scholarly journals Intensity-Duration-Frequency relationships: stochastic modeling and disaggregation of daily rainfall in the lagoa Mirim watershed, Rio Grande do Sul, Brazil

2016 ◽  
Vol 36 (3) ◽  
pp. 492-502 ◽  
Author(s):  
Rita de C. F Damé ◽  
Claudia F. A. Teixeira-Gandra ◽  
Hugo A. S. Guedes ◽  
Gisele M. da Silva ◽  
Suélen C. R. da Silveira
Keyword(s):  
Stochastic Modeling ◽  
Mean Squared Error ◽  
Transition Probabilities ◽  
Information Gain ◽  
Daily Rainfall ◽  
Gumbel Distribution ◽  
Rain Gauge ◽  
Rainfall Occurrence ◽  
Intensity Duration Frequency ◽  
Annual Maximum Daily Rainfall

ABSTRACT This study aimed to investigate information gain by using rainfall intensity-duration-frequency (IDF) relationships, with data gathered within N+M years from seven rain gauge stations located in the Lagoa Mirim Watershed (South Atlantic basin). After N years of daily rainfall, the transition probabilities of a time homogeneous two-state Markov chain were defined to simulate rainfall occurrence, as well as gamma distribution to measure it; for that, daily rainfall series were composed of N+M years, with M being the generated series. The series were adjusted to Gumbel distribution, being used in annual maximum daily rainfall disaggregation for durations of 10, 20, 30, 40, 50, 60, 120, 360, 720 and 1440 min. Daily rainfall disaggregation was validated through IDF relationships taken from pluviograph records of N years and from N+M years, using the “t” test of relative mean squared error. We can infer that there was information gain using IDF relationships of rainfall occurrence when using N years of observed data and M years of generated data by stochastic modeling compared to those obtained from a composed series of N years.

scholarly journals Analysis of the relationship intensity, duration, frequency of disaggregated daily rainfall in southern Rio Grande do Sul, Brazil

2014 ◽  
Vol 34 (4) ◽  
pp. 660-670 ◽  
Author(s):  
Rita de C. F. Damé ◽  
Claudia F. A. Teixeira-Gandra ◽  
Francisco A. Villela ◽  
Jacira P. dos Santos ◽  
Antoniony S. Winkler
Keyword(s):  
Daily Rainfall ◽  
Rio Grande ◽  
Rain Gauge ◽  
Point Of View ◽  
Rio Grande Do Sul ◽  
Rainfall Occurrence ◽  
Feasible Alternative ◽  
Southern Half ◽  
Intensity Duration Frequency ◽  
Frequency Relationship

The intensity, duration, and frequency relationship (IDF) of rainfall occurrence may be done through continuous records of pluviographs or daily pluviometer values . The objective of this study was to estimate the intensity-duration-frequency relationships of precipitation, using the method of daily rainfall disaggregation, at weather stations located to the southern half of the state of Rio Grande do Sul; comparing them with those obtained by rain gauge records, in places considered homogeneous from the meteorological point of view. The IDF equation parameters were estimated from daily rainfall disaggregation data, using the method of nonlinear optimization. To validate the equations confidence indices and efficiency and the "t" Student test, among maximum intensity values obtained from the disaggregated daily rainfall durations of 10; 30; 60 min and 6; 12 and 24 h and those extracted from existing IDF equations. For all studied stations and return periods, the trust index values were regarded as "optimal", i.e., greater than 0.85. The maximal intensity of rainfall obtained by daily rainfall disaggregation have similarity with those obtained by relations IDF standards. Thus, the method constitutes a feasible alternative in obtaining the IDF relationships.

scholarly journals Heavy rainfall equations for Santa Catarina, Brazil

2011 ◽  
Vol 35 (6) ◽  
pp. 2127-2134 ◽  
Author(s):  
Álvaro José Back ◽  
Alan Henn ◽  
José Luiz Rocha Oliveira
Keyword(s):  
Coastal Region ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Annual Maximum ◽  
Santa Catarina ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Significance Level ◽  
Intensity Duration Frequency ◽  
Annual Maximum Rainfall

