Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

This study aims to evaluate the ability of the ECHAM5 GCM model output data in estimating monthly rainfall on the island of Lombok. The data used in this study are ECHAM5 monthly rainfall data and automatic rainfall recorder (ARR) measurement rain data for 2000-2018 obtained from ARR Gunung Sari. Correction of bias is conducted by using the mean ratio method and the regression method. The method that produces the best approach is then used to obtain rain data projections and a simple regression method. Evaluation and validation used the Pearson correlation coefficient (r), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values. The results obtained are that the daily and monthly rainfall data from the ECHAM5 model cannot be directly used to replace the rain measurement data because of its very low accuracy. The downscaling technique performed on daily and monthly rainfall data using the average ratio method does not show satisfactory performance where the efficiency figures produced are still low even gave a slight increasing number. However, the ECHAM5 model data can be used to obtain rainfall projections on a monthly and seasonal scale with a good and satisfactory correlation. Key words: mean ratio method; global climate model; ECHAM5; monthly rainfall.

ANALISIS KETERSEDIAAN AIR PADA DAS INDRAGIRI HILIR MENGGUNAKAN MODEL IHACRES

The imbalance that occurs between the availability of water and the water needs needed in Indragiri Hilir requires a conseptual review and evaluation. The all-time distribution of water availability is greatly influenced by the distribution of rain throughout the year. Conceptual analysis of water discharge with the help of IHACRES software can help analyze DAS indragiri Hilir discharge. Rainfall-runoff modeling is used to predict the value against the runoff, using the IHACRES model. The IHACRES model produces nonlinear loss module parameters and linear unit hydrograph modules. AWLR will be used, namely Bt. Kuantan Rengat station, Rain Data which will be used from Tembilahan station and climatology used from Air Molek station. Determination of success in the model used the equations R2 and R to calculate the deviation that occurs. The calibration, verification and simulation phases begin in 2010-2015. The results of conceptual analysis of water discharge in Indragiri Hilir watershed, mainstay discharge results for irrigation purposes with a probability of 80% maximum discharge occurred in February by 4.33 m3 / s and minimum discharge occurred in April by 0.34 m3/s. Overall availability of water on site is available throughout the year. but it cannot be used for hydropower needs because the available discharge may be affected by tidal factors. Ketidakseimbangan yang terjadi antara ketersediaan air dan kebutuhan air yang diperlukan di Indragiri Hilir memerlukan peninjauan dan evaluasi yang konseptual. Distribusi ketersedian air sepanjang waktu sangat dipengaruhi oleh distribusi hujan sepanjang tahun . Analisis konseptual debit air dengan bantuan software IHACRES dapat membantu menganalisis debit DAS indragiri hilir. Pemodelan rainfall-runoff digunakan untuk memprediksi nilai terhadap runoff salah satunya yaitu menggunakan model IHACRES. Model IHACRES menghasilkan parameter nonlinier loss module dan linier unit hydrograph module. AWLR akan digunakan yaitu stasiun Bt. Kuantan Rengat, Data Hujan yang akan digunakan yaitu dari stasiun Tembilahan dan klimatologi yang digunakan dari stasiun Air Molek. Penentuan keberhasilan pada model digunakan persamaan R2 dan R untuk menghitung simpangan yang terjadi. Tahap kalibrasi, verifikasi dan simulasi dimulai tahun 2010-2015. Hasil analisis konseptual debit air pada DAS Indragiri Hilir, hasil debit andalan untuk keperluan irigasi dengan probabilitas 80% debit maksimum terjadi pada bulan Februari sebesar 4,33 m3/s dan debit minimum terjadi pada bulan April sebesar 0,34 m3/s. Secara keseluruhan ketersediaan air di lokasi tersedia sepanjang tahun. tetapi tidak bisa digunakan untuk kebutuhan PLTA karena debit yang tersedia mungkin dipengaruhi faktor pasang surut

Mapping past, present and future dew and rain water resources for biocrust evolution in southern Africa

Biocrust sustainability relies on dew and rain availability. A study of dew and rain resources in amplitude and frequency and their evolution is presented from year 2001 to 2020 in southern Africa (Namibia, Botswana, South Africa) where many biocrust sites have been identified. The evaluation of dew is made from a classical energy balance model using meteorological data collected in 18 stations, where are also collected rain data. One observes a strong correlation between the frequency of dew and rain and the corresponding amplitudes. There is a general tendency to see a decrease in dew yield and dew frequency with increasing distance from the oceans, located west, east and south, due to decreasing RH, with a relative minimum in the desert of Kalahari (Namibia). Rain amplitude and frequency decreases when going to west and north. Short-term dew/rain correlation shows that largest dew yields clearly occur during about three days after rainfall, particularly in the sites where humidity is less. The evolution in the period corresponds to a decrease of rain precipitations and frequency, chiefly after 2010, an effect which has been cyclic since now. The effect is more noticeable towards north. An increase of dew yield and frequency is observed, mainly in north and south-east. It results in an increase of the dew contribution with respect to rain, especially after 2010. As no drastic changes in the distribution of biomass of biocrusts have been reported in this period, it is likely that dew should compensate for the decrease in rain precipitation. Since the growth of biocrust is related to dew and rain amplitude and frequency, future evolution should be characterized by either the rain cycle or, due to global change, an acceleration of the present tendency, with more dew and less rainfalls.

Geographic Information Systems Application to Drought Distribution Identification with Standardized Precipitation Index (SPI) Method in Trenggalek Regency

The widespread drought area in Trenggalek Regency in 2019 needs to be analyzed to reduce negative impacts and as a monitoring tool to anticipate future drought events. The Standardized Precipitation Index (SPI) is a drought analysis method by calculating the rainwater deficit at various time scales used to identify the distribution of drought in Trenggalek Regency. This study using rain data on 13 rain stations for the period 1990-2019 and agricultural production data for 2019. The calculation results show that the highest SPI value occurred in March at the highly wet level with a value of 2.11. The lowest SPI value occurred in May at the extremely dry level with a value of -2.31. The results are then mapped using ArcGIS with the Inverse Distance Weighted (IDW) method to identify the spatial distribution of drought.

