Daily Rainfall Measurements Research Articles

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts

Study regionThis study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focusThis study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the raw numerical weather prediction (NWP) provided by Weather Research and Forecasting model (WRF): quantile mapping bias correction (QM) and random forest regression (RF). Rainfall forecasts, updated with two pre-processing methods, WRF-QM and WRF-RF, were evaluated against daily rainfall measurements from HII’s stations in each subcatchment. Six hydrological re-forecasting experiments were conducted using a hydrological model to compare runoff forecasts with and without preprocessing method as well as with in-situ rainfall, climatology and persistence benchmark. We assessed rainfall and runoff predictions during training (2016–2019) and testing periods (2020–2021). New Hydrological Insights for the RegionUtilizing pre-processing methods in rainfall prediction enhances the accuracy for raw rainfall and runoff predictions. The WRF-QM and WRF-RF methods improved rainfall prediction by 12% and 18% in RMSE’s terms during testing period, respectively. Overall performance results indicate runoff forecasting with WRF-QM and WRF-RF pre-processing reduces RMSE by 34% and 40%, respectively, compared to Raw WRF. Wilcoxon signed-test confirmed significant improvement with pre-processing methods. Our study demonstrates the potential of pre-processed NWP to enhance the skill of hydrologic forecasting systems. Pre-processing methods boost flood forecasting reliability, addressing challenges caused by more frequent and severe hydrologic extremes.

Association of exposure to extreme rainfall events with cause-specific mortality in North Carolina, US

Extreme rainfall events could influence human health. However, the associations between extreme rainfall events and mortality remain rarely explored. Here, we conducted a time-series study using county-level mortality data in North Carolina during 2015–2018 to estimate the associations between extreme rainfall events and cause-specific mortality. We defined an extreme rainfall event as a day when a county’s daily total precipitation exceeded the 95th percentile of daily rainfall measurements from all of North Carolina’s counties during the study period. We employed a two-stage analysis where we first estimated the associations for each county and then used the estimates to obtain the state-wide associations by meta-analysis. Exposure to an extreme rainfall event was significantly associated with an increase in total, non-accidental, cardiovascular disease, respiratory disease, and external mortality by 2.24% (95% CI: 0.67%, 3.83%), 2.38% (95% CI: 0.76%, 4.03%), 3.60% (95% CI: 0.69%, 6.60%), 6.58% (95% CI: 1.59%, 11.82%), and 6.92% (95% CI: 1.28%, 12.86%), respectively. We did not find significant differences in the mortality risks within age, sex, or race groups or by seasonality. Our findings suggest that extreme rainfall events may trigger the risk of mortality, especially from non-accidental diseases such as respiratory mortality.

Optimal vetiver hedgerow spacing for mitigating sediment and runoff erosion on steep slopes in Malawi

This study assessed the optimum vetiver hedgerow spacing capable of reducing sediment and runoff erosion on a 17% slope. Three treatments were used: 2-meter vetiver hedgerow spacing, 4-meter vetiver hedgerow spacing, and a control plot without vetiver. Erosion pins were strategically placed on the plots and measured periodically over a period of 6 months, accompanied by daily rainfall measurements. The findings revealed the effectiveness of vetiver hedgerows in reducing sediment erosion and runoff from the plots. Notably, the 2-meter vetiver hedgerow spacing proved more efficient in reducing erosion and minimizing soil accumulation within the plot, with an average change in erosional pin height of 1.2 cm, whereas the 4-meter hedgerow spacing exhibited higher erosion rates at 2 cm, which was statistically significant. These results underscore the importance of optimizing vetiver hedgerow spacing to effectively combat soil erosion and runoff on steep slopes.

