Rainfall Distribution Research Articles

Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

Unveiling the Urban Impact on Mesoscale Convective System Rainfall in the Pearl River Delta Urban Agglomeration Under Typical Synoptic Backgrounds

AbstractMesoscale convective systems (MCSs), the primary drivers of extreme rainfall over the Pearl River Delta (PRD) urban agglomeration, are strongly influenced by synoptic circulations and local geographical environments, including water bodies and topography. However, the urban impact on MCS rainfall under various synoptic backgrounds remain inadequately understood. Using a 20‐year high‐resolution MCS tracking database and self‐organizing map clustering, three typical backgrounds for MCSs, namely weak monsoon‐like, strong monsoon‐like, and low‐pressure system (Types‐1 to 3), impacting the PRD are identified. These backgrounds exhibit pronounced disparities in MCS tracks and temporal variations as well as rainfall distributions. Urban heat island (UHI) significantly alters the spatial patterns under Types‐1 and 2. Specifically, under weak UHI condition, MCS rainfall typically occurs offshore in the morning and shifts inland in the afternoon driven by the land‐sea breeze. However, UHI modifies the low‐level thermal structure, leading to anomalous convergence and instability, which causes morning rainfall to concentrate near coastal cities, while afternoon rainfall expands further inland to the northern rural region. Additionally, the strong southwesterly winds associated with Type‐2 enhance the interaction between topography and urban impact, resulting in even higher rainfall anomalies (+28.9%) over the northeastern region. The findings highlight the crucial role of urban impact and their synergistic effect with synoptic backgrounds and other land surface processes on MCSs.

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

Effects of different karst fissures and rainfall distribution on the biomass, mineral nutrient elements, antioxidant substances, and photosynthesis of two coniferous seedlings

BackgroundStudying the physiological growth status of Pinus yunnanensis Franch and Pinus elliottii Engelm. seedlings under different karst fissure thicknesses and rainfall distributions is of great significance for the management, vegetation restoration, and tree species selection in karst rocky desertification areas. In this study, we used a two-factor block experiment and set different rainfall durations, namely reduced rainfall duration (I3d), natural rainfall duration (I6d), and extended rainfall duration (I9d); Different karst small habitats, i.e., stone-free soil (S0), less stone and more soil (S1/4), and half stone and half soil (S1/2), are simulated at these three levels. Analyze the changes in physiological growth and photosynthetic characteristics in two coniferous seedlings under different treatments with different karst thicknesses.ResultsThe results showed that with the increase of karst thickness, the growth volumes of height and diameter of P. yunnanensis seedlings, the biomass of various organs, and the accumulation of K+, Ca2+, Na+, and Mg2+ showed a significant change pattern of first increasing and then decreasing (P < 0.05); P. elliottii seedlings show a gradually decreasing trend (except for Ca2+). The biomass accumulation of each organ in two coniferous seedlings showed that leaves > stems > roots. The K+, Ca2+, and Mg2+ content in various organs of P. yunnanensis seedlings showed that leaves > roots > stems, while Na+ shows the order of roots > leaves > stems. The accumulation of mineral elements in various organs of P. elliottii seedlings is manifested as roots > stems > leaves and the accumulation of mineral elements in both coniferous seedlings is manifested as Ca2+ > Mg2+ > K+ > Na+. Root length, root volume, root surface area, root diameter, SOD, POD, SP, photosynthetic pigment content, fluorescence parameters, and gas exchange parameters of P. yunnanensis seedlings gradually increase with the increase of karst thickness (except for the 9-day rainfall duration), while those of P. elliottii seedlings gradually decrease. The light saturation point of P. yunnanensis seedlings is highest under the I6dS1/2 treatment, while that of P. elliottii is highest under the I3dS0 treatment.ConclusionsIn summary, prolonging rainfall duration has an inhibitory effect on the growth of two types of coniferous seedlings. Increasing karst thickness inhibits the growth of P. elliottii seedlings, and to some extent, promotes the growth and development of P. yunnanensis seedlings. I6dS1/4 and I3dS0 treatments have the best growth effects on P. yunnanensis and P. elliottii seedlings. Therefore, we give priority to P. yunnanensis as the tree species for vegetation restoration or rocky desertification management in karst areas. Our study reveals the role of limestone-filled different karst fissures in mitigating the effects of drought as “containers” for plant growth. These findings help us understand the response of plants to drought stress and provide valuable insights for vegetation restoration in karst environments affected by global climate change. Therefore, further experiments with various karst fissure sizes are necessary to test the universality of the reactions of various plants under different karst fissures.

