Comparison of Normal Ratio Method and Distance Power Method for Estimating Missing Rainfall Data with Three Neighboring Stations

This study compared the performances of Normal Ratio Method and Distance Power Method as a tool for estimating missing rainfall data. The data utilized are the rainfall data of the three neighboring station of Catarman, Northern Samar, Philippines. These stations are Catbalogan Station (Samar Province), Legazpi (Bicol Province) and Masbate (Masbate Province). The observed daily rainfall data for the Catarman (Northern Samar), Catbalogan, Legazpi, and Masbate were obtained from the Philippine Atmospheric Geographical Astronomical Services Administration. The monthly rainfall were computed for the three (3) neighboring stations (Catbalogan, Legazpi, Masbate). The evaluation used the T-test for correlated samples and the Pearson’s Correlation Coefficient for the monthly rainfall data computed of the three neighboring Station of Catarman, Northern Samar with the three neighboring stations. Based from the results, Normal Ratio Method performs better than Distance Power Method as applied to three neighboring stations.

Fitting of a Markov chain model for daily rainfall data at Calcutta

A Markov chain probability model has been fitted to the daily rainfall data recorded at Calcutta. The 'wet spell' and 'weather cycles' are found to obey geometric distribution, The distribution of the number of rainy days per week has been calculated and compared with the actual data.

Impact of Extreme Rainfall on Flood Hydrographs

Extreme rainfall is the main factor triggering flooding in various regions of the world including Indonesia. The increase in intensity and duration of current extreme rainfall is predicted as a result of global climate change. This paper aims to analyze the impact of extreme rainfall to the peak discharge of flood hydrographs at a watershed outlet in Palu, Sulawesi, Indonesia. Maximum daily rainfall data for the period 1990-1999 recorded at the Palu Meteorological Station, Central Sulawesi were selected using the Annual Maximum Series Method, and grouped into two types. Type I is the maximum daily rainfall data with extreme events and Type II is the maximum daily rainfall data without extreme events. Frequency analysis was applied to the two data groups using the best distribution method of: Normal, Normal Log, Pearson III Log, and Gumbel to obtain the design rainfall of each data group. In the next stage, the design rainfall transformation into a flood hydrograph is performed using the Nakayasu Synthetic Unit Hydrograph based on a number of return periods in one of the rivers flowing into Palu Bay, namely the Poboya River. The analysis results show that the design rainfall graphs with both extreme rainfall and without extreme rainfall are identical at the low return period and divergent at the high return period with a difference of up to 21.6% at the 1000-year return period. Correspondingly, extreme rainfall has a greater impact at the peak of the flood hydrograph with increasing return periods ranging from -1.28% to 26.81% over the entire return period.

Uncertainty of Intensity-Duration-Frequency Curves Due to Adoption or Otherwise of the Temperature Climate Variable in Rainfall Disaggregation

Most areas around the world lack fine rainfall records which are needed to derive Intensity-Duration-Frequency (IDF) curves, and those that are available are in the form of daily data. Thus, the disaggregation of rainfall data from coarse to fine temporal resolution may offer a solution to that problem. Most of the previous studies have adopted only historical rainfall data as the predictor to disaggregate daily rainfall data to hourly resolution, while only a few studies have adopted other historical climate variables besides rainfall for such a purpose. Therefore, this study adopts and assesses the performance of two methods of rainfall disaggregation one uses for historical temperature and rainfall variables while the other uses only historical rainfall data for disaggregation. The two methods are applied to disaggregate the current observed and projected modeled daily rainfall data to an hourly scale for a small urban area in the United Kingdom. Then, the IDF curves for the current and future climates are derived for each case of disaggregation and compared. After which, the uncertainties associated with the difference between the two cases are assessed. The constructed IDF curves (for the two cases of disaggregation) agree in the sense that they both show that there is a big difference between the current and future climates for all durations and frequencies. However, the uncertainty related to the difference between the results of the constructed IDF curves (for the two cases of disaggregation) for each climate is considerable, especially for short durations and long return periods. In addition, the projected and current rainfall values based on disaggregation case which adopts historical temperature and rainfall variables were higher than the corresponding projections and current values based on only rainfall data for the disaggregation.

