhanced evapotranspiration in the drier months, limiting shallow soil infiltration. Under the soil layers, highly permeable pyroclastic deposits and andesitic lavas promote deep percolation. The results highlight the degree of dissimilarity of hydrological processes in Andean settings, but unravelling their complexity seems plausible using streamflow signatures and causal explanatory models.

Changing Spatio-Temporal Rainfall Fluctuation over North East India

This study is analysis of changes in rainfall fluctuation in North East India. Using the longest instrumental monthly rainfall data of well spread 316 stations across India available from Indian Institute of Tropical Meteorology (IITM), the fluctuation characteristics as well as the spatial-temporal variability of the seasonal, monsoon monthly and annual rainfall pattern over the north eastern region of India (NER) have been examined. On an average, NER receives about 2450 mm of rainfall with 1093.0 mm as its annual evapo transpiration. The winter (JF) rainfall contributes 2.1% to the annual rainfall; summer (MAM) rainfall 24.3%; summer monsoon (JJAS) rainfall 65.1% and the post-monsoon (OND) rainfall contributes 8.5% to the annual rainfall. The region shows great variation in surface temperature regime (15oC to 32oC in summer and 0 to 26oC in winter). The spatial-temporal rainfall variability shows random fluctuating characteristics of expansion / contraction of desert area but overall it is showing a slight decreasing over the NER as well as whole India.

Rainfall Pattern Analysis in the Three Major Valley Systems of Bengaluru

Bengaluru has three major valley systems, i.e., Vrishabhavathi Valley (V-Valley), Hebbal Valley and Kormangala-Challaghatta Valley (K-C Valley) which are the repositories of many lakes. Due to the increasing population, urbanization and industrialization, the lakes and their drainage areas are being encroached. Bengaluru is facing the problem of flooding. Hence, the rainfall analysis is very important to understand the rainfall pattern, trend as well as its quantity. Rainfall analysis helps to predict urban floods. In the present study, the Taluk level station rainfall data was collected from Karnataka State Natural Disaster Monitoring Cell (KSNDMC) for the period of 50 years from 1971 to 2020 and the same was analyzed for monthly, seasonal and annual periods using the Thiessen polygon method. Based on the monthly rainfall analysis carried out in the valley systems, it is observed that the highest rainfall occurs during the month of September, i.e., nearly 20% of the total annual rainfall, whereas the January month contributes the least amount of rainfall. Based on the seasonal rainfall analysis, it is known that the South-West Monsoon contributes to the highest percentage of rainfall when compared to other two season. The South-West Monsoon contributed to nearly 54.7%, 55% and 55.4% of the total annual rainfall in the Hebbal Valley, K-C Valley and Vrishabhavathi Valley respectively. The normal average annual rainfall is observed to be 830.1mm, 890.2mm and 1002.2mm respectively for Hebbal Valley, K-C Valley and Vrishabhavathi Valley when calculated from the past 50 years data (1971 to 2020). Hebbal Valley has received a lowest rainfall of 455mm during 2012 and Vrishabhavathi Valley has recorded the highest rainfall of 1556.2mm during 2005. When analyzed annually, it was observed that the highest rainfall of 1321.2 mm (2005) and lowest rainfall of 455mm (2012) was recorded in the Hebbal Valley. In the K-C Valley, the maximum rainfall observed was 1225.3mm (2004) and the minimum rainfall of 623.9mm was observed (1985). In the Vrishabhavathi Valley, the highest rainfall of 1556.2mm (2005) was recorded and the least rainfall of 551.3mm (2012) was recorded. Since all the valleys are located spatially adjacent to each other, they experience similar kinds of monthly and seasonal rainfall pattern. The rainfall pattern in the three valleys shows an increasing trend.

A meta-analysis of the possible impact of climate change on global cotton yield based on crop simulation approaches

