March Rainfall Research Articles

Temporal variability in soybean sowing and harvesting according to K-means and silhouette scores

Understanding the impact of seasonal variations on soybean productivity is crucial for optimizing agricultural practices. Given the influence of climatic factors such as temperature and rainfall on crop phenology, this study aims to analyze the effects of sowing and harvesting soybean cultivars in different seasons. This research investigates whether sowing soybeans in November and December leads to varying outcomes in terms of productivity and morphological characteristics, focusing on identifying the most stable cultivars across different climatic conditions. The study employed a comprehensive methodology, including data standardization, statistical tests (Levene, Shapiro-Wilk, ANOVA, Wilcoxon), and K-means clustering, to analyze 40 soybean cultivars across two seasons. Statistical preprocessing ensured data accuracy, while clustering helped identify cultivars with consistent responses to climatic changes. All computational analyses were performed using Python in the Google Colab environment. The findings revealed no significant difference in productivity between the two seasons, despite variations in temperature and rainfall. However, the moisture content of the grains (MTG) showed significant differences, influenced by higher rainfall in March and April and increased temperatures in December. K-means clustering highlighted SYN2282IPRO as the most stable cultivar and 77HO111I2X-GUAPORÉ as the most sensitive to climatic changes. The results emphasize the need for careful cultivar selection based on specific adaptability to seasonal variations. This study underscores the potential of computational tools like K-means clustering in agricultural optimization, offering a data-driven approach to selecting stable soybean cultivars. The adaptable methodology can be tailored to different geographical regions, soil types, and climate conditions, enhancing its relevance and applicability. These insights contribute to a better understanding of the complex interactions between climatic variables and soybean phenology, providing a foundation for improving agricultural practices in the face of climate change.

Assessment of Selected Agroclimatic Indices on Maize Yield Forecasting Under Climate Change in Nigeria

This study investigates the relationship between climate, agroclimatic indices, and maize yield in Nigeria, focusing on diverse agroecological zones. Climate change is poised to significantly impact agricultural output. Analysis of historical data reveals varying sensitivities to weather changes in Nigeria’s agroecological regions. Regional climate impact assessments typically use annual statistical models, which may not capture sub-seasonal weather variations and often assume a constant relationship between crops and weather. Crop yield anomalies were created to remove non-weather-related influences from a time series dataset. Also, agroclimatic indices were incorporated into forecasting models as inputs to offer more relevant information for estimating crop output. The research demonstrates the critical role of climate factors such as rainfall in March and minimum temperatures in shaping maize yield in Nigeria. By expanding the scope to include a broader range of climate-related elements, this study has illustrated how incorporating agroclimatic indices into crop yield forecasting models can enhance forecast accuracy and reliability. The study reveals that different agroecological zones may face varied outcomes with regions in the south recording more negative maize yield anomalies as oppose to the north. The research underscores the complexity of the relationship between climate, agroclimatic indices, and crop yield in Nigeria. It provides essential insights for policymakers, farmers, and researchers to make informed decisions and develop strategies for ensuring food security and agricultural sustainability in the midst of a changing climate in Nigeria.

Spatiotemporal assessment of rainfall and drought projection for integrated dam management in Benut River Basin, Malaysia under CMIP6 scenarios

