Rainfall Regime Research Articles

Spatial and Temporal Patterns of Rainfall Erosivity in Southern Africa in Extreme Wet and Dry Years

Soil erosivity is a key indicator of the effectiveness of precipitation acting on the land’s surface and is mainly controlled by event-scale and seasonal weather and climatic factors but is also influenced by the nature of the land’s surface, including relief and vegetation cover. The aim of this study is to examine spatial and temporal variations in soil erosivity across southern Africa using rainfall data for the period 2000–2023 and a gridded raster spatial modelling approach. The two wettest and driest years in the record (±>1.5 standard deviation of rainfall values) were identified, which were 2000 and 2006, and 2003 and 2019, respectively. Monthly rainfall values in these extreme wet/dry years were then analyzed for four rainfall regions (arid, semiarid, subhumid, humid), identified according to their annual rainfall totals. These data were then used to calculate Precipitation Concentration Index (PCI) values as an expression of rainfall seasonality, and the modified Fournier index (MFI) was used to quantify rainfall erosivity. The results show that there are significant differences in erosivity between the different climate regions based on rainfall seasonality and also their distinctive environmental settings. In turn, these reflect the synoptic climatic conditions in these regions, their different precipitation sources, and rainfall totals. The results of this study show that calculated MFI values at the national scale, which is the approach taken in most previous studies, cannot effectively describe or account for erosivity values that characterize different climatic regions at the sub-national scale. Furthermore, the mismatch between PCI and MFI spatial patterns across the region highlights that, under semiarid, and highly seasonal rainfall regimes, episodic rainfall events interspersed with periods of dryness result in significant variability in erosivity values that are unaccounted for by rainfall totals or seasonality alone. In these environments, flash floods and wind erosion result in regional-scale soil erosion and land degradation, but these processes and outcomes are not clear when considering MFI values alone. Fully evaluating spatial and temporal patterns of erosivity in their climatic and environmental contexts, as developed in this study, has implications for sediment and carbon exports, as well as identifying the major regions in which land degradation is an environmental and agricultural issue.

Understanding competition between two invasive woody plants of India under an altered rainfall regime

Understanding competition between two invasive woody plants of India under an altered rainfall regime

Nitrogen fertilization management and seeding density differently affect net blotch incidence and grain yield in one two-row and one six-row cultivar of barley

A two-year field experiment was carried out on barley (Hordeum vulgare L.) to test the effect of cultivar, N fertilization management and seeding density on crop growth, net blotch (NB) disease occurrence and yield. The three variables were factorially combined in a split-split-plot design as follows: 1) two barley cultivars, one two-row (H. v. distichum, cv Callas) and one six-row (H. v. hexastichum, cv Azzurro); 2) four N application schedules differing for rates and/or timing of N supply: unfertilized control; fertilized with 35kgha-1 of N at BBCH29 plus 30kgha-1 of N at BBCH31; fertilized with 70kgha-1 of N at BBCH29 plus 60kgha-1 of N at BBCH31; fertilized with 130kgha-1 of N simultaneously at BBCH31; 3) four seeding densities: 100, 200, 400 and 800 viable seeds m2.The three variables affected crop growth and NB occurrence resulting in different grain yield and quality. Results varied with the seasonal trend, in particular with rainfall regime and air temperature, for their possible effect on soil N dynamics and the presence of NB, which resulted in different source capacity, and for their effect on grain setting and filling, i.e., the sink capacity. Both N fertilization and seeding density increased NB pressure, with seeding density affecting the disease even under unfavourable weather conditions for NB development. At the maximum N fertilization rate, delaying N application helped limit plant height and the related risk of lodging, as well as NB symptoms, resulting in greater hectolitre weight and protein content together with no appreciable decrease of grain yield, provided seeding density was close to optimal (i.e., 400 seeds m-2) or higher.

