Variation of temperature and rainfall at Patna

In this paper, the variation of temperature and rainfall at Patna are analysed using simple non-parametric tests. The trends in the annual maximum and minimum daily temperatures, annual rainfall, annual maximum daily rainfall, number of rainy days in a year, the annual average rainfall per rainy day and the ratio of maximum to average rainfall per rainy day at Patna have been examined. Tends in total monthly rainfall, Highest daily rainfall in a month and number of rainy days in a month have also been determined for every month in a year. The monthly trends of data using simple Mann-Kendall test indicated statistically significant changes in rainfall pattern for the city.

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.

ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia

In recent years, the weather and climate are unpredictable and the most visible is the rotation of the rainy season and the dry season. The extreme changes in rainfall can cause disasters and losses for the community. For that we need to predict the rainfall to anticipate the worst events. Rainfall is included in the periodic series data, so the forecasting method that can be used is the ARIMAX model which is ARIMA model expanded by adding the exogen variable. The aim of this research is to predict the rainfall data in Pangkalpinang City, Indonesia. The best model for each rainfall is ARIMAX (0,1,3) for monthly rainfall data and ARIMAX (0,1,2) for maximum daily rainfall. This research shows that there is an influence maximum wind speed variable to monthly rainfall and maximum daily rainfall in the Pangkalpinang City. Nevertheless, when viewed from the ARIMA and ARIMAX models based on the obtained AIC value, the ARIMAX value is still better than ARIMA. However, the prediction value using ARIMAX needs to increase again to take into account seasonal data rainfall. Then, possible to add other exogeneous factors besides maximum wind speed.

Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall

Extreme rain events can cause social and economic impacts in various sectors. Knowing the risk of occurrences of extreme events is fundamental for the establishment of mitigation measures and for risk management. The analysis of frequencies of historical series of observed rain through theoretical probability distributions is the most commonly used method. The generalized extreme value (GEV) and Gumbel probability distributions stand out among those applied to estimate the maximum daily rainfall. The indication of the best distribution depends on characteristics of the data series used to adjust parameters and criteria used for selection. This study compares GEV and Gumbel distributions and analyzes different criteria used to select the best distribution. We used 224 series of annual maximums of rainfall stations in Santa Catarina (Brazil), with sizes between 12 and 90 years and asymmetry coefficient ranging from -0.277 to 3.917. We used the Anderson–Darling, Kolmogorov-Smirnov (KS), and Filliben adhesion tests. For an indication of the best distribution, we used the standard error of estimate, Akaike’s criterion, and the ranking with adhesion tests. KS test proved to be less rigorous and only rejected 0.25% of distributions tested, while Anderson–Darling and Filliben tests rejected 9.06% and 8.8% of distributions, respectively. GEV distribution proved to be the most indicated for most stations. High agreement (73.7%) was only found in the indication of the best distribution between Filliben tests and the standard error of estimate.

Changes in climate and its effect on timing of snowmelt and intensity-duration-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.

Design of edges in contour and half moons from edaphoclimatic parameters, for the endorrheic basin of lagunas de tajzara - ramsar site 1030

The collection of water is proposed from the design of contour borders and half moons, green infrastructure measures, to reduce surface runoff and increase the availability of water for vegetation. The contour and crescent ridges have land ridges with a trapezoidal section, which follow the contour lines, to compartmentalize the slope into smaller hydrological units, the ends of which are located on contour lines. With the data of maximum rainfall every 24 hours and parameters of Gumbel's Law modified, the equations of maximum daily rainfall height (hdT), rainfall height for a duration ´´t´´ (htT), and the Intensity Duration Frequency curve (ItT), for a duration of t <2h. Then considering the values of basic infiltration, vegetation cover, soil type and hydrological condition, the curve numbers were determined for different soil moisture conditions, later the separation length (L) between the Half Moons, and the borders was calculated. in contour, which were designed by means of 10 configurations between diameter and height, for the two infrastructures, being in Copacabana Valle, the greatest separation distance.

Natural hazards in the mountainous region of Rio de Janeiro state, Brazil: rainfall erosivity as an early warning index

Rainfall erosivity is defined as the potential of rain to cause erosion. It has great potential for application in studies related to landslides and floods, in addition to water erosion. The objectives of this study were: i) to model the Rday using a seasonal model for the Mountainous Region of the State of Rio de Janeiro (MRRJ); ii) to adjust thresholds of the Rday index based on catastrophic events which occurred in the last two decades; and iii) to map the maximum daily rainfall erosivity (Rmaxday) to assess the region’s susceptibility to rainfall hazards according to the established Rday limits. The fitted Rday model presented a satisfactory result, thereby enabling its application as an estimator of the daily rainfall erosivity in MRRJ. Events that resulted in Rday > 1,500 MJ.ha-1.mm.h-1.day-1 were those with the highest number of fatalities. The spatial distribution of Rmaxday showed that the entire MRRJ has presented values that can cause major rainfall. The Rday index proved to be a promising indicator of rainfall hazards, which is more effective than those normally used that are only based on quantity (mm) and/or intensity (mm.h-1) of the rain.

