scholarly journals Entropy-Based Temporal Downscaling of Precipitation as Tool for Sediment Delivery Ratio Assessment

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1615
Author(s):  
Pedro Henrique Lima Alencar ◽  
Eva Nora Paton ◽  
José Carlos de Araújo
Keyword(s):  
Maximum Entropy ◽  
Sediment Yield ◽  
Hydrological Modeling ◽  
Daily Precipitation ◽  
Absolute Error ◽  
Delivery Ratio ◽  
Combined Method ◽  
Sediment Delivery ◽  
Principle Of Maximum Entropy ◽  
Principle Of Maximum

Many regions around the globe are subjected to precipitation-data scarcity that often hinders the capacity of hydrological modeling. The entropy theory and the principle of maximum entropy can help hydrologists to extract useful information from the scarce data available. In this work, we propose a new method to assess sub-daily precipitation features such as duration and intensity based on daily precipitation using the principle of maximum entropy. Particularly in arid and semiarid regions, such sub-daily features are of central importance for modeling sediment transport and deposition. The obtained features were used as input to the SYPoME model (sediment yield using the principle of maximum entropy). The combined method was implemented in seven catchments in Northeast Brazil with drainage areas ranging from 10−3 to 10+2 km2 in assessing sediment yield and delivery ratio. The results show significant improvement when compared with conventional deterministic modeling, with Nash–Sutcliffe efficiency (NSE) of 0.96 and absolute error of 21% for our method against NSE of −4.49 and absolute error of 105% for the deterministic approach.

scholarly journals Entropy-Based Method for Temporal Downscaling of Precipitation to Improve Sediment Delivery Ratio Assessment

2020 ◽  
Author(s):  
Pedro Alencar ◽  
Eva Paton ◽  
José de Araújo
Keyword(s):  
Maximum Entropy ◽  
Sediment Yield ◽  
Absolute Error ◽  
Reanalysis Data ◽  
Delivery Ratio ◽  
Sediment Delivery ◽  
Precipitation Characteristics ◽  
Erosion Modelling ◽  
Novel Method ◽  
Made In

Scarcity of precipitation data is still a problem in erosion modelling, especially when working in remote and data-scare areas. While much effort was made in the past to use remote sensing or reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration - MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for the modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with areas varying from 10 to 10 km and a broad timespan of measured data (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21% and a Nash Sutcliffe Efficiency of 0.96, (rather than 105% and -4.49, respectively).

scholarly journals Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID: a method for precipitation temporal downscaling for sediment delivery assessment

2021 ◽  
Author(s):  
Pedro Henrique Lima Alencar ◽  
Eva Nora Paton ◽  
José Carlos de Araújo
Keyword(s):  
Maximum Entropy ◽  
Sediment Yield ◽  
Rainfall Intensity ◽  
Absolute Error ◽  
Reanalysis Data ◽  
Sediment Delivery ◽  
Precipitation Characteristics ◽  
Erosion Modelling ◽  
Novel Method ◽  
Maximum Entropy Distribution

Abstract. Scarcity of precipitation data is yet a problem in erosion modelling, especially when working in remote and data scarce areas. While much effort was made to use remote sensing and reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with area varying from 10−3 to 10+2 km2 and broad timespan of monitoring (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for the rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21 % and a Nash-Sutcliffe Efficiency of 0.96 (rather than 105 % and −4.49, respectively

KAJIAN EROSI LAHAN DI KAWASAN AIR STRIP RUNWAY 2600 BANDARA DEPATI AMIR (PGK) BERDASARKAN TATA GUNA LAHAN MASTERPLAN ULTIMATE

2019 ◽  
Vol 7 (2) ◽  
pp. 100-111
Author(s):  
Miskar Maini ◽  
Junita Eka Susanti
Keyword(s):  
Sediment Yield ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Delivery Ratio ◽  
Sediment Delivery Ratio ◽  
Sediment Delivery ◽  
Soil Loss Equation

Standar permintaan engineering pesawat agar desain bangunan infrastruktur di area Air Strip Runway 2600 yang ada dapat mempunyai fungsi lain. Sedangkan kondisi lain sangat menentukan keselamatan karena lahan di sekitar Air Strip Runway 2600 Bandara Depati Amir (PGK) jika tidak ditutupi vegetasi seperti rumput, kondisi lain lahan yang belum ditutupi vegetasi di sekitar Air Strip Runway 2600 berpotensi akan mengalami erosi lahan, kemudian hasil erosi lahan ini akan terbawa oleh aliran air sehingga akan masuk ke saluran drainase yang akan menyebabkan sedimentasi pada saluran drainase tersebut, akhirnya akan berkurang efektifitas kinerja saluran drainase tersebut. Metode yang digunakan untuk memprediksi laju rata-rata erosi di area Air Strip Runway 2600 dengan memperhitungkan faktor erosivitas hujan, erodibilitas tanah, kemiringan lereng atau panjang lereng, pengelolaan tanaman dan konservasi tanah, yang masing masing tata guna lahan tersebut mengacu pada Masterplan Ultimate Bandara Depati Amir (PGK). Perhitungan dilakukan menggunakan persamaan USLE (Universal Soil Loss Equation) yang dikembangkan oleh Wischmeier dan Smith (1965, 1978), kemudian Sediment Delivery Ratio (SDR) dan Sediment Yield.Hasil penelitian ini, prediksi laju erosi permukaan pada area Air Strip Runway 2600 Bandara Depati Amir (PGK) tahun pertama yang mencapai 5,60 mm/tahun atau 100,76 Ton/Ha/tahun, laju erosi tahun kedua mencapai 3,38 mm/tahun atau 60,84 Ton/Ha/tahun dapat diklasifikasikan ke dalam kelas bahaya erosi sedang (kelas III) dan nilai SDR adalah sebesar 56,3%, nilai sediment yield (SR) pada tahun pertama sebesar 5.887,59 Ton/Tahun, pada tahun kedua ketika rumput pada area Air Strip telah tumbuh dengan sempurna terjadi penurunan hasil sediment yield yaitu nilai SR sebesar 3.554,85 Ton/Tahun.

scholarly journals To Be or to Have Been Lucky, That Is the Question

Philosophies ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 57
Author(s):  
Antony Lesage ◽  
Jean-Marc Victor
Keyword(s):  
High Risk ◽  
Maximum Entropy ◽  
Chronic Diseases ◽  
Statistical Test ◽  
Low Risk ◽  
Principle Of Maximum Entropy ◽  
Ex Ante ◽  
Social Opportunities ◽  
Global Population ◽  
Principle Of Maximum

Is it possible to measure the dispersion of ex ante chances (i.e., chances “before the event”) among people, be it gambling, health, or social opportunities? We explore this question and provide some tools, including a statistical test, to evidence the actual dispersion of ex ante chances in various areas, with a focus on chronic diseases. Using the principle of maximum entropy, we derive the distribution of the risk of becoming ill in the global population as well as in the population of affected people. We find that affected people are either at very low risk, like the overwhelming majority of the population, but still were unlucky to become ill, or are at extremely high risk and were bound to become ill.

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