Modification of basic hydrology formulation based on an approach of the rational method at field measurement

The rational method was a simple technique for estimating the design discharge of a small watershed and can be used in a probabilistic approach. The main parameter in the rational method formula was the C coefficient. The rational method was the primary method for determining the peak discharge from surface runoff flow. At the end of the dry season and the beginning of the rainy season, it placed an automatic water level recorder and automatic rain gauges for the initial water level. This field research aimed to correct a simple hydrological formulation with field measurements and build a numerical rainfall modelling based on rainfall simulation, infiltration, land use, and flow parameters according to the relationship model of precipitation. The hypothesis in this study, the basic formulation of hydrology, is simple: it requires experience to fit the theoretical hydrological formula so simply (simplicity). The theory needs to be simplified because it is an experience of field necessary to explain.

Flow Behavior of Boyong River as Revealed by Long-term Hydro-monitoring System

Boyong River is one of many rivers originating from Mt. Merapi, flows across three autonomy administrative of Sleman Regency, Yogyakarta City, and Bantul Regency. The river experiences flood in the form of lava flow several times, and the 1994 and 2010 occurrences were considered the biggest ones along with the river history. In line with the rapid development of information and communication technology, efforts to develop the early warning system due to the Mt. Merapi disaster have been implemented by the Hydraulic Laboratory of Gadjah Mada University 2006. This paper presents the study results of Boyong River flow behavior by analyzing the data obtained from the monitoring system. The Gemawang Weir at Boyong River was selected as the river control point understudy; those include the catchment boundary, the catchment characteristics, and the hydraulic features. Monitoring equipment consists of an automatic water level recorder (AWLR), the flow visualization using a Brinno camera, and the hydrophone monitoring system. The flow hydrograph characteristics and its corresponding sediment transport rate are considered two parameters for identifying the flow behavior. The results show that the precursive and recession times of the flood hydrograph are about 1-3 hours and 3.5-5 hours, respectively.

Using Rose’ and Manning’s Equations to Spatially Quantify Soil Erosion in Lagawe River Sub- Watershed, Ifugao, Philippines

A dynamic, physical model was created to predict soil erosion of Lagawe River Sub-watershed, a sub-watershed of Magat River Watershed, Philippines. Tipping-bucket rain gauge was installed to gather event-based rainfall data and a water-level recorder was installed on a straight segment of Lagawe River to gather water depth. Sediment samples were taken during rainstorm events and were used to calibrate the model. Manning’s equation was used to calculate surface runoff and stream flow velocity. Rose’ and Freebairn’s Equation was used to calculate sediment mass. Geographic Information System was utilized as a tool for modelling using PCRaster Software. The model estimated a total of 57,905,000 m3 of eroded sediments which was generated during Typhoon Koppu (local name, Lando) in year 2015. A Welch Two Sample t-value of -0.25 and a p-value of 0.81 was achieved on the statistical analysis between the measured sediment yield and the output of the model. Since the p-value is greater than 0.05 (5%), there is no significant difference between the output of the physical dynamic model and the measured value for sediment yield. Likewise, the correlation analysis supports this conclusion with a linearly positive R2 value of 0.74.

Pengembangan Automatic Water Level Recorder (AWLR) Berbasis IoT Sebagai Alat Mitigasi Resiko Potensi Bencana Banjir di Kota Bontang

Hujan deras pada tahun 2019 telah menyebabkan terjadinya banjir besar di kota Bontang yang mengakibatkan kerugian material pada masyarakat. Setelah kejadian tersebut, masyarakat tidak merasa tenang ketika terjadi musim hujan karena munculnya berbagai informasi yang kurang akurat tentang ketinggian muka air sungai Bontang. Tidak akuratnya informasi menyebabkan masyarakat bingung ketika harus membuat keputusan untuk menyelamatkan benda dan jiwanya. Untuk menyediakan informasi akurat tentang kondisi DAS sungai Bontang, agar masyarakat dapat menjadikannya sebagai pertimbangan untuk membuat keputusan, dikembangkanlah sebuah system AWLR berbasis IoT yang menggunakan teknologi komunikasi LoRa dan internet. Hasil pengujian terhadap system yang telah dirancang bangun menunjukkan bahwa sistem dapat bekerja dengan baik dan memberikan informasi yang tepat tentang tinggi muka air di DAS sungai Bontang kepada msyarakat.

