Daily Rainfall Amounts Research Articles

Identifying rainfall patterns using Fourier series: A case of daily rainfall data in Sarawak, Malaysia

The a im for this paper is to fit Fourier series on the daily rainfall data to describe the rainfall patterns and to identify the time frame of the wettest and the driest period for the studied stations. The estimated parameter of the Fourier series in modelling the mean daily rainfall amount received were obtained using the method of Generalized Linear Model and the performance was evaluated using the analysis of deviance. Following the objective, the daily rainfall data ranges from 41 to 58 years over four stations in Sarawak namely Kuching, Miri, Bintulu a nd Sibu are studied. Results showed three harmonics is appropriate in modelling the mean daily rainfall received for Kuching, Sibu and Miri stations. However, only two harmonics was found to be fitted the mean daily rainfall amount received significantly for Bintulu station. The wettest month of the studied stations wa s estimated to occur in late December to early January, whereas the driest month is in March for Miri station, April for Bintulu, July for Kuching a nd June for Sibu station. The information gained from this study can be used to monitor the occurrence of flood and drought events in the area so that future action can be taken to reduce the impacts.

Spatial variability of south Asian summer monsoon extreme rainfall events and their association with global climate indices

The study explores the spatial variability and related characteristics of south Asian summer monsoon rainfall and extreme rainfall events based on APHRODITE rainfall products. An attempt was also made to investigate their association with global climate indices. Most of the South Asian regions are vulnerable to extreme rainfall events and many of these lead to major hydrological disasters. Such events are adversely affecting flora and fauna, causing large-scale damage to the livelihood and economy. We have analysed the characteristics of extreme rainfall events during the summer monsoon season for 65 years (1951–2015). The statistical method based on the percentile of daily rainfall amount (>99th percentile) was used to identify the extreme rainfall zones. We have also attempted to explore the spatio-temporal features and heterogeneity of these extreme events at a finer spatial resolution. We examined the linear trend of these extreme rain events on the spatial domain and found that extreme rainfall decreases over northcentral Indian and southwestern coastal belts. An increase in the frequency and amount of rainfall from extreme rain events were observed in the western coastal belts between 16° to 20° N and central Indian regions along with the coastal areas of Bangladesh and Myanmar. The linear trends of extremes exhibit remarkable differences from the trends of seasonal rainfall. The results from the probability density function (PDF) analysis are also supporting the trend analysis of mean and extremes in all the five regions. It was also observed a distinct shift in the PDF curve during the pre and post 1980 periods. Moreover, we have analysed the relationship of various climate indices (Niño3.4, Modoki, DMI, PDO, AMO, and AZM) with the mean and extreme rainfalls in addition to the spatial trends. Most of the indices show a significant inverse relationship with mean and extreme rainfalls. The areas of significant correlation are less in the case of extreme rainfall while comparing with that of the mean rainfall.

Changes in precipitation patterns in Houston, Texas

There has been an alarming increase in the frequency of major flooding events along the Gulf Coast over the last three decades, primarily due to events of unprecedented, or extreme, rainfall. Using data from 63 rain gauges maintained by the Harris County Flood Control District's Flood Warning System (HCFCD FWS), this study examines the changes in daily precipitation amounts in the highly urbanized city of Houston, Texas, USA. The potential shift in annual precipitation patterns over a period of three decades (1989-2018) was examined by investigating the numbers of dry and wet days as well as annual precipitation totals over the study period. Wet days were then further scrutinized based on daily rainfall amounts (e.g., R10, R20, R30, R40, R50, R100) to determine if extreme events are beginning to dominate annual rainfall amounts. Trends were analyzed for statistical significance temporally using the Mann-Kendall and Sen's slope methods and for spatial trends using GIS applications. The results indicate a statistically significant increase in extreme rainfall at the expense of light, moderate, and heavy rainfall over time. The only negative relationship is found in dry days. The most statistically significant trends exist in the 99th percentile, maximum, and R100 parameters with p-values of 0.07, 0.08, and 0.11, respectively. There has been rapid growth and intensive development in the Houston area in recent decades that continues to this day, and land cover change has been significant as 12.6% of Harris County changed to one of four National Land Cover Database (NLCD) Urban classes (e.g., developed: barren, low intensity, medium intensity, high intensity). This confirms that urbanization has continued to increase while total vegetative and wetland coverage has decreased. The findings of this study provide essential guidance for city and state planners and engineers.

