Temporal Distribution Of Rainfall Research Articles

Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021

Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021

Role of rainfall temporal distribution on effective infiltration in the loess slope and prediction model

Rainfall is one of the main causes of landslides, particularly in the Loess Plateau. When rainfall occurs, rainfall thresholds can be used to predict whether it will cause landslides in monitoring and early-warning system. However, most rainfall thresholds are established based on constant rainfall processes. Therefore, this study takes the temporal distribution of rainfall into account in rainfall threshold studies and proposes four well-delineated temporal distribution types and an innovative three-step model. Rainfall thresholds are derived from 45 months of rainfall data and slope moisture data obtained through in-situ monitoring of the loess slope in Yan'an city, northwest China. Our analysis focuses on effective rainfall infiltration rather than shallow landslides as the monitoring index for thresholds. The results revealed a total of 189 rainfall events over the course of 45 months. Among these, 167 were classified as ineffective rainfall events with an infiltration depth of <10 cm, while 22 were categorized as effective rainfall events with an infiltration depth exceeding 10 cm, accounting for only 11.6% of the total. In this study, the temporal distribution of rainfall was classified into four types: transitory, constant, unimodal, and multimodal. Analysis of the monitoring data showed that transitory rainfall events were all effective, constant rainfall events were mostly effective (> 76%), unimodal rainfall events were 86.7% effective, and all multimodal rainfall events were effective, with the deepest infiltration depth. A three-step method was employed to establish thresholds for different types of rainfall. Firstly, cumulative rainfall exceeding 34 mm at a single time point was considered the most effective rainfall. Secondly, multimodal or constant rainfall with a constant value of Ac > 3.5 mm/3 h and a peak value of Ap > 9.5 mm/3 h was deemed the most effective rainfall. Lastly, a temperature difference Tr during rainfall below 7.7 °C was indicative of mostly ineffective rainfall. The model demonstrated an accuracy of 96%, as validated by existing monitoring data in the nearby loess area. This model has been demonstrated to be superior to previous model and is currently suitable for application in loess regions of China.

Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing

Climate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. Rainfall forecasting is considered an important tool that could help decisionmakers for managing water resources. Syria is facing several drought events that may affect its water resources. Due to the lack of available rainfall data from land stations in Syria after the civil war started in 2010 till now, one of the aims of this study is to depend on rainfall data from satellite images after correcting these data using the available land stations data. This study also aims to assess rainfall trends in Syria due to climate change and forecasting rainfall. The rainfall data were collected from 71 land stations from 1991 to 2009. Satellite images from Climate Data Record (CDR) were downloaded from 1983 to 2020 and ArcMap was used to extract rainfall data at locations of land stations. The extracted rainfall data from satellite images were corrected by land stations data from 1991 to 2009. Then Statistical Package for the Social Sciences (SPSS) program with Auto-Regressive Integrated Moving Average (ARIMA) models are used to fit and forecasting rainfall in Syria. The rainfall trends at the studied stations were analyzed for 40 years (1983–2022) and a forecast of one year is conducted. The results showed decreasing trends at all the stations that lead to a decrease the quantity of water received every year which could affect the water resources in Syria and agriculture and food security will be also affected. The results are consistent with Intergovernmental Panel on Climate Change (IPCC) reports that concluded a decrease in rainfall at southern and eastern Mediterranean regions. The results of the current study could help in the management of water resources in Syria and forecasting using ARIMA is recommended for rainfall predictions.

