scholarly journals Analisis Limpasan Langsung Metode SCS Menggunakan Data Hujan TRMM Studi Kasus Subdas Code Hulu

2021 ◽  
Vol 1 (2) ◽  
pp. 64-72
Author(s):  
Puji Harsanto ◽  
Muhammad Sufyan Tsaury
Keyword(s):  
Satellite Data ◽  
Incomplete Data ◽  
Data Availability ◽  
Conformity Index ◽  
Observation Data ◽  
Efficiency Coefficient ◽  
Average Value ◽  
Low Efficiency ◽  
Efficiency Data ◽  
Fast Access

Ketersediaan data menjadi hal yang krusial dalam analisis hidrologi. Banyak permasalahan menyangkut ketersediaan data yang seringkali ditemui di lapangan, seperti minimnya data, data yang tidak kontinyu, atau sebaran stasiun yang tidak merata. Seiring berkembangnya teknologi, permasalahan tersebut dapat diselesaikan dengan memanfaatkan data pengamatan satelit yang memiliki resolusi spasial dan temporal tinggi, cakupan luas, akses cepat, dan ekonomis. Akan tetapi, data satelit perlu divalidasi dengan data pengamatan nyata di lapangan. Penelitian ini dilakukan untuk validasi data satelit TRMM terhadap data observasi berbasis darat dengan membandingkan debit limpasan dari data hujan terukur di darat atau ARR (Automatic Rainfall Recorder) dengan data hujan TRMM,  lalu dikoreksi dengan debit limpasan terukur di stasiun AWLR (Automatic Water Level Recorder) Gemawang. Debit limpasan dari hujan dihitung dengan menggunakan Metode SCS. Hasil penelitian menunjukan jeda waktu rata-rata pengukuran hujan TRMM dan ARR sekitar 8,5 jam. Ditemukan perbedaan bentuk hidrograf limpasanTRMM. Pada data 18 Januari 2018, terdapat kesalahan bentuk gelombang hidrograf (Ew) sebesar 11.843. Dari analisis indeks kesesuaian dan efisiensi, data satelit TRMM mendapat hasil koefisien korelasi rata-rata debit ARR-AWLR dan TRMM-AWLR tergolong rendah yaitu masing-masing sebesar 0,2416 dan 0,1041, sedangkan koefisien efisiensinya 1,67 yang dikategorikan sebagai data yang efisien. Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data. 

scholarly journals PEMANFAATAN DATA SATELIT TROPICAL RAINFALL MEASURING MISSION (TRMM) UNTUK PEMETAAN ZONA AGROKLIMAT OLDEMAN DI KALIMANTAN SELATAN

2016 ◽  
Vol 12 (3) ◽  
pp. 267 ◽  
Author(s):  
Riza Arian Noor ◽  
Muhammad Ruslan ◽  
Gusti Rusmayadi ◽  
Badaruddin Badaruddin
Keyword(s):  
Satellite Data ◽  
Tropical Rainfall Measuring Mission ◽  
Data Availability ◽  
Observation Data ◽  
Resources Management ◽  
Agricultural Resources ◽  
Monthly Average ◽  
Surface Rainfall ◽  
Calculation Results ◽  
Arc Gis

The irregularity of observation sites distribution and network density, lack data availability and discontinuity are the obstacles to analyzing and producing the information of agroclimate zone in South Kalimantan. TRMM satellite needs to be researched to overcome the limitations of surface observation data. This study intended to validate TRMM 3B43 satellite data with surface rainfall, to produce Oldeman agroclimate zone based on TRMM satellite data and to analyze the agroclimate zone for agricultural resources management. Data validation is done using the statistical method by analyzing the correlation value (r) and RMSE (Root Mean Square Error). The agroclimate zone is classified based on Oldeman climate classification type. The calculation results are mapped spatially using Arc GIS 10.2 software. The validation result of the TRMM satellite and surface rainfall data shows a high correlation value for the monthly average. The value of correlation coefficient is 0,97 and 25 mm for RMSE value. Oldeman agroclimate zone based on TRMM satellite data in south Kalimantan is divided into five climate zones, such as B1, B2, C1, C2, and D1.