Synoptic Analysis for the Daily Rains of the City of Baghdad

To know the reasons for changing the daily rainfall amount, cases study were chosen for Baghdad station by specifying the maximum and minimum daily rain for the months of the rainy season for each year of the study period (2007-2018). The behavior of daily rainfall for the rainy season varying temporally with maximum (89 mm) and minimum (0.1mm). Synoptic analysis of weather chart for pressure levels (300, 500, 700, 850, and surface) was performed. Visible images of satellites were used to determine the quantities of the cloud for the selected cases. The results showed that synoptic analysis in all cases occurs due to the presence of a jet stream in the upper atmosphere, accompanied by the presence of a basin at the level of 300 hectopascals with an enhancement of relative humidity exceeding 70% at the level of 700 hectopascals, but the presence of the front makes the amount of rain greater in the absence of it. At the level of 850 hectopascals and the surface, the depressions that affect Iraq also differ in their severity and the amount of moisture loaded.

The role of extreme rain events in driving tree growth across a continental‐scale climatic range in Australia

Rainfall regimes in many parts of the world have become increasingly dominated by fewer, but more extreme, rainfall events. Understanding how tree growth responds to changes in the frequency and intensity of rain events is critical to predicting how climate change will impact on forests and woodlands in the future. In this study, we used five tree‐ring records of the native Australian conifer Callitris columellaris that span a large (> 20°) latitudinal and climatic gradient from the mesic (tropical) north to the xeric (semi‐arid) south of Australia to investigate how inter‐annual and spatial variation in the delivery of rainfall (the intensity and frequency of rain events) influences tree growth. In semi‐arid biomes (~300–400 mm rainfall annually), tree growth is most strongly related to the amount of rainfall from heavy (> 75th percentile) rain days or the number of extreme (> 90th percentile) rain days, regardless of differences in the seasonal distribution and average intensity of rainfall among sites. Our findings also indicate that there is likely a minimum threshold amount of daily rainfall (~5 mm) that is required to stimulate tree growth in the semi‐arid zone. In contrast, in the tropics (> 800 mm annual rainfall), inter‐annual variation in growth is best explained by total growing season rainfall or the number of rain days > ~5 mm (~50th percentile of rain days) rather than extreme rainfall. Our findings indicate that not all rain events are important for driving tree growth, which has important implications for interpreting climatic signals in tree rings. Our findings also indicate that projected increases in the intensity of extreme rain events are likely to have contrasting impacts on tree growth across biomes, with greater and positive impacts on growth in semi‐arid biomes and potentially negative impacts on growth in tropical biomes of Australia.

Daily Rainfall Estimate by Emissivity Temporal Variation from 10 Satellites

AbstractRainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

Estimating the average daily rainfall in Thailand using confidence intervals for the common mean of several delta-lognormal distributions.

The daily average natural rainfall amounts in the five regions of Thailand can be estimated using the confidence intervals for the common mean of several delta-lognormal distributions based on the fiducial generalized confidence interval (FGCI), large sample (LS), method of variance estimates recovery (MOVER), parametric bootstrap (PB), and highest posterior density intervals based on Jeffreys’ rule (HPD-JR) and normal-gamma-beta (HPD-NGB) priors. Monte Carlo simulation was conducted to assess the performance in terms of the coverage probability and average length of the proposed methods. The numerical results indicate that MOVER and PB provided better performances than the other methods in a variety of situations, even when the sample case was large. The efficacies of the proposed methods were illustrated by applying them to real rainfall datasets from the five regions of Thailand.

