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Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the im, Validerad;2024;Nivå 2;2024-06-26 (joosat);Full text license: CC BY 4.0;Funder: King Saud University, Riyadh, Saudi Arabia (RSP2024R327)

Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the im, Validerad;2024;Nivå 2;2024-06-26 (joosat);Full text license: CC BY 4.0;Funder: King Saud University, Riyadh, Saudi Arabia (RSP2024R327)

Climate change is one of the most important factors impacting hydrological regimes. In this paper, climate change impact on streamflow of Loukkos basin (northwestern Morocco) is evaluated using SWAT model for three future periods: near (2021–2040), mid (2041–2070), and far (2071–2100), compared to baseline 1981–2020. A set of bias-corrected climate models was used: five regional climate models (EURO-CORDEX), four global climate models (CMIP6) and their ensemble mean, under two representative concentration pathways respectively (RCP 4.5; RCP 8.5) and (SSP2-4.5; SSP5-8.5). Furthermore, SUFI-2 algorithm in SWAT-CUP was performed to calibrate (1981–1997), validate (1998–2015), and analyze uncertainty for each dataset at ten hydrological stations. In most stations, statistical performance indicated a good simulation, with a Nash–Sutcliffe efficiency (NSE) greater than 0.77 and percent bias (PBIAS) within ±10% on a monthly basis. Overall, 82% of models indicated that future climate could decline streamflow. The largest decrease would be for 2071–2100 and under RCP 8.5/SSP5-8.5. Our findings could help planners and policymakers in developing reasonable water management policies and climate change adaptation measures.

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite's (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani's (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith's (1948) equations were cited in 9307 research studies, but the, Validerad;2022;Nivå 2;2022-09-08 (sofila)

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite's (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani's (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith's (1948) equations were cited in 9307 research studies, but the, Validerad;2022;Nivå 2;2022-09-08 (sofila)

Afin d’évaluer l’impact des changements climatiques sur les ressources en eau, nous réalisons dans ce travail une analyse statistique spatio-temporelle de certaines variables climatiques du bilan hydrique. En effet, pour comprendre les variations climatiques ayant eu lieu dans le passé, l’analyse statistique doit se faire sur des séries chronologiques de données représentatives aussi bien sur le plan spatial que temporel. Toutefois, ces séries de données présentent souvent des lacunes dans les mesures et des irrégularités qui entravent leur utilisation directe. Pour cela, l’analyse statistique adoptée dans cette étude s’est appuyée sur trois étapes : i) le comblement des données manquantes par deux méthodes d’imputation, à savoir la méthode d’imputation multivariée par des équations enchainées (MICE) et la méthode des k plus proches voisins (k-NN); ii) la détection des changements significatifs ayant eu lieu dans le passé dans les séries chronologiques. Les dates de ces changements sont marquées par des ruptures au niveau de la moyenne des variables climatiques étudiées. Ces ruptures ont été validées par trois tests statistiques (Pettitt, Buishand et homogénéité normale SNH); iii) l’investigation de tendances potentielles dans les données climatiques par les tests statistiques (Sen et Mann-Kendall) ainsi que par une projection future. Comme application, nous avons traité les données climatiques pour les périodes 1974-2016 et 2021-2050 issues de cinq stations météorologiques du périmètre du Gharb situé au nord du Maroc. Les résultats obtenus montrent que la méthode d’imputation de MICE est la plus performante pour toutes les stations. Pour les tendances, les séries de températures, d’évapotranspiration potentielle et du déficit hydrique présentaient des tendances significatives à la hausse sur tous les pas de temps. Tandis que pour les séries de précipitations, les tendances étaient non significatives. Les projections à l’horizon 2021-2050 ont fait ressortir que nous, In order to evaluate the climate change impact on water resources, we carry out in this work, a spatