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Jai Prakash Mishra (IJISRTREW13)
| Professeur adjoint | ECE Dept. | VIT Jaipur | Rajasthan | Inde
Harsh Gupta (IJISRTREW02)
| Département de microélectronique | Université Manipal | Jaipur | Rajasthan | Inde
Diwakar Gautam (IJISRTREW05)
| Professeur adjoint | ECE Dept. | Université Sharda
Tarun Badiwal (IJISRTREW09)
| Professeur adjoint | Département électrique | Université Jaggannath | Jaipur | Rajasthan | Inde
Virendra Swami (IJISRTREW105)
| Professeur adjoint | ECE Dept. | Collège Maharshi Arvind | Jaipur | Rajasthan | Inde
Nishant Chauhan (IJISRTREW79)
| Professeur adjoint | Département électrique | Collège Mahershi Arvind | Jaipur | Rajasthan | Inde
Prince Ja.cob (IJISRTREW91)
| Professeur adjoint | Département électrique | Collège Mahershi Arvind | Jaipur | Rajasthan | Inde
Dr S.Saira Banu (IJISRTREW10)
| Professeur associé | ECE Dept. | Université de Karpagam | Coimbatore | Tamil Nadu | Inde
Balaji Velusamy (IJISRTREW500)
| Professeur associé | Info Institut d'ingénierie | Coimbatore | Tamil Nadu | Inde
Lalit Mohan Nainwal (IJISRTREW501)
| École des sciences et recherches pharmaceutiques | Jamia Hamdard | Delhi | Inde
Bais Nirav Kishorkumar (IJISRTREW502)
| Professeur adjoint | Université-Institut de technologie de Ganpat | Ahmedabad | Gujarat | Inde
Raj Kumar Gupta (IJISRTREW503)
| Professeur adjoint | Université Amity | Jaipur | Rajasthan | Inde
Dr Neeta Saxena (IJISRTREW504)
| Professeur adjoint | Université Amity | Gwalior | Madhya Pradesh | Inde
Dr Nageswara Rao Moparthi (IJISRTREW505)
| Professeur associé | Collège d'ingénierie Velgapudi Ramakrishna Siddhartha | Vijayawada | Andhra Pradesh | Inde
R. Narendran (IJISRTREW506)
| Faculté des sciences marines | Université Annamalai | Parangipettai | Tamil Nadu | Inde
Mahadeva.M (IJISRTREW507)
| Professeur adjoint | Collège d'ingénierie Shri Pillappa | Bangalore | Karnataka | Inde
Dr Richa Mehrotra (IJISRTREW508)
| Professeur adjoint | Université Amity | Bangalore | Uttar Pradesh | Inde
Dr Chetan. S. Patali. (IJISRTREW509)
| Directeur | Institut d'infirmières de Dhanush, derrière Durga Vihar | Bagalkot | Inde
Suvarna S Pinnapati (IJISRTREW510)
| Vice-principal | Institut Dhanush des sciences infirmières | Bagalkot | Inde
Nugzar Aleksidze (IJISRTREW511)
| Professeur de la Chaire de Biochimie et Biotechnologie de Iv. Université d'État de Djavakhishvili Tbilissi | Géorgie |
Dr Abdul Rahmat (IJISRTREW513)
| Professeur à l'Université d'État de Gorontalo | Gorontalo | Indonésie |
Elhadi Ibrahim Miskeen (IJISRTREW514)
| Faculté de médecine, Université de Bisha | Bisha | Arabie saoudite |
Dr Ming-An Chung (IJISRTREW515)
| TECHNOLOGIE MICROELECTRONIQUE Inc., | | Taiwan |
Selwa Yousif Abdeldafie Mohammed (IJISRTREW516)
| Professeur adjoint au département des soins infirmiers et infirmiers pédiatriques, | | KSA |
MOHD. MUNTJIR (IJISRTREW517)
| À l'Université de Taif | | Arabie saoudite |
RAJESH AS (IJISRTREW518)
| Professeur adjoint au Département de génie mécanique du MIT | Mysuru | Inde |
Anyira, Isaac Echezonam (CLN) (IJISRTREW519)
| Maître de conférences au Département de bibliothèque et des sciences de l'information | Ogwashi-Uku | Nigeria |
Dr MEHBOOB ALAM PASHA (IJISRTREW520)
| Professeur agrégé de chirurgie, Faculté de médecine | Kedah | Malaisie |
Ahmed Abdelhalim Ahmed Mohamed (IJISRTREW521)
| Département de géologie, Faculté des sciences, Université d'Assiut | Assiut | Égypte |
Lateef Ayodele AGBETUNDE (IJISRTREW522)
