Thomas Fiering Research Articles

Comparison of Monte Carlo Schemes in the Modeling of Extreme Flood in Tropical Rain Forest Basins

Insufficient length of streamflow record poses challenges in capturing extreme events, frequencies, and sequences crucial for effective water resources systems design. Consequently, simulation and application of statistically similar streamflow values becomes imperative. In this study, two Markov-based models, namely the Thomas-Fiering (T-F) model and the Autoregressive (AR) model, were developed based on a log-normal probability distribution to synthesize monthly and annual flows using a decade of historical data (1980–1989) collected from the Umulokpa gauging station in the Adada River catchment of Enugu State, Nigeria. Except for the month of December (p = 0.03), no significant difference (p > 0.05) was observed between the monthly and annual model-predicted streamflows. However, both models demonstrated limitations in fully capturing the characteristics of the observed flows, particularly regarding Kurtosis. Further accuracy assessment from computed correlation coefficients (0.018 for T-F and 0.122 for AR), along with root mean square error (RMSE) values (12.84 for T-F and 11.95 for AR), indicate that while both models deviated from the observed flows, the AR model demonstrated superior performance compared to the T-F model. However, to apply these models reliably, for future flow prediction in the Adada catchment, streamflow data from rivers with comparable catchment characteristics is essential.

Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa

Abstract Over recent decades, water quality at the Mthatha River Catchment (MRC) within the Eastern Cape Province of South Africa has been threatened by various pollutants. The continuous effluent concentration discharges from the Mthatha Prison and the Efata School for the Blind and Deaf have caused ineffable damage to the Mthatha River's water quality. Thus, the time series-measured data between 2012 and 2020 were analysed to determine the trends and enable forecasting of selected water quality parameters using the Thomas–Fiering (T–F) stochastic model. The Kendall's τ test trends show an increase in the coefficient of variation of 0.498 and 0.394 at the Mthatha Prison and Efata School, respectively, for abrupt changes, whereas the mean monthly T–F forecasted model shows a good correlation value range from 0.79 to 0.87 for the various predicted variables. The simulated predicted models and trends could serve as a measure to forecast selected water quality parameters' occurrence and a likelihood period when the river pollutants could be controlled. Water managers and researchers would find usefulness in the employed tools for an effective control planning of the river pollutants.

Spatial Climate Forecasting for Climatology Disaster Mitigation

Climate is the average weather conditions over a relatively long time. Climate change is a problem for living things, such as the agricultural sector. It affects the availability of community food reserves, public health, and socio-economics. The impact of climate change causes dramatic changes in agricultural patterns and shifts in planting time. Water availability for agricultural needs is complicated to predict, so the failure rate of agricultural production is getting bigger. Failure is due to a mismatch between the availability of water and the applied cropping pattern. This study aims to analyze and predict the spatial distribution of the Schmidt-Ferguson climate classification on Lombok Island from 2018-2035. This study generated rainfall data using the Thomas Fiering method from 18 rain stations on Lombok Island for 24 years (1994-2017). Determine the dry, moist, and wet months based on the Schmidt-Ferguson classification. From the analysis results, the most significant Q value is at the Geres Daya, Sapit, and Sopak rain stations with the classification

Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm

The production and use of energy from hydropower generation play a vital role in the economy. Besides, the presence of uncertainty further increases the complexity in optimizing the reservoir operation. A synthetic streamflow generation based on historical inflow records was employed using the Thomas–Fiering model for handling the uncertainty and variability of reservoir inflows. However, under the circumstances of water deficiency, the hydropower output is significantly reduced. In this study, an investigation of a parameter free Jaya algorithm as an optimization method for reservoir operation was carried out. When deriving the optimal operational rule a hedging strategy is introduced to attenuate the impact of reduced water supply. This strategy can effectively counterbalance the lack of water supply with reservoir storage requirements. The higher amount of hydropower generated by the proposed algorithm than the other algorithms used in this study, such as genetic algorithm (GA), the ant colony algorithm (ACO), the bat algorithm (BA), the particle swarm optimization (PSO) algorithm, chicken swarm optimization (CSO) algorithm, grasshopper optimization algorithm (GOA), equilibrium optimizer (EO) and firefly algorithm (FA), has shown its efficiency in the reservoir system. Several reservoir performance indices, such as total hydropower generation, reliability, and resilience, were used to access the proposed algorithm and other algorithms efficiency