Spatiotemporal analysis of wildfires and their relationship with climate and land use in the Gran Chaco and Pantanal ecoregions

The Gran Chaco and Pantanal ecoregions are the largest remaining dry forest areas in South America. Supporting diverse savanna, woodland and wetland ecosystems, these ecoregions are experiencing rapid changes in land use and fire occurrence with implications for ecosystem integrity. Our study characterizes the spatiotemporal patterns of wildfires in the Gran Chaco and Pantanal, and then examines the relationship between patterns of fire occurrence and climatic and anthropogenic drivers. We evaluated fire data of the last two decades (2001-2020) using the MODIS Collection 6.1 and the Global Fire Atlas products. Results of the fire pattern characterization were then used to model the probability of fire occurrence across each ecoregion (Random Forest, Generalized Linear Model, and Generalized Additive Model).Our results indicated that most of the total burned area belonged to the Humid Chaco, while the largest individual burned areas were mainly observed in the Pantanal. Fires primarily occurred during the dry season, with the majority of burned areas recorded during this period. Findings from the three modelling approaches consistently illustrated the spatial distribution of fire occurrence, depicting a declining probability of fire occurrence from East to West. All models underscored the importance of three variables to predict fire occurrence: temperature, livestock abundance and forest cover. Fire occurrence increased with increasing maximum temperatures and livestock presence and decreased with tree cover. This research helps to clarify the potential consequences of changes in land use, rainfall regime and temperature, and uncontrolled burning practices on the current fire activity in the Gran Chaco and Pantanal ecoregions. Understanding the spatiotemporal patterns of fire occurrence and their relationship with climatic, environmental and anthropogenic drivers can help to design more effective management strategies to mitigate fire impacts and to preserve the ecological integrity of these highly diverse regions

Community resilience to socio-environmental disasters in Itajaí Valley, Brazil

The Itajaí Valley (Brazil) is historically hit by disasters characterized by landslides and floods. Despite some level of preparedness, the changing rainfall regimes which became less predictable and more severe, is exacerbated by uncertainties of climate change challenging the state of resilience in the area. Community perception in disaster risk areas is one of the key components for identifying the state of resilience to disasters. Hence, to better understand the nature of changing resilience, this research aims to identify and compare the state of resilience of communities (SC) in the Itajaí Valley by analyzing risk perception of local communities. A descriptive-evaluative methodology is used through a quantitative survey approach in the at-risk areas in two distinct phases. Data collection in the first phase took place between March and July 2023, a period temporally distant from the last major disaster, which occurred in 2008. The second phase of data collection took place between the floods of October and November 2023. The results of the research indicate that exposure to the October 2023 floods had a substantial impact on the population's perception of risk, in which changes were observed in all the aspects of community resilience analyzed. In this sense, there was a significant increase in resilience during the reorganization phase, strengthening the consensus with studies related to adaptive cycles. The results enable a more precise understanding of vulnerable areas, allowing decision-makers to pinpoint where resources and efforts should be directed with greater accuracy.

Unravelling the performance of atmospheric radiative transfer schemes in the simulation of mean surface climate in Central Africa using the RegCM5 climate model

AbstractThe theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10‐year period, from January 2002 to December 2011 preceded by 1 year as spin‐up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5‐CCM3 and RegCM5‐RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa.

Sedimentological and geochemical proxies reveal changes in the South Atlantic Convergence Zone in Southeast South America during the Late-Holocene

Today, the South Atlantic Convergence Zone (SACZ), a convective band that extends across central Brazil to the western South Atlantic Ocean, modulates Southern Hemisphere summer rainfall in Southeastern Brazil. During the Holocene, however, the role of the SACZ over the rainfall regime in this part of South America is still under debate. This study aims to provide new insights into the SACZ Late-Holocene variability through analysis of the geochemical (XRF-based bulk sediment Metal/Metal ratios) and sedimentological (grain size and end-member modeling) variations along four cores retrieved in a coastal area (24–49 m water depth) located in the core of modern SACZ. Our records show an increase in Ti/Ca ratios, suggesting increased rainfall and that the terrigenous input to the Brazilian southeastern inner shelf changed in the Late-Holocene (last 4200 years). These changes are also followed by a decrease in the K/Ti ratio, indicative of an increase in chemical weathering in the sediment source, and an increase in the Mn/Ti, suggesting an increase in oxidation. Additionally, decadal-scale variability with periodicities of 25 and 55 years obtained by a time-series (Redfit) analysis highlights a potential role of the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation, respectively, as drivers of the SACZ variability over the last 4200 years.

Untangling the environmental drivers of gross primary productivity in African rangelands

Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa.

Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?