| Université Fountain | Osogbo | Nigeria |
OTUU CHIDIEBERE AGHA (IJISRTREW523)
| Département de zoologie et de biologie environnementale | Nsukka | Nigeria |
Oui Kim Huat (IJISRTREW524)
| Westlake International School Position: Professeur de physique de niveau A | Kampar | Malaisie |
Luqman Kareem SALATI (IJISRTREW525)
| Département d'ingénierie des ressources minérales et pétrolières, Kaduna Polytechnic | Kaduna | Nigeria |
Mostafa Ibrahim Hassan (IJISRTREW526)
| Département de zoologie, Faculté des sciences (hommes), Université Al-Azhar | Nasr City, Le Caire | Égypte |
Dibua Emmanuel Chijioke (IJISRTREW527)
| Université Nnamdi Azikiwe | Awka | Nigeria |
Dr Mochammad Munir Rachman (IJISRTREW528)
| La désignation en tant que conférencier de Faculity Economic and Business | Surabaya | Indonésie |
Akinola Yinka Paul Ojelade (IJISRTREW529)
| Le prévôt, Collège fédéral d'éducation | Abeokuta | Nigeria |
Dr PRAKASH BETHAPUDI (IJISRTREW530)
| Institut VIGNAN d'Ingénierie pour les Femmes, HOD, IT | Visakhapatnam | Inde |
Dr T. velumani (IJISRTREW531)
| Professeur adjoint, Collège des arts et des sciences Rathinam (autonome) | Coimbatore | Inde |
PANCHAL RONAKKUMAR KANTILAL (IJISRTREW532)
| Professeur adjoint au Vidyabharti Trust College of BBA & BCA | Umrakh | Inde |
RAGHAVENDRA D (IJISRTREW533)
| Professeur assistant au Département de génie civil, Institut de technologie Don Bosco | New Delhi | Inde |
D. Vijaychendar (IJISRTREW534)
| CONFÉRENCIER AU RKLK College of Education | Suryapet | Inde |
Ajay B. Gadicha (IJISRTREW535)
| .R.Pote College of Engg and Management Amravati en tant que professeur assistant en science informatique. & Département Engg | Amravati | Inde |
Dr Neshat Anjum (IJISRTREW536)
| Professeur adjoint, Université musulmane d'Aligarh | Aligarh | Inde |
PREM KUMAR (IJISRTREW537)
| Project Fellow dans le cadre du projet de recherche UGC-SAP (DRS II) au Dept | Pondichéry | Inde |
BHARATH V (IJISRTREW538)
| Professeur adjoint, Brindavan College of Engineering, Yelahanka | Bangalore | Inde |
Dr AMRITA MEHTA (IJISRTREW539)
| Maître de conférences au Département de MBA de l'Université de Singhania | Udaipur | Inde |
Dr G. Vedanthadesikan (IJISRTREW540)
| Professeur associé et directeur du centre de développement rural de l'Université d'Annamalai | Annamalainagar | Inde |
SOUPTIK BHATTACHARYA (IJISRTREW541)
| Ingénieur civil exécutif, Baguiati | Kolkata | Inde |
Dr R. RAJESHKANNA (IJISRTREW542)
| Professeur agrégé et chef du département de technologie de l'information au Dr.NGP Arts & Science College | Coimbatore | Inde |
Dr Y. Md. Riyazuddin (IJISRTREW543)
| Professeur assistant Département informatique, Université GITAM | Hyderabad | Inde |
Dr BALAJI. AP B (IJISRTREW544)
| Chercheur principal - Division de chimie analytique ”in Vanta Bioscience Pvt Ltd | Chennai | Inde |
Dr UMESH KUMAR SHARMA (IJISRTREW545)
| Professeur Réunions du Conseil académique et autres activités administratives quotidiennes dans une université | | Inde |
Dr Tamilselvan (IJISRTREW546)
| Professeur agrégé, Collège de pharmacie du KMCH | Coimbatore | Inde |
Dr Somnath Thigale (IJISRTREW547)
| Coordinateur pour AAKASH for Education de l'IIT Bombay | Mumbai | Inde |
Maya Kumari (IJISRTREW548)
| Professeur adjoint, Université Amity | Noida | Inde |
Dr ABHISHEK JAI (IJISRTREW549)
| Développeur de contenu et faculté pour CSIR NET LIFE SCIENCES à l'Académie des sciences médicales de Delhi | Dehli | Inde |
Dr M. VINOD KUMAR (IJISRTREW550)
| Professeur associé à Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology | Chennai | Inde |
Jaime L. Coyne (Berry) (IJISRTREW551)
| Professeur associé, Département de l’école d’enseignement et d’apprentissage College of Education Sam Houston State University | Huntsville | Texas |
DR. SHAILENDRA YADAV (IJISRTREW552)
| Professeur assistant, Département de chimie, (Sciences fondamentales) AKS UNIVERSITY | SATNA | Inde |
Dr ANNIE P. ALEXANDER (IJISRTREW553)
| Vice-principal et HOD Upasana College of Nursing | Kollam | Inde |
Dr Manoj Kumar Banjare (IJISRTREW554)
| Professeur assistant Sciences biologiques et chimiques, Université MATS | Pagaria | Inde |
Dr A. SUJIN JOSE (IJISRTREW555)
| PROFESSEUR ASSOCIÉ NEW HORIZON COLLEGE OF ENGINEERING AND TECHNOLOGY | BANGLORE | Inde |
Dr S. Gopinath (IJISRTREW556)
| Professeur associé, Institut de technologie de Karpagam | Coimbatore | Inde |
Dr N. SURESHKUMAR (IJISRTREW557)
| Professeur agrégé au département ECE AVS Emgineering College | Salem | Inde |
Dr Ashim Bora (IJISRTREW558)
| HoD et professeur agrégé Département de mathématiques Diphu Government College | Diphu | Inde |
Dr SANDEEP KAUR (IJISRTREW559)
| Professeur adjoint (Département des sciences de l'alimentation) au MCM DAV College for Women, Secteur 36 | Chandigarh | Inde |
SELVAGANAPATHY MANOHARAN (IJISRTREW560)
| Professeur adjoint au CK COLLEGE OF ENGINEERING AND TECHNOLOGY | CUDDALORE | Inde |
Amarjeet Singh (IJISRTREW561)
| COD Pyramid College of Business & Technology | Phagwara | Inde |
Dr R. SARAVANAN (IJISRTREW562)
| Professeur associé Balaji Institute of Technology & Science | Laknepally | Inde |
Dr Arvind Kumar Shukla (IJISRTREW563)
| Professeur assistant à la School of Computer Applications, IFTM University | Moradabad | Inde |
Dr Ayad M. Fadhil Al-Quraishi (IJISRTREW564)
| Professeur de télédétection environnementale et chef du département d'ingénierie environnementale, Collège d'ingénierie | Erbil | Irak |
Dr KV SHALINI (IJISRTREW565)
| Professeur adjoint Rathnavel Subramaniam College of Arts and Science | Coimbatore | Inde |
Dr Devendra S. Shirode (IJISRTREW566)
| Professeur associé au Dr DY Patil College of Pharmacy | Pune | Inde |
Dr Satish Geeri (IJISRTREW567)
| Professeur agrégé Pragati Engineering College | Surampalem | Inde |
V. PRADEEPA (IJISRTREW568)
| Professeur adjoint Seethalakshmi Achi College pour femmes | Pallathur | Inde |
Dr S.THULASEE KRISHNA (IJISRTREW569)
| Professeur en CSE au Chadalawada Ramanamma Engineering College | Tirupathi | Inde |
Dr K. HUSSAIN (IJISRTREW570)
| Professeur associé Sharad Institute of Technology, College of Engineering | Yadrav | Inde |
Mohamed Ashik (IJISRTREW571)
| Professeur adjoint en statistique Département de mathématiques, Merit Arts and Science College | Tirunelveli | Inde |
Dr SUBHASH LAXMAN GADHAVE (IJISRTREW572)
| Professeur agrégé à, Institut de technologie Dr.DYPatil | Pimpri | Inde |
DR. WANJARA AMOS OTIENO (IJISRTREW573)
| Université de Kisii | Kisii | Kenya |
Dr Pawan Singh (IJISRTREW574)
| Asst. professeur IFTM University | Moradabad | Inde |
M.PARVATHY (IJISRTREW575)
| Conférencier Sethu Institute of Technology | Virudhunagar | Inde |
K.SRIPRASADH (IJISRTREW576)
| Professeur associé Département de génie informatique Thirumalai Engineering College | Kancheepuram | Inde |
R.BHOOPATHI (IJISRTREW577)
| Professeur adjoint Grade -II Sri Sairam Engineering | Chennai | Inde |
Narendra Kumar (IJISRTREW578)