MODELING FLOOD SUSCEPTIBILITY IN HYDROLOGICAL DATA-SCARCE STUDY AREAS

Hydrological information is essential for estimating the magnitude of floods, thus helping reduce losses and damage. However, when this data is scarce, it challenges flood modeling and risk assessment. In Brazil's west of Rio Grande do Sul, we used the Santa Maria hydrographic basin as a case study to estimate the river flow for different RP based on the statistical distribution of rainfall and river flow data and to simulate flood scenarios using a 2D IBER hydrological model.River flow estimates for 5, 10, 20, 30, and 50-year RP were obtained using the Giandotti equation. The results showed a high correlation between the river flows calculated from rainfall and the fluviometric data, using GEV and GumbelMax distribution. The areas susceptible to flooding are consistent with past flood events, validated with fieldwork and Sentinel imagery. River flows estimated from rainfall data can promote hydrological studies where fluviometric data is absent. Keywords: River Flow Estimation; Generalized Extreme Values; Flood Susceptibility; Rosário Do Sul; Brazil.

Climatic and edaphic controls of root-tip production and mortality in five temperate tree species

Root tips are the main components of absorptive fine roots, but their seasonal dynamics and relationship to environmental factors remain unclear due to the difficulties in methodology. In this study, we explored the temporal patterns of root-tip production and mortality in monoculture plantations of five temperate tree species at a common site in northeastern China, and identified the general environmental controls on such processes. We made monthly in-situ assessments of root tip length (RTL) production and mortality in two hardwood and three coniferous species with a minirhizotron (MR) method during the growing seasons of 2008 and 2009. Air temperature, rainfall, soil temperature and water content at 10 cm depth were determined concurrently. RTL production in all species exhibited consistent peaks in summer (June–August) in two growing seasons. RTL mortality showed substantial interannual and interspecific variability, with peaks in autumn and winter in 2008, but various patterns in 2009. RTL production positively correlated with monthly soil and air temperature across all species, and with monthly rainfall in three coniferous species. However, there was no significant correlation between RTL production and soil water content. By contrast, RTL mortality was weakly related to environmental factors, showing positive correlations with soil temperature in Korean spruce, and with rainfall in Korean pine and Korean spruce. Our findings suggest that the seasonal patterns of RTL production are convergent across the five temperate tree species due to the overlapped distribution of heat and rainfall, which can conduce roots to maximizing the acquisition of nutrient resources in the soil.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Identifying the Layout of Retrofitted Rainwater Harvesting Systems with Passive Release for the Dual Purposes of Water Supply and Stormwater Management in Northern Taiwan