Determination of Harvest Maturity for Mango (<i>Mangifera indica</i> L.) Fruit by Non-Destructive Criteria

Haden, Kent, Palmer, and Keitt mango varieties were studied to establish the relationship of harvest time to 1) seasonal accumulated day-degrees or heat units (˚C), 2) daily rainfall amount (mm), and 3) physical fruit development attributes in order to fix maturity standards for export and local markets. Randomized Complete Block Design with four replications was used. In each case of Haden, Kent, Palmer, and Keitt varieties, physical fruit development attributes established as standard harvest maturity values were: weight (640 g, 836 g, 837 g, and 1104 g, respectively), length (16.31 cm, 16.19 cm, 21.22 cm, and 19 cm, respectively), width (30.97 cm, 33.47 cm, 30.86 cm, and 35.91 cm, respectively), volume (598 cm3, 807 cm3, 772 cm3, and 959 cm3, respectively), density (1.147 g/cm3, 1.076 g/cm3, 1.084 g/cm3, and 1.189 g/cm3, respectively), and indentation (0.25 cm, 0.49 cm, and 0.50 cm, respectively). The intensity of grooves around the stylar-scar end of Palmer fruits was studied and used as maturity index. Index values of 0.075 mls, 0.150 mls, 0.425 mls, and 0.116 mls, respectively, for Haden, Kent, Palmer, and Keitt varieties were recorded as latex exuded at harvest since these values tallied with the other physical harvest maturity index values, and also with those of rain fall and temperature values. Temperature, rainfall, and physical characteristics are therefore important non-destructive criteria for fixing maturity index values for mango fruits.

Spatio-temporal analysis of flood data from South Carolina

To investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

Confidence intervals for the common coefficient of variation of rainfall in Thailand.

The log-normal distribution is often used to analyze environmental data like daily rainfall amounts. The rainfall is of interest in Thailand because high variable climates can lead to periodic water stress and scarcity. The mean, standard deviation or coefficient of variation of the rainfall in the area is usually estimated. The climate moisture index is the ratio of plant water demand to precipitation. The climate moisture index should use the coefficient of variation instead of the standard deviation for comparison between areas with widely different means. The larger coefficient of variation indicates greater dispersion, whereas the lower coefficient of variation indicates the lower risk. The common coefficient of variation, is the weighted coefficients of variation based on k areas, presents the average daily rainfall. Therefore, the common coefficient of variation is used to describe overall water problems of k areas. In this paper, we propose four novel approaches for the confidence interval estimation of the common coefficient of variation of log-normal distributions based on the fiducial generalized confidence interval (FGCI), method of variance estimates recovery (MOVER), computational, and Bayesian approaches. A Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths of the confidence intervals. In terms of coverage probability, the results show that the FGCI approach provided the best confidence interval estimates for most cases except for when the sample case was equal to six populations (k = 6) and the sample sizes were small (nI < 50), for which the MOVER confidence interval estimates were the best. The efficacies of the proposed approaches are illustrated with example using real-life daily rainfall datasets from regions of Thailand.

Parameter Estimation of Extreme Rainfall Distribution in Johor using Bayesian Markov Chain Monte Carlo

Heavy rainfall and the associated floods occur frequently in the Malaysia and have caused huge economic losses as well as massive impact on agriculture and people. As a consequence, it is necessary to understand the distribution of extreme rainfall in order to improve the managements in a country. Thus, the aim of this paper is to determine the best method to estimate parameters of Generalized Extreme Value (GEV) distribution that represent the annual maximum series (AMS) data of daily rainfall by using method of moments (MOM), maximum likelihood estimators (MLE) and Bayesian Markov Chain Monte Carlo (MCMC). The daily precipitation rainfall amount of 12 rain gauge stations in Johor from year 1975 to 2008 were used and the AMS data of each year were fitted with GEV distribution. Based on goodness-of-fit tests, namely Relative Root Mean Square Error (RRMSE) and Relative Absolute Square Error (RASE), the performances of three parameters of GEV distribution estimated by MOM, MLE and Bayesian MCMC were compared for each station. The results indicated that Bayesian MCMC method was performed better than MOM and MLE method in estimating the parameters of GEV distribution.