CONTEXT:Cotton is one of the most widely planted fiber crops in the world. Its growth, development, and yield are influenced by elevated atmospheric CO2 concentrations, temperature increases, and seasonal rainfall patterns. However, due to differences in research methods (such as crop models, climate models, and climate scenarios), there are uncertainties in the magnitude and direction of the impact of future climate change on cotton yields in existing studies. OBJECTIVETo comprehensively assess the potential impact of climate change and adaptation on yieldand analyze the associated uncertainties, 27 published studies (including 1353 samples) were used to establish a meta database of changes in future cotton yield. METHODSThe responses of cotton relative yield change to changes in mean temperature, minimum temperature, maximum temperature, precipitation, and CO2 concentration were studied using local polynomial (Loess) regression in a full dataset. A linear mixed-effect model was then used to explore the quantitative relationship between them in a restricted dataset. RESULTS AND CONCLUSIONSUsing the established full dataset, we found that when the average temperature exceeded 4.3 °C, or the average precipitation decreased or increased too much (>200%), the simulated cotton yield decreased. Elevated CO2 concentrations and appropriate management measures could alleviate the negative impact of climate change. By establishing a linear mixed-effect model, we found that temperature, precipitation, CO2 concentration changes, adaptive measures, study area, climate models, and climate scenarios had significant impacts on cotton yield change. For every 1 °C increase in average temperature, the cotton yield decreased by 1.64%. For every 1% increase in precipitation and 1 ppm increase in CO2 concentration, cotton yield increased by 0.09% and 0.05%, respectively. Cotton yield under adaptation measures was 8.97% higher than without any adaptation. SIGNIFICANCEThe first systematic meta-analysis of the impact of climate change on cotton yield in major cotton-planting regionswas conducted herein. The findings provide new information for policies related to the impact of climate change and enhance our understanding of the future resilience of the cotton system.

Land use influence on distribution and abundance of herbivores in Samburu-Laikipia, Kenya

The distribution and abundance of different wildlife herbivores was studied in Samburu-Laikipia landscape. The study sites included; Mpala and Oljogi, both commercial ranches in Laikipia district; Oldonyiro and Kipsing community areas in Isiolo district; West Gate Conservancy, Ngaroni Community area, Kalama Community area and Sessia-Barsalinga Community area in Samburu district; and Buffalo Spring National Reserve and Samburu National Reserve both protected areas in the landscape. The objectives of the study were: 1) Determine the influence of different land use on seasonal abundances and distribution wildlife species and 2) Examine the influence of livestock, human settlements and water on wildlife species in Samburu-Laikipia landscape. Distance sampling was used to estimate wildlife, livestock and bomas densities. Distance to nearest water was projected from GPS coordinates for both wildlife and livestock sighting using ARCGIS. Our analysis showed non-uniform distributions of wildlife groups across the Samburu-Laikipia ecosystem largely driven by seasonal rainfall patterns and land use types. Like predicted, most wildlife groups occurred in higher abundances on protected areas, Laikipia commercial ranches and community conservancies unlike in community grazing areas in both dry and wet season. However, large grazers increased substantially in community grazing areas over the wet season when livestock grazing was heavy, stimulating growth of short annuals plants of high-quality nutrients. Human activities had negative influences on all wildlife groups. Our findings indicate that the type of land -use influenced herbivore distribution and abundance in Samburu-Laikipia landscape. This suggests that human activities, including pastoralism, in conjunction with season rainfall patterns and land-use shape herbivore distribution and abundance in the area. Conservation strategies for successfully increasing survival of wildlife therefore, requires maintenance of a mixture of land-use types with well controlled and sustainable development.

Trends of daily extreme and non‐extreme rainfall indices and intercomparison with different gridded data sets over Mexico and the southern United States

AbstractTrends of 15 extreme and non‐extreme daily rainfall indices for Mexico and the southern United States are analyzed for the extended boreal winter (November–March) and summer (June–October) during 1981–2010. The analysis includes gridded observations (CHIRPS and Livneh), reanalyses (ERA5, ERA‐Interim, MERRA2, and CFSR), the regional climate model RegCM4.7 (forced by ERA‐Interim), and four general circulation models (GCMs) from the CMIP5 (HadGEM2‐ES, CNRM‐CM5, MPI‐ESM‐MR, and GFDL‐ESM2M). The domain is divided into 13 regions with similar seasonal rainfall patterns according to the k‐means algorithm applied to CHIRPS monthly precipitation, as a reference. Gridded observations and reanalyses (O&R) show similar regional annual cycles and metrics of precipitation. The two observational data sets show the largest differences in the magnitude of some indices in the wettest region (southeastern Mexico) and in the monsoon. During summer, O&R show positive significant trends in extreme rainfall indices in regions commonly affected by tropical cyclones (southern Baja California Peninsula, northeastern Mexico, and the southeastern United States), while negative significant trends of seasonal accumulation of rainfall are seen in the Mediterranean Californias and the Sonoran Desert region. There is an agreement among most data sets that the frequency and intensity of extreme winter precipitation indices have declined in northwest Mexico and the southwestern United States, but trends are not statistically significant, while southeastern Mexico shows significant increases in 5‐days accumulation (rx5d). ERA5 followed by MERRA2 show the best performance. RegCM4.7 reproduces the annual cycle of precipitation in most of the regions and captures the main trend signals especially in winter, but it has difficulties in reproducing the summer interannual variability of few indices. The GCMs tend to overestimate (underestimate) the annual cycle of precipitation and the median values of extreme indices in the driest (wettest) regions. They are not able to capture the regional trends of the indices.