MODEL PERAMALAN DEBIT SUNGAI MENGGUNAKAN ANN- SOM ANN PADA SUB DAS TAPUNG KIRI

Sub daerah aliran sungai Tapung Kiri merupakan bagian dari daerah aliran sungai Siak yang dikategorikan sebagai daerah aliran sungai kritis dan kawasan rawan bencana banjir. Potensi terjadinya banjir disebabkan oleh tingginya curah hujan diwilayah tengah, hulu dan di sepanjang daerah aliran sungai yang telah mengalami perubahan tata guna lahan. Pada penelitian ini, dilakukan model peramalan debit dengan menggunakan metode artificial neural network, diharapkan untuk menyediakan data peramalan debit yang lebih terpercaya untuk masa yang akan datang sehingga dapat menjadi acuan bagi sistem peringatan dini. Data yang digunakan adalah data ketinggian muka air sungai Tapung Kiri dari peralatan automatic water level recorder Pantai Cermin dengan panjang data selama 15 tahun, dimulai pada tahun 2005 sampai dengan tahun 2019. Penelitian ini diharapkan bisa memberikan gambaran tentang keunggulan dari artificial neural network untuk digunakan sebagai sistem peringatan dini terhadap banjir. Hasil penelitian menunjukkan bahwa menggunakan metode articial neural network menghasilkan nilai koefesien korelasi pada tahap pelatihan sebesar 0,98, pada tahap pengujian sebesar 0,85 dan pada tahap validasi sebesar 0,89. Jumlah iterasi yang dibutuhkan pada metode artificial neural network adalah sebanyak 7515 epoch.

PENERAPAN SISTEM PERINGATAN DINI SUNGAI KELAY UNTUK PENGAMANAN RESIKO BANJIR PADA PIT BLOK-7 TAMBANG BINUNGAN – PT BERAU COAL