This study assesses the influence of climate change on rainfall and drought in the Benut River Basin (BRB) to support the integrated management of Machap dam. The study seeks to reshape dam safety components in the near future through a technical focus group discussion (FGD) with key stakeholders. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset for the immediate future (2020–2040) under various scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), historical Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall datasets (1981 to 2021) are employed. A multi-filtration process ranks twenty-three Global Climate Models (GCMs) using seven relative importance metrics (RIMs). This work's novelty lies in a focused GCM evaluation through individual contributions quantified with RIMs, providing a refined ranking that address the limitations of traditional metrics. Then, the Compromise Programming Index (CPI) aggregates the ranked models, with the top subset selected through Jenks Optimised Classification (JOC), identifying ACCESS-ESM1–5 and CMCC-ESM2 as top models. The selected GCMs were bias-corrected using the linear scaling method and validated before ensembled, effectively replicating the probability distribution function (PDF) of CHIRPS data, as confirmed by a Taylor diagram indicating the closest agreement. The study reveals a generally wetter tendency in the northernmost area of Machap dam under all SSPs, with 5–9 % maximum increasing percentages. Conversely, downstream areas anticipate decreasing rainfall. June (6–22 %) and July (7–22 %) project higher rainfall, indicating increased water availability in a relatively dry period, while December (3–11 %) suggests increasing flood risk. The South West monsoon (SWM) anticipates lower rainfall in early March (3–7 %) and more severe dry periods in August (8–11 %). Future drought occurrences are projected homogeneously across scenarios, with drought months ranging from 15 % to 19 % of the 252 months under all severity classifications. In particular, the study enhances myDAMS by proposing continuous development of the guideline under diverse scenario for Machap dam's overall management and sustainability. Projected scenarios, coupled with outcomes from FGD, can inform basin-scale dam management, aiding risk mitigation for critical dam infrastructure.

Long Term Trends in Rainfall and Temperature Effects on Food Security in Pakistan: An Analysis of 75 Years (1947-2021)

This study investigates the rainfall and temperature impact on food security (Wheat production) in Pakistan. The data nature is quarterly, and the time period is from 1947 to 2021. Econometrics approach simple OLS used. The wheat production is based on January, March, and November rainfall and temperature. In the findings of model 1, the rainfall in January and wheat production are negatively correlated. Besides, temperature and wheat production are directly correlated with each other. In Model 2, the rainfall has a significant and positive impact on wheat production. In the same month, the temperature was insignificant. The combined effect of rainfall and temperature has a negative impact on wheat production. It suggests that the combined effect of March rainfall and March temperature has a significant impact on wheat production at 10%. In model 3, November rainfall and wheat production are negatively correlated. The combined impact of November rainfall and November temperature has a positive and significant impact on the dependent variable. The study suggested the government reduce CO2 emissions in various sectors as well as improve technology and hybrid seeds. Besides, the state also adopts long-term reduction policy such as other developing countries adopts.

Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil

The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.

Legume options for summer-active pastures in a temperate rainfall environment of south-eastern Australia

Context High-quality, summer-active pastures could improve meat production in south-eastern Australia by facilitating livestock finishing over summer, with legumes critical for enhancing the nutritive value of pasture mixes. Available legumes vary in their ability to withstand moisture stress and grazing. Aims We aimed to identify legumes suitable for a summer–autumn finishing system. Methods We tested pure swards of 12 cultivars across eight legume species in replicated small-plot experiments at Goulburn and Bombala, New South Wales, assessing productivity, persistence and warm-season nutritive characteristics over 2–3 years. Key results Lucerne (Medicago sativa) was clearly the most productive species during summer and outperformed the clovers (Trifolium spp.) in terms of persistence and productivity throughout most of the experimental period at both sites, except during autumn 2021 after high rainfall in March. Caucasian clover (T. ambiguum) was also highly persistent at both sites. Talish clover (T. tumens) and strawberry clover (T. fragiferum) were more persistent than white clover (T. repens) and red clover (T. pratense). White clover recovered strongly under high rainfall after drought, whereas red clover established rapidly but showed less capacity for post-drought recovery. Hybrid Caucasian × white clover was the least productive legume. Alternative clover species sometimes had slightly lower values of nutritive characteristics than white clover; red clover sometimes had distinctly lower values. Conclusions Lucerne performed best but several clovers were also productive, persistent and of high nutritive value over the summer–autumn period. Implications Talish, Caucasian and strawberry clovers warrant further investigation for inclusion in summer-active pastures in south-eastern Australia.