| Professeur assistant au Département des études de gestion, Université de Kumaun | Nainital | Inde |
Dr Ajay B. Gadicha (IJISRTREW579)
| Professeur adjoint PRPote College of Engg and Management | Amravati | Inde |
Dr G.SWAMINATHAN (IJISRTREW580)
| Professeur adjoint SRM Institute of Science and Technology | Chennai | Inde |
DR. ANIMESH KUMAR SHARMA (IJISRTREW581)
| SV College of Agricultural Engineering & Technology and Research Station, IGKV Campus | Raipur | Inde |
Dr Anupam Tiwari (IJISRTREW582)
| Professeur adjoint Janta Vaidik College | Meerut | Inde |
Dr Anand Kumar (IJISRTREW583)
| Professeur adjoint, Faculté de commerce et de gestion, Université Maharishi | Mundiya Purohitan | Inde |
Dr Manoj Kumar Banjare (IJISRTREW584)
| Professeur assistant Sciences biologiques et chimiques, Université MATS | Pagaria | Inde |
Dr SRINIVASA RAO P (IJISRTREW585)
| Professeur associé à l'institut LENDI d'ingénierie et de technologie | Vizianagaram | Inde |
Dr T.velumani (IJISRTREW586)
| Professeur adjoint au Département d'informatique du Kongu Arts & Science College | Coimbatore | Inde |
Dr Shubham Agarwal (IJISRTREW587)
| Professeur associé Institut de gestion de New Delhi | New Delhi | Inde |
Dr P. ALAGURAJA (IJISRTREW588)
| Professeur associé Jay Shriram Group of Institutions, Anna University | Tirunelveli | Inde |
Dr Ramesh Saudagar Ghogare (IJISRTREW589)
| Professeur assistant chimie au BNN College | Thane | Inde |
Dr RAGHU CHAND R (IJISRTREW590)
| Professeur et vice-principal, Département de génie mécanique, Institut de technologie de Karavali | Bondathila | Inde |
Dr Ashim Bora (IJISRTREW591)
| HoD et professeur agrégé Département de mathématiques Diphu Government College | Diphu | Inde |
Mekala Ramesh (IJISRTREW592)
| rofessor Science and Engineering Institut Sri Ranganathar d'ingénierie et de technologie | Coimbatore | Inde |
Dr Dnyaneshwar M.Mate (IJISRTREW593)
| Rajarshi Shahu College of Engg, professeur agrégé JSPM. | Pune | Inde |
Volume 5, Issue 12, December – 2020 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT20DEC258 www.ijisrt.com 1274
Performance of Multi-site Stochastic Weather
Generator MulGETS : Application to the Lobo
Watershed (Western Center of Côte d’Ivoire) Vami Hermann N’Guessan Bi1,2, Tchepko Théodore Adjakpa3 , Fabrice Blanchard Allechy1,
Marc Youan Ta1,2, Assa Fabrice Yapi1 , Affian Kouadio1
1 Soil, Water and Geomaterials Laboratory, Felix Houphouët Boigny University, Côte d’Ivoire 2 University Center for Research and Application in Remote Sensing, Felix Houphouët Boigny University, Côte d’Ivoire
3 Interfaculty Center for Training and Research in Environment for Sustainable Development, Abomey Calavi University, Benin
Abstract:- The study of the impact of climate variability
and change requires long series of quality meteorological
data from several sites. Measuring stations are sparse
with data gaps and short durations, so time generators are
indispensable to generate climate data that are
statistically similar to the observed data. This study
describes a Matlab-based multi-site stochastic time
generator (MulGETS) for generating daily precipitation
and temperature data. The daily observation weather
stations of Séguéla, Vavoua, Zoukougbeu, Daloa, Issia and
Grand-zattry were used and the results show that the
model adequately reproduces the meteorological data.
The observed and simulated values show a very good
correlation with the coefficients of determination very
near to 1, indicating the performance of the model.