Rainfall prediction for data scares areas using metrological satellites in the case of the Lake Tana sub-basin, Ethiopia

Abstract In African nations with complex topographies, alternative rainfall estimation methods such as satellites are crucial. This study is aimed at predicting the spatial and temporal distribution of rainfall in the Lake Tana sub-basin from 1990 to 2020. A satellite-based rainfall estimate of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) was used with the same spanning period (1990–2020). The validation process employs point-to-pixel analysis, comparing CHIRPS estimates with observed data at specific gauge stations. The findings showed that CHIRPS had well-estimated rainfall incidence in the highland areas and significantly overestimated it in the lowland areas. The Mann–Kendall trends for January, June, and August indicate decreasing trends, while the Bega and spring seasons show notable declines. Regression analysis reveals a non-significant decrease in annual rainfall with the highest rainfall in the summer and relatively dry winters. In addition, the coefficient of variation value of 26.37% suggests a moderate level of variability around the mean annual rainfall. In conclusion, the CHIRPS satellite exhibited varied performance across the Tana Sub-basin, with site-specific discrepancies and notable inaccuracies at certain stations. The study underscores the importance of considering local factors and topography in satellite-based rainfall assessments, providing valuable insights for agricultural planning in the region.

Impacts of rainfall on peat fire during the dry season and wet dry season on degraded tropical peatland in South Sumatra, Indonesia

Peat fires on cleared and drained tropical peatlands continue to cause tremendous damage to their ecological functions, contribute to global warming, and reduce in health of local communities, where the fire incidents in 2019 and 2020 correlated with weather conditions. The research aims to determine and clarify the correlation between rainfall dynamics and the occurrence and severity of peat fires in degraded tropical peatlands in South Sumatra, Indonesia. The research was conducted by recording rainfall Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) data, surface and peat fuel, and peat fire area primer data from 2019 to 2020. The results show that two types of weather correlate with the size of the burned area in the Ogan Komering Ilir (OKI) regency. Temporal rainfall distribution, quantity, and duration a determining factor for the transition of surface fires to peat fires, especially in the role they play in the hydrology and moisture conditions of peat and surface fuels. This study will present the effect of rainfall in the dry and wet dry seasons on peatland fires and the extent of peat fires that occurred in two years.

Optimal allocation of soil and water resources in agriculture under the total regional evapotranspiration indicator: A case study in Guangping, China

There is a mismatch between crop water demand, the spatial and temporal distribution of natural rainfall, surface water sources, and arable land resources in the North China Plain. This has been compensated for by massive groundwater exploitation, and as a result, ground water levels have continuously declined and groundwater landing funnels have formed in some areas. Consequently, there is an urgent need for the rational allocation of soil and water resources. In this study, regional soil types and the corresponding crops in Guangping County, Hebei Province, China, alongside the regional annual average total water consumption were used to determine reasonable allocations for crop planting structures, with an aim of maximizing the water utilization efficiency and economic benefits while also achieving a regional groundwater dynamic equilibrium. Daily meteorological data from 1973 to 2021, including precipitation, temperature, sunshine hours, wind speed, and humidity, as well as crop data, including irrigation systems and crop growth status, were collected. Water consumption models for eight soil types and seven typical crops forming 56 calculation partitions were established to obtain annual average water consumption values for the different crops. The maximum economic benefit and water-use efficiency were then used as the objective functions and total water consumption as the control condition for the non-dominated sorted genetic algorithm-Ⅱ (NSGA-II), to optimize the allocation of soil and water resources in the region; the adjusted quantities for the different crops cultivated under different soil types in the study area were obtained. The areas growing sweet potato and vegetable-fruits were found to increase by 0.5255 km2 and 4.4452 km2, respectively, when compared to the pre-optimization period, whereas the areas with winter wheat-summer maize, millet, peanut, cotton, and summer maize decreased by 1.3481 km2, 0.1812 km2, 0.7316 km2, 2.0896 km2, and 0.62 km2, respectively. The economic benefit increased from 810.37 to 853.64 million yuan, while the water-use efficiency increased from 1.5426 kg/m3 to 1.5859 kg/m3. The results provide a basis for soil and water resource management in the region and a technical reference for similar areas to improve agricultural water-use efficiency and economic benefits.

Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia

Australia is a unique continent, surrounded by the ocean, and the majority of its catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia, whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The spatial and temporal distribution of mean annual rainfall and potential evaporation (PET) vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the daily GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves, and daily scatter plots; and (ii) performance metrics, including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models, respectively, were found to be ‘good’. There was no relationship found between the catchment area and the model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments, where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using the GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor-gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions—the Carpentaria Coast (CC) and the Tanami–Timor Sea Coast (TTS)—where ungauged areas comprised 51% and 43%, respectively. The mean gauged annual streamflow varied significantly across drainage divisions—230 gigalitres (GL) from the South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and lead to improvements in marine modelling systems and tools.

The analysis of fire hotspot distribution in Kalimantan and its relation- ship with ENSO phases

Kalimantan experiences fire hazards almost every year, which threaten the largest tropical forest in South- east Asia. Climatic conditions, such as increasing surface temperature and decreasing rainfall, become important es- pecially when El Nino Southern Oscillation (ENSO) occurs. Studies on fire are commonly conducted based on the climatic condition such as the dry or wet season, but those which focused on analysis of fire occurrences with the specific ENSO phases are still limited. This study aims to identify the spatial and temporal distribution of rainfall, land surface temperature, and soil moisture and analyses the distribution of hotspots in Kalimantan from 2014 to 2020 dur- ing different ENSO phases. The data used are Moderate Resolution Imaging Spectroradiometer (MODIS) for hotspot analysis, Global Precipitation Measurement (GPM) for rainfall analysis, MODIS Land Surface Temperature (LST) for surface temperature analysis and Soil Moisture Active Passive (SMAP) for soil moisture analysis. The methods used were descriptive and spatial analyses based on each ENSO phase, which were then combined to analyse the temporal and spatial distribution of fire, rainfall, LST and soil moisture. The temporal distribution shows a positive relationship between ENSO, rainfall, LST, soil moisture and hotspots with a confidence level of 90% in the dry months of August– October. Fire occurred in most parts of West and Central Kalimantan, associated with low elevation, organic soil types and agricultural peatland. The average trend of increasing hotspots is 17.4% in the El Nino phase and decreasing hot- spots by 84.7% in the La Nina phase during August–October in Kalimantan.

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49–0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67–0.95 for climate change.

Quantitative rainfall analysis of the 2021 mid-July flood event in Belgium

Abstract. The exceptional flood of July 2021 in central Europe impacted Belgium severely. As rainfall was the triggering factor of this event, this study aims to characterize rainfall amounts in Belgium from 13 to 16 July 2021 based on two types of observational data. First, observations recorded by high-quality rain gauges operated by weather and hydrological services in Belgium have been compiled and quality checked. Second, a radar-based rainfall product has been improved to provide a reliable estimation of quantitative precipitation at high spatial and temporal resolutions over Belgium. Several analyses of these data are performed here to describe the spatial and temporal distribution of rainfall during the event. These analyses indicate that the rainfall accumulations during the event reached unprecedented levels over large areas. Accumulations over durations from 1 to 3 d significantly exceeded the 200-year return level in several places, with up to 90 % of exceedance over the 200-year return level for 2 and 3 d values locally in the Vesdre Basin. Such a record-breaking event needs to be documented as much as possible, and available observational data must be shared with the scientific community for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming to identify and understand factors leading to such disaster. The corresponding rainfall data are therefore provided freely in a supplement (Journée et al., 2023; Goudenhoofdt et al., 2023).

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

AbstractThis paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study.

The Effect of the Climate Change Looming for Indian Summer Monsoon

In recent years, with global temperature rise, extreme weather and climate events, especially high temperature heat waves, floods and droughts, occur more frequently. Recent observational and simulation projections with paleoclimate research reported that monsoon rainfall would become uncertain in the forthcoming century. One of the major problem in predicting the ISM is the accumulation of uncertainties arising from the complex role of Anthropocene activities on the climate system. This, in turn, would result in indecisions in the projected Gross Domestic Product (GDP), sustainable growth, and economy. About 21% of the Indian GDP is contributed by Agriculture (arguably the backbone of India's Economy) which itself highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper reviews the projection of the Indian Summer Monsoon (ISM) in looming climate change. Evaluating the uncertainties in future monsoon behavior predictions would facilitate informed decision-making while formulating strategies for a sustainable Economy and energy policy. Reliable predictions of future ISM in the context of climate change are required while formulating adaptation strategies for sustainable growth and water availability.