FLOOD MODELLING OF BADENG RIVER USING HEC-RAS IN SINGOJURUH SUB DISTRICT, BANYUWANGI REGENCY, EAST JAVA, INDONESIA

The flood disaster is a severe threat in Indonesia due to the enormous impacts on environmental degradation, social and economic sectors. One flood event due to the overflow is the Badeng River's flooding in 2018 at Singojuruh Subdistrict, Banyuwangi Regency. The flood had a detrimental impact on the local community, especially on agricultural land and residential. Anticipatory steps need to be taken to minimize losses due to flooding in the future. Inundation modelling in this research is purposed to predict flood hazards. Hence it can have appropriate anticipatory steps in the future. The software used to model the inundation in this study was the HEC-RAS Program. Data needed in this study are river geometry, manning coefficient, and maximum daily rainfall from the year 2010 until 2019. The research e stages in this study consist of (1) Calculation of watershed morphometry, (2) Calculation of average regional rainfall, (3) Calculation of rainfall plan, (4) Rain Data Suitability Test, (5) Calculation of Rain Intensity, (6) Calculation of Flood Discharge Plan, (7) Geometry Modelling, (8) Extraction of Manning Coefficient, and (9) Inundation Simulation. The results of the Gama 1 method's peak discharge plan showed an increase in each return period. The area with the highest level of susceptibility around the Badeng River occurs in Alasmalang Village, Singojuruh Subdistrict. This area has the smallest river storage capacity than other river crossings. Hence it has the most significant potential for flooding.Keywords: inundation modelling, flood, HEC-RAS, Badeng RiverBencana banjir menjadi ancaman serius bagi negara Indonesia karena memberikan dampak yang besar terhadap kerusakan lingkungan, sosial maupun ekonomi. Salah satu kejadiannya adalah banjir yang terjadi akibat luapan sungai Badeng pada tahun 2018 di Kecamatan Singojuruh, Kabupaten Banyuwangi. Kejadian Banjir tersebut memberikan dampak yang merugikan bagi masyarakat setempat, terutama pada lahan pertanian dan permukiman. Langkah antisipasi perlu dilakukan untuk meminimalisir kerugian akibat bencana banjir di masa mendatang. Pemodelan genangan dalam penelitian ini dibuat bertujuan untuk memprediksi bahaya banjir, sehingga dapat dilakukan langkah antisipasi yang tepat. Software yang digunakan untuk memodelkan genangan dalam penelitian ini adalah Program HEC-RAS. Data yang dibutuhkan berupa data geometri sungai, koefisien manning dan curah hujan harian maksimum selama periode tahun 2010 sampai 2019. Beberapa tahapan dalam penelitian ini meliputi (1) Perhitungan morfometri DAS, (2) Perhitungan hujan rerata wilayah, (3) Perhitungan curah hujan rencana, (4) Uji Kesesuaian Data Hujan, (5) Perhitungan Intensitas Hujan, (6) Perhitungan Debit banjir rencana, (7) Pemodelan geometri, (8) Ekstraksi angka kekasaran manning, dan (9) Simulasi Genangan. Hasil perhitungan debit puncak rencana metode Gama 1 menunjukkan peningkatan pada setiap periode ulang. Daerah yang mempunyai tingkat kerawanan paling besar adalah areal sekitar Sungai Badeng yang berada di Desa Alasmalang Kecamatan Singojuruh. Daerah ini memiliki kapasitas tampung sungai yang paling kecil daripada penampang sungai yang lainnya, sehingga memiliki potensi terjadinya banjir paling besar. Kata kunci: pemodelan genangan, banjir, HEC-RAS, Sungai Badeng

Analysis of Annual Maximum Rainfall Series using Method of L-Moments in Some Selected Sites in South-South Geographical Zone, Nigeria

Determination of the extent of peak rainfall for different return periods is an essential ingredient for the accurate design of hydraulic structures such as drains, dams and culverts as well as detection of flood risk areas. The focus of this study is to analyze annual maximum daily rainfall series in some selected sites within the coastal region of Nigeria using three parameter probability distribution models, namely, Generalized Logistics (GLO), Generalized Extreme Value (GEV) and Generalized Pareto (GPA) with the view of identifying the best fit probability distribution model per station that can be employed to estimate the rainfall magnitude for selected return periods. Specific time series analysis test, namely, detection of outlier and homogeneity test were performed to certify that the data utilized are adequate and suitable. Descriptive statistics such as sample mean, variance, standard deviation, kurtosis, skewness, and coefficient of variation were computed using basic statistical equations. The probability weighted moment parameters (b0, b1, b2 and b3), L-moment values (λ1, λ2, λ3 and λ4) and ratios (τ2, τ 3 and τ4) including the distribution parameters, namely, shape (k), scale (α) and location (ξ) parameters were computed based on L-moments procedures. To select the best-fit probability distribution model per station, carefully chosen goodness-of-fit statistics, namely, root mean square error, relative root mean square error, maximum absolute deviation index, maximum absolute error and probability plot correlation coefficient were employed since they can adequately assess the fitted distribution at a site. Results obtained indicate that the GLO is the best fit distribution for analyzing annual maximum daily rainfall series from Warri and Calabar while GPA for Port Harcourt and Uyo.