Rain fade margin of terrestrial line-of-sight (LOS) links for 5G networks in Peninsular Malaysia

Advanced telecommunication systems are moving toward a high data transfer rate and wider bandwidth. The 5G communication network has recently been implemented for such aims. However, 5G networks operating with high operating frequency (typically above 20 GHz) could lead to impairments because of the atmospheric phenomena mainly precipitation and especially heavy rain. To address this, an optimum rain fade margin for the 5G network in Peninsular Malaysia is proposed using 77 sites of the rain-gauge network, which convert 1-h rain data to 1-min rain data by means of the international telecommunication union recommendation (ITU-R) P.837-7 model. Long-term rain attenuation statistics are obtained from ITU-R P.530-17 and the synthetic storm technique. The predicted rain attenuation is also presented in monthly statistics and in rain attenuation contour maps. The analysis showed that at 99.99% of link availability, the optimum rain fade margin operating at 26 GHz link should be in the range of 6.50 to 10 dB and 7 to 11 dB at 28 GHz link for a 5G network. Such information is useful for network operators and system engineers for the operation of 5G terrestrial microwave links in heavy rain regions.

Utilising Citizen Science Rain Data for Improved Rainfall Estimation in Urban Pluvial Flooding

<p>Did you know there are millions of rain observations from thousands of privately owned automated weather stations located throughout Britain (and beyond) held in a freely accessible online archive? Citizen Scientists are sharing detailed sub-daily weather observations, including from locations where other gauge data is not available, often in close to real-time. There is distinct clustering of rain gauges in British urban areas, and with an anticipated increase in convective storms resulting in localised pluvial flooding, such high-resolution data should not be ignored. The aims of this research are to assess data quality, investigate how access to the data can be made easier, and to explore how the data can be used to support improved flood risk assessment.</p><p>British rain observations are presented, spanning 10 years from more than 3000 unique citizen science weather stations via the Met Office WOW archive. These citizen science observations have the potential to fill gaps in the official monitoring network run by the Met Office and agencies responsible for flooding in Britain. Analysis indicates that if the official ground based rain gauge network was interpolated on a 5km grid there would be coverage for 36% of Britain, but if citizen science weather stations were included that figure increases to over 50%. A methodology to identify poor quality observations has been developed; the preliminary findings show that even where absolute values may be inaccurate, citizen science gauges can capture the pattern of extreme rainfall. Examples are shown from work in progress showing how combining citizen science observations with official rain data (radar and ground based gauges) can improve delineation of specific events that resulted in pluvial flooding.</p>

Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events

Rain impacts roadways such as wet pavement, standing water, decreased visibility, and wind gusts and can lead to hazardous driving conditions. This study investigates the use of high fidelity Doppler data at 1 km spatial and 2-minute temporal resolution in combination with commercial probe speed data on freeways. Segment-based space-mean speeds were used and drops in speeds during rainfall events of 5.5 mm/hour or greater over a one-month period on a section of four to six-lane interstate were assessed. Speed reductions were evaluated as a time series over a 1-hour window with the rain data. Three interpolation methods for estimating rainfall rates were tested and seven metrics were developed for the analysis. The study found sharp drops in speed of more than 40 mph occurred at estimated rainfall rates of 30 mm/hour or greater, but the drops did not become more severe beyond this threshold. The average time of first detected rainfall to impacting speeds was 17 minutes. The bilinear method detected the greatest number of events during the 1-month period, with the most conservative rate of predicted rainfall. The range of rainfall intensities were estimated between 7.5 to 106 mm/hour for the 39 events. This range was much greater than the heavy rainfall categorization at 16 mm/hour in previous studies reported in the literature. The bilinear interpolation method for Doppler data is recommended because it detected the greatest number of events and had the longest rain duration and lowest estimated maximum rainfall out of three methods tested, suggesting the method balanced awareness of the weather conditions around the roadway with isolated, localized rain intensities.

Debit Banjir Rancangan DAS Tojo Metode HSS ITB 1

Floods often occur in several regions in Indonesia. The problem is the flooding with its uncertain characteristics is one of the environmental problems that has not been handled optimally. The method of converting rain data into discharge data for flood analysis has been widely presented in previous studies. The methods used to analyze flood discharge also vary, starting from rational, empirical, statistical models to the unit hydrograph model. This research aims to determine the flood discharge design for return periods 2, 5, 10, 20, 25, 50, and 100 years in Tojo watershed, Tojo Una-una Regency using the synthetic unit hydrograph method of ITB-1. Research methods are data collection and data analysis. Data collection was carried out at several agencies and collecting from online sources. Results of this research design flood discharge that was analyzed by synthetic unit hydrograph of ITB-1 method. The maximum design flood discharge at Tojo watershed are 82.375m3/s for a 2-year, 98.21 m3/s for a 5-year, 104.77 m3/s for a 10-year, 111.83 m3/s for a 20-year, 113.3 m3/s for a 25-year, 118.87 m3/s for a 50-year, 123.86 m3/s for a 100-year return period

A MATHEMATICAL STUDY OF RAIN DROP SOUND

In this work we first download the recorded sound of drizzling rain and convert them to digits using Octave. Then we analyze this time series formed to interpret the rain data. We find that his data is nonlinear and yet have the similarity of being a linear data. We perform both linear and nonlinear time series analysis here in our work using the computational tool TISEAN. We present our results in this paper.