Lateral moraine failure in the valley of the Djankuat Catchment (Central Caucasus) and subsequent morphodynamics

Moraine failures are severe events in proglacial areas that lead to major surface changes. On July 1, 2015, a lateral moraine collapsed within the 9.1 km2 glacierized Djankuat Catchment in the Northern Caucasus. This study quantifies the surface changes over a 7-year period after the collapse and proposes the main event triggers using hydrometeorological measurements and remote sensing data. Digital Elevation Models (DEMs), derived from Unmanned Aerial Vehicle (UAV) surveys and satellite images for the 2010–2022 period, were used to estimate erosion/deposition associated with the event and further surface changes in several zones: the breach, the fan, the headward erosion area and gully erosion on lateral moraine slopes. The failure occurred due to an extreme precipitation event (7-day cumulative sum: 227 mm, 24 h prior: 60 mm), simultaneous snowmelt within the whole upstream catchment, and continuous positive temperatures. During the 2015 event, approximately 200,000 m3 of material was instantly eroded, corresponding to a surface lowering of 1.91 m a−1 (over 52,227 m2). Only 25 % of the eroded material redeposited on the fan. Later in 2017–2019, the breach underwent infilling with a 10 m deposition layer in the bottom (net change ca. 14,600 m3), while the stream in the headward erosion area continued to incise (ca. −12,900 m3). From 2019 to 2022, the dynamics decreased, and a positive sediment budget was assessed for all zones except the intensive local erosion on the fan in 2021–2022 (ca. −15,200 m3). Gully changes suggested a constant erosion of 90–100 mm a−1. Based on daily rainfall measurements for the 2008–2022 period, we proposed a 3-day and prior 15-day cumulative precipitation threshold that outlines major erosional events in the Djankuat River valley. A cumulative 3-day precipitation of 100 mm could be a sufficient but not necessary condition since some significant sediment export was recorded below that threshold.

ANALYSIS OF THE LOCAL PRECIPITATION TRENDS IN THE PODKARPACKIE AND LUBUSKIE VOIVODESHIPS

The type of precipitation is one of the factors taken into consideration when deciding on the most optimal drainage system. Drainage systems are used to prevent the landslides caused by water erosion. Rainfall affect the rate of infiltration and the intensity of surface runoff and thus the occurrence, course and effectiveness of erosion processes [1, 2, 3]. Knowledge of local precipitation trends will help to apply precautions and thus minimise the risk of adverse events such as landslides. What is more it can help more effectively manage projects risks and costs. The aim of this study was to analyse more than 30 years of data from daily rainfall measurements from the Podkarpackie Voivodeship and to check whether the occurrence of precipitation is a random event or whether it indicates long-term trends that may affect changes in ground stability. The non-parametric Mann-Kendall test and correlation test were used for the analysis.

Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications

Accurate rainfall datasets with high temporal and spatial resolutions are crucial for most hydrological applications. One potentially valuable source of rainfall data that has consistent spatial and temporal resolutions are atmospheric reanalysis products. However, while such data sets can provide a physically consistent representation of rainfall over large spatial and temporal extents, they are generally less accurate than observed datasets at daily scales. In contrast, while the gauge measurements are accurate source of rainfall data, sub-daily observations are spatially sparse and are of shorter length than daily observations. While the temporal resolution of daily observations can be enhanced using temporal disaggregation methods, they are often applied stochastically with a focus on capturing the fine-scale statistical properties rather than generating a best estimate time series useful for hindcasting purposes. The increasing availability of high resolution regional reanalysis products prompts the question whether they can be used to temporally disaggregate daily observations to derive high-resolution estimates of sub-daily rainfalls suitable for hydrologic applications. This study investigates the efficacy of a simple disaggregation approach to temporally disaggregate daily rainfalls to hourly values using a regional reanalysis at moderate spatial resolutions. The approach is tested on attributes relevant to a wide range of hydrological applications. The selected performance metrics include the distribution and frequency of various sub-daily rainfall accumulations, statistics characterising the sequencing and central tendency of sub-daily rainfalls, and the efficacy of areal estimates of sub-daily rainfalls for simulating catchment streamflows. Categorical evaluation shows that the disaggregated rainfalls reduce the frequency of false alarms and improves the probability of detection compared to the use of raw reanalysis estimates. However, a mixed performance in capturing fine-scale statistical characteristics suggests that the disaggregation approach is less robust for applications that rely solely on high-resolution rainfall characteristics. In hydrological evaluation, when compared to estimates based on raw reanalysis or uniformly disaggregated daily observations, the disaggregated catchment rainfalls improve the simulation of the magnitude and timing of peak flows, and the accuracy of derived flood frequency estimates. The proposed disaggregation method is easily applied to any high-resolution dataset and has the potential to be used in hydrological applications that rely heavily on sub-daily characterisation, with a varying performance across the target applications.