Abstract. An experimental study aimed at identifying special rainfall regimes with the help of co-located disdrometers is performed. Eight potentially special events (i.e., four number-controlled events and four size-controlled events) are identified and examined. However, a detailed cross-check with additional, independent radar measurements reveals no clear evidence of special rainfall dynamics. The research underscores the difficulty of experimentally confirming seemingly straightforward questions about rainfall patterns and dynamics that have been theorized in the literature for several decades but never formally validated experimentally. The study also questions the reliability of previous claims and serves as a reminder to approach such problems with more caution, emphasizing the need for rigorous uncertainty analysis and multiple cross-checks between sensors to avoid misinterpretation.

Understanding the seasonal growth patterns of seedlings in forest restoration of post-bauxite mining area: insights for monitoring in the Atlantic Forest

Understanding the behavior of planted seedlings and how they respond to changes in the rainfall regime is a challenge for forest restoration projects. This study aimed to assess the phytosociological structure and compare height and diameter at ground level (DGL) increments between the dry and rainy seasons in a forest restoration area post-bauxite mining in Descoberto-MG, Southeast Brazil. The study area features a dry season (from April to September) and a rainy season (from October to March). Three assessments were conducted for height and DGL increments: at the beginning, end of the dry season, and end of the rainy season. Three plots of 15x15m were established, and all planted individuals were measured over one year. Phytosociological parameters and diversity indices were calculated to select the 10 species with the highest importance value index (IVI). Welch's t-test (p<0.05) was used to compare average increments between seasons. Height and DGL increments during the rainy season were statistically higher than during the dry season for all 10 evaluated species. In height, the greatest increments were observed in Inga vera (590%), Enterolobium contortisiliquum (458.8%), and Piptadenia gonoacantha (343.8%). In DGL, the highest increments were observed in Schinus terebinthifolia (1427.2%), Anadenanthera peregrina (849.2%), and Peltophorum dubium (745.7%). Seasonal monitoring of planted seedlings is essential to understanding growth patterns, especially in Atlantic Forest restoration projects. These findings enable adjustment of management strategies, optimization of maintenance actions, and assistance in selecting species adapted to specific environmental conditions, particularly regarding water availability.

Environmental Filters Structure Cushion Bogs' Floristic Composition along the Southern South American Latitudinal Gradient.

The environmental filtering hypothesis predicts that abiotic factors restrict communities by selecting species capable of survival and persistence under specific conditions, resulting in variations in beta diversity, phylogenetic clustering, and niche differentiation among communities when studying environmental gradients. Cushion bogs and high-altitude wetlands along the Andes display homogeneous flora contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems are subject to diverse environmental effects. Here, we test the environmental filtering hypothesis on the structure of cushion bog communities along a broad-scale latitudinal gradient from 15° S to 42° S. We analyzed 421 bogs and 293 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species' niche overlap and the influence of environmental filters on the communities' phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23° S-24° S) highlights the impact of environmental filtering. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.

Semi-Arid to Arid Scenario Shift: Is the Cabrobó Desertification Nucleus Becoming Arid?

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis.

Environmental genome-wide association studies across precipitation regimes reveal that the E3 ubiquitin ligase MBR1 regulates plant adaptation to rainy environments

In an era characterized by rapidly changing and less-predictable weather conditions fueled by the climate crisis, understanding the mechanisms underlying local adaptation in plants is of paramount importance for the conservation of species. As the frequency and intensity of extreme precipitation events increase, so are the flooding events resulting from soil water saturation. The subsequent onset of hypoxic stress is one of the leading causes of crop damage and yield loss. By combining genomics and remote sensing data, it is now possible to probe natural plant populations that have evolved in different rainfall regimes and look for molecular adaptation to hypoxia. Here, using an environmental genome-wide association study (eGWAS) of 934 non-redundant georeferenced Arabidopsis ecotypes, we have identified functional variants of the gene MED25 BINDING RING-H2 PROTEIN 1 (MBR1). This gene encodes a ubiquitin-protein ligase that regulates MEDIATOR25 (MED25), part of a multiprotein complex that interacts with transcription factors that act as key drivers of the hypoxic response in Arabidopsis, namely the RELATED TO AP2 proteins RAP2.2 and RAP2.12. Through experimental validation, we show that natural variants of MBR1 have different effects on the stability of MED25 and, in turn, on hypoxia tolerance. This study also highlights the pivotal role of the MBR1/MED25 module in establishing a comprehensive hypoxic response. Our findings show that molecular candidates for plant environmental adaptation can be effectively mined from large datasets. This thus supports the need for integration of forward and reverse genetics with robust molecular physiology validation of outcomes.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.