Fitting statistical distribution of extreme rainfall data for the purpose of simulation

&lt;span&gt;In this study, several types of probability distributions were used to fit the daily torrential rainfall data from 15 monitoring stations of Peninsular Malaysia from the period of 1975 to 2007. The study of fitting statistical distribution is important to find the most suitable model that could anticipate extreme events of certain natural phenomena such as flood and tsunamis. The aim of the study is to determine which distribution fits well with the daily torrential Malaysian rainfall data. Generalized Pareto, Lognormal and Gamma distributions were the distributions that had been tested to fit the daily torrential rainfall amount in Peninsular Malaysia. First, the appropriate distribution of the daily torrential rainfall was identified within the selected distributions for rainfall stations. Then, data sets were generated based on probability distributions that mimic a daily torrential rainfall data. Graphical representation and goodness of fit tests were used in finding the best fit model. The Generalized Pareto was found to be the most appropriate distribution in describing the daily torrential rainfall amounts of Peninsular Malaysia. The outputs can be beneficial for the purpose of generating several sets of simulated data matrices that mimic the same characteristics of rainfall data in order to assess the performance of the modification method compared to classical method. &lt;/span&gt;

Estimation and analysis of the effective rainfall In Tabuk area – Saudi Arabia

this study examined the effective rainfall in Tabuk area by applying the Lang rainfall factor, De Martonne Index and the (UNEP) arid index. The study methodology was based on a statistical tests using the Normality test (Shapiro-Wilk), the Homogeneity test (Leven test), the ANOVA (LSD test) of the monthly mean of rainfall, the daily maximum rainfall and the total number of rainfall days. The multiple comparison (Tehmane’s Test) had been applied between the studied stations. The results of the Shapiro-Wilk test shows that the distribution of monthly rainfall averages follows normal distribution in all stations except stations Tabuk and Al Bad’ and that the maximum daily rainfall distribution at Duba, Shuw?q, Al Kurr stations is also normal distribution. The Leven test results showed that the significance level was greater than 0.05 and the Leven Test was greater than 0.05 for the average monthly precipitation, for the daily maximum rainfall and for the number of rain days and it indicates the homogeneity of the rainfall variances in the studied stations.The ANOVA analysis of the averages and maximum daily rainfall, shows that the significance level is greater than 0.05 and the LSD test is greater than 0.05 indicating that the differences are not significant. While the LSD was greater than 0.05 for the number of rain days, indicating that the differences between the number of rain days and their distribution at the studied stations are statistically significant differences, and therefore there is no homogeneity in their differences. The results of the Tamhane’s Test of multiple comparison confirmed that the mean level of monthly mean rainfall and daily maximum rainfall is greater than 0.05 indicates that the differences between the monthly rain averages of and the daily maximum rainfall amounts are not significant for 22.2% of total comparisons.The effective rainfall estimate of Lang’s rainfall index shows that the threshold of effective mean rainfall is between 1.3 and 27.4 mm and that the threshold of maximum daily rainfall is 154.1 mm at the station (Al Bad’). Also, the effective rainfall estimate by the DeMartonne index shows that the threshold of average effective rainfall is between 1.2 and 15.2 mm and that the threshold of maximum daily rainfall is 80.4 mm at the station (Al Bad’). Therefore, the effective rainfall estimate by the UNEP index shows that the threshold of average effective rainfall is between 1.8 and 30.3 mm and that the threshold of maximum daily rainfall is 130.5 mm at the station (Al Bad’). Finally, the effective rainfall estimate by the difference between the rainfall and Pan Class“A” Evaporation shows that the threshold of average effective rainfall is between 1.2 and 25.8 mm and that the threshold of maximum daily rainfall is 137.1 mm at the station (Al Bad’).- Key Words:Effective rainfall average, Effective maximum daily rainfall , Number of rainy days, Statistical tests, Lang rainfall factor, DeMartonne index, UNEP arid index, Tabuk area, Saudi Arabia.

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

In this paper, a Markov Chain Analogue Year model is used to describe the occurrence process of daily rainfall and the amount of daily rainfall on wet days is presented using Gamma, Exponential and mixed Exponential distributions. Combining the occurrence and the amount process models together, we developed Markov Chain Analogue Year Gamma model, Markov Chain Analogue Year Exponential model and Markov Chain Analogue Year Mixed Exponential model. The result shows that all the three models perform very well in the simulation of precipitation process in the study area and their performances are nearly the same with a slight difference. Using the results of these models, option prices for Teff and wheat crops for different months in their growing season are calculated. The calculated option price has an excellent accuracy with maximum absolute error of 0.54.