Life-history characteristics and climate correlates of dioecious plant species in central southern Australia

Context The proportion of dioecious species can vary considerably among climates and habitats. However, studies often involve isolated communities or large diverse areas and fail to capture how proportions vary across diverse landscapes.Aims To identify (1) life-history associations of terrestrial dioecious plant species in central southern Australia, (2) whether proportion of dioecy varies spatially across central southern Australia, and (3) whether proportion of dioecy is correlated with life-history and/or climate factors.Methods Species growth form, pollination mechanisms and seed-dispersal features were extracted from herbarium databases to determine potential dioecy-linked traits. Distribution data for native terrestrial species in 66 Interim Biogeographical Regionalisation of Australia subregions were extracted from the Australasian Virtual Herbarium to calculate the proportion of total native species richness that are dioecious. Climate data for each subregion were also obtained from Terrestrial Ecology Research Network databases to investigate relationships among climate, life-history traits and dioecy.Key results Woodiness, abiotic pollination and endozoochory were more prevalent in dioecious than non-dioecious taxa. Proportion of dioecy ranged from 1.7% to 8.5% among subregions and correlated negatively with annual temperature range, January to March rainfall and precipitation seasonality and with average annual daily mean, minimum, maximum and average annual minimum temperature. The highest-ranked models of dioecy incorporated the additive effects of the relative proportion of woody species and either annual temperature ranges, January to March rainfall or average annual daily maximum temperature.Conclusions Dioecy was associated with woodiness, abiotic pollination and endozoochory, in line with studies of other flora, with the model of stable temperature range and woodiness being the highest-ranked model of dioecy.Implications Areas with higher proportions of dioecy can be targeted for future investigations into dioecious plant ecology to aid conservation and ecosystem management.

Interdecadal change of external forcings of March rainfall interannual variation over southern China

Interdecadal change of external forcings of March rainfall interannual variation over southern China

Field-Based Biochar, Pumice, and Mycorrhizae Application on Dryland Agriculture in Reducing Soil Erosion

Biochar, pumice, and mycorrhizae applications using direct testing methods in the field have not been widely carried out. The application of biochar in this study was used as a conservation material to control runoff and erosion. The research was conducted using a field plot during the peak of the rainy season (March-April) of 2021. The study was conducted in areas where the soil material is dominated by clay (>40%) and steep slope angles (>60%). The cropping pattern at the research site is generally cassava in the dry season and corn in the rainy season. Four 1 × 10 m field plots with corn stands were prepared with biochar, pumice, mycorrhizae, and control treatments. Runoff and sediment measurements were carried out by calculating the volume of water and suspension in the storage tank. The effect of three treatments was observed and measured through some soil characteristics such as bulk density (BD), specific gravity (SG), porosity, organic matter content (OM), cation exchange capacity (CEC), and aggregate stability. The highest rainfall in March and April reached 441 mm/month, with the highest intensity reaching 150 mm/week. Under intense rainfall, biochar application provides better performance than pumice and mycorrhizae. Runoff reduction from biochar is the highest, with 51.67%. On the other hand, pumice and mycorrhizae show a lower effectivity in decreasing runoff with 40.15% and 37.92%, respectively. The effectivity on lowering runoff translates to each ameliorant’s performance in reducing soil loss. Biochar decreases soil loss by 50.78%, while pumice and mycorrhizae decrease soil loss by 37.9% and 26.26%. The application of biochar reduced the rate of erosion by altering soil characteristics. Biochar application provides better soil characteristics by reducing BD and SG while at the same time increasing the porosity, OM, CEC, and aggregate stability. The changes provided by biochar can provide means to both soil conservation and increase in soil productivity.