Rainfall modeling based on early predicted and season zone characteristic in the BMKG season zone over Lombok river basin

Planning water resources for long-term (annual, monthly) and operational (daily) requires a more accurate analysis of water availability calculations. The Meteorology, Climatology and Geophysics Agency (BMKG) publishes bulletin of forecasting every year in the form of the early wet season and the characteristics of the wet and dry season in each Season Zone (ZOM). BMKG conducts analysis and forecasting with complex climate parameters. Thomas Fiering analysis with the help of Excel Visual Basic Application (VBA) software is used to forecast rainfall at each rainfall station in BMKG Season Zone. The average error of forecasting rainfall during the wet season on the BMKG season zone in 2015/2016, 2016/2017 and 2017/2018 were 20%, 30% and 26%. While the average error for the dry season on the BMKG season zone is 44%, 34% and 52%. The existence of this deviation is caused by several factors including the uncertain season changes every year, as well as the influence of “El Nino.”.

Stochastic analysis for prediction of future performance of Mosul storage

An investigation of the Mosul reservoir system within the Tigris river basin in Iraq was conducted to determine the ability of the system to generate hydroelectric power. A reproduction model utilizing the Simulink environment on the MATLAB platform was used to imitate the Mosul reservoir system. The reliability of the system under various future scenarios of data sources was also examined by employing a stochastic model used to create an inflow time series. The Thomas–Fiering model was chosen for this reason, which provided a wide range of data sources (inflows) to generate hydropower from the reservoir system under examination. Generally, the annual potential capacity of the Mosul basin for energy generation reaches 20,000 GW·h–1. Realizing that Iraq’s energy requirements are approximately 12 GW of power, and the integrating power production of the basin under examination is about 1.5 GW, this would cover around 12% of the total demand, which is significant.

Water Supply Risk Analysis Based on Runoff Sequence Simulation with Change Point under Changing Environment

This study investigates the water supply risk of Panjiakou reservoir in the Luanhe River basin of China during 1956–2016 under environmental change. Since the monthly runoff series during 1956–2016 is a time series with change points, it is necessary to find a new stochastic streamflow series generation approach to preserve the statistical characteristics of the original series and to refine the reliability of water supply risk analysis. This paper improves a known stochastic streamflow simulation method of previous research to better reflect the characteristics of series with change points. And this paper simulates the monthly runoff series with change point of Panjiakou reservoir during 1956–2016 by three different methods, including Thomas–Fiering model, copula function stochastic simulation method, and copula function stochastic simulation method with the mixed distribution model. Among the three methods, the copula function stochastic simulation method with the mixed distribution model which is improved on the basis of copula function stochastic simulation method in this study performs best in simulating the observed monthly runoff series during 1956–2016, and the water supply risk indices including reliability (time-based and volume-based), resilience, vulnerability, drought risk index (DRI), and sustainability index (SUI) are evaluated for Panjiakou reservoir and analyzed by using the stochastic simulation results. By comparing with the previous studies, all indicators are between the corresponding results of 1956–1979 and 1980–2016 with stationary inflows; it can be seen that change point seriously affects the water supply risk of Panjiakou reservoir. These results make it easy to formulate water supply strategies and schemes in changing environment for water resources managers.

Hydrological Drought Frequency Analysis A Case Study in South Kerala

Hydrological droughts refer to the shortfalls on surface or subsurface water supply like streamflow, reservoir, groundwater etc. Many people consider droughts to be a rare event, when in fact they are a normal and recurrent feature in the cycle of climatic events. Droughts in Indian peninsula seem to be primarily associated with the failure of monsoon. With climate change studies predicting even more hydrological extremes for the future, already competing demand for water resources may further aggravate. Thus, the importance of water resources aspect of drought is being increasingly recognised. In this paper, the severity of droughts in terms of the cumulative water deficiency is studied by two methods by proper identification of the threshold levels in two major basins of Kerala in the south. The streamflow for the basins are generated for a period of over two decades using the 'Thomas Fiering model'. An application of the drought frequency analysis by Yevjevich and Dracup methods is performed and presented.