Penerapan Model Pembelajaran Kooperatif Tipe STAD untuk Meningkatkan Hasil Belajar Siswa pada Penentuan Sifat Larutan Asam – Basa dan Derajat Keasaman (Ph) Larutan di Kelas XI IPA SMAN 2 Tanjung Jabung Timur

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Nurasiah Nurasiah
Keyword(s):  
Learning Outcomes ◽  
Learning Process ◽  
Descriptive Analysis ◽  
Observation Data ◽  
Activity Data ◽  
Student Activities ◽  
Average Value ◽  
Base Solution ◽  
Student Questionnaire ◽  
Motivation And Learning

The purpose of this study was to improve students' motivation and learning outcomes of students of class XI IPA SMAN 2 Tanjung Jabung Timur. The classroom action research was conducted in the second semester of the academic year 2013-2014 in period of January until March, as much as four times the meetings which were divided into two cycles. Each cycle was performed twice meetings and one evaluation. The subjects of the study were the  students of class XI IPA 1. To measure students' motivation and learning outcome used student’s activity data during the learning process with guidance observation data, questionnaire data and achievement test data. Then these data were analysed using descriptive analysis method. In the first cycle shows the percentage of student activities, at the first meeting and the second meeting of 45, 71% and 74, 28%. While in the second cycle, the first meeting and the second meeting of 88, 57% and 94, 28%. The Increasing of learning outcomes in the first cycle was shown learning mastery of 59, 38% with an average value of 70, 17. Whereas in second cycle was shown learning mastery of 87,50% with an  average value of 82,76. In addition, the student’s responses are positive towards learning process by implementation of cooperative learning (model STAD) in determination of acid-base solution properties and acidity of solution using natural indicators. It is based on student questionnaire answers which feel happy or satisfied (agree) was 86, 89%. Keywords: Learning outcomes, Learning activities, STAD model, Natural indicators, Learning mastery

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Streamlining Data and Service Centers for Easier Access to Data and Analytical Services: The Strategy of ODATIS as the Gateway to French Marine Data

2020 ◽  
Vol 7 ◽  
Author(s):  
Sabine Schmidt ◽  
Gilbert Maudire ◽  
Cécile Nys ◽  
Joël Sudre ◽  
Valérie Harscoat ◽  
...  
Keyword(s):  
Data Availability ◽  
Research Infrastructure ◽  
Observation Data ◽  
Use Of Data ◽  
Frequency Monitoring ◽  
Fair Principles ◽  
Marine Data ◽  
Access To Data ◽  
Analytical Services ◽  
Induced Changes

The past few decades have seen a marked acceleration in the amount of marine observation data derived using both in situ and remote sensing measurements. For example, high-frequency monitoring of key physical-chemical parameters has become an essential tool for assessing natural and human-induced changes in coastal waters as well as their consequences on society. The number and variety of data acquisition techniques require efficient methods of improving data availability. The challenge is to make ocean data available via interoperable portals, which facilitate data sharing according to Findable, Accessible, Interoperable, and Reusable (FAIR) principles for producers and users. Ocean DAta Information and Services (ODATIS) aims to become a unique gateway to all French marine data, regardless of the discipline (e.g., physics, chemistry, biogeochemistry, biology, sedimentology). ODATIS is the ocean cluster of the Data Terra research infrastructure for Earth data, which relies on a network of data and service centers (DSC) supported by the major French oceanic research organizations (CNRS, CNES, Ifremer, IRD, SHOM; Marine Universities). ODATIS, through its components, is involved in European and international initiatives such as Copernicus, SeaDataCloud, and EMODnet. The first challenge of ODATIS is to catalog all open ocean and coastal data and facilitate data collection and access (discovery, visualization, extraction) through its web portal. A specific task is to develop tools for handling large amounts of data and generate products for policymakers, practitioners, and academics. This study presents the strategy used by ODATIS to implement the FAIR and CoreTrustSeal requirements in each of its DSCs and promote adherence within the scientific community (the main data producer) regarding the upload and/or use of data and suggestion of new products. A second challenge is to cover the end-user needs ranging from proximity to the producer to cross-analysis of data from all Earth compartments. This involves defining and organizing a classification of DSCs in the network, which will be developed within the framework of the French Data Terra research infrastructure, the only framework capable of providing the necessary IT and human resources.