Farmers’ perceptions of climate change, long-term variability and trends in rainfall in Apac district, northern Uganda

BackgroundClimate change poses severe threats to smallholder farmers' agricultural livelihoods and food security in Sub Saharan Africa. Understanding long-term rainfall trends of variability and extremes at local scales and perceptions regarding long-term changes in climate variables is important in planning appropriate adaptation measures to climate change. This paper examines the perception of farmers in Apac district regarding long-term changes in climate variables and analyzes the trend of occurrence in seasonal and annual rainfall in Apac district, northern Uganda. A cross-sectional survey design was employed to collect data on farmers' perceptions regarding long-term changes in climate from 260 randomly selected smallholder farmers’ households across two sub-counties in Apac district by administering semi-structured questionnaires in February 2018. Monthly rainfall data sets from the Uganda Meteorological Authority (UMA) for the period 1980 to 2019 for Apac district were also used to analyze trends of occurrences in seasonal and annual rainfall in the study area. The non-parametric Sequential Mann–Kendall (SMK) tests were employed at a 5% significance level to detect mean seasonal rainfall trends and abrupt change points.ResultsThe majority of the respondents (87%) perceived a decrease in precipitation over the past 39 years. The plot of forward regression u(ti) values and backward regression u’(ti) values showed interactions indicating rainfall trends, rainfall lower and upper limits and abrupt change points in the different cropping seasons. Analysis of historical series of mean monthly and annual rainfall showed an abrupt change in rainfall in March, April, May (MAM) season in 1982. Although the September, October and November (SON) season did not show a significant abrupt change, there was a significant (p < 0.05) increase in rainfall above the upper limit from 1994 to date.ConclusionThe mean seasonal rainfall for MAM and SON cropping seasons in Apac district were highly variable from different time points within the past 39 years (1980–2019), while June, July, and August (JJA) did not realize a significant change in rainfall within the same study period that the two cropping seasons (MAM and SON) in the district experienced remarkable variations in rainfall. This, therefore, provides a basis for the government to strengthen the provision of an effective climate tailored agricultural advisory service to aid farmers’ adaptation planning at the local level and to assist smallholder farmers and land-use managers in developing effective adaptation management strategies to the effects of climate change.

Soil moisture-based winter–spring drought variability over West Karbi Anglong region, Assam, Northeast India using tree-rings of Pinus kesiya

Drought is increasingly becoming more frequent and intense over Northeast India (NEI). To understand the drought variability over the region, we developed a 151-year (1868–2018 CE) long tree-ring width chronology of Pinus kesiya from Assam, NEI. The correlation analyses of tree-ring width chronology with temperature, rainfall, normalized difference vegetation index (NDVI) and soil moisture were conducted. The tree-ring chronology showed significant positive correlation with previous October through current year March rainfall and negative correlation with mean temperature of March–June. Similarly, we observed positive correlation with NDVI and soil moisture for April–May and January–April, respectively. Soil-moisture being the most significant variable for winter-spring (January to April), was reconstructed since 1868. This reconstruction accounted for 23.3% of the variance of the observed soil moisture in the calibration period of 1982–2018 CE. The years of soil moisture deficit (drought) and wet soil moisture conditions were identified based on the percentile distribution. The major droughts and famines that occurred in India during the late 19th and early 20th century were observed in the reconstruction. This study adds additional input towards agricultural planning and management in NEI.

Growth response of seven multipurpose tree species to climatic factors: A case study from northwestern Himalayas, India

Identification of the species for dendrochronological studies is of great relevance to understand various aspects of climate change. However, in the northwestern Himalayan region, dendroclimatological investigations are confined to conifer species, with broadleaved species being disregarded. Thus, the present study was conducted to assess the growth response of seven multipurpose tree species (MPTs), namely Bauhinia variegata, Celtis australis, Grewia optiva, Paulownia fortunei, Toona ciliata, Ulmus villosa and Melia composita to local climate variables, viz. temperature as well as rainfall (seasonal, monthly, average) and CO2 level by evaluating the climatic signal in tree ring chronologies at Solan district, India (altitude 1 250 m) in the mid-hills of the northwestern Himalayas. The results indicated that only the maximum, rainy season temperature and CO2 level varied significantly (P &amp;lt; 0.05) between 1991 and 2017. Only G. optiva exhibited a significant (P &amp;lt; 0.05) tendency toward increased growth. C. australis has a remarkable negative correlation with temperature variables, viz. average, maximum, spring season, March temperature, whereas T. ciliata exhibits a positive correlation with temperature variables, such as rainy season, average and April temperature. Similarly, winter, total and December rainfall have a profound effect on P. fortunei, while March rainfall adversely affected the growth of B. variegata. On the other hand, G. optiva demonstrated sensitivity to both temperature (February and May) and rainfall variables (winter, February and May). U. villosa recorded a positive correlation with rainfall (autumn and October rainfall) but a negative correlation with temperature variables (maximum and April temperature). Elevated CO2 levels affected only two species (G. optiva, M. composita) out of the seven selected species. Our findings will contribute to a better understanding of the climate growth relationships of investigated tree species, as a result, to more accurate projections of the effects of climate change on these MPTs and directing future studies.