Stochastic Time series analysis of Runoff data of the Blue Nile at Eldeim, Sudan

To improve the management of operation system for the Roseires reservoir it is necessary to know the hydrological system of the Blue Nile river, which is the main water source of the reservoir. In this work, a Modified Thomas Fiering model for generating and forecasting monthly flow is used. The methodological procedure is applied on the data obtained at the gauging station of Eldeim in Blue Nile, Sudan. The study uses the monthly flows data for years 1965 to 2009. After estimation the model parameters, the synthetic time series of monthly flows are simulated. The results revealed that the model maintained most of the basic statistical descriptive parameters of historical data. Also, the Modified Thomas Fiering model is applied to predict the values of the next fifty-five years, with excellent results that conserved most basic statistical characteristics of runoff historical series. The Modified Thomas Fiering model is able to realistically reconstruct and predict the annual data and shows promising statistical indices.

Generation of Synthetic Streamflow of Jakham River, Rajasthan Using Thomas-Fiering Model

A study was undertaken to develop and apply Thomas-Fiering (T-F) model for generation of synthetic streamflow of Jakham river, Rajasthan. Streamflow data of 40 years (June,1975 to May, 2015) was used, of which 360 month-data (June,1975 to May, 2005) were used to develop the model and 120 month-data (June, 2005 to May, 2015) were used to predict the streamflow. Performance of the model was evaluated using four statistical criteria, viz. correlation coefficient (R), root mean square error (RMSE), modified NashSutcliffe efficiency (MNSE), and modified index of agreement (MIA).The results showed satisfactory performance of the model in generating synthetic monthly streamflow based on R, RMSE, MNSE and MIA values of 0.73, 28.47×106 m3 , 0.456 and 0.73, respectively. The developed model was used to generate 100-year synthetic streamflows.

Reliability assessment of Thomas Fiering's method of stream flow prediction

This study was carried out to assess the reliability of Thomas-Fiering’s method of stream flow prediction. The 19 years gauged data of 1955-1973 was extended to 2016 using the model. Model calibration was done by multiple linear regressions of the gauged and synthetic data of 1956-1973. The linear equations developed for January to December were used for adjustment of the three sets of stream flow data generated for 1974-2016. The reliability assessment was done based on the extent to which the unbiased statistics (mean, standard deviation and correlation coefficients) of the 1955-1973 stream flow data were preserved in the synthetic stream flow for 1955-2016. The comparison was done using linear regression and One-Way ANOVA (95% Confidence level) to check for the reliability of the generated data. The coefficients of determination, P-values, F-values and critical F-values were used to estimate the reliability index. Synthetic data was found to be 95.9% reliable. Keywords : Ofu River, Reliability, Stream flow, Synthetic, Thomas Fiering’s model

Generation of Synthetic Streamflow of Jakham River, Rajasthan Using Thomas-Fiering Model

A study was undertaken to develop and apply Thomas-Fiering (T-F) model for generation of synthetic streamflow of Jakham river, Rajasthan. Streamflow data of 40 years (June,1975 to May, 2015) was used, of which 360 month-data (June,1975 to May, 2005) were used to develop the model and 120 month-data (June, 2005 to May, 2015) were used to predict the streamflow. Performance of the model was evaluated using four statistical criteria, viz. correlation coefficient (R), root mean square error (RMSE), modified NashSutcliffe efficiency (MNSE), and modified index of agreement (MIA).The results showed satisfactory performance of the model in generating synthetic monthly streamflow based on R, RMSE, MNSE and MIA values of 0.73, 28.47×106 m3 , 0.456 and 0.73, respectively. The developed model was used to generate 100-year synthetic streamflows.

Pembangkitan Data Debit dan Skenario Pola Tanam Daerah Irigasi Embung Suruhan

The availability of historical data used in hydrological analysis is often incomplete and very short, so the information obtained from the data is also very little. If continue to use incomplete historical data or a little period of time, the analysis results will not match the circumstances in the field. Hydrology analysis in planning of Embung Suruhan uses rainfall data from Jiken Station, Blora Station and Bogorejo Station from 1986-2006 and climatology data of Tempuran Reservoir Station 1986-2005 with 4.6 million m3. The next study, capacity 6.69 million m3 with rainfall data from five stations, namely: Blora station, Jiken, Gayam, Greneng, and Tempuran from 1997-2012 and climatological data of Tempuran Reservoir and Kedung Ombo Reservoir. This research is to get the requirement of optimal irrigation with forecasting of discharge data and simulation of initial variation of planting and some scenario type of planting pattern from Embung Suruhan. Discharge was analyzed by Mock method and then raised up to 25 years with Thomas Fiering model and to get the water availability used Basic Year. The discharge data has characteristics that are close to historical data and in 2018 as the basic year used for water availability.

Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation

The capability of ANN to generate synthetic series of river discharge averaged over different time steps with limited data has been investigated in the present study. While an ANN model with certain input parameters can generate a monthly averaged streamflow series efficiently; it fails to generate a series of smaller time steps with the same accuracy. The scope of improving efficiency of ANN in generating synthetic streamflow by using different combinations of input data has been analyzed. The developed models have been assessed through their application in the river Subansiri in India. Efficiency of the ANN models has been evaluated by comparing ANN generated series with the historical series and the series generated by Thomas-Fiering model on the basis of three statistical parameters-periodical mean, periodical standard deviation and skewness of the series. The results reveal that the periodical mean of the series generated by both Thomas–Fiering and ANN models are in good agreement with that of the historical series. However, periodical standard deviation and skewness coefficient of the series generated by Thomas–Fiering model is inferior to that of the series generated by ANN.

Data-driven stochastic modeling for multi-purpose reservoir simulation

This paper presents an integration of data-driven modeling and stochastic models for simulation of reservoir operation. The simulation model developed in this study was applied to the Ruhr river reservoirs system in Germany. An adaptive neuro-fuzzy inference system, Thomas–Fiering model and hidden Markov model were integrated in a simulation model. The set of model input included the time of the year, reservoir storage, inflow and Standardized Precipitation Index; and the target output was the reservoir release. Predicted and observed release values were evaluated using several common evaluation criteria. Results of model performance showed that the proposed model is capable of simulating reservoir operation and provides reliable reservoir release prediction. Results showed also that the proposed approach could be a good tool at the real-time operation stage to quickly check operational alternatives due to emergency events or planning and real-time incongruence.

Improved Thomas–Fiering and wavelet neural network models for cumulative errors reduction in reservoir inflow forecast

Inflow forecasting for reservoirs is a key component for reservoir operation, water resources management, and inter-regional water transfer. In this paper, to obtain enhanced accuracy in forecasting reservoir inflow, the Thomas–Fiering (TF) model as a typical stochastic model was improved through pre-defining an array in the model that was previously supposed to be a random one to find the suitable parameters for the data series for forecast errors' reduction. Also, a wavelet neural network (WNN) model was utilized to represent a machine learning approach which was improved through using an input–output matrix for the model parameter training to timely adjust input parameters according to the errors produced in the model application process, so as to reduce overall forecast errors as much as possible. Through comparing the performance of the two models within a test year, the TF model was performing better in the wet season and the WNN model had better performance in the dry seasons at the Danjiangkou Reservoir, an important water-providing area of the South-North Water Transfer Project in China. Through comparing the abilities of the improved TF and the WNN models to forecast reservoir inflow, consistencies, differences and reasonable suggestions could be obtained for helping select appropriate models over different hydrologic seasons. Thus, these two models were combined to support the forecast of reservoir inflow within an entire hydrological year. Using these models improved in this paper, the forecasted inflow to the Danjiangkou Reservoir was 4.52 × 1010 m3 in the coming year (here is 2006), which is approximately 70% of incoming frequency based on daily observed inflow data from 1980 to 2005.