Climate change and small-scale agriculture in Africa: Does indigenous knowledge matter? Insights from Kenya and South Africa

Africa is highly vulnerable to changes in global climatic conditions due to its low adaptive capacity and sensitivity to changes in climatic variables, particularly in the agricultural sector. A key attribute of studies on climate change coping strategies and adaptation mechanisms in Africa is that they lack local specificity. Within a discourse dominated by large-scale attempts to measure the extent of climate change and its impacts with methods drawn from physical and biological sciences, there is little focus on how locally-specific knowledge and practices help communities to cope with effects of adverse environmental conditions on their agriculture at the farm level. From a sample of 115 respondents drawn from South Africa and Kenya and through interviews, discussions and interactions, this paper demonstrates that local residents deploy their indigenous knowledge in predicting seasonal weather and rainfall patterns, determining wind speed and direction, preserving grains for planting purposes and various traditional farming support systems to lessen the impacts of climate change on their agricultural activities. The paper concludes that merging local knowledge with modern science in Africa could help develop a syncretic agronomical knowledge amongst farmers in handling climate change.

Variability and Trend Analysis of the Rainfall of the Past 119 (1901-2019) Years using Statistical Techniques: A Case Study of Uttar Dinajpur, India

The present study aims to identify and measure the impact of climate change on rainfall patterns in the Uttar Dinajpur district of West Bengal. The hydro-meteorological time series rainfall data was collected from the IMD and CHRS data portals and subsequently analysed using various statistical methods. Agriculture in this district is the main economic activity, but the rainfall propensity is very unpredictable and sporadic that has a significant impact on agriculture. The rainfall results (1901-2019) were examined and assessed using statistical techniques for Mann-Kendall’s Z-statistic and Sen’s slope estimators. From the estimation, it is understood that the pre-monsoon, monsoon, and winter seasons have positive trends in rainfall, whereas the post-monsoon rainfall shows a negative trend and both Mann-Kendall and Sen’s slope projections depict the same. Likewise, January, February, April, May, June, July, August, and December reflect upward positive change, while a downward trend (decline trend) was recorded in March, September, and October. The winter Kharif crops are more impacted by this negative or decreasing pattern of seasonal rainfall than other crops. The maximum average monthly rainfall in July (892.1 mm) and January showed the lowest average monthly rainfall of 63.3 mm. The results revealed that during the monsoon season the maximum rainfall (75.2%) occurred and the coefficient of variance value is 20.4%. In the winter season, the minimal rainfall (2.87%) with a coefficient of variance (CV) is 72.9%. The rainfall forecast using SMOreg and linear regression methods has been calculated. This research contributes greatly to adopting different strategies by the planners, researchers, numerous government institutions, and NGOs for the overall development of the study area. This study may also be effective in the management of water resources in the study region.

Influence of Substrates and Hydrogels on Spearmint (Mentha spicata) Growth and Flowering in a Rooftop Garden

Agricultural hydrogels improve water retention in a variety of substrates. However, little is known about their impact on herb cultivation in rooftop farming. To identify the optimal substrate and hydrogel concentration for the growth and flowering of spearmint (Mentha spicata) in rooftop gardens, coir and perlite were mixed in three different ratios of 80% to 20% (v/v; referred to as C4P1), 50% to 50% (C1P1), or 20% to 80% (C1P4). Hydrogels were added into different substrates at different concentrations including 0 (control), 0.25, 0.5, 1.0, or 2.0 kg⋅m–3. Substrate composition significantly affected the growth (P < 0.001) and flowering (P < 0.05) properties of spearmint. The addition of hydrogels into substrates resulted in a significant (P < 0.05) increase in growth parameters for spearmint during the dry season. However, plants grown in C4P1, which has the highest ratio of coir, displayed inferior growth and flowering compared with those of the other two substrates during the rainy season. Therefore, a perlite-based substrate, such as C1P4 with added hydrogel, provides a suitable environment for the cultivation of spearmint in rooftop gardens regardless of seasonal rainfall patterns.