Soybean phenological changes in response to climate warming in three northeastern provinces of China

ContextSoybean plays an important role in agricultural production in three northeastern provinces of China; furthermore, climate change, particularly temperature and rainfall changes, can significantly regulate soybean phenological periods. Research QuestionCurrent research on the spatial and temporal characteristics of soybean phenology and its relationship with climate change in the three northeastern provinces of China is still inadequate. MethodsIn this study, phenological and meteorological data from 26 agrometeorological stations in northeastern China were used to evaluate temporal and spatial changes and their correlations with soybean temperature, rainfall, phenological stage, and growth duration over the long term in three northeastern provinces. ResultsOn average, 22 of the 26 stations showed increases in temperature, and 15 stations showed increases in rainfall during the survey stage. All of soybean growth nodes were delayed, except R8, which advanced by 0.28 days, and all soybean growth durations were shortened except for during the V3-R1 period. In addition, temperature was negatively correlated with growth duration, while rainfall was positively correlated with growth duration. Further, the spatial and temporal distribution of temperature and rainfall showed that an increase in temperature led to an increase in thermal conditions and a significant northward and eastward expansion of the cumulative temperature zone, with the most significant increase in temperature occurring in most areas of Heilongjiang, followed by Liaoning. ConclusionsSoybean planting practices in the three eastern provinces should be adjusted to medium- and late-maturing varieties, which can adapt to climate change and mitigate its effects during the phenological period, due their tolerance to high heat. SignificanceThese findings fill a knowledge gap for understanding the relationship between soybean phenological changes and climate change in the three northeastern provinces.

Assessment of Rainfall Variability over Karnataka state in India

Karnataka state situated in the southern part of India is surrounded by a long coast line and western ghat mountain regions on the western side. The rainfall distribution shows a large variability in different parts of the state. North interior part in the state receives less rainfall as compared to the south interior region, which receives moderate rainfall. Although coastal Karnataka witnesses a high rainfall and is one of the most important regions in the country, in recent years the state has faced drought because of less rainfall which may be attributed to the regional climate change. Because of the decreasing trend in rainfall almost all the important sectors like agriculture and water resources have been affected a lot in Karnataka. In this work, assessment of the spatial and temporal distribution of monsoon rainfall is presented in terms of the climatology, variability, trend etc. The time series analysis also indicated the increasing trend of rainfall as a whole but the trend in recent decades is very less as compared to the long term. The El-Niño Southern Oscillation (ENSO) and the regional rainfall pattern relation is also explored. The increase in heavy rainfall in recent times is observed from the analysis. This study can be useful in addressing regional climate change, understanding the local and large scale variability impacts and in can be a good input for preparing the policy formulation and pro-active disaster management.

Assessing the performance of techniques for disaggregating daily rainfall in South Africa

Design flood estimation (DFE) methods are used to limit the risk of failure and ensure the safe design of hydrological and related infrastructure, and to inform water resources management. In order to improve DFE methods which are based on event or continuous simulation rainfall–runoff models, it is generally necessary to use sub-daily rainfall data. However, sub-daily rainfall gauges are relatively sparse and have shorter record lengths than daily rainfall gauges in South Africa. Rainfall temporal disaggregation (RTD) techniques can be used to produce finer resolution data from coarser resolution daily rainfall data. Several RTD approaches have been developed and are used in South Africa. However, there is a need to review and assess the performance of the available RTD methods. This paper contains an overview of selected RTD approaches and the performance of the methods at selected sites in South Africa, for disaggregating daily rainfall into 15-min intervals. Temporal distributions of rainfall were represented by dimensionless Huff curves, which served as the basis for comparison of observed and disaggregated rainfall. In a pilot study it was found that the SCS-SA (Soil Conservation Service model South Africa) distributions and the Knoesen model approaches performed considerably better than the other approaches. The RTD approaches were further assessed using data from 14 additional rainfall stations. For the additional stations, the Knoesen model and SCS-SA disaggregated rainfall generally provided the most realistic temporal distributions.