Seasonal Effects of Wildfires on the Physical and Chemical Properties of Soil in Andean Grassland Ecosystems in Cusco, Peru: Pending Challenges

Soils are a valuable renewable resource on human timescales, and they interact with distinctive grassland ecosystems characterized by unique biodiversity and essential provision of ecosystem services, such as water supply and carbon sequestration. However, knowledge of the effects of wildfires on soil properties and nutrient availability in the Andes remains limited. Andean grasslands are currently one of the ecosystems of the Peruvian Andes most affected by wildfires. Our objective is to analyze the effect of fire activity on the physicochemical properties of soil and analyze its social context in Cusco, in the southern Andes of Peru. Soil samples were collected during five periods, spanning both the dry and rainy seasons, to characterize changes in soil properties and monitor vegetation recovery post-fire in two local communities dedicated to livestock activities. The vegetation restored after the wildfire was measured by the “step transect” method. Post-fire changes in soil properties indicate slight increases in pH, electrical conductivity, organic matter, nitrogen, phosphorus, and potassium during the onset of the rainy season; thereafter, a gradual reduction in these values was observed. This reduction can be attributed to leaching associated with the seasonal rainfall and runoff regime. Our findings indicate that one-year post-fire, the biomass in burned areas is reduced to 30–46% of the biomass in unburned areas. A complete regeneration is likely to occur in up to 4 years; this assertion is supported by the perceptions of the affected population, as expressed in interviews conducted in the two farming communities. These results are significant for decision-makers formulation of policies and regulations regarding grasslands and their seasonal restoration.

Environmental and genetic drivers of physiological and functional traits in a key canopy species

The resilience of forests worldwide is challenged by climate change. Large-scale tree mortality and dieback events have been documented across continents in recent decades. The adaptive capacity of forests is important for predicting forest resistance and resilience to future climates yet remains largely unknown. We grew 12 populations of a widespread foundation tree species (Corymbia calophylla), originating from different temperature and rainfall regimes, in two common garden trials in Western Australia that had similar temperature but contrasting rainfall conditions. We quantified intraspecific trait variation at these two sites to estimate genetically determined trait variation with climate origin (genetic adaptation) and trait variation associated with environment (phenotypic plasticity). We aimed to determine the 1) contribution of genetic and environmental factors on growth, functional, and physiological trait variation; 2) coordination of leaf traits within the context of the leaf economic spectrum (LES) in variable rainfall conditions; and 3) role of local or regional climate adaptation influencing tree growth and water use efficiency. Growth and physiological traits were differentially expressed across populations and sites, highlighting the importance of genetic adaptation and phenotypic plasticity. Leaf traits reflected a more water conservative strategy with higher water use efficiency, high foliar nitrogen content, and low specific leaf area, as predicted by the LES, in trees at the dry site measured in autumn after the warm summer. Local adaptation was detected in growth and leaf water use efficiency traits at the regional climate, not the local population, scale. Plants from the cool region had greater performance than those from the warm region in most plant traits. Home-site rainfall was not a good predictor of trait expression. The capacity of C. calophylla to respond to low water availability through genetic adaptation and phenotypic plasticity may enable it to maintain optimal performance in drier conditions associated with climate change.

Interplanted crops and rainfall variability regulate soil erosion in rainfed maize of the Indian Himalayas

AbstractSoil erosion is a serious threat to agriculture and environmental sustainability in the risk‐prone rainfed Himalayan ecosystem. Hence, runoff and soil loss mitigation ability of four maize‐based intensified systems; maize (Zea mays) + turmeric (Curcuma longa), maize + ginger (Zingiber officinale), maize + colocasia (Colocasia esculenta), and maize + sweet potato (Ipomoea batatas) were tested against the sole maize. The study recorded 41 erosive events during the 2020–2022 monsoon season. A principal component analysis (PCA) was performed to transform correlated rainfall parameters into orthogonal principal components. A two‐dimensional biplot analysis examined the relationship between PCs, rainfall events, and rainfall parameters. The rainfall events were divided into three regimes based on cluster analysis using PC. Rainfall regime 1 was characterized by low rainfall, I30, rainfall erosivity (E), and moderate rainfall duration (D), regime2 with moderate rainfall, short duration, high I30, and E, and regime3 with high D, I30, and rainfall erosivity. The soil loss of regime‐3 was 9.29 and 3.24 times higher than regime‐1 and regime‐2, respectively. Cover crops reduced runoff by 21.5%–69.3% and soil loss by 54.1%–77.0% over control. Redundancy analysis (RDA) showed that rainfall parameters (I30, I60, D, and E) had a significant direct influence across the systems, explaining 37.7%–54.9% of response variable variance, with E being the most influential (47.4%–54.9%), indicating varied impacts on runoff and soil loss. The sweet potato was best suited with maize as an intercrop to minimize soil erosion and maximize the system's profitability.