Knowledge of intensity-duration-frequency (IDF) relationships of rainfall events is extremely important to determine the dimensions of surface drainage structures and soil erosion control. The purpose of this study was to obtain IDF equations of 13 rain gauge stations in the state of Santa Catarina in Brazil: Chapecó, Urussanga, Campos Novos, Florianópolis, Lages, Caçador, Itajaí, Itá, Ponte Serrada, Porto União, Videira, Laguna and São Joaquim. The daily rainfall data charts of each station were digitized and then the annual maximum rainfall series were determined for durations ranging from 5 to 1440 min. Based on these, with the Gumbel-Chow distribution, the maximum rainfall was estimated for durations ranging from 5 min to 24 h, considering return periods of 2, 5, 10, 20, 25, 50, and 100 years,. Data agreement with the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test, at 5 % significance level. For each rain gauge station, two IDF equations of rainfall events were adjusted, one for durations from 5 to 120 min and the other from 120 to 1440 min. The results show a high variability in maximum intensity of rainfall events among the studied stations. Highest values of coefficients of variation in the annual maximum series of rainfall were observed for durations of over 600 min at the stations of the coastal region of Santa Catarina.

scholarly journals Development and evaluation of a stochastic daily rainfall model with long-term variability

2017 ◽  
Vol 21 (12) ◽  
pp. 6541-6558 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-gamma model with different parameterisations. The key finding is that if the parameters of the gamma distribution are randomly sampled each year from fitted distributions rather than fixed parameters with time, the variability of rainfall depths at both short and longer temporal resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decadally varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

2019 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Vanessa Conceição dos Santos ◽  
Claudio Blanco ◽  
José Francisco de Oliveira Júnior
Keyword(s):  
River Basin ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Probability Of Occurrence ◽  
Rainfall Occurrence ◽  
Order Markov Chain ◽  
Historical Series ◽  
The Matrix ◽  
Geographical Position ◽  
Homogeneous Regions

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

scholarly journals Performance Assessment of GSMaP and GPM IMERG Products during Typhoon Mangkhut

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 134
Author(s):  
Xiaoyu Li ◽  
Sheng Chen ◽  
Zhenqing Liang ◽  
Chaoying Huang ◽  
Zhi Li ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Southern China ◽  
Eastern China ◽  
Mainland China ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Critical Success ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

This paper evaluated the latest version 6.0 Global Satellite Mapping of Precipitation (GSMaP) and version 6.0 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products during 2018 Typhoon Mangkhut in China. The reference data is the rain gauge datasets from Gauge-Calibrated Climate Prediction Centre (CPC) Morphing Technique (CMORPHGC). The products for comparison include the GSMaP near-real-time, Microwave-IR merged, and gauge-calibrated (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) and the IMERG Early, Final, and Final gauge-calibrated (IMERG_ERUncal, IMERG_FRUncal, and IMERG_FRCal) products. The results show that (1) both GSMaP_Gauge and IMERG_FRCal considerably reduced the bias of their satellite-only products. GSMaP_Gauge outperforms IMERG_FRCal with higher Correlation Coefficient (CC) values of about 0.85, 0.78, and 0.50; lower Fractional Standard Error (FSE) values of about 18.00, 18.85, and 29.30; and Root-Mean-Squared Error (RMSE) values of about 12.12, 33.35, and 32.99 mm in the rainfall centers over mainland China, southern China, and eastern China, respectively. (2) GSMaP products perform better than IMERG products, with higher Probability of Detection (POD) and Critical Success Index (CSI) and lower False Alarm Ratio (FAR) in detecting rainfall occurrence, especially for high rainfall rates. (3) For area-mean rainfall, IMERG performs worse than GSMaP in the rainfall centers over mainland China and southern China but shows better performance in the rainfall center over eastern China. GSMaP_Gauge and IMERG_FRCal perform well in the three regions with a high CC (0.79 vs. 0.94, 0.81 vs. 0.96, and 0.95 vs. 0.97) and a low RMSE (0.04 vs. 0.06, 0.40 vs. 0.59, and 0.19 vs. 0.34 mm). These useful findings will help algorithm developers and data users to better understand the performance of GSMaP and IMERG products during typhoon precipitation events.