Keywords:- Performance, weather generator MulGETS,
precipitation, temperature, Lobo watershed.
I. INTRODUCTION
Many problems in hydrology and agricultural science
require extensive rainfall records from several sites [1].
However, it is clear that the measuring stations are not very
dense and the observed data are not consistent or available in
sufficient quantities. There are gaps and high levels of
uncertainty. Climate generators are thus developed to generate climate data that are statistically similar to observed
data. They have been widely used to generate climate
variables simultaneously at several sites ([2], [3], [4], [5], [6],
[7], [8], [9], [10]). Most time generators are based on a single
site and are unable to represent the spatial attributes of the
observed time series. In other words, although time series can
be generated over multiple sites, they are spatially
independent even when the stations are correlated. The lack
of spatial correlation, particularly for precipitation, makes
single-site time generators useless for many applications.
However, the simultaneous generation of meteorological
variables at several locations is of great importance for hydrological and agricultural applications ([11] [1] [12]). [13]
thus presented an algorithm to efficiently find the desired
correlation matrices for precipitation occurrence and
amounts. They solved the problem of spatial intermittency of
precipitation using an index of occurrence approach.
Following this algorithm, a Matlab-based multi-site weather
generator, called the Multi-Site Weather Generator of the
École de Technologie Supérieure (MulGETS), was developed
to generate daily precipitation and temperature data. This
study was carried out to describe and evaluate the performance of the MulGETS multi-site stochastic time
generator in the Lobo watershed at six stations. This paper
has been structured in four sections for its understanding. The
first section introduced the study area. The second section
dealt with the materials and methods used. The results
obtained, their interpretation and discussion constituted the
third section of this work followed by the conclusion in the
fourth section.
II. PRESENTATION OF STUDY AERA
The Lobo watershed is located in central-western Côte d'Ivoire between longitudes 6°05' and 6°55' West and
latitudes 6°02' and 7°55' North (Fig. 1).
Fig. 1. Localisation of the Lobo watershed
The major part of the watershed belongs to the Haut-
Sassandra region, the capital of the region is Daloa. It covers
the departments of Daloa, Issia, Vavoua and Zoukougbeu; the
extreme north belongs to the department of Séguéla; while in
the south it overflows into the department of Soubré. The
Lobo watershed is located in a transition zone where there are
two types of climate: the equatorial transition climate
(Baouléen climate) which is observed in the northern half of
Volume 5, Issue 12, December – 2020 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT20DEC258 www.ijisrt.com 1275
the watershed and the equatorial transition climate (Attiéen
climate) which is observed in the extreme south. Two main types of relief share the watershed. These are the plains
whose altitude varies between 160 and 240 m, located in the
south of the watershed, and the plateaus occupying the major
part of the watershed correspond to altitudes varying between
240 and 320 m [14]. The soils are essentially of ferrallitic
type, strongly or moderately desaturated, modally reworked
with an overlay of schists and granites.
III. MATERIAL AND METHOD
A. Data and material
The data used in this study are daily precipitation and temperature data from six weather stations over the period
1980 to 2013 (34 years). These data are obtained from the
National Centers for Environmental Prediction
(NCEP) Climate Forecast System Reanalysis (CFSR) and are
available on the website (http://globalweather.tamu.edu/ ).
The MulGETS package implemented under the Matlab
environment was used to generate the precipitation,
maximum and minimum temperature data.
B. Method Multi-site stochastic weather generator (MulGETS)
developed by [15], is based on the Matlab environment and
allows to generate daily rainfall, maximum and minimum
temperatures (Tmax and Tmin) of unlimited length on several
sites based on historical data. It is an extension of the single-
site Weather Generator of École de Technologie Supérieure
(WeaGETS) and is suitable for small watersheds where a
single station can be used to represent the entire watershed
[15]. The basic input data includes a file name for the
observed meteorological data, a file name for storing the
generated data, a precipitation threshold value (a minimum
amount of rain in ''mm'' for a day considered rainy) and the number of years of data to be generated (Fig. 2).