Calibration and validation of rainfall erosivity estimators for application in Rwanda

Rainfall erosivity is one of the most important erosion factors in tropical humid areas including Rwanda. Its current application in erosion modelling is often restricted to the use of externally validated erosivity indices based on annual or monthly data, even though daily rainfall data have become more readily available. The present study aimed to calibrate and validate six different models predicting monthly and annual rainfall erosivity from daily, monthly and annual precipitation records. The reference dataset constituted mid-term average (1980–1989) monthly REI30 erosivities reported by Ryumugabe and Berding (1992) at five stations from mid and high altitude zones of Rwanda. The analysis is partly supported by data collected during a short-term field experiment with bare erosion plots at Tangata, located in the northern highlands of Rwanda. Rwanda’s mid altitude zone exhibits a bimodal rainfall erosivity, reflecting both short and long rains. Analysis of this reference dataset revealed moderate to strong linear relations of monthly rainfall erosivity to monthly rainfall amounts. The erosivity density, retrieved from the REI30 data, proved strongly variable by season, month and station. The field experiment showed a stronger correlation of event-based soil losses to rainfall amount times intensity than to rainfall amount. Since no unique rainfall amount threshold value for erosive rainfall events could be retrieved, total daily rainfall amount values were used for developing the erosivity models (estimators).Both regionally (all stations) and locally (one station) calibrated models were tested. At regional scale, estimators with a daily rainfall support performed best. The daily power function adopting monthly-calibrated coefficients (Model 1 by Richardson et al., 1983) proved most suited to capture the irregular patterns in erosivity densities, reported at most stations. In case only monthly rainfall data are available, the power function at monthly temporal resolution (model 4 by Wu, 1994) is to be recommended. Variable performances between stations are explained by the diversity in rainfall generators active at short distances in Rwanda west of Kigali. A local calibration of the selected models confirms the improved performance obtained with daily input data, even when monthly varying model coefficients are replaced by a seasonal function (Yu and Rosewell, 1996), except for Kigali. At this latter station, the power (Wu, 1994) and linear (model 3 by Moore, 1979 or Zhou et al., 1995) functions with monthly support perform best. At Gisenyi, a linear model 5 (Angulo-Martínez and Beguería, 2009) using rainfall amount and number of wet days in the month performs well. Differences in performance reflect the variable capacity of the different models to deal with either seasonal or irregular fluctuations in erosivity density, as well either outstanding or limited differences in erosivity between both rainfall seasons within Rwanda. This frequently leads to over- or underestimations of rainfall erosivity in one of the seasons. The models however perform very well when used to estimate annual erosivity. The monthly power model 4 distinctly outperformed all other models, even those at daily rainfall support, with a prediction quality that is slightly better than a linear regression based on the modified Fournier index (MFI), using all stations except for Kigali. This confirms the power of the MFI as a rainfall erosivity index, but also highlights the importance of this regional calibration within Rwanda and the need for at least two different regression equations.

Option pricing of weather derivatives based on a stochastic daily rainfall model with Analogue Year component

In this study, we analyzed option pricing of rainfall derivatives based on stochastic daily rainfall model. We used Markov Chain Analogue Year model (MCAY) in order to describe occurrence process of daily rainfall. We have included the Analogue Year (AY) component in the Markov Chain (MC), which is a new component incorporated in this study and pricing rainfall derivatives. The inclusion of AY in the MC, provides excellent description of the occurrence process of daily rainfall. The amount of daily rainfall on wet days is obtained using Mixed Exponential distribution because it has the advantage of a better representation of extreme events. Combining the occurrence and amount model, we obtained Markov Chain Analogue Year Mixed Exponential model (MCAYMEM). Daily rainfall data from 2005 to 2017 were taken from Ethiopia National Meteorology Agency (ENMA) in order to assess the model performance. Based on the results of the daily rainfall models, we calculated an option price for different months. The price calculated using MCAYMEM gave an excellent result compared to the price calculated using MCMEM. This accuracy is mainly because of AY component included in the MC in the modeling of occurrence process.