Evaluation of Lake Simcoe watershed rainfall characterisics

Lake Simcoe Region Conservation Authority is monitoring the phosphorous loading in Lake Simcoe and to understand the changes in phosphorous loading due to runoff, it is prudent to characterize the rainfall data of the watershed contributing to Lake Simcoe. In this project, hourly and daily rainfall data from 13 different raingage statistics surrounding Lake Simcoe was analyzed to identify event, monthly, seasonal and annual statistics and their trend and thereby to identify the driest and wettest and average annual rainfall. After initial analysis, daily rainfall data from only 4 stations with consistent data for an approximate period of 20 years were chosen for further analysis. The results showed that hydrological year 1995-1996 was the wettest and hydrologic year 1991-1992 was the driest year. Similarly summer season and the month of June were the wettest and winter season and month of February were the driest for the watershed. No significant trend was observed in the yearly and monthly rainfall data while an increasing trend was observed at 3 stations for the winter season.

Evaluation of Lake Simcoe watershed rainfall characterisics

Lake Simcoe Region Conservation Authority is monitoring the phosphorous loading in Lake Simcoe and to understand the changes in phosphorous loading due to runoff, it is prudent to characterize the rainfall data of the watershed contributing to Lake Simcoe. In this project, hourly and daily rainfall data from 13 different raingage statistics surrounding Lake Simcoe was analyzed to identify event, monthly, seasonal and annual statistics and their trend and thereby to identify the driest and wettest and average annual rainfall. After initial analysis, daily rainfall data from only 4 stations with consistent data for an approximate period of 20 years were chosen for further analysis. The results showed that hydrological year 1995-1996 was the wettest and hydrologic year 1991-1992 was the driest year. Similarly summer season and the month of June were the wettest and winter season and month of February were the driest for the watershed. No significant trend was observed in the yearly and monthly rainfall data while an increasing trend was observed at 3 stations for the winter season.

Temporal disaggregation of daily rainfall data in SuDS design: a case study in Tuscany

<p>Sustainable urban Drainage Systems (SuDS), by themselves or combined with grey traditional infrastructures, help to diminish the runoff volume and peak flow, as well as to improve the water quality. Hydrological design of SuDS is usually based on rainfall volumetric percentiles as the number of rainfall events, N<sub>x</sub>, or the accumulated volume of the rainfall series, V<sub>x</sub>, to be managed. Sub-index x refers to common qualities used in SuDS design, like 80, 85, 90 and 95%. Usually, only daily rainfall data are available. Nevertheless, due to the characteristics of the urban watershed involved in the SuDS implementation, the quantification of design parameters for these facilities needs sub-hourly rainfall time series. To overcome this issue, a temporal disaggregation methodology was proposed based on the use of a stochastic rainfall generator model (RainSim V3). We analysed the case of Florence University rain gauge (Tuscany, Italy), by collecting 20 years (in the period from 1998 to 2018) of observed data at 15 minutes time step. First, we verified the ability of RainSim model to reproduce observed rainfall patterns at 15 minutes time-step. The parameters of the stochastic model were estimated using observed data with 24 hours time-step. We generated 100 series of 20 years each with a time step of 15 minutes. We accounted two variables to implement the storm events extraction: a) the Minimum Inter-event Time (MIT) between storm events; 2) the storm volume threshold. We obtained a better characterization of the rainfall regime by applying the temporal disaggregation methodology than using daily-observed data. Second, we compared the SuDS design parameters N<sub>x</sub> and V<sub>x</sub>, obtained by using the stochastically generated rainfall, the observed daily and 15 minutes data. Moreover, the effect of different MITs and different thresholds on N<sub>x</sub> and V<sub>x </sub>were evaluated. In all the cases, results show that N<sub>x</sub> and V<sub>x</sub> obtained with the median of the simulated series were closer to the actual observed parameters based on 15 minutes time step than the ones calculated with the observed daily data. Therefore, the proposed temporal disaggregation method arises as an efficient technique to overcome the lack of sub-hourly rainfall data, necessary to adequately design SuDS.</p>