CLIMATE VARIABILITY AND IMPACT VULNERABILITY STATUS OF IRRIGATION WATER RESOURCES ON RICE AND TOMATO PRODUCTION DOWNSTREAM OF TIGA DAM, NIGERIA

2021 ◽  
Vol 5 (1) ◽  
pp. 126-134
Author(s):  
A. B. Adegbehin ◽  
E. O. Iguisi ◽  
Y. O. Yusuf ◽  
C. K. Dauda
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Level ◽  
Water Availability ◽  
Irrigation Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Tomato Production ◽  
Rainfall Index ◽  
The Government

The focus of this empirical study is to investigate the trends of some hydro metrological parameters and Impact Vulnerability Status (IVS) of irrigation water resources on rice and tomato production in the downstream of Tiga station. Investigation was conducted using data on rainfall, temperature, evaporation and reservoir water level for 30 years in Tiga station. The data collected was used to show the trend fluctuations of each parameter for the period of study. The rainfall data was also used to analyze the Normalized Rainfall Index (NRI) in order to know periods of surplus, deficit and optimal water availability as against the required water for rice and tomato production. The rainfall pattern and water level showed increasing trend while temperature and evaporation showed a general decrease in trend. The NRI used to investigate the IVS in Tiga station downstream revealed that rice and tomato were not vulnerable to drought and flooding for 18 years while every other years were vulnerable or slightly vulnerable. However, only year 1993 appears to be very wet and highly susceptible to flooding. Findings from focus group revealed that 80% of the farmers reported floods occurrences during rainy season and deficit of water between January and March of each year. In conclusion, the IVS of farmers to climate change revealed periods of deficit, optimal and excess water availability for rice and tomato production and their vulnerability status. It was recommended that the government should strengthen laws and policies relevant in addressing climate change

scholarly journals Impacts of Climate Change on Water Availability and Reservoir Based Hydropower

2019 ◽  
Vol 1 (1) ◽  
pp. 52-68 ◽  
Author(s):  
Suman Ghimire ◽  
Nabin Dhungana ◽  
Suraj Upadhaya
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Water Availability ◽  
Meteorological Data ◽  
Secondary Data ◽  
Primary Data ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Monthly Data ◽  
Impacts Of Climate Change

The impacts of climate change (CC) are observed in several sectors, and water resource is one of them. This study explored the impacts of CC on water availability and reservoir based hydropower. It determined the impacts of CC in the reservoir water level and major watershed characteristics and has explored the perception of people on CC impacts in the reservoir. The primary data were collected through questionnaire and field survey and secondary data were gathered from different literatures. The analysis of meteorological data generated from meteorological station. temperature and rainfall data, discharge of Kulekhani River, monthly data of reservoir level and annual energy generation revealed increasing pattern of temperature and decreasing seasonal and annual precipitation in the study area. Similarly, because of the increased sedimentation, the water level of the reservoir has been increasing though the precipitation has been observed declining. Consideration could be taken while designing such hydropowers to hold water year-round, resulting minimal power shortage. A clear institutional direction and strategies could make reservoir based hydropower climate resilient and enable sustainable generation of electricity.

scholarly journals Multiyear rainfall variability in the Mono river basin and its impacts on Nangbeto hydropower scheme

2021 ◽  
Vol 384 ◽  
pp. 343-347
Author(s):  
Salomon Obahoundje ◽  
Ernest Amoussou ◽  
Marc Youan Ta ◽  
Lazare Kouakou Kouassi ◽  
Arona Diedhiou
Keyword(s):  
Water Level ◽  
Water Availability ◽  
Rainfall Variability ◽  
Energy Generation ◽  
Hydropower Plant ◽  
Hydropower Generation ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Hydropower Plants ◽  
Change Context

Abstract. Hydropower energy, the main renewable energy source in West Africa, contributes to more than half of the Togo and Benin National electrification. This resource highly depends on water availability in rivers or reservoirs. The water availability heavily relies on climate patterns of the area. In the climate change context, the sustainability of hydropower plants is at risk. This work aims to assess the sensitivity of the Nangbeto hydropower plant to multiyear climate variability using statistical analysis. The results show that energy generation at Nangbeto hydropower is more modulated by four main variables namely inflow to reservoir, water level, rainfall of the actual and the previous year. The energy generation is found to be strongly and significantly correlated to inflow to reservoir, water level, and rainfall. Overall, the Nangbeto hydropower generation is more sensitive to inflow which is controlled by climate variables (rainfall, temperature) and land use/cover change. Therefore, the probable future change in these variables is suggested to be deeply investigated.

scholarly journals Improvement of Downstream Flow by Modifying SWAT Reservoir Operation Considering Irrigation Water and Environmental Flow from Agricultural Reservoirs in South Korea

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2543
Author(s):  
Jinuk Kim ◽  
Jiwan Lee ◽  
Jongyoon Park ◽  
Sehoon Kim ◽  
Seongjoon Kim
Keyword(s):  
Water Supply ◽  
Water Level ◽  
Irrigation Water ◽  
Reservoir Operation ◽  
Rice Paddy ◽  
Environmental Flow ◽  
Water Levels ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Downstream Flow

This study aims to develop a reservoir operation rule adding downstream environmental flow release (EFR) to the exclusive use of irrigation water supply (IWS) from agricultural reservoirs through canals to rice paddy areas. A reservoir operation option was added in the Soil and Water Assessment Tool (SWAT) to handle both EFR and IWS. For a 366.5 km2 watershed including three agricultural reservoirs and a rice paddy irrigation area of 4744.7 ha, the SWAT was calibrated and validated using 21 years (1998–2018) of daily reservoir water levels and downstream flow data at Gongdo (GD) station. For reservoir water level and streamflow, the average root means square error (RMSE) ranged from 19.70 mm to 19.54 mm, and the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) had no effect on the improved SWAT. By applying the new reservoir option, the EFR amount for a day was controlled by keeping the reservoir water level up in order to ensure that the IWS was definitely satisfied in any case. The downstream mean wet streamflow (Q95) decreased to 5.70 m3/sec from 5.71 m3/sec and the mean minimum flow (Q355) increased to 1.05 m3/sec from 0.94 m3/sec. Through the development of a SWAT reservoir operation module that satisfies multiple water supply needs such as IWR and EFR, it is possible to manage agricultural water in the irrigation period and control the environmental flow in non-irrigation periods. This study provides useful information to evaluate and understand the future impacts of various changes in climate and environmental flows at other sites.

scholarly journals Projecting The Impact of Human Activities And Climate Change On Water Resources In The Transboundary Sre Pok River Basin

2021 ◽  
Author(s):  
Pragya Pradhan ◽  
Trang Thi Huyen Pham ◽  
Sangam Shrestha ◽  
Loc Ho ◽  
Edward Park
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Water Availability ◽  
Irrigation Water ◽  
Human Activities ◽  
Annual Rainfall ◽  
Hydropower Generation ◽  
Drought Conditions ◽  
The Future

Abstract This study aims to project the compound impacts of climate change and human activities, including agriculture expansion and hydropower generation, on the future water availability in the Sre Pok River Basin. The five regional climate models (RCMs): ACESS, REMO2009, MPI, NorESM, CNRM were selected for the future climate projection under two scenarios i.e., RCP 4.5 and RCP 8.5. Our results reveal that the future annual rainfall is expected to decrease by 200 mm whereas the average temperature is expected to increase by 0.69°C to 4.16°C under future scenarios. The future water availability of Sre Pok River Basin was projected using soil and water assessment tool (SWAT). Next, the CROPWAT model was used to examine the irrigation water requirement and the HEC-ResSim model to simulate the hydropower generation of Buon Tuar Sarh reservoir. The future simulation indicates the decrease in future water availability, increasing demand for irrigation water and decreases in hydropower generation for the future periods. The irrigated areas are increases from 700 ha to 1500 ha as per the provincial development plan. This study also examines the present and future drought conditions of Sre Pok River via streamflow drought index (SDI). Our results expect to contribute toward supporting the planning and management of water resources for agriculture and to efficiently cope with drought conditions in the studied basin and beyond.

scholarly journals The Monitoring-Based Analysis on Deformation-Controlling Factors and Slope Stability of Reservoir Landslide: Hongyanzi Landslide in the Southwest of China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Han ◽  
Bin Tong ◽  
Jinkai Yan ◽  
Chunrong Yin ◽  
Liang Chen ◽  
...  
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Monitoring Program ◽  
Significant Risk ◽  
Controlling Factors ◽  
Seepage Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
Reservoir Landslide

Reservoir landslide is a type of commonly seen geological hazards in reservoir area and could potentially cause significant risk to the routine operation of reservoir and hydropower station. It has been accepted that reservoir landslides are mainly induced by periodic variations of reservoir water level during the impoundment and drawdown process. In this study, to better understand the deformation characters and controlling factors of the reservoir landslide, a multiparameter-based monitoring program was conducted on a reservoir landslide—the Hongyanzi landslide located in Pubugou reservoir area in the southwest of China. The results indicated that significant deformation occurred to the landslide during the drawdown period; otherwise, the landslide remained stable. The major reason of reservoir landslide deformation is the generation of seepage water pressure caused by the rapidly growing water level difference inside and outside of the slope. The influences of precipitation and earthquake on the slope deformation of the Hongyanzi landslide were insignificant.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

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