DETERMINATION OF SEDIMENT YIELD BY TRANSFERRING RAINFALL DATA

1977 ◽  
Vol 13 (3) ◽  
pp. 529-541 ◽  
Author(s):  
Jeffrey H. Smith ◽  
Donald R. Davis ◽  
Martin Fogel
Keyword(s):  
Sediment Yield ◽  
Rainfall Data

scholarly journals Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

2021 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Bilal Ahmad Munir ◽  
Sajid Rashid Ahmad ◽  
Raja Rehan
Keyword(s):  
Sediment Yield ◽  
Soil Loss ◽  
Rainwater Harvesting ◽  
Suitability Analysis ◽  
Valley Bottom ◽  
The North ◽  
Roughness Index ◽  
Dam Site ◽  
Very High

In this study, a relation-based dam suitability analysis (RDSA) technique is developed to identify the most suitable sites for dams. The methodology focused on a group of the most important parameters/indicators (stream order, terrain roughness index, slope, multiresolution valley bottom flatness index, closed depression, valley depth, and downslope gradient difference) and their relation to the dam wall and reservoir suitability. Quantitative assessment results in an elevation-area-capacity (EAC) curve substantiating the capacity determination of selected sites. The methodology also incorporates the estimation of soil erosion (SE) using the Revised Universal Soil Loss Equation (RUSLE) model and sediment yield at the selected dam sites. The RDSA technique identifies two suitable dam sites (A and B) with a maximum collective capacity of approximately 1202 million m3. The RDSA technique was validated with the existing dam, Gomal-Zam, in the north of Sanghar catchment, where RDSA classified the Gomal-Zam Dam in a very high suitability class. The SE estimates show an average of 75 t-ha−1y−1 of soil loss occurs in the study area. The result shows approximately 298,073 and 318,000 tons of annual average sediment yield (SY) will feed the dam A and B respectively. The SE-based sediment yield substantiates the approximate life of Dam-A and Dam-B to be 87 and 90 years, respectively. The approach is dynamic and can be applied for any other location globally for dam site selection and SE estimation.

scholarly journals PERKIRAAN PELUANG HUJAN UNTUK MENENTUKAN WAKTU TANAM KAPAS DI NUSA TENGGARA BARAT

2020 ◽  
Vol 9 (2) ◽  
pp. 39
Author(s):  
PRIMA D. RIAJAYA ◽  
F. T. KADARWATI ◽  
MOCH. MACHFUD
Keyword(s):  
Gossypium Hirsutum ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Cotton Yield ◽  
Rainfall Analysis ◽  
First Order ◽  
Successful Prediction ◽  
Probability Methods ◽  
Climatic Elements

<p>Curah hujan merupakan salah salu unsur iklim yang sangal berpengaruh terhadap produksi kapas Variasi hujan di lahan tadah hujan sangat linggi. Waklu tanam yang telah dilentukan sebelumnya hanya berdasarkan data curah hujan selama 1 0 Uihun Untuk mcmpcrbaiki waktu tanam tersebut, perlu dilakukan analisis hujan berdasarkan data curah hujan selama lebih dari 20 tahun untuk mendapatkan angka peluang yang lebih stabil. Analisis dilakukan berdasarkan data curah hujan lebih dari 20 tahun yang lerkumpul dari 16 slasiun hujan yang tersebar di Kabupaten Lombok Timur. lombok Tengah. Lombok Barat, Sumbawa, Bima, dan Dompu. Data dianalisis menggunakan metode peluang Markov Ordc Pertama dan perhilungan peluang sclang kering beturut-turut Waktu tanam kapas di sebagian besar I-ombok dan Sumbawa berkisar minggu pertama sampai minggu kedua Desember, minggu ketiga sampai keempal Desember di Kawo, Lombok Tengah dan Rasanae, Bima, dan minggu pertama Januari di Moyohilir, Sumbawa dan Bayan, Lombok Barat. Daerah yang beresiko linggi untuk pengembangan kapas adalah di wilayah sekilar Pringgabaya (Lombok Timur), Ulhan (Sumbawa), Donggo dan Wawo di Bima Daerah lainnya dengan kandungan air tersedia yang rendah dengan kandungan pasir lebih dari 50% seperti di 1-ape (Sumbawa) penanaman kapas hendaknya dilakukan lebih awal. Tipe iklim didominasi iklim kering dengan musim hujan yang sangat pendek sehingga tidak memungkinkan adanya pergiliran tanaman palawija-kapas Kapas hendaknya ditanam bersamaan dengan palawija mcngingal pendeknya periode hujan.</p><p>Kata kunci : Gossypium hirsutum, waktu tanam. periode kering, masa tanam</p><p> </p><p><strong>ABSTRACT </strong></p><p><strong>Prediction of rainfall probability for determination of cotton sowing times in West Nusa Tenggara</strong></p><p>Climatic elements paticularly rainfall strongly influences successful prediction of rainfed cotton yield. Rainfall vaiability varies amongst Ihe season The previous planting times were determined based on 10 years daily rainfall data. I-ongterm rainfall data arc required for rainfall analysis to get reliable probabilities. The rainfall analysis was done using Markov Chain First Order Probability and dryspell probability methods Ihe rainfall data were collected from 16 rainfall stations in West Nusa Tcnggara (Eas( Lombok, Central I-ombok, West Lombok, Sumbawa, Bima, and Dompu). Ihe planting times varied from the irst week to the second week of December for most areas of I-ombok and Sumbawa The planting limes in Kawo, Central Lombok and Rasanae, Bima were mid December: and early January in Moyohilir, Sumbawa and Bayan, West l.ombok The areas which high risk to drought are around Pringgabaya (Hast lombok), Uthan (Sumbawa), Donggo and Wawo (Bima). On sandy- areas such as I-ape (Sumbawa) cotton should be planted earlier Type of climate in most areas is dry with limited rainy season, thai relay-planting of these areas is not practiced.</p><p>Key words: Gossypium hirsutum, planting time, dryspcll, seasonal patern</p>

scholarly journals Applicability of CHIRPS-based satellite rainfall estimates for South Africa