scholarly journals Penerapan Model Pembelajaran Kooperatif Tipe Make a Match untuk Meningkatkan Prestasi Belajar Siswa Kelas III A SDN 38 Kota Bengkulu

2020 ◽  
Vol 3 (2) ◽  
pp. 235-242
Author(s):  
Akhiri Putri ◽  
Johanes Sapri ◽  
Herman Lusa
Keyword(s):  
Cooperative Learning ◽  
Student Learning ◽  
Learning Model ◽  
Average Score ◽  
Observation Data ◽  
Class Iii ◽  
Learning Achievement ◽  
Average Value ◽  
Thematic Learning ◽  
Improve Student Learning

This study aims to improve student learning achievement in Thematic learning class III A SDN 38 Bengkulu City by applying the Cooperative learning model type Make a Match. The subjects of the study were students of class III A SDN 38 Bengkulu City. The research instruments were observation sheets and test sheets. The observation data analysis technique used the formula of average score, highest score, lowest score, difference in score, and range of values for each criterion. Test data were analyzed using the formula of the average value and the percentage of classical learning completeness. Thus the application of the Make a Match type Cooperative learning model can improve student learning achievement in Thematic learning class III A SDN 38 Bengkulu City

scholarly journals Variations of carbon monoxide in the atmosphere of Antarctica according to ground-based and satellite measurements

2019 ◽  
pp. 97-106 ◽  
Author(s):  
V. P. Ustinov ◽  
E. L. Baranova ◽  
K. N. Visheratin ◽  
M. I. Grachev ◽  
A. V. Kalsin
Keyword(s):  
Satellite Data ◽  
Total Content ◽  
Mixing Ratio ◽  
Satellite Measurements ◽  
Average Volume ◽  
Average Deviation ◽  
Average Value ◽  
Surface Measurements ◽  
Good Agreement ◽  
Measurement Period

The results of systematic (2003–2017) measurements of the total content and the volume mixing ratio of CO at Novolazarevskaya station with a spectrometer with a resolution of 0.2 cm– 1 are presented. The inverse problem of determining the total CO content, as well as interfering gases (H2O and N2O), was solved using the SFIT4 software package. Data analysis showed that over the measurement period the average total CO content at Novolazarevskaya amounted to (8 ± 2) 1017 molec/cm2, and the average volume mixing ratio amounted to (37 ± 8) ppb. The obtained data are compared with variations in the total content of CO in Arrival-Heights station, with MOPITT satellite data, as well as with surface values of CO concentration at Syova station. The maximum values of CO are observed in September, the minimum — in January–February. For all the considered series, the trends are insignificant, while there are periods of increased CO content (2010). In recent years (2014–2017) there is a tendency towards an increase in the minimum values of CO. For  Novolazarevskaya and  Arrival-Heights satellite data are characterized by the excess of over ground data, amounting to 19% and 14%, respectively, while there is a seasonal dependence of the deviation with the minimum in December–January. Surface measurements of the total CO content are in fairly good agreement at Novolazarevskaya and Arrival-Heights, and since 2010 the average deviation is 2.4%. The average value of the concentration of CO on Syova 51 ppb is higher than the average volume mixing ratio at Novolazarevskaya. According to the spectral, wavelet and composite analyzes, in all the considered series there are oscillations in the range of 6–45 months with closely coinciding periods and phases.

