Gorka Landeras

Forecasting Weekly Evapotranspiration with ARIMA and Artificial Neural Network Models

Information about the parameters defining water resources availability is a key factor in their management. Reference evapotranspiration ( ET0 ) prediction is fundamental in planning, design, and management of water resource systems for irrigation. The application of time series analysis methodologies, which allow evapotranspiration prediction, is of great use for the latter. The objective of the present study was the comparison of weekly evapotranspiration ARIMA and artificial neural network (ANN)-based forecasts with regard to a model based on weekly averages, in the region of Alava situated in the Basque Country (northern Spain). The application of both ARIMA and ANN models improved the performance of 1 week in advance weekly evapotranspiration predictions compared to the model based on means (mean year model). The ARIMA and ANN models reduced the prediction root mean square differences with respect to the mean year model (based on historical averages) by 6–8%, and reduced the standard deviation differ...

Independent testing for assessing the calibration of the Hargreaves-Samani equation

We modeled evapotranspiration using temperature based approaches.Hargreaves-Samani was compared with GEP models.Locally trained models gave the superior results.Externally trained GEP is a good alternative to locally trained model. There is multitude of models for estimating daily reference evapotranspiration (ET0) using meteorological parameters. Among others, the temperature-based Hargreaves-Samani (HS) model is one of the frequently applied models for estimating ET0 when meteorological parameters in the studied station are limited. However, this method tends to require a preliminary local calibration. Most calibration procedures usually apply the same data sets for calibrating and testing. At the most, some studies reserve an independent test set for evaluating the calibrated model, but considering a single data set assignment. In the present study, the HS model and its calibrated version were assessed using meteorological parameters from 29 weather stations in Iran, through complete temporal and spatial data scanning, using a k-fold testing approach. A similar procedure was also repeated using the Gene Expression Programming (GEP) technique relying on the same input variables of the HS model. The results showed the importance of adopting k-fold based independent testing approach in order to avoid problems related to the influence of selected test period on the performance of the GEP models.

Predicting daily pan evaporation by soft computing models with limited climatic data

Abstract Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran. Editor D. Koutsoyiannis

Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations

Evapotranspiration estimation is of crucial importance in arid and hyper-arid regions, which suffer from water shortage, increasing dryness and heat. A modeling study is reported here to cross-station assessment between hyper-arid and humid conditions. The derived equations estimate ET0 values based on temperature-, radiation-, and mass transfer-based configurations. Using data from two meteorological stations in a hyper-arid region of Iran and two meteorological stations in a humid region of Spain, different local and cross-station approaches are applied for developing and validating the derived equations. The comparison of the gene expression programming (GEP)-based-derived equations with corresponding empirical-semi empirical ET0 estimation equations reveals the superiority of new formulas in comparison with the corresponding empirical equations. Therefore, the derived models can be successfully applied in these hyper-arid and humid regions as well as similar climatic contexts especially in data-lack situations. The results also show that when relying on proper input configurations, cross-station might be a promising alternative for locally trained models for the stations with data scarcity.

New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Accurate estimation of reference evapotranspiration (ET0) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET0 estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET0) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET0 estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET0 estimation in West Africa.