The Analysis of the Contribution of the Southwestern Indian Ocean Tropical Cyclones (TCs) to December, January, February, March (DJFM) Rainfall Season over Tanzania

The contribution of Southwestern Indian Ocean (SWIO) Tropical cyclones (TCs) to the December to March (DJFM) rainfall season over Tanzania was analyzed. Seasonal, monthly and daily rainfall data were obtained from Tanzania Meteorological Authority (TMA), while data for sea surface temperatures (SSTs), relative and specific humidity, zonal and meridional winds were acquired from NOAA (NCEP – NCAR). Tropical Rainfall Measuring Mission (TRMM) daily average gridded rainfall estimates were used. The observed and gridded rainfall data for each TC day were extracted and computed to monthly and seasonal totals for all TCs and non TCs days. Correlation between DJFM TCs and DJF/DJFM rainfall were determined. Inter annual variability between TCs and DJF rainfall was analyzed. Wind patterns at 850 - 200 mb, relative humidity (850-700mb), vertically integrated horizontal wind and moisture divergence (1000-500 mb) for two TC events (Izilda and Fobane) were analyzed. The results revealed that, TCs contribution to the DJFM rainfall was very significant. The influence of TCs to monthly total rainfall showed a general increasing trend from north to south along the coast of Tanzania, with a southwest to northeast alignment. On the other hand correlation between DJFM TCs with DJF rainfall was relatively weak at some stations while few stations including Dodoma and Tabora were showing relatively higher correlations of about 0.39 (p ≤ 0.05) suggesting that these stations are highly vulnerable to the TCs impacts. Moreover, results had shown that the distribution of TCs rainfall prone areas run from southern parts (Mtwara) through central parts (Dodoma) to northeastern highlands (Kilimanjaro). Analyses revealed that 700 mb wind vectors, integrated moisture fluxes and their convergence from Congo air mass, northwesterly and northeastern Indian Ocean air masses were the main contributors of rainfall over Tanzania during TCs events. Conclusively, the study showed that TCs has significant contribution to the DJF and DJFM rainfall in Tanzania, thus more studies for understanding the dynamic of TCs in relation to rainfall is required for improving the weather forecasts in Tanzania.

Novel Along-Track Processing of GRACE Follow-On Laser Ranging Measurements Found Abrupt Water Storage Increase and Land Subsidence During the 2021 March Australian Flooding.

Following extreme drought during the 2019–2020 bushfire summer, the eastern part of Australia suffered from a week‐long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low‐latency (1–3 days), high‐precision intersatellite laser ranging measurements from GRACE Follow‐On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60–70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ∼5 mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ∼80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021.

Influence of atmospheric macroprocesses on the spatial distribution of spring precipitation within the territory of Ukraine