Selecting “the best” nonstationary Generalized Extreme Value (GEV) distribution: on the influence of different numbers of GEV-models

ABSTRACT The selection of an appropriate nonstationary Generalized Extreme Value (GEV) distribution is frequently based on methods, such as Akaike information criterion (AIC), second-order Akaike information criterion (AICc), Bayesian information criterion (BIC) and likelihood ratio test (LRT). Since these methods compare all GEV-models considered within a selection process, the hypothesis that the number of candidate GEV-models considered in such process affects its own outcomehas been proposed. Thus, this study evaluated the performance of these four selection criteria as function of sample sizes, GEV-shape parameters and different numbers candidate GEV-models. Synthetic series generated from Monte Carlo experiments and annual maximum daily rainfall amounts generated by the climate model MIROC5 (2006-2099; State of Sao Paulo-Brazil) were subjected to three distinct fitting processes, which considered different numbers of increasingly complex GEV-models. The AIC, AICc, BIC and LRT were used to select “the most appropriate” model for each series within each fitting process.BIC outperformed all other criteria when the synthetic series were generated from stationary GEV-models or from GEV-models allowing changes only in the location parameter (linear or quadratic). However, this latter method performed poorly when the variance of the series varied over time. In such cases, AIC and AICc should be preferred over BIC and LRT. The performance of all selection criteria varied with the different number of GEV-models considered in each fitting processes. In general, the higher the number of GEV-models considered within aselection process, the worse the performance of the selection criteria. In conclusion, the number of GEV-models to be used within a selection process should be set with parsimony.

Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

Predictability of the rainy season onset date in Central Highlands of Vietnam

AbstractIn this study, different criteria to determine the rainy season onset date (RSOD) and its predictability in the Central Highlands (CH) of Vietnam are investigated. Using daily rainfall amounts from 10 meteorological stations for the period 1981–2015, four criteria to determine the RSOD were tested in order to select the one that most reasonably depicts the climatology of the RSOD over the CH. Results show that the RSOD varies strongly from year to year. In the long‐term mean, the onset starts first in southern parts of the CH and then progresses to the northern parts. The earliest onset date is around the beginning of April, and the latest in the second half of May. The average RSOD is on 28 April with a standard deviation of 14 days. Thus, the RSOD is distinct from the summer monsoon onset, namely, leading the mean summer monsoon onset by about 3 weeks in some years. In terms of remote influences, the RSOD in the CH has a high correlation with the El Niño–Southern Oscillation phenomenon, with most RSODs being later during El Niño years, while being earlier during La Niña years. The RSOD in the CH also shows high correlations with sea surface temperatures (SSTs), 850‐hPa zonal winds (U850) and mean sea level pressures (PMSL) over certain regions of the Pacific and Indian Oceans. Building on these relationships, the RSOD can be successfully predicted using large‐scale fields of SST, U850 and PMSL as predictors in two different approaches, namely the field mean and principal component analysis. This suggests that the overall approach can also be applied in predicting the RSOD in the CH on sub‐seasonal to seasonal timescales.

Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

This paper presents an assessment of the feasibility of using artificial neural networks (ANNs) with different input and output configurations to simulate streamflows for Brazilian Atlantic Rainforest basins. Hydrological data consisted of potential evapotranspiration (PET), rainfall (P), and streamflow (including runoff and baseflow) daily series (32 years in extent) of 12 watersheds of the Itapemirim River Basin (IRB). As a novelty, the input parameters for the ANNs were chosen basing on the statistical correlation between rainfall and flow data. Daily rainfall amounts until the third antecedent day were strongly correlated to runoff and streamflow. Accumulated rainfall until the 90 antecedent days were strongly correlated to baseflow and streamflow. Also as novelty, the ANNs were trained, for the downstream watershed, considering three different input and output configurations. The 1st configuration (ANNP) simulates daily streamflow considering input variables only related to rainfall. The 2nd (ANNP-PET) also simulates daily streamflow, but considers the differences between precipitation and potential evapotranspiration as input variables. The 3rd (ANN*P_streamflow) also considers input variables only related to rainfall, but the outputs were daily runoff and daily baseflow, which were used to later calculate total streamflow. Better streamflow simulations were provided by ANNP_streamflow, followed by ANNP-PET and ANNP. However, the three ANNs performances were quite similar. Thus, we indicate the use of ANNP for regions with lack of meteorological data (such as IRB) due to the need for fewer input parameters (only rainfall) and no need of splitting runoff and baseflow. After, the trained ANNs were applied to simulate the streamflow of each watershed. The results show that all the ANNs were able to satisfactorily simulate the streamflow of 11 watersheds, with better performances for the largest ones.