Fig. 2. Organisation chart of the structure of the MulGETS
weather generator
Generation of rainfall occurrence
The two-state first-order Markov chain is one of the most widely used methods for generating rainfall occurrence
([15]). The probability of precipitation for a given day is
based on the wet or dry state of the previous day, which can
be defined in terms of two transition probabilities P01 and
P11 according to equations 1 and 2 :
P01=Pr{precipitation on day t ǀ no precipitation on day t-
1}(1)
P11=Pr{Precipitation on day t ǀ precipitation on day t -1}
(2)
The two complementary transition probabilities are :
P00 = 1 - P01, and P10 = 1 - P11
Generation of precipitation amount
MulGETS offers the possibility to use multi-gamma
distribution and multi-exponential distribution to simulate
daily rainfall amounts. Precipitation sequences are produced
using two different distributions: a one-parameter exponential
and a two-parameter gamma. The probability density function
of the exponential distribution is given by equation 3:
f(x)= λe-λx (3)
where x is the amount of daily precipitation and its
parameter λ is equal to 1/average. The multi-exponential distribution combines several exponential distributions, each
of which has its own parameter. The probability density
distribution of gamma is given by equation 4 :
f(x)= ((x/β)〗^(α-1) exp[-x/β])/(βΓ(α)) (4)
where α and β are the shape and scale parameters
respectively, and Г(α) refers to the gamma function
calculated on α. The two parameters (𝛼 and 𝛽) necessary to
use the gamma distribution are directly related to the mean
(𝜇) and standard deviation (𝜎). They are defined as equations
5 and 6 :
μ= α / β (5)
σ= √(α / β) (6)
Generation of maximum and minimum temperature
MulGETS uses a 1st order autoregressive model to
generate the maximum and minimum temperature data. The
observed time series is first reduced to the residuals by
subtracting the daily means and dividing by the standard
deviations. The means and standard deviations are conditioned by wet or dry conditions. The residual series are
then generated by equation 7 :
χ_(p,i) (j)=A χ_(p,i-1) (j)+Bε_(p,i) (j) (7)
where χp,i(j) is a matrix (2×1) for day i of year p whose
elements A and B are matrices (2×2) whose elements are
defined so that the new sequences have the desired
autocorrelation and intercorrelation coefficients. The matrices
A and B are determined by equations 8 and 9 :
𝐴 = 𝑀1𝑀0−1 (8)
Volume 5, Issue 12, December – 2020 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT20DEC258 www.ijisrt.com 1276
𝐵𝐵𝑇 = 𝑀0 − 𝑀1𝑀0−1𝑀1
𝑇 (9)
where the exponents -1 and T denote the inverse and
transposition of the matrix, respectively, and M0 and M1 are
the covariance matrices of delay 0 and delay 1. The daily
values of Tmax and Tmin are obtained by multiplying the
residuals by the standard deviation (σ) and adding the mean
(μ) (equations 10 and 11) :
Tmax= μmax+ σmax × χ(p,i) (10)
Tmin= μmin+ σmin × χ(p,i) (11)
Performance of the MulGETS weather generator
Model performance is achieved using monthly
precipitation data from 1997 to 2013 (17 years). It consists in
making simulations using the model obtained over a period
(1997 to 2013) for which precipitation data exist and then
comparing the actual data with the data estimated by the
Markov model. This process leads to the determination of the
correlation coefficient that measures the quality of prediction
of meteorological data.
IV. RESULTS
Daily data from the stations of Séguéla, Vavoua,
Zoukougbeu, Daloa, Issia and Grand-zattry were used as
input data for the MulGETS time generator. The monthly
averages of precipitation, maximum and minimum
temperatures for the seventeen (17) years (1997-2013)
recorded by each of the 30 (thirty) simulations were used to
illustrate the performance of the model.
A. Precipitation amount
The superposition of the curves of observed monthly
mean rainfall heights and those simulated by the model for the validation period from 1997 to 2013 over all stations is
given in Fig. 3.
Fig. 3. Comparison of observed (blue) and simulated (red)
average monthly rainfall curves for 1997-2013.
These graphs show a good superposition of the curves,
which translates into a good estimate of the average monthly rainfall from January to February and from August to
September at the stations of Séguéla, Vavoua and
Zoukougbeu. This good estimate is also observed in January
at the Daloa station, from January to February and from July
to November at the Issia and Grand-zattry stations. Rainfall
heights are slightly overestimated by the model from March
to June at all stations. The performance of the model was also
evaluated through the coefficient of determination which
varies from 0.8175 to 0.916 (Fig. 4) indicating a good
correlation between observed and simulated rainfall amounts.