The article presents the results of a comprehensive statistical study of the spatial distribution of spring precipitation in Ukraine and its relationship with the main low-frequency oscillations of the Northern Hemisphere. The research focuses on the monthly amount of rainfalls in March, April and May at 40 long-range stations of Ukraine that are evenly distributed throughout its territory, and the indices of North Atlantic (NAO) and North Sea-Caspian (NCP) fluctuations over a 45-year period (1962-2006). The use of multiyear data allowed for objective clusterization and division of the Ukrainian territory into regions with different types of weather observed in the event of precipitation, with each of them being physically substantiated. To ensure clarity, the article presents regional statistical models in the form of schematic maps of probable relationships between distribution of spring precipitation in Ukraine and the North Atlantic and Euro-Mediterranean macroprocesses. In order to forecast the monthly rainfall in March, it is advisable to take into account the state of the North Atlantic fluctuations. The response of this fluctuation was found within Chernivtsi, Vinnytsia, Cherkasy, Kyiv Regions, northern part of Odesa Region, western districts of Chernihiv Region and southern part of Zhytomyr Region. In the western regions of Ukraine, as well as in Vinnytsia, Kyiv, Zhytomyr and Chernihiv Regions, the formation of precipitation in April depends on the state of both NAO and NCP; in the rest of Ukraine – only of NCP. The 90% probability linear statistical relationship between precipitation in May and the main climatic signals of the Northern Hemisphere under study was not found only in some northern regions of Ukraine, namely: Kyiv, Chernihiv, Sumy Regions and central (Poltava and northern part of Cherkasy Region) part of Ukraine. A study of the influence of North Atlantic and Euro-Mediterranean macroprocesses on the distribution of monthly rainfall in Ukraine, conducted using a statistical approach, shows its ambiguity during different spring months and within different regions of Ukraine, and requires further research to solve the general scientific problem – study of climate-conditioned natural resources required to ensure sustainable socio-economic development of Ukraine under conditions of global climate change.

Variation in physical characteristics of rainfall in Iran, determined using daily rainfall concentration index and monthly rainfall percentage index

Variations in rainfall characteristics play a key role in available water resources for a country. In this study, spatial and temporal variations in rainfall in Iran were determined using the daily rainfall concentration index (DRCI) and monthly rainfall percentage index (MRPI), based on 30-year (1987–2016) daily precipitation records from 80 meteorological stations throughout Iran. The results showed that MRPI differed between locations within Iran, with increasing or decreasing trends observed in different areas. The highest significant decreasing trend in MRPI (3–7% per decade) was found for March rainfall in western Iran, and the highest increasing trend in MRPI (3–7% per decade) for November rainfall in eastern and southern Iran. The DRCI values obtained varied from 0.57 to 0.71, indicating moderate and high rainfall concentrations, with the highest DRCI values in coastal zones of Iran near the Caspian Sea and the Persian Gulf. Trend analysis showed increasing trends in DRCI values at 80% of meteorological stations, and these trends were significant at 37% of those stations.

Contrasting controls on Congo Basin evaporation at the two rainfall peaks

Evaporation is a crucial driver of Congo Basin climate, but the dynamics controlling the seasonality of basin evaporation are not well understood. This study aims to discover why evaporation on the basin-wide average is lower at the November rainfall peak than the March rainfall peak, despite similar rainfall. Using 16-year mean LandFlux-EVAL data, we find that evaporation is lower in November than March in the rainforest and the eastern savannah. The ERA5-Land reanalysis, which effectively reproduces this pattern, shows that transpiration is the main component responsible for lower evaporation in these regions. Using ERA5-Land, we find the following contrasting controls on transpiration, and therefore evaporation, at the two rainfall peaks: (a) In the northern rainforest, there is lower leaf area index (LAI) in November, driven by lower surface downward shortwave radiation (DSR), and lower vapour pressure deficit (VPD) in November, driven by lower sensible heat flux that results from lower net radiation. The combination of lower LAI and VPD explains lower transpiration, and therefore lower evaporation, in November. (b) In the southern rainforest, and in the north-eastern savannah, there is lower LAI in November, driven by lower surface DSR, and this explains lower transpiration, and therefore lower evaporation, in November. (c) In the south-eastern savannah, there is lower LAI in November, driven by lower volumetric water content (VWC), and this explains lower transpiration, and therefore lower evaporation, in November. Collectively, these contrasting controls at the two rainfall peaks explain why the basin-wide average evaporation is lower in November than March.