Due to its unique climate and geography, Taiwan experiences abundant rainfall but still faces significant water scarcity. As a result, rainwater harvesting systems (RWHSs) have been recognized as potential water resources within both water legal and green building policies. However, the effects of climate change—manifested in more frequent extreme rainfall events and uneven rainfall distribution—have heightened the risks of both droughts and floods. This underscores the need to retrofit existing RWHSs to function as stormwater management tools and water supply sources. In Taiwan, the use of simple and cost-effective passive release systems is particularly suitable for such retrofits. Four key considerations are central to designing passive release RWHSs: the type of discharge outlet, the size of the outlet, the location of the outlet, and the system’s operational strategy. This study analyzes three commonly used outlet types—namely, the orifice, short stub fitting, and drainage pipe. Their respective discharge flow formulas and design charts have been developed and compared. To determine the appropriate outlet size, design storms with 2-, 5-, and 10-year return periods in the Taipei area were utilized to examine three different representative buildings. Selected combinations of outlet diameters and five different outlet locations were assessed. Additionally, probably hazardous rainfall events between 2014 and 2023 were used to verify the results obtained from the design storm analysis. Based on these analyses, the short stub fitting outlet type with a 15 mm outlet diameter was selected and verified. For determining the suitable discharge outlet location, a three-step process is recommended. First, the average annual water supply reliability for different scenarios and outlet locations in each representative building is calculated. Using this information, the maximum allowable decline in water supply reliability and the corresponding outlet location can be identified for each scenario. Second, break-even points between average annual water supply and regulated stormwater release curves, as well as the corresponding outlet locations, are identified. Finally, incremental analyses of average annual water supply and regulated stormwater release curves are conducted to determine the suitable outlet location for each scenario and representative building. For the representative detached house (DH), scenario 2, which designates 50% of the tank’s volume as detention space (i.e., the discharge outlet located halfway up the tank), and scenario 3, which designates 75% (i.e., the discharge outlet at one-quarter of the tank height), are the most suitable options. For the four-story building (FSB), the outlet located at one-quarter of the tank’s height is suitable for both scenarios 2 and 3. For the eight-story building (ESB), scenario 2, with the outlet at one-quarter of the tank’s height, and scenario 3, with the outlet at the lowest point on the tank’s side, are preferred. The framework developed in this study provides drainage designers with a systematic method for determining the key parameters in passive-release RWHS design at the household scale.

Assessment of surface irrigation potential and crop water demand in Daramalo Wereda, southern Ethiopia

AbstractAssessing available water and land resources as well as their potential for surface irrigation use is vital for sustainable agricultural development and planning. Agricultural irrigation also plays a crucial role in ensuring food security and reducing poverty in Ethiopia. However, it is dominated by rain‐fed agriculture, with rainfall distribution and intensity vary spatially and temporally. Enhancing agricultural water management and irrigation has the potential to increase agricultural productivity. The objective of the study was to evaluate surface water potential and the irrigation water demand of Zage River sub‐watershed, which were estimated using hydrological model of Soil and Water Assessment Tool (SWAT) and CROPWAT8.0 software, respectively. Slope, soil type, drainage densities, land use/cover, and river proximity were the factors utilized to determine irrigation land suitability. A weighted overlay was completed, with 17.32% of irrigable lands found to be highly suitable, 41.29% moderately suitable, 37.61% marginally suitable, and 3.78% not suitable. The SWAT model result gave very good model performance indicator values of NE = 0.773 and R2 = 0.872 for calibration and NE = 0.771 &amp; R2 = 0.945 for validation. The SWAT model result showed a total of 33.248 million m3 of surface runoff generated annually. Compared with irrigation water demand of 21.068 million m3, this suggests the watershed has high surface water potential for surface irrigation for selected crop types of the watershed. The findings of this research are relevant to improving local water management policies and practices and enable stakeholders to make decisions that enhance agricultural productivity and sustainable agricultural practices and help ensure the food security of local farmers.