Fig. 4. Correlation between observed average monthly
rainfall amounts and simulated average monthly rainfall
amounts from 1997 to 2013.
B. Maximum and minimum temperatures Fig. 5 shows the superimposition of the observed and
simulated monthly mean maximum temperature curves from
1997 to 2013 for each station.
Fig. 5. Comparison of observed (blue) and simulated (red)
monthly mean maximum temperature curves for 1997-2013.
Volume 5, Issue 12, December – 2020 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT20DEC258 www.ijisrt.com 1277
The observed and simulated monthly mean maximum
temperature curves show a perfect overlap and similar trends in each station. The MulGETS multi-site approach adequately
reproduces the maximum temperature data. The observed
values are almost identical to the simulated maximum
temperature values. The observed and simulated monthly
mean maximum temperatures show a very high correlation as
indicated by the coefficients of determination which are very
close to 1 (Fig. 6) showing a good performance of the model.
Fig. 6. Correlation between observed monthly average maximum temperatures and simulated monthly average
rainfall amounts from 1997 to 2013
As with maximum temperatures, the observed and
simulated monthly mean minimum temperature curves are
well overlaid and show similar trends (Fig. 7). The MulGETS
model adequately reproduces the minimum temperature data.
Fig. 7. Comparison of observed (blue) and simulated (red)
monthly mean minimum temperature curves from 1997-2013
Figure 8 illustrates the correlations between observed
monthly mean minimum temperatures and the monthly mean minimum temperatures simulated by MulGETS. The R2
coefficients, which range from 0.85 to 0.906 across all
stations, are very close to 1, meaning a very good correlation
between observed and simulated minimum temperatures.
Fig. 8. Correlation between observed monthly mean
minimum temperatures and simulated monthly mean rainfall amounts from 1997 to 2013
V. DISCUSSION
This work describes the Multi-site stochastic weather
generator of the School of Higher Technology (MulGETS)
for generating daily meteorological data (precipitation and
temperature) in the watershed of the Lobo river. The daily
precipitation and temperature data used in this study come
from the Climate Prediction System Reanalysis Centre
(CFSR). The use of these data is based on their wide use,
availability and daily time step. They have been used and validated by several authors including [16]; [17]; [18]; [19]
[20]; [21]; [22]. The Markov chains used in this study have
been the subject of several works relating to rainfall analysis
([23]; [24]; [25]; [26]. These authors have shown that Markov
chains describe daily rainfall fields well. They have the
advantage of taking into account the memory effect to give a
better description of rainfall. [27] in his work on Markov
rainfall field modelling, came to the same conclusion. The
performance of the MulGETS weather generator was
evaluated in relation to the generation of precipitation and
temperature at six stations. The results show that the model adequately reproduces precipitation and temperature data.
The coefficients of determination show a very good
correlation between observed and simulated data at all
stations. These results are similar to the work of [28] which
showed that MulGETS satisfactorily reproduces the observed
correlation for both the occurrence and amount of
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ISSN No:-2456-2165
IJISRT20DEC258 www.ijisrt.com 1278
precipitation and maximum and minimum temperatures in a
part of the UK north-east of Ireland. Work by [29] also indicated improved model performance in the climate
statistics space over the five sub-watersheds of the South
Nation watershed, located in eastern Ontario, Canada. [30] in
their study on the evaluation of multi-site precipitation
generators at different scales, confirm the performance of
MulGETS in preserving most of the observed statistics in
contrast to the modified Wilks approach, the Stochastic
Climate Library (SCL) and the Weather GENerator (WGEN)
in simulating precipitation in Taiwan.
VI. CONCLUSION
MulGETS is a stochastic daily weather generator based
on Matlab that allows the generation of time series of
precipitation and temperature data of unlimited length. Six
stations in the Lobo watershed have been selected to illustrate
the performance of the model. The results show that the
model adequately reproduces daily precipitation and
temperature data but overestimates the wet periods (rainy
season period). The values of the observed and simulated data
are almost similar and show a very good correlation with
coefficients of determination ranging from 0.8 to 0.98 over all
stations. MulGETS is an efficient and effective model for generating meteorological data.
ACKNOWLEDGMENT
This study was supported by the International Joint
Laboratory NEXUS water climate agriculture and energy.
The authors express their gratitude to the reviewers and the
editor for their outstanding relevance, which has enabled us to
improve this article.
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