Rare Earth Elements in Andaman Island Surface Seawater: Geochemical Tracers for the Monsoon?

The Asian summer monsoon affects the lives of billions of people. With the aim of identifying geochemical tracers for the monsoon related freshwater input from the major rivers draining into the Bay of Bengal and Andaman Sea we have taken surface seawater samples from various locations up and down the Andaman Islands during 2011. Importantly, in some locations samples have been taken in March, July and November, covering most of a seasonal cycle and different monsoon phases. Samples were collected from the side of small wooden boats or while swimming and were filtered within a few hours at 0.45 or 0.22 microns using the vacuum produced by a water jet or a hand operated peristaltic pump. Filtered and unfiltered samples were acidified to < pH 2 and analysed for Y and the REEs with an automated online preconcentration ICP-MS technique [1]. The local input of REEs from streams and sediment rich areas such as mangrove environments is clearly identified by middle REE enrichments in the shale normalised patterns of some samples. These middle REE bulges accompany large increases in dissolved REE concentrations at some locations, especially for the July samples obtained during the peak monsoon season with frequent storms. Y/Ho fractionation aslo occurs during the local input of dissolved REEs with affected samples having lower Y/Ho ratios. Conversly, some samples, in particular those taken after heavy rainfall in March, show strong REE scavenging accompanied by the prefferential removal of dissolved light REEs and higher Y/Ho ratios. The time series at a location away from local input sources shows remarkably similar REE patterns and concentraions in March and July. Then in October-November, following the peak in monsoon river discharge, the dissolved REE concentration increases by almost a factor of 2. The notable exception to this seasonal pattern is the Ce anomally which is around 0.3 in March and November but 0.6 in July, implying less oxidative removal of Ce(IV) during the peak summer monsoon rains. With the exception of elevated dissolved Ce concentrations, the North Pacific Deep Water normalised REE patterns are similar to those reported for offshore samples from the Bay of Bengal and Andaman Sea [2]. These seawater normalised patterns are distinctive having a middle REE enriched arc with similar light and heavy REE values suggesting the input from large rivers in the region is traceable using seawater REE chemistry. [1] Hathorne et al. (2012), Online preconcentration ICP-MS analysis of rare earth elements in seawater, Geochem. Geophys. Geosyst., 13, Q01020, doi:10.1029/2011GC003907. [2] Amakawa et al. (2000), Geochim. Cosmochim. Acta 64, 1715-1727.

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

Taiwanese rainfall variability associated with large-scale climate phenomena

Examining the physical mechanisms through which large scale climate indicators, e.g., El Niño-Southern Oscillation and Indian Ocean Dipole, affect hydroclimatic variables in the tropics and extratropics is a scientific challenge. In this study, climatic relationships between large-scale climate indices (CIs) and Taiwan rainfall variability were examined. Not only leading patterns of observed monthly total and extreme rainfall were estimated through an empirical orthogonal teleconnection method (EOT), but also correlation and regression analyses were performed for the leading rainfall patterns and various CIs based on atmospheric-ocean circulation dataset. From the spatial structure of the leading EOT patterns for total and extreme rainfall, the north-coast mode in the cold seasons and the middle-inland mode for the warm seasons were detected. The temporal cycle of the leading EOT patterns indicates decreasing trends in the cold seasons and oscillations mainly on decadal timescales in the warm seasons. The leading EOT patterns of total rainfall showed more widespread coherent patterns than those of extreme rainfall with more variance in rainfall variability. The findings from this study illustrate that tropical ENSO forcing has a coherent association with March and October–November rainfall patterns, while the Indian Ocean Dipole is identified as a driver for rainfall variability during spring and fall. The western North Pacific monsoon activity has a negative (positive) correlation with March (October and December) rainfall variabilities and tropical cyclone indices also exhibit significant positive correlation with October rainfalls. The leading patterns of the October and following-year March rainfall time series are predictable at up to nine-month lead time from the tropical Pacific sea surface temperatures (SSTs).