ABSTRAK Pit E dan Pit 7B West adalah Pit yang berada di Blok-7 Tambang Binungan PT Berau Coal yang berdekatan dengan bibir sungai. Kedua pit ini memiliki resiko tinggi terhadap potensi banjir luapan air sungai. Sungai Kelay termasuk kategori Sungai Besar karena memiliki luas daerah aliran sungai (DAS) 7,027km2 (>500km2) dan panjang ±155 km. Seiring dengan peningkatan target produksi PT Berau Coal (PTBC) tahun 2019 maka, bukaan penambangan menuju kearah utara mendekati bibir sungai. Kondisi ini akan berpotensi meluapnya air Sungai Kelay menuju Pit Blok-7 karena menerima kiriman banjir dari hulu. Banjir yang terjadi di area penambangan akan mempengaruhi kondisi keselamatan dan operasional penambangan serta ketercapaian produksi PTBC pada tahun 2019 secara keseluruhan. Berdasarkan historis kejadian banjir di Pit Blok-7 yang terjadi pada bulan Juni tahun 2017, luapan banjir dari Sungai Kelay mencapai elevasi +8.3MRL. Arah aliran banjir melalui area rawa pada sisi utara Pit dan menggenangi Pit 7B West. Sistem mitigasi telah dibangun di akhir tahun 2017 dan selesai pada akhir tahun 2018. Sistem mitigasi ini mencakup pengambilan data batimetri, HEC-HMS, HEC-RAS hingga analisis Neural Network yang menghasilkan waktu prediksi banjir untuk setiap titik pantau terdekat Pit yang telah ditentukan. Waktu prediksi banjir akan digunakan sebagai waktu tenggang untuk evakuasi alat beserta manusia. Sistem ini didukung oleh pembangunan instrumen monitoring otomatis berupa Automatic Rain Gauge dan Automatic Water Level Recorder pada titik hulu dan hilir Sungai Kelay, sehingga mampu menghasilkan analisis yang lebih cepat dan akurat terhadap kondisi aktual di lapangan serta dapat menggambarkan daerah terdampak banjir untuk kala ulang 50 tahun dan rekomendasi geometri tanggul penahan banjir yang sesuai. Sistem mitigasi dibagi menjadi 4 kode berdasarkan sisa waktu evakuasi mulai dari aman hingga bahaya yaitu hijau (≥12h), kuning (≤12h), oranye (≤5h), dan merah(≤2h). Dalam keberjalanannya selama periode Des’18-Juli’19, ketinggian elevasi muka air Sungai Kelay beberapa kali melewati batas hijau dan kuning. Ketika kondisi ini terjadi, instrumen pemantauan secara otomatis mengirimkan peringatan melalui surel, sms, serta fitur whatsapp sesuai dengan peruntukan tingkatan peringatannya. Sistem mitigasi ini juga memberikan peringatan secara otomatis melalui alarm yang terpasang di Control Room. Dengan adanya notifikasi secara otomatis, informasi peringatan dapat tersampaikan dengan cepat sehingga proses evakuasi di lapangan dapat segera dilakukan sesuai prosedur mitigasi bencana banjir PT Berau Coal. Kata kunci: peringatan dini, mitigasi banjir, hec-hms, hec-ras, neural network ABSTRACT Pit E and Pit 7B West are Pits located in Block-7 Binungan Mine Operation of PT Berau Coal which is adjacent to the river bank. The Pits have high risk of flood potential from the river. Kelay River belongs to the Great River category because the river basin area (DAS) is covering 7,027km2 (>500km2) and the length of ± 155km. In line with the increase of PT Berau Coal's (PTBC) production target in 2019 resulting the advancement of Pits mining sequence towards the north approching the river bank. This condition will cause a high risk of overflowing the Kelay River water to Pit Block-7 especially when the heavy rain occurred in the upstream of the river. Flood that occurs inside the mining area will affect safety conditions of mining operations and the overall production achievements of PT Berau Coal in 2019. Historically, flood had been occurred in June, 2017 where the flood from Kelay River overflowed at +8.3 elevation through the swamp area in the north and inundated Pit 7B West. Thus, the mitigation system has built at the end of 2017 and completed by the end of 2018. The mitigation system covered from bathymetry data collection, HEC-HMS, HEC-RAS to Neural Network analysis which results the water elevation prediction for each predetermined monitoring station. This prediction time will be used as lag time for the evacuation of equipments and human. This system is also integrated with automatic monitoring instruments as off Automatic Rain Gauge and Automatic Water Level Recorder at the Kelay River’s upstream and downstream station. The overall system will generate fast and accurate analysis results based on actual condition, also describe affected flood area for 50 year return period, and recommendations for the appropriate flood embankment geometry in areas that need to be protected. Mitigation system is divided into 4 codes based on remaining evacuation time as off green(≥12h), yellow (≤12h), orange (≤5h), and red (≤2h). Along December 2018 to July 2019, several elevations were exceeded the green and yellow code. This event triggered the monitoring instrument to automatically sends warning notification via e-mail, message, and whatsapp. The system also provides an automatic warning through alarm installed in Control Room. With the automatic notifications, warning can be conveyed quickly to carry out field evacuation process immediately according to Flood Mitigation Procedure of PT Berau Coal. Keywords: early warning, flood mitigation, hec-hms, hec-ras, neural network

Analisis Model Ketersediaan Air Pada Sungai Siak Bagian Hulu Dengan Menggunakan Model GR2M (Studi Kasus: Daerah Aliran Sungai Siak Bagian Hulu, AWLR Pantai Cermin)

Pengalihragaman hujan menjadi debit merupakan suatu proses untukmenghitung debit dengan data masukan berupa data hujan di lapangan.Terdapat beberapa metode perhitungan transformasi hujan – debit yangdigunakan dalam penelitian di Indonesia seperti Mock, NRECA dan TankModel. Metode yang digunakan dalam penelitian ini adalah metode GR2Mkarena lebih sederhana dibanding dengan metode lainnya. GR2M hanyamemiliki dua variabel yang akan digunakan dalam perhitungan. Lokasipenelitian di DAS Siak Bagian Hulu AWLR Pantai Cermin. Penelitian inidilakukan untuk mengetahui efektifitas model GR2M dalam analisis hujanaliranpada DAS Siak Bagian Hulu AWLR (Automatic Water Level Recorder)stasiun Pantai Cermin. Hasil penelitian menunjukkan keefektifan metodeGR2M yang sangat efisien dengan nilai ketelitian efiseinsi sebesar 90%.

