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Published in Abias Uwimana, Effects of Wetland Conversion to Farming on Water Quality and Sediment and Nutrient Retention in a Tropical Catchment, 2019
Water flows were quantified in terms of inflows (surface inflow and rainfall), outflows (surface outflow and evaporation) and groundwater exchange during Period 2 from September 2012 to May 2013. Surface inflow and outflow were measured at inlet and outlet pipes by measuring the time required for a container of a known volume to be filled with water. Rainfall was recorded using a rain gauge installed at the study site. Evaporation was measured as pan evaporation. Evapotranspiration was estimated based on evaporation and a crop coefficient depending on the four growth stages of rice (initial, development, mid-season and late season with 30, 30, 80 and 40 days and crop coefficients of 1.05, 1.20, 0.90–0.60 and 1.00 respectively; FAO 1998). For the mid-season stage, 0.75 as the average was used. The crop coefficient for wetland vegetation was assumed to be 0.95 (Drexler et al., 2008). Net groundwater exchange (So - Si) was calculated based on the water balance (1). So−Si=P+Qi+R−(E+Qo+ΔH)
Evaporation Pans
Published in Frank E. Jones, Evaporation of Water, 1992
In 1982, the U.S. National Weather Service (NWS) Office of Hydrology published Evaporation Atlas for the Contiguous 48 States.2 In the present context we will be interested in pan evaporation and particularly the Class-A pan as discussed in this report. Pan evaporation, as used in the report, means “evaporation observed at a standard NWS Class-Α pan installation by observers following standard techniques.” Free water surface (FWS) evaporation was defined as “evaporation from a thin film of water having no appreciable heat storage.” It is considered to be “approximately equivalent to potential evaporation from a shallow water surface and to potential evapotranspiration from a vegetative surface with an unlimited supply of water.” FWS can be computed from meteorological factors.
Drip Irrigation and Fertigation for Horticultural Crops: Scope, Principle, Basic Components, and Methods
Published in Ajai Singh, Megh R. Goyal, Micro Irrigation Engineering for Horticultural Crops, 2017
The pan evaporation method has been successfully used for calculating the crop water requirement of plants on daily basis (in mm/day or cm3/ day). The various weather parameters, pan factor, and crop factor are used in the pan evaporation method. The pan evaporation method is simple and practical method of crop water requirement calculation.
Estimation of daily pan evaporation using neural networks and meta-heuristic approaches
Published in ISH Journal of Hydraulic Engineering, 2020
Afshin Ashrafzadeh, Anurag Malik, Vinayakam Jothiprakash, Mohammad Ali Ghorbani, Seyed Mostafa Biazar
Pan evaporation is an energy-consuming phenomenon influenced by several meteorological variables, such as vapor pressure and wind speed. Extensive work has been reported on linear/nonlinear regression equations to estimate pan evaporation (e.g. Hanson 1989; Cahoon et al. 1991; Kovoor and Nandagiri 2007; Almedeij 2012; Xiong et al. 2012). In most of the cases, pan evaporation is estimated as a linear/nonlinear function of meteorological variables. Over the past two decades, there has been growing interest to exploit more sophisticated mathematical modeling algorithms such as Artificial Neural Network (ANN), Fuzzy Inference System (FIS), and Support Vector Machine (SVM) to tackle the problem of pan evaporation estimation. Bruton et al. (2000), Keskin and Terzi (2006), Rahimikhoob (2009), Piri et al. (2009), Tabari et al. (2010), Abghari et al. (2012), Nourani and Sayyah Fard (2012) and Arunkumar et al. (2017) used ANN models to estimate daily pan evaporation. Keskin et al. (2009) used Adaptive-Network-based Fuzzy Inference System (ANFIS), a combination of an ANN and a FIS, to estimate daily pan evaporation. Kim and Kim (2008) used a hybrid ANN-GA (Genetic Algorithm) model to estimate pan evaporation and reference evapotranspiration. Eslamian et al. (2008) assessed ANN and SVM models in simulating monthly pan evaporation. Moghaddamnia et al. (2009), Goyal et al. (2014), Malik and Kumar (2015), and Malik et al. (2017) compared the capabilities of different ANN models with that of ANFIS type models in simulating daily pan evaporation.
A novel approach for knowledge extraction from Artificial Neural Networks
Published in ISH Journal of Hydraulic Engineering, 2019
Shreenivas N. Londhe, Shalaka Shah
Evaporation is a key component of hydrological cycle and its estimation is of prime importance in planning and design of many water resources projects. Pan evaporation data is a long-standing need to agronomists, hydrologists, hydro meteorologists, agro meteorologists, command area development authorities, irrigation engineers for various purposes ranging from irrigation scheduling to water balance studies. Forecasting of evaporation is of vital importance in coastal hydrology and agro meteorology. Traditionally, evaporation is determined using: water budget (Guitjens 1982); energy budget (Fritschen 1966); mass transfer (Harbeck 1962); empirical (Kohler et al. 1955); combination of energy budget and mass transfer method (Penman 1948). The process of evaporation is highly non-linear in nature, as is evidenced by many of the estimation procedures (Chaudhari et al. 2012). Empirical methods are data intensive; they require measurement of many meteorological variables. Chaudhari et al. (2012) therefore felt the necessity to try alterative techniques to estimate pan evaporation with reasonable accuracy and avoiding excessive data measurement. They developed ANN models for Nashik climatic centre, located in Maharashtra, India, where they showed that ANN models work reasonably well in terms of estimation accuracy for pan evaporation modelling. For the present work, the evaporation models were devised for Nashik and two other neighbouring stations. Once the evaporation models were trained and tested, attempts were made to extract knowledge from them.
Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran
Published in Engineering Applications of Computational Fluid Mechanics, 2018
Mohammad Ali Ghorbani, Reza Kazempour, Kwok-Wing Chau, Shahaboddin Shamshirband, Pezhman Taherei Ghazvinei
A forecast model for daily pan evaporation can be an important decision-support tool in water engineering, agriculture, rural and urban water systems, water policy planning and design of hydrologic structures (e.g. dams or irrigations). The incorporation of an optimizer algorithm where a standalone artificial intelligence model is integrated with a global search algorithm to deduce optimal model’s internal parameters, and consequently improving the overall predictive performance, is gaining prominence in hydrologic research. In the present research, the hybrid artificial intelligence procedure based on a multi-layer perceptron framework incorporated with the MLP-QPSO procedure, was established and evaluated for its preciseness in the estimation of daily pan evaporation. For a specific case study region in Northern Iran, the present study has utilized key meteorological parameters including the maximum and minimum temperature, sunshine hours, relative humidity, and wind speed datasets as the predictor variables. Besides this, the predictive ability of the developed hybrid method (i.e. the MLP-QPSO model) was evaluated and compared to a standalone MLP and a hybrid MLP-PSO model. The findings of the present study showed a much improved accuracy of the hybrid MLP-QPSO model in respect to a hybrid MLP-PSO and a standalone MLP model applied in the context of daily pan evaporation.