Analysis of Rainfall Distribution in Malaysia through the Employment of Hydro-Estimator Data

Analysis of Rainfall Distribution in Malaysia through the Employment of Hydro-Estimator Data

Rainfall characteristics over the Congo Air Boundary Region in southern Africa: A comparison of station and gridded rainfall products

Strong meridional rainfall gradients exist between the tropics and subtropics in southwestern Africa, bounded to the north by the moist Congo basin and to the south by the Kalahari Desert. This domain received relatively little scientific attention compared to the rest of southern Africa. In this study, the limited available station data are assessed against six gridded rainfall products (CHIRPS, PERSIANN-CDR, ERA5, GPCC and CPC) for various rainfall characteristics. The nearest neighbour approach was used to match the closest rainfall dataset pixel to each station location, with the assumption that each rain gauge represents observation of various pixels of products, irrespective of product resolution. Results reveal that ERA5, CHIRPS and PERSIANN-CDR tend to represent the monthly rainfall totals and number of rainy days well for most stations although magnitudes and monthly peaks differ. CPC and GPCC tend to perform poorly for magnitudes of rainfall, rainy days and monthly cycles especially for Angolan stations. These products also fail to adequately capture spatial distributions of rainfall, with poor representation of the strong gradients found in the region.Correlations between various gridded rainfall products mostly show good agreement in rainfall totals and rainy days. For early summer (October–November-December) moderate rainy days, ERA5 and CHIRPS products tend to have more days than the stations while CPC and GPCC products perform poorly over Angola and in the south. ERA5 generally overestimates rainfall in mountainous regions, while other products tend to underestimate it. Based on the Simple Daily Intensity Index, it was found that for most of the gridded rainfall products tend to overestimate rainfall during rainy days in the northern and wetter part of the domain. Furthermore, for heavy rainfall, CPC and GPCC tend to compare fairly well with stations in the southern and eastern parts of the domain but poorly with those in the western parts. PERSIANN-CDR tends to underestimate heavy rainy days for most stations in early and late summer (January–February-March-April). However, CHIRPS compares well at several stations, while ERA5 performs well for stations located in the south. This study helps provide useful guidance in choosing suitable rainfall gridded datasets for assessing long term rainfall cycles, daily rainfall characteristics as well as extremes over southwestern Africa.

Rainfall water collection and irrigation via stone bud and karren on karst rocky desertification slopes: Application and benefit analysis

Lack of surface water is a key factor leading to agricultural drought in karst regions and exacerbates the karst rocky desertification, which is characterized by high rate of exposed bedrock. With the characteristics of impermeability, runoff collection, and wide distribution, stone bud and karren (SBK), a unique karst landscape type composed of exposed bedrock and gullies, has a great potential to solve water shortage in agricultural irrigation, especially in the rain-rich karst regions of Guizhou Province, China. Using unmanned aerial vehicle images to identify SBK and measured rainfall–runoff data for validation, we applied SBK as a basic unit to harvest rainfall water on rocky desertification slopes and analyzed the benefits of rainfall water collection and irrigation via SBK. The results showed the following: (1) Karrens on rocky desertification slopes could be identified using a method coupling terrain opening difference and depression filling, and the simulated runoff on SBK could achieved the average NSE and R2 of 0.82 and 0.86, with RMSE and RSR less than 0.03 m3 and 0.51, respectively. (2) The exposed bedrock utilization rate and rainfall water collection coefficient of SBK were 0.62 and 0.55, respectively, such that the area ratio of exposed bedrock to the cultivated land meeting irrigation water demand reached 1:3.1, 1:3.7 and 1:7.0 under 90 %, 80 % and 50 % possibilities of irrigation. (3) Implementing SBK for rainfall water harvesting and irrigation could efficiently avoid crop failure in the karst regions where crops faced drought within only one week after rainfall, and was at an affordable cost to local farmers with limited environmental damage. The use of SBK to harvest rainfall water could also alleviate surface water shortages in karst regions caused by problems such as uneven temporal distribution of rainfall and engineering water shortages. Consequently, SBK demonstrate a strong application potential to alleviate agricultural drought in karst rocky desertification regions.

Progress in Research on Deep Learning-Based Crop Yield Prediction

In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research.

District wise spatiotemporal analysis of precipitation trend during 1900-2022 in Bihar state, India

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900–2022. Major findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter, and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post-monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas, and extreme eastern districts like Purnia, Katihar, Araria, and Supaul had negative trend.