scholarly journals Introduction of a simple formula for estimating approximate intensity-duration-frequency curves from daily rain gauge data

2021 ◽  
Author(s):  
Rasmus Benestad ◽  
Julia Lutz ◽  
Anita Verpe Dyrrdal ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Simple Formula ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Complete Representation ◽  
Orographic Rainfall ◽  
Return Levels ◽  
Gauge Data ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented. It was derived from a combination of simple mathematical principles, approximations and fitted to 10-year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula has subsequently been evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as a means of downscaling regarding the timescale, given an approximate power-law dependency between temporal scales. One clear benefit of this framework is that observational data is far more abundant for 24 hr rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity and is well-suited for projecting IDF curves for a future climate. This method also provides a framework that strengthens the connection between climatology and meteorology to hydrology, and can be applied to risk management in terms of flash flooding. The proposed formula can also serve as a 'yardstick' to study how different meteorological phenomena with different timescales influence the local precipitation, such as convection, weather fronts, cyclones, atmospheric rivers, or orographic rainfall. An interesting question is whether the slopes of the IDF curves change as a consequence of climate change and if it is possible to predict how they change. One way to address this question is to apply the framework to simulations by convective-permitting regional climate models that offer a complete representation of both sub-daily and daily precipitation over time and space. </p>

scholarly journals Development and Evaluation of a Stochastic Daily Rainfall Model with Long Term Variability

2017 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability, but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov Chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a Gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-Gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-Gamma model with different parameterisations. The key finding is that if the parameters of the Gamma distribution are randomly sampled from fitted distributions prior to simulating the rainfall for each year, the variability of rainfall depths at longer resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decade-varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Changes in climate and its effect on timing of snowmelt and intensity-duration-frequency curves

2021 ◽  
Author(s):  
Anna Wagner ◽  
Christopher Hiemstra ◽  
Glen Liston ◽  
Katrina Bennett ◽  
Dan Cooley ◽  
...  
Keyword(s):  
North Dakota ◽  
Climate Changes ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Flood Frequency ◽  
Return Levels ◽  
Intensity Duration Frequency ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall ◽  
Frequency Curves

Snow is a critical water resource for much of the U.S. and failure to account for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall intensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

scholarly journals Daily precipitation records over mainland Spain and the Balearic Islands

2013 ◽  
Vol 13 (10) ◽  
pp. 2483-2491 ◽  
Author(s):  
C. Ramis ◽  
V. Homar ◽  
A. Amengual ◽  
R. Romero ◽  
S. Alonso
Keyword(s):  
Extreme Events ◽  
Extreme Values ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Balearic Islands ◽  
Precipitation Event ◽  
Similar Distribution ◽  
Major Interest ◽  
Precipitation Records ◽  
Different Characteristics

Abstract. Understanding the spatial distribution of extreme precipitations is of major interest in order to improve our knowledge of the climate of a region and its relationship with society. These analyses inevitably require the use of directly observed values to account for the actual extreme amounts rather than analyzed gridded values. A study of daily rainfall extremes observed over mainland Spain and the Balearic Islands is performed by using records from 8135 rain gauge stations from the Spanish Weather Agency (AEMET). Results show that the heaviest daily precipitations have been observed mainly on the coastal Mediterranean zone from Gibraltar to the Pyrenees. In particular, a record value of 817 mm was recorded in the Valencia region in 1987. The current map of daily records in Spain, which updates the pioneering work of the Spanish meteorologist Font, shows similar distribution of extreme events but with notably higher amounts. Generalized extreme values distributions fit the Mediterranean and Atlantic rain gauge measurements and shows the different characteristics of the extreme daily precipitations in both regions. We identify the most extreme events (above 500 mm per day) and provide a brief description of a typical meteorological situation in which these damaging events occur. An analysis of the low-level circulation patterns producing such extremes – by means of simple indices such as NAO, WeMOi and IBEI – confirms the relevance of local flows in the generation of either Mediterranean or Atlantic episodes. WeMOi, and even more IBEI, are good discriminants of the region affected by the record precipitation event.

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