Development of Rainfall Intensity, Duration and Frequency Relationship on a Daily and Sub-Daily Basis (Case Study: Yalamlam Area, Saudi Arabia)

Realistic runoff estimates are crucial for the accurate design of stormwater drainage systems, particularly in developing urban catchments which are prone to overland flow and street inundation following extreme rainstorms. This paper derives new intensity–duration–frequency (IDF) curves for the Yalamlam area in the Kingdom of Saudi Arabia. These curves were obtained based on daily rainfall measurements and, in some short durations, across the entire study area over 30 years. The study is based on applying two distributions—the Log-Pearson type III and Gumbel—to estimate the average rainfall for the different return periods. The results show that there are slight differences between the Log-Pearson type III distribution and the Gumbel distribution, so the average parameters were used to construct the IDF curve in the Yalamlam area. The maximum daily rainfall was converted into sub-daily intervals using two methods and compared with the observed value. The new ratios were calculated using the converting rainfall from daily to sub-daily. These ratios are recommended for application in the Yalamlam area if there are no short-time-interval data available. The following ratios for 1-day rainfall were proposed: 0.37, 0.40, 0.46, 0.53, 0.61, 0.66, 0.70, 0.76, 0.80, and 0.87 for 10 min, 15 min, 30 min, 1 h, 2 h, 3 h, 4 h, 6 h, 8 h, and 12 h rainfall, respectively. The developed IDF curve for the Yalamlam district was built based on the daily and sub-daily observed data.

The USA National Phenology Network's Buffelgrass Green‐up Forecast map products

Abstract Buffelgrass (Cenchrus ciliaris syn. Pennisetum ciliare) was introduced to Sonoran Desert in the early 20th century and has become widespread at low elevations. This perennial bunchgrass accumulates abundant biomass that can carry fires through ecosystems not adapted to fire, resulting in devastating impacts for native cacti and other plant and animal life. Buffelgrass is most effectively managed through the application of herbicide when the grass is at least 50% green. Because the grass rapidly greens up following summer monsoon rainfall, it is possible to forecast green‐up using daily rainfall measurements. In 2019, the USA National Phenology Network (USA‐NPN) released daily Buffelgrass Green‐up Forecast maps for the state of Arizona based on the PRISM 4 km daily total precipitation product. The daily digital Buffelgrass Green‐up Forecast maps are a freely available data product and meet the FAIR principles of findability, accessibility, interoperability and reusability. They are permanently archived and publicly accessible as raster and image layers from the USA‐NPN website. These map layers support planning the timing of management activities to maximize buffelgrass treatment efficacy and researchers seeking to incorporate daily estimates of buffelgrass greenness in their analyses.

Measurement of Enteric Methane Emissions by the SF6 Technique Is Not Affected by Ambient Weather Conditions.

Simple SummaryAlthough the SF6 technique was developed over 25 years ago with the intention that it could be used to measure enteric methane production from ruminants outdoors, no experiments have reported the influence of ambient wind speed, temperature, humidity or rainfall on the accuracy of the technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Individual cow feed intakes and daily methane productions were measured over 5 consecutive days and an automatic weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily temperature, wind speed, humidity and rainfall to the average daily dry matter intake, methane production and methane yield of each cohort of cows. It was concluded that the modified SF6 technique can be used outdoors during a range of weather conditions without a significant effect on the measurement of methane production or methane yield of dairy cows.Despite the fact that the sulphur hexafluoride (SF6) tracer technique was developed over 25 years ago to measure methane production from grazing and non-housed animals, no studies have specifically investigated whether ambient wind speed, temperature, relative humidity and rainfall influence the accuracy of the method. The aim of this research was to investigate how these weather factors influence the measurement of enteric methane production by the SF6 technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Methane production from individual cows was measured daily over the last 5 days of each 32-day period. An automated weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily wind speed, average daily temperature, average daily relative humidity and total daily rainfall measurements to dry matter intake, average daily methane production and methane yield of each cohort of cows. It was concluded that the modified SF6 technique can be used outdoors during a range of wind speeds, ambient temperatures, relative humidities and rainfall conditions without causing a significant effect on the measurement of methane production or methane yield of dairy cows.