Temporal Distribution of Extreme Precipitation in Barcelona (Spain) under Multi-Fractal n-Index with Breaking Point

Rainfall regimes are experiencing variations due to climate change, and these variations are adequately simulated by Earth System Models at a daily scale for most regions. However, there are not enough raw outputs to study extreme and sub-daily precipitation patterns on a local scale. To address this challenge, Monjo developed the n-index by characterizing the intensity and concentration of precipitation based on mono-fractal theory. In this study, we explore the use of a multi-fractal approach to establish a more accurate method of time scaling useful to study extreme precipitation events at a finer temporal resolution. This study was carried out on the reference station of Barcelona (Spain) and its surroundings in order to be representative of the Mediterranean climate. For return periods between 2 and 50 years, two variables were analyzed: the n-index and the reference intensity I0. Moreover, a new parameter, the so-called “breaking point”, was designed here to describe the reference intensity I0, which is predominant for low time ranges. The results showed that both parameters are dependent on the time steps and the return period, and the scores confirmed the validity of our approach. Finally, the n-index was projected under downscaled CMIP6 climate scenarios by 2100, showing a sustained increase of up to +10%.

Characterization of the starting conditions of the rainy season in Senegal: highlighting the constraints of crop establishment

The start of the rainy season in Senegal is characterized by critical variability, resulting in many crop failures after seed planting when a long dry spell occurs. The objective of this study is to characterize the starting conditions of the rainy season in different areas of Senegal in relation to crop success at the early stage. An analysis of four seasonal components determined from a daily rainfall database of 95 stations from 1950 to 2015. These seasonal components are the sowing date (Sowing), the Onset of the rainy season date (Onset), the length of the longest dry spell (DryMax), and the total rainfall (TotRain) during the 30 days after sowing. Statistical methods of time series homogeneity determination such as the Pettitt test, the Buishand test, the Von Newmann test, and the segmentation method have been applied to determine actual breakpoints and to obtain the most recent and homogeneous period to define the component in each site. The results indicate that these components have not exhibited statistically significant changes since 1950. Indeed, 3% of the stations show breakpoints for the Sowing, 4% for the TotRain, and 2% for the Onset. The start of the season follows a South East-North West gradient. It begins in the extreme South-East part of the country in the second decade of June while the first waves of sowing take place in the South-East center part from the second half of June. The north zone remains exposed to false start events with important seasonality. In the Northern and Central zones, the early or late character of the sowing passes more on the DryMax than the TotRain. It would be interesting to elucidate the effects of the rainfall regime at the early stage on the rainy season profile to gain better control of the pluvial crop yields.

Is snow drought a messenger for the upcoming severe drought period? A case study in the Upper Mississippi River Basin

The exacerbation of floods and the extension of droughts, attributed to climate change and human-induced factors, are posing a substantial risk to communities by causing water scarcity. The significance of safeguarding water resources and managing them is increasingly gaining prominence. Snow is an efficient source of water for recharging groundwater compared to rainfall. This is attributed to its gradual melting process and capacity to infiltrate the soil, thereby providing sustenance to the groundwater. Thus, snow drought can be considered a major contributing factor to the issue of water scarcity. The objective of this study was to investigate the evolution of snow drought over the period since 1980, as well as its impact on agricultural drought across the Upper Mississippi River Basin (UMRB). An analysis is conducted for comparison between the spatial estimations of snow drought in the UMRB and two drought indicators, namely the evaporative demand drought index (EDDI) and water deficit amounts. The effects of the El Niño and La Niña phenomena on the UMRB as well as the results of the summer agricultural drought conditions were investigated since 2000. The results point to two important findings: 1) the snow-drought-affected zones show an increasing trend since 1980 and 2) severe snow droughts in the winter (from October to April) of a water year trigger severe agricultural droughts in the summer months of the same water year since 2000. It is seen that monitoring snow droughts is as essential as following rainfall regimes in the planning of water resources, agricultural production, and irrigation methods.