Parametric models of rainfall temporal distribution at the Yabú meteorological station in Villa Clara province, Cuba

La obtención de patrones de distribución temporal de la lluvia mediante curvas de masa sintéticas o hietogramas es un recurso aplicado a nivel internacional para elaborar la tormenta de diseño. En el presente artículo se realizó un análisis de 243 eventos lluviosos convectivos de más de 25 mm ocurridos en la estación meteorológica Yabú de la provincia Villa Clara, Cuba, en el periodo comprendido desde 1989 hasta 2019, con el objetivo de elaborar los hietogramas sintéticos característicos de la estación mediante el método de Huff. Se categorizaron las lluvias y se identificaron tres tipos SC-T1, SC-T2, SC-T3 de acuerdo con la relación entre el tiempo de duración del aguacero y el tiempo donde ocurren las mayores intensidades. Las curvas de masa obtenidas para cada probabilidad de ocurrencia se expresaron en hietogramas adimensionales, los cuales fueron ajustados a los modelos paramétricos de Sherman, Wenzel y uno propio elaborado por los autores, que describen la distribución de las intensidades con respecto al tiempo. Este resultado permitió obtener las curvas de intensidad-frecuencia-duración para cada tipo de aguacero, y cada probabilidad de ocurrencia. Se lograron elevados coeficientes de correlación de Pearson y el modelo elaborado por los autores mostró el mejor desempeño. Los resultados indican que los hietogramas adimensionales obtenidos reflejan satisfactoriamente el fenómeno de lluvias convectivas en la localidad de estudio

Guidance for parameterizing post‐fire hydrologic models with in situ infiltration measurements

AbstractWildfire can alter soil‐hydraulic properties, often resulting in an increased prevalence of infiltration‐excess overland flow and greater potential for debris‐flow hazards. Mini disk tension infiltrometers (MDIs) can be used to estimate soil hydraulic properties, such as field‐saturated hydraulic conductivity (Kfs) and wetting front potential (Hf), and their spatial variability following wildfire. However, the small (point‐scale) footprint of MDI measurements makes it challenging to use these data to parameterize hydrologic models at the hillslope and watershed scales where hydrologic hazards, such as debris flows, initiate. Here, we designed numerical experiments to estimate spatially constant or watershed‐scale effective hydrologic parameters (EHPs) that approximate the response of spatially variable hydrologic parameters with distributions derived from MDI measurements at five sites in the southwestern United States. We found that it is possible to define EHPs for both Kfs and Hf based on the MDI measurements that lead to reasonable approximations of run‐off hydrographs at the outlets of small watersheds (&lt;1 km2). We found that watershed EHPs are functions of rainfall characteristics, although they are most sensitive to rainfall intensity and relatively less sensitive to the temporal distribution of rainfall. EHPs are lower than the arithmetic mean of the MDI measurements and are better approximated by the median or geometric mean of the MDI measurements, particularly for storms with recurrence intervals of approximately 1 year or less that commonly initiate post‐fire debris flows. This work demonstrated that using the proposed upscaling method to estimate watershed‐scale EHPs, as opposed to approximating EHPs based on the arithmetic mean of the MDI measurements, improved the ability of a hydrologic model to identify storms that are likely to produce debris flows. Results improved our ability to link point‐scale MDI measurements and watershed‐scale EHPs in post‐fire settings and helped guide our ability to use MDI data to parameterize post‐fire hydrologic models.