Characteristics of Rainfall in Peninsular Malaysia

This study presents the rainfall statistics, conditional probability structure and statistical dependence of rainfall amount of several gauging stations located around Peninsular Malaysia, namely Subang, Senai and Kota Bharu. Daily rainfall measurements for all stations were collected from the Department of Meteorology, Malaysia are long and reliable, with at least 40 years of data. The average annual rainfall estimated for Kota Bharu, Subang and Senai are 2,627 ± 574 mm, 2581 ± 399 mm and 2499 ± 340 mm, respectively. The effect of monsoon seasons on the monthly rainfall amount is evident in this study. The most significant variation in the average monthly rainfall is noticed for Kota Bharu. There was some variation in the average monthly rainfall for Subang and Senai. The conditional probability structure for t-consecutive wet and dry days show that the multi-day events are time-dependent. For example, the probability of occurrence for a single dry day is 0.458, 0.453 and 0.553 and increased significantly to 0.696, 0.780 and 0.817 for 8-consecutive dry days at Subang, Senai and Kota Bharu, respectively. The dependence of rainfall amount was analyzed using the auto correlation function (ACF). The range of ACFs estimated for all stations were very low, i.e. 0.0050 to 0.0209, 0.0093 to 0.0857 and 0.0633 to 0.3700 for Subang, Senai and Kota Bharu, respectively. This result shows that the rainfall amounts are independent of each other. Overall, the analysis shows that the east coast region received more annual rainfall with higher variability, as compared to the central and south parts of Peninsular Malaysia. Additionally, the total amount of rainfall observed for all stations varies spatially and temporarily.

Deciphering the performance of satellite-based daily rainfall products over Zambia

The amount and distribution of precipitation plays a vital role in the management of water resources, agriculture and flood-risk preparedness. Unfortunately, Zambia like many other developing countries is a highly data-scarce country with few and unevenly distributed meteorological stations. The objective of this study was to run a comparative analysis of satellite-based rainfall products (SRPs) and gauge data to ascertain the reliability of using SRPs for daily rainfall measurements in Zambia. The four daily SRPs examined in this study include the following: The Tropical Applications of Meteorology using Satellite and ground-based observations version 3 (TAMSATv3), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the Climate Hazards group InfraRed Precipitation with Station data version 2 (CHIRPSv2.0), and the African Rainfall Climatology Version 2 (ARCv2). SRPs were compared to rain gauge data from 35 meteorological, agrometeorological, and climatological stations in Zambia for the period 1998–2015. Statistical analyses were extensively carried out at temporal scales inter alia daily, monthly, seasonal and annual. Comparisons were also done for three stations lying at the highest, middle and lowest elevations to examine the ability of SRPs to capture precipitation occurrences on complex topography. Strong coefficient of determination (> 0.9) of all the SRPs and gauge data were found at the monthly scale even over multifaceted topography. However, the ability of these products to capture rain gauge data at daily, seasonal and annual scales differs markedly. Specifically, PERSIANN outperforms all the other SRPs at all scales, CHIRPSv2.0 is rated second, followed by TAMSATv3 and ARCv2, respectively. These results suggest that PERSIANN can reliably be used in studies that seek to estimate rainfall in data-sparse regions of Zambia at any temporal scale and arrive at similar results to rain gauge data.

Assessing rainfall erosivity and erosivity density over a western Himalayan catchment, India