2021 ◽  
Vol 63 (3) ◽  
pp. 1-12
Author(s):  
J A du Plessis ◽  
J K Kibii
Keyword(s):  
South Africa ◽  
Temporal Distribution ◽  
Pairwise Comparison ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Good Precision ◽  
Alternative Source

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

scholarly journals PENDEKATAN Z-SCORE DAN FUZZY DALAM PENGUJIAN AKURASI PERAMALAN CURAH HUJAN

2020 ◽  
Vol 3 (2) ◽  
pp. 149-156
Author(s):  
Made Leo Radhitya ◽  
Gede Iwan Sudipa
Keyword(s):  
Numerical Data ◽  
Rainfall Data ◽  
Training Data ◽  
Z Score ◽  
Rainfall Forecasting ◽  
Average Value ◽  
Score Method ◽  
Prediction Techniques ◽  
The City

Determination of rainfall is important to determine the intensity of rain that occurs in an area. Rain intensity that is too high will certainly have a bad impact. Forecasting or prediction techniques are used to determine the likelihood of intensity occurring in the following year. However, rainfall data are continuous numerical data. Measurement of accuracy becomes more difficult if the data type is like that. So, this study tests the accuracy of rainfall forecasting in the city of Denpasar from a different perspective. This test combines the Z-score method and the Fuzzy set theory to normalize and classify rainfall data. This combination determines the degree of rainfall membership divided into Upper, Middle, and Lower levels. Based on the results of rainfall accuracy testing starting in 2012-2016 obtained an average value of accuracy of 85% with training data that is data in 2007-2015. The normalization process greatly affects the value of the training data.

scholarly journals A Review on Rainfall Data Resolution and Its Role in the Hydrological Practice

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1012
Author(s):  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Jacopo Dari ◽  
Alessia Flammini
Keyword(s):  
Review Paper ◽  
Temporal Evolution ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Second Step ◽  
Data Resolution ◽  
Historical Series ◽  
Correct Determination

Data collected by gauges represent a fundamental force in most hydrological studies. On the basis of sensor type and recording system, such records are characterized by different aggregation time, ta. In this review paper, a comprehensive rainfall database of rain gauge networks operative worldwide is used to determine the temporal evolution of ta. As a second step, issues related to the limited and heterogeneous temporal resolution of rainfall data are discussed with regard to avoiding possible errors in the analysis of historical series. Particular attention is focused on quantifying the effects on the estimation of extreme rainfalls that play a crucial role in designing hydraulic structures. To this aim, algebraic relations for improving a correct determination of extreme rainfall are also provided.

scholarly journals KALIBRASI PARAMETER HIDROGRAF SATUAN SINTETIK NAKAYASU (STUDI SUNGAI-SUNGAI DI SULAWESI SELATAN )

GANEC SWARA ◽  
2019 ◽  
Vol 13 (2) ◽  
pp. 369
Author(s):  
MUHAMAD YAMIN ◽  
BAGUS WIDHI DHARMA S
Keyword(s):  
Research Study ◽  
Rainfall Data ◽  
Watershed Characteristics ◽  
South Sulawesi ◽  
Determination Of Parameters ◽  
Unit Hydrographs

   Many synthetic unit hydrographs have been developed including the Nakayasu Synthetic Hydriograph based on empirical observations in Japan. Although the determination of parameters has been presented with various criteria, so far the results are still relatively distorted if applied to watersheds in Indonesia, so it is necessary to calibrate some of the parameters used.   This research is a research study conducted in 3 sub-watersheds in South Sulawesi Province by using a type of spread, namely the Maros sub-watershed, Tallo sub-watersheds and Jeneberang sub-watersheds. There are 2 parameters to be calibrated, namely the coefficient α and Ctg with tg = 0.04-0.0058L, the calibration done is obtained α and Ctg values corresponding to each watershed.   The results of the study indicate that the use of the Nakayasu method for the purposes of the analysis of flood hydrographs which is a change in the variety of rainfall data in Indonesia, needs to be modified, especially to the parameters used. To get a range of parameters based on the parameters of watershed characteristics, it is necessary to conduct further studies on many watersheds based on the type of watershed.

scholarly journals Erosive rainfall in Rio do Peixe Valley in Santa Catarina, Brazil: Part I - Determination of the erosivity index

2017 ◽  
Vol 21 (11) ◽  
pp. 774-779
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Rain Gauge ◽  
The State ◽  
Rainfall Data ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Santa Catarina ◽  
R Factor ◽  
Erosivity Index ◽  
Erosive Rainfall

ABSTRACT This study aimed to determine the rainfall erosivity index in the Valley of Rio do Peixe, in the state of Santa Catarina, Brazil. The data series of three rain gauge stations in the cities of Campos Novos, Videira, and Caçador were used to determine the rainfall erosivity based on the EI30 index and to adjust the equations in order to estimate the EI30 value from the rainfall coefficient. On average, it was observed that erosive rains represents 81.4-88.5% of the annual precipitation. The adjusted equations can be used to estimate rainfall erosivity in locations with only rainfall data. The regional equation specified for the erosivity estimation is EI30 = 74.23 Rc0.8087. The R factor is 8,704.8; 7,340.8; and 6,387.1 MJ mm ha-1 h-1 year-1 for Campos Novos, Videira, and Caçador, respectively. In Campos Novos and Videira, the erosivity was classified as high, while in Caçador, it was classified as average.

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