Application of statistical and geostatistical approaches in temporal and spatial estimations of rainfall

Abstract This study is the first study that worked on the temporal and spatial distributions of annual rainfall (Pyear) and maximum 24-h rainfall (Pmax24h) in the Semnan province. For this purpose, different statistical distributions were used to estimate the temporal Pyear and Pmax24h in the Semnan province. Six synoptic stations across the province were studied and all stations had complete Pyear and Pmax24h data. Different return periods were studied. The goodness fit test of statistical distributions for Pyear showed that about 67% of the stations follow the Generalized Pareto (GP) distribution. Considering the Pmax24h, 50% of the stations follow the GP distribution, and for the ratio of Pmax24h to Pyear, 50% of stations follow the Generalized Extreme Value (GEV) distribution. The spatial distribution of Pyear and Pmax24h showed that in all return periods, by moving to the southeast of the province, precipitation amounts decreased. While moving toward the Shahmirzad station, the amounts of Pyear and Pmax24h increased. Also, there was a logical relationship between the Pyear and Pmax24h. Consequently, the minimum value and the maximum value of the R2 coefficient in different return periods were equal to 0.992 and 0.980, respectively.

Spatial and temporal distributions of temperature and rainfall on tropical islands of Vietnam

Abstract This study presents the spatial-temporal distribution of temperature and rainfall on the tropical islands of Vietnam using observed data, and historical and future climate data extracted from the ACCESS-ESM1.5. Regarding spatial distribution, the average temperature increased from North to South while rainfall did not vary significantly. Regarding temporal distribution, this research employed the Mann–Kendall test at a 5% significance level to analyze the trend of climate change. According to the calculation result, only annual temperature tended to increase in all stations but at different rates. Other trends of climate change in maximum, temperature, minimum temperature, annual rainfall, and maximum 1-day rainfall are inconsistent and only found significant in islands with a long-observed period. Using 1970–2000 as the reference period with all R2 above 0.94, this research extracted and predicted climate trends in the period of 2021–2100 for areas above 1 km2 based on four scenarios which are SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5. The projected maximum and minimum temperatures have shown increasing trends whereas the change in annual rainfall is not clear. The results of this study provide important implications for climate change adaptation scenarios and territorial planning of tropical islands.

Sensitivity of cloud microphysics on the simulation of heavy rainfall in WRF- a case study for the 7–10 August 2019 event over Kerala, India

This work investigates the mechanism of the extremely heavy rainfall event during 7–10 August 2019 in Kerala. As the skill of WRF model for simulating the heavy rainfall strongly depends on the choice of cloud microphysics (CMP), sensitivity analysis is performed using cloud resolving WRF simulations with different CMP schemes to assess their performance. Total five sensitivity experiments are conducted by varying CMP schemes namely Thompson, Goddard, WSM6, Kessler and Purdue-Lin. Model results along with the fifth generation European Reanalysis (ERA5) data indicate that the heavy rainfall occurred in association with the monsoon depression, intensification of off-shore trough, and low-level jet (LLJ) phenomena and associated moisture transport and convergence. Our results clearly suggest that the heavy rainfall occurred during 7–10 August 2019 was less intensive compared to the August 2018 event. One of the prime reasons for meagre intensity is the presence of the upper tropospheric cyclonic flow over southeast Arabian Sea off Kerala, which reduced the intensity of low-level westerly flow, moisture transport and convection in 2019 event relative to the 2018 event. Comparison of simulated rainfall against station and gridded rainfall data indicate that the spatial and temporal variations of rainfall could be reproduced except that the model slightly over-estimated rainfall at a few stations in the central parts of Kerala. Out of the five CMP schemes, Thompson scheme simulates large amounts of cloud, rain, and snow during the period of peak rainfall with relatively low amounts of ice. Moreover, Thompson indicates a high warming in the 4–6 km layer due to large snow production while Lin and Goddard configurations exhibit altitude shift in this pattern i.e., more heating in the upper troposphere (9–15 km layer) due to producing more solid hydrometeor distributions and associated freezing processes. Among the CMP schemes, Thompson followed by Goddard better simulated the spatial and temporal distribution of rainfall as they produced the strongest convergence, CAPE and vertical motions compared to WSM6, Lin and Kessler schemes. Moreover, the simulated hydrometeor mixing ratios in the 2019 heavy rainfall event are less by 60–80% as compared to that of 2018 due to differences in the synoptic settings.