Large discrepancies on nitrate loading estimates from sparse measurements by SWAT and statistical models at catchment scale

2021 ◽  
Author(s):  
Kang Liang ◽  
Yefang Jiang ◽  
Fan-Rui Meng
Keyword(s):  
Water Quality ◽  
Agricultural Watershed ◽  
Data Availability ◽  
Accurate Estimation ◽  
Observation Data ◽  
Load Estimation ◽  
Concentration Data ◽  
Regression Methods ◽  
Nitrate Load ◽  
Sparse Measurements

<p>Nitrogen (N) is one of the major pollutants to aquatic ecosystems. One of the key steps for efficient N reduction management at watershed scale is accurate quantification of N load. High frequency monitoring of stream water N concentration has not been common, and this has largely been the limiting factor for accurate estimation of N loading worldwide. N loads have often been estimated from sparse measurements. The objective of this study was to investigate the performance of the physical-based SWAT (Soil and Water Assessment Tool) model and three commonly used regression methods, namely LI (linear interpolation), WRTDS (Weighted Regression on Time, Discharge, and Season), and the LOADEST (LOAD ESTimator) on estimating nitrate load from sparse measurements through a case study in an agricultural watershed in eastern Canada. The range of daily nitrate load of SWAT and LOADEST was 0.05-1.29 and 0.14 - 1.35 t day<sup>-1</sup>, compared with 0.13 - 13.08 t day<sup>-1  </sup>and 0.15 - 16.75 t day<sup>-1 </sup>for LI and WRTDS, respectively. Mean daily nitrate load estimated by the four methods followed the order: WRTDS > LI > LOADEST > SWAT. The large discrepancies were mainly occurred during the non-growing season during which there was observation data available. As regression methods use concentration data from dry seasons to estimate the concentrations of wet seasons, there is a strong likelihood of overestimation of nitrate load for wet seasons. The results of this study shed new light on nitrate load estimation under conditions of different data availability. Under situations of limited water quality measurement, policy makers or researchers are likely to benefit from using hydrological models such as SWAT for constituent load estimation. However, the selection of the most appropriate method for load estimation should be seen as a dynamic process, and case by case evaluation is required especially when only sparsely measured data is available. As agri-environmental water quality issues become more pressing, it is critical that data collection strategies that encompass seasonal variation in streamflow and nitrate concentration be employed in regions like Atlantic Canada in the future.</p>

scholarly journals Research on GF-6 Data Selection Technology for Fine Classification of Forest Land

2019 ◽  
Vol 131 ◽  
pp. 01064 ◽  
Author(s):  
Guang Deng ◽  
Peng Zhang ◽  
Zhiyong Li ◽  
xin Tian
Keyword(s):  
Satellite Data ◽  
Selection Process ◽  
Data Availability ◽  
Data Selection ◽  
Wide Field ◽  
Large Area ◽  
Business Applications ◽  
The Face ◽  
Screening Efficiency ◽  
Selection Stage

GF-6 satellite is a kind of high-resolution satellites launched by China in recent years. Its sensors have the characteristics of multispectrals, wide field of view, high spatial resolution and high frequency imaging. In order to carry out fine identification of forest types, this paper proposes a method to improve data screening efficiency and data availability rate in GF-6 satellite data selection stage. This paper describes the selection process and key technical methods of GF-6 satellite data, and gives a verification program. It has been proved that the program meets the design objectives and can quickly scree out the required fast screening technologies in the face of massive data and large-area business applications, thus increasing the degree of automation and reducing the workload of manual visual selection.

Count Models to Represent the Impacts of Weather and Infrastructure on Flight Disruptions

2020 ◽  
Vol 2674 (5) ◽  
pp. 510-521
Author(s):  
Sabrena Jahan Ohi ◽  
Amy M. Kim
Keyword(s):  
Incomplete Data ◽  
Performance Metrics ◽  
Quantitative Model ◽  
Data Availability ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Count Models ◽  
The World ◽  
The Relationship

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

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