Pembangunan Sistem Pengukuran Muka Air Otomatis (Automatic Water Level Recorder) Berbasis Gelombang Akustik Untuk Pengamatan Pasang Surut Laut

AbstrakPasang surut adalah fenomena naik turunnya muka air. Pasut dapat diukur dengan berbagai macam metode, baik manual maupu otomatis. Pengukuran otomatis dengan menggunakan alat pengukur pasut, khususnya untuk pengukuran jangka panjang dinilai relatif lebih berbiaya rendah dibandingkan dengan pengukuran manual, akan tetapi alat pengukur pasut otomatis hampir semuanya memiliki harga yang relatif mahal sehingga diperlukan peralatan yang lebih terjangkau dan andal. Pengembangan sistem automatic water level recorder (AWLR) berbasis gelombang akustik dilakukan dengan membangun dan merancang sistem perangkat lunak maupun perangkat keras alat dengan berbasiskan perangkat open source Arduino. Alat yang dihasilkan dapat mengukur dengan baik di skala laboratorium maupun lapangan. Pengukuran skala lapangan menunjukkan RMSE 36,6 cm di daerah terpencil dan RMSE 11 cm untuk daerah yang memungkinkan alat dipasang dengan stabil.Kata Kunci : AWLR, pengukur pasut otomatis, skala lapangan, skala laboratorium.AbstractTides were phenomenon of rising water levels. Tides could be measured by various methods, manual or automatic way. Measurements using automatic tide gauges, especially for long-term measurements, usually needed lower cost compared to manual ones, but in facts automatic tide gauges were relatively more expensive prices, so it was worthy to develop the reliable equipment with lower cost. This automatic water level recorder (AWLR) system using acoustic waves was developed by building and designing a software and hardware system based on an open source device named Arduino. The builded equipment had could reached well level in scales, laboratory or field scales. Field scale measurements showed that RMSE in outlying areas reached 36.6 centimeters and could be better for areas where tide gauges could be installed stably (11 centimeters).Keywords: AWLR, automatic tide gauges, field scale, laboratory scale

Stream’s regime coefficient in upstream Rokan watershed of Riau Province

Flood disaster in Rokan Hulu Regency often occurs every year. High streamflow conditions during rainy season as one of the causes of the flood. Stream’s Regime Coefficient (KRS) is the ratio of maximum and minimum discharge in a watershed that shows the watershed condition. The higher KRS means the worse the watershed condition. The study areas are located in the upstream Rokan watershed. Upstream Rokan watershed with the AWLR (Automatic Water Level Recorder) Lubuk Bendahara and Pasir Pangaraian in Riau Province were picked up as study area of this research. The results show the indicator of stream’s regime coefficient at Lubuk Bendahara AWLR valued 217.45 which placed on poor class and the indicator of stream’s regime coefficient at Pasir Pangaraian AWLR valued 38.25 which placed on good class.

Automatic calibration and sensitivity analysis of DISPRIN model parameters: A case study on Lesti watershed in East Java, Indonesia

The Dee Investigation Simulation Program for Regulating Network (DISPRIN) model consists of eight tanks that are mutually interconnected. It contains 25 parameters involved in the process of transforming rainfall into runoff data. This complexity factor is the appeal to be explored in order to more efficiently. Parameterization process in this research is done by using Differential Evolution (DE) algorithm while parameters sensitivity analysis is done by using Monte Carlo simulation method. Software application models of merging the two concepts are called DISPRIN25-DE model and compiled using code program M-FILE from MATLAB. Results of research on Lesti watershed at the control point Tawangrejeni automatic water level recorder (AWLR) station (319.14 km2) in East Java Indonesia indicate that the model can work effectively for transforming rainfall into runoff data series. Model performance at the calibration stage provide value of NSE = 0.871 and PME = 0.343 while in the validation stage provide value of NSE = 0.823 and PME = 0.180. Good performance in the calibration process indicates that DE algorithm is able to solve problems of global optimization of the equations system with a large number of variables. The results of the sensitivity analysis of 25 parameters showed that 3 parameters have a strong sensitivity level, 7 parameters with a medium level and 15 other parameters showed weak sensitivity level to performance of DISPRIN model.