Simulasi Operasi Waduk Lamong untuk Kepentingan Air Baku dan Irigasi

Daerah Aliran Sungai (DAS) Lamong berada di wilayah Kabupaten Lamongan dan Mojokerto untuk bagian hulu, Kabupaten Gresik dan Kodya Surabaya untuk bagian hilir. Luas DAS Lamong ± 720 km2 dengan panjang alur sungai utama ± 103 km. Keadaan hidrologi DAS Lamong dalam keadaan kritis, akibatnya, sebagian besar wilayah DAS Lamong yang berada di wilayah Kabupaten Gresik dan Kota Surabaya, setiap tahun mengalami banjir akibat luapan Kali Lamong. Sebaliknya di bagian hulu yaitu Kabupaten Lamongan selalu kekurangan air pada musim kemarau. Penelitian ini, inflow Waduk Lamong diperoleh dengan metode bangkitan data dengan menggunakan metode Thomas Fiering. Data aliran historis diperoleh melalui simulasi pengalihragaman hujan menjadi aliran model Mock dengan menggunakan data hujan Stasiun Bluluk, data hujan Stasiun Ngimbang dan data hujan Stasiun Mantup dan data pengukuran debit Stasiun Boboh tahun 1995 - 1997. Perhitungan kebutuhan air baku berdasarkan kriteria perencanaan air bersih Ditjen Cipta Karya, Kementerian PU tahun 2000, sedangkan kebutuhan air irigasi dihitung berdasarkan Standar Perencanaan Irigasi (KP-01) tahun 1986. Simulasi yang digunakan yaitu dari tahun 2016-2025 dengan menggunakan Standard Operating Rule (SOR). Hasil penelitian menunjukkan bahwa kebutuhan air baku masyarakat yang diasumsikan akan dilayani oleh Waduk Lamong yaitu sebesar 5,70 juta m3/thn pada tahun 2015 dan 5,74 juta m3/thn pada tahun 2025. Reliabiltas Waduk Lamong dalam melayani kebutuhan air baku akan tercapai 99,17% dengan luas areal tanam yang seluas 2.117 Ha dengan reliabilitas dalam melayani air irigasi 96,25%.

Software Application for Data Driven Prediction Models for Intermittent Streamflow for Narmada River Basin

Synthetic generation of streamflow data facilitates the planning and operation of water resource projects. Significance of streamflow forecasting for intermittent river increases many fold in order to use available water yearlong for multipurpose water resources project. In the present study, monthly streamflow data has been used for intermittent river Goi in Narmada river basin. The performance of stochastic stream flow generation models– seasonal autoregressive integrated moving average (SARIMA) and Thomas-Fiering model are compared with Artificial Neural Network (ANN) approach. The performance of these models is evaluated on the basis of root mean square error (RMSE) and coefficient of determination (R). The study reveals that SARIMA performs better than Thomas-Fiering and ANN models. Thomas Fiering model is least reliable model among other two models. However Thomas-Fiering model performed well in case of high flow prediction whereas SARIMA and ANN performed well for lower and moderate flow. The predicted data can be used for the small hydropower projects development.

Stream flow Simulation and Synthetic Flow Calculation by Modified Thomas Fiering Model

In this paper , Thomas-Fiering T-F model was used twice for simulation of Khassa Chi river in Kirkuk city by using historical monthly stream flow sequences for a period (1941-2001) . A modification to Thomas-Fiering model was done by extracting the persistency from the monthly flow values of Khassa Chi river and including the regression values between the monthly values of the flow without persistence. It was concluded that Thomas-Fiering model is very suitable to simulate Khassa Chi behavior. The modified model MT-F was more capable to reverse the monthly and annual statistical parameters especially the monthly and annual standard deviation and it was more sensitive in reversing the drought times which reflects its capability in other simulation operations for variables which are suffering from drought times. Key words :Thomas-Fiering , Persistence Simulation

Risk Evaluation of Water Shortage in Source Area of Middle Route Project for South-to-North Water Transfer in China

Water diversion causes changes in the downstream flow regime, which may intensify the crisis of water shortage. The effect of the diversion on water shortage depends on the volumes of water transferred and water demand of source area, the upstream inflow and the way the reservoir is operated. This paper reports the findings of a study to assess the impact of water diversion from Danjiangkou reservoir on middle and lower Hanjiang River, part of the source area of South-to-North Water Transfer Project, China. The risk evaluation model consists of four parts, including inflow generation, water demand, simulation, and performance evaluation. Thomas–Fiering model and Mont-Carlo method are utilized to simulate monthly reservoir inflow data and a 12-dimensional random vector is used to describe the 12-month water demand in middle and lower Hanjiang River. A reservoir simulation model is established for optimum operation of Danjiangkou reservoir. Several scenarios including different water diversion scales are run by the risk evaluation model, whose outputs provide valuable information for decision making.