The present study aims to assess rainfall erosivity and erosivty density both in space and time over the Suketi River catchment of western Himalayan region in India during 1971–2015. The data used comprises of daily rainfall measurements at three rain gauge stations, which are sparsely distributed over the catchment. Rainfall erosivity was assessed by employing Wischmeier and Smith algorithms, whereas erosivity density was estimated by applying Kinnell’s algorithm. The spatial distribution of both algorithms was analyzed through Kriging method based on geographical information system. The obtained results indicate remarkable year-to-year, seasonal and monthly variations in average annual rainfall erosivity and erosivity density. Apart from this, individual cases of high and very high rainfall erosivity and erosivity density were noticed. The long-term average annual rainfall erosivity and erosivity density revealed a general decreasing trend. This decreasing trend in rainfall erosivity was found to be statistically significant at 0.05 significance level, whereas it was found to be non-significant for erosivity density. The highest values of both indices were observed in the month of July followed by August and June particularly in northern parts. These results indicate that July month followed by August and June are the most susceptible months for soil erosion over the Suketi River catchment with lower reaches (northern) being the most vulnerable one. Finally, results of this study will be valuable for farmers, agronomists and regional planners in chalking out best management practices for reducing water erosion in vulnerable areas of the catchment.

Interpolation of daily rainfall data using censored Bayesian spatially varying model

This paper illustrates a Bayesian spatially varying space-time model to analyse daily precipitation in the Murray Darling Basin (MDB) of Australia. The model is capable of addressing data censoring at zero rainfall days using a latent Gaussian spatial field. The model is also useful for analysing data obtained from sparse locations, where spatially varying parameters are used to interpolate the rainfall estimates in high-density grids across the MDB region. We find that the proposed censored method provides a better out-of-sample predictive performance for the daily rainfall measurements compared to a non-truncated method. Additionally, it identifies location specific trends over the study region. The model is constructed under the Bayesian paradigm and the Markov chain Monte Carlo algorithm is used to obtain inferences of the model parameters. The R software spTDyn is hence extended to version 2.0 to implement the model.

Study of very long-period extreme precipitation records in Thessaloniki, Greece

The objective on this study is to fully statistically determine and describe the rainfall regime characteristics of the very long precipitation records of 124-year period (1892–2015) in Thessaloniki and moreover to apply extreme value statistical methodologies to extreme daily rainfall information (period, 1931–2015), in order to define the extreme precipitation probability distributions. To meet these objectives, the observed annual, monthly and daily rainfall measurements obtained at the Meteorological Station of the Department of Meteorology and Climatology of the Aristotle University of Thessaloniki are used. The use of both Mann-Kendall test and moving average methods are used to determine the profile of the examined very long-period rainfall regime in Thessaloniki, on annual, seasonal, and monthly basis. Taking into consideration the climatic change effect upon precipitation regimes, the extreme daily rainfall measurements are statistically analyzed with extreme value statistical methodologies, defining thus the extreme precipitation probability distribution functions. The Taylor diagram analysis, through combinations of appropriate correlation coefficients, standard deviations, and root-mean-square differences, are constructed to statistically quantify the degrees of similarity between reference points (mean annual precipitation) and results on seasonal and agricultural (agro-) seasonal basis. All the above tests are applied to detect possible changes in precipitation characteristics, over the study region, throughout the extreme long period of 124years. The resulted probability distributions might potentially contribute to the prediction of extreme rainfall events, and be used for future climatic change projections. Moreover, based upon the resulted-adopted probability distributions, emerging from this extreme long study period, return periods for different extreme values of precipitation observed are calculated.

Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference

The common practice of infrequent (e.g., monthly) stream water quality sampling for state of the environment monitoring may, when combined with high resolution stream flow data, provide sufficient information to accurately characterise the dominant nutrient transfer pathways and predict annual catchment yields. In the proposed approach, we use the spatially lumped catchment model StreamGEM to predict daily stream flow and nitrate concentration (mg L−1 NO3-N) in four contrasting mesoscale headwater catchments based on four years of daily rainfall, potential evapotranspiration, and stream flow measurements, and monthly or daily nitrate concentrations. Posterior model parameter distributions were estimated using the Markov Chain Monte Carlo sampling code DREAMZS and a log-likelihood function assuming heteroscedastic, t-distributed residuals. Despite high uncertainty in some model parameters, the flow and nitrate calibration data was well reproduced across all catchments (Nash-Sutcliffe efficiency against Log transformed data, NSL, in the range 0.62–0.83 for daily flow and 0.17–0.88 for nitrate concentration). The slight increase in the size of the residuals for a separate validation period was considered acceptable (NSL in the range 0.60–0.89 for daily flow and 0.10–0.74 for nitrate concentration, excluding one data set with limited validation data). Proportions of flow and nitrate discharge attributed to near-surface, fast seasonal groundwater and slow deeper groundwater were consistent with expectations based on catchment geology. The results for the Weida Stream in Thuringia, Germany, using monthly as opposed to daily nitrate data were, for all intents and purposes, identical, suggesting that four years of monthly nitrate sampling provides sufficient information for calibration of the StreamGEM model and prediction of catchment dynamics. This study highlights the remarkable effectiveness of process based, spatially lumped modelling with commonly available monthly stream sample data, to elucidate high resolution catchment function, when appropriate calibration methods are used that correctly handle the inherent uncertainties.

Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain

AbstractIn the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns.Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.

Rainfall variation and child health: effect of rainfall on diarrhea among under 5 children in Rwanda, 2010.

BackgroundDiarrhea among children under 5 years of age has long been a major public health concern. Previous studies have suggested an association between rainfall and diarrhea. Here, we examined the association between Rwandan rainfall patterns and childhood diarrhea and the impact of household sanitation variables on this relationship.MethodsWe derived a series of rain-related variables in Rwanda based on daily rainfall measurements and hydrological models built from daily precipitation measurements collected between 2009 and 2011. Using these data and the 2010 Rwanda Demographic and Health Survey database, we measured the association between total monthly rainfall, monthly rainfall intensity, runoff water and anomalous rainfall and the occurrence of diarrhea in children under 5 years of age.ResultsAmong the 8601 children under 5 years of age included in the survey, 13.2 % reported having diarrhea within the 2 weeks prior to the survey. We found that higher levels of runoff were protective against diarrhea compared to low levels among children who lived in households with unimproved toilet facilities (OR = 0.54, 95 % CI: [0.34, 0.87] for moderate runoff and OR = 0.50, 95 % CI: [0.29, 0.86] for high runoff) but had no impact among children in household with improved toilets.ConclusionOur finding that children in households with unimproved toilets were less likely to report diarrhea during periods of high runoff highlights the vulnerabilities of those living without adequate sanitation to the negative health impacts of environmental events.

Extreme value modeling for environmental data analysis

This article deals with the problem of stochastic modeling the total maximum amount of flushed rainfall in a watershed when data come from daily rainfall measurements in various locations inside a watershed. The annual total maximum \(Y\) of a random number \(N\) of independent total extremes in a given year is of great interest, for instance when dealing with the prediction of the largest flood that might occur in a given year in order to construct hydraulic protections in populated areas. Here, we propose a joint probability model for \(Y\) and \(N\) on the basis of the peaks over a threshold method. From this model, it is possible to obtain the marginal distribution of \(Y\), which includes information contained in the \(N\) variable. It is also possible to obtain the conditional distribution of \(N\) given that \(Y\) is less than a given value \(y\), which provides information about predicting the mean number of extreme events \(N\) in a year. Some discussion is included on two methods for estimating the parameters involved in the distribution of \(Y\). An application of this model is provided where daily rainfall data coming from some basins located in a southeast region of Mexico are analyzed.

A hidden seasonal switching model for multisite daily rainfall

[1] A hidden seasonal switching model for daily rainfall over a region is proposed where season onset times are stochastic and can vary from year to year. The model allows seasons to occur earlier or later than expected and have varying lengths. This stochastic seasonal variation accommodates considerably more of the observed intraannual rainfall variability than can be represented using seasonal models with standard fixed seasons. In essence, the model dynamically classifies daily rainfall time series into seasons whose onsets vary from year to year and within which the model parameters are assumed to be time homogeneous. A variety of nonseasonal models could have been used to describe daily rainfall within seasons. Here a generalization of the Richardson model is adopted which has rainfall states (dry, light rain, and heavy rain) some of which are hidden or unobserved. It is further assumed that the rainfall states generate rainfall that is independent of season (seasonally invariant), so it is only the dynamics of the rainfall states that vary from season to season. A suitable estimation strategy based on maximum likelihood and the EM algorithm is developed for fitting the model across a region. This strategy is validated on simulated data. Various forms of the model are fitted to daily rainfall measurements from 12 sites in southern New Zealand. These results are discussed and compared to those from fitting standard fixed season models.