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

Spatiotemporal Rainfall Analysis of El Nino, Neutral and La Nina Years Across Belg and Kiremt Seasons Over Oromia Region Ethiopia

ENSO is the primary natural hazard that contributes to food insecurity and poverty. It has been influencing countries’ climatic regimes for decades, bringing frequent floods and droughts. There is limited knowledge on El Niño and La Niña occurrences that affect agricultural activities in the Oromia Region and occur during the Belg and Kiremt seasons when rainfall is distributed. Evaluating the spatiotemporal rainfall performance of ENSO years spanning Belg and Kiremt seasons rainfall distribution over the research area is the primary goal. For the study’s spatial and temporal analyses, satellite rainfall data from 1981 to 2021 was obtained from CHG-UCSB and https://earlywarning.usgs.gov/fews/ewx_lite/index.html, respectively. The study’s findings demonstrated a substantial association between the Belg and Kiremt seasons spanning El Niño and La Niña periods, with a range of correlation values indicating a weak, moderate, and strong relationship. Because of the spatial and temporal dynamics of this study area, ENSO phases indicate that exceptionally dry years are probably linked to El Niño occurrences in the region, whereas extraordinarily rainy years are likely linked to La Niña events. Seasonal rainfall performance and rainfall variability are impacted by ENSO events and other regional systems, with both positive and negative effects observed throughout the research area. In addition to examining the ENSO (Neutral, El Niño, and La Niña) that have a major impact on the seasonal rainfall performances in the Oromia region, this study also looked at the water availability, yield production, pastoralist and agro-pastoralist activities, and water availability. In summary, the results of this research can help us better understand the mechanisms underlying regional ENSO events and their causes, which will be especially useful when future climate conditions change.

Plasticity response of desert shrubs to intense drought events at different phenophases under the context of climate change

Global climate change has led to the frequent occurrence of intense drought events in mid-latitude desert ecosystems, coupled with uneven rainfall distribution across phenophases within the year. However, the impact of intense drought events at different phenophases on plant growth and drought resistance strategies still lacks clear conclusions. We selected the typical desert semi-shrubs Artemisia ordosica as our research object, and constructed a rain shelter to simulate intense drought events (without rainfall for 30 consecutive days) during the sprouting, vegetative growth, flowering and fruiting stages. Based on this, we analyzed the differential impacts of intense drought events at different phenophases on the phenotypic characteristics (e.g. shrub height, cover, volume, specific leaf area) and functional traits (ANPP accumulation and allocation) of A. ordosica. The experiment employed a randomized complete block design with three replicates for each treatment. The results indicate that: (1) Under intense drought events at different phenophases, all phenotypic characteristic indicators of A. ordosica significantly decreased. (2) Contrary to the significant negative correlation between twig number and twig size in response to rainfall variations, under intense drought conditions, there is a significant positive correlation between the two, indicating a synergistic effect. (3) The impact of intense drought events at different phenophases on the ANPP (aboveground net primary production) accumulation of A. ordosica varied significantly. The degree of impact is as follows: the flowering and fruiting stage > the sprouting stage > the vegetative growth stage. (4) A. ordosica adapted to intense drought by increasing the proportion of reproductive growth and decreasing the proportion of vegetative growth. Our results reveal the phenotypic and functional trait plasticity response mechanisms of A. ordosica to intense droughts at different phenophases, laying a foundation for predicting the impacts of climate change on desert ecosystems.

An Investigation on Microphysical Characteristics of Early‐, Late‐, and Post‐Mei‐yu Season Rainfall Over Taiwan

AbstractOver Taiwan, Mei‐yu season (May and June), which is primarily linked to frontal systems, is the transition period between winter and summer. Using the Global Precipitation Measurement Mission dual‐frequency precipitation radar (GPM DPR), the current study examined the rain and microphysical characteristics of the Mei‐yu season in Taiwan. In order to examine the areal and intra‐seasonal aspects, May and June months are divided into three sub‐seasons: early‐Mei‐yu, late‐Mei‐yu, and post‐Mei‐yu. The three sub‐seasons exhibited differences in rainfall and raindrop size distributions, with abundance of large drops in the post‐Mei‐yu. Additionally, there were noticeable variations in the raindrop size distributions among the south, central, north, and eastern parts of Taiwan, with more large drops in central Taiwan. To comprehend the microphysical progressions causing the regional and intra‐seasonal fluctuations, CFADs (contoured frequency by altitude diagrams) of rain parameters are utilized. Compared to other two sub‐seasons, the early‐Mei‐yu season exhibited weaker convection. Dominance of stratiform precipitation in late‐Mei‐yu season, and convective precipitation in post‐Mei‐yu season resulted in higher rainfall amounts in these two sub‐seasons (more particularly in post‐Mei‐yu) than the early‐Mei‐yu season. Examination of warm rain microphysical processes (below 5 km height) among these sub‐seasons revealed that the early‐Mei‐yu season is dominated with break‐up process, late‐Mei‐yu season with breakup and coalescence balance, and post‐Mei‐yu with coalescence, size‐sorting and evaporation processes.

Effect of Anti-Transpiration and Potassium on Water Use Efficiency and Water Consumption under Water Stress Conditions to Climate Change Resistance

Climate change impact serious threats to global food security due to changes in water requirements resulting from the variation and instability of the spatial and temporal distribution of rainfall and the lack of water availability. Therefore, our research aims to use anti-transpiration agents that reduce plant water consumption, thus providing adequate amounts of water for agriculture in arid areas. A field experiment was conducted under water stress conditions during the winter seasons 2022-2023 and 2023-2024. The study included three levels of water stress, which are depletion of 40, 55 and 70% of the available water at a depth of 20 cm for the first three irrigations and a depth of 30 cm for the remaining irrigations and for the three treatments in succession. Anti-transpiration was added from different sources, which are kaolin, paraffin wax, silicon and potassium, in addition to the comparison treatment (spraying with water only). The results showed significant differences between the depletion treatments, as the 40% depletion treatment of the available water was significantly superior in most of the characteristics of the crop and its components, as the amount of increase in the grain yield was 62.24 and 64.22% for the two seasons in succession, compared to the 70% depletion treatment of the available water, which gave the lowest averages for all the studied characteristics. The irrigation treatment after depleting 40% of the available water was characterized by the best water use efficiency of 1.60 and 1.44 kg m-3 for the two seasons respectively, compared to the treatment of depleting 70% of the available water, which gave the lowest water use efficiency of 1.14 and 1.02 kg m-3 for the two seasons respectively. Spraying with anti-transpiration and potassium led to a significant increase in the yield and all its components compared to the comparison treatment (spraying with water only), which gave the lowest averages for all the studied traits, as spraying with potassium and kaolin gave a significant increase in most of the studied traits. Also, anti-transpiration and potassium, represented by potassium and kaolin, had a significant effect on water use efficiency, as the increase amount when spraying with potassium was 45.87 and 46.93% for the two seasons respectively compared to the comparison treatment (spraying with water only). As for the actual water consumption, we find an increase in the values ​​of water consumption and the number of irrigations in the comparison treatment (spraying with water only) compared to the anti-transpiration and potassium treatments, as the anti-transpiration and potassium provided water at a depth of 550, 414 and 405 m3 ha-1 for the first season and 615, 375 and 393 m3 ha-1 for the second season for the depletion treatments of 40, 55 and 70% of the available water, respectively, compared to the comparison treatment (spraying with water only). This increase is not small when applied to large areas and providing additional agricultural areas that can be invested. These findings suggest that targeted use of these treatments can effectively mitigate the effects of water stress, improving wheat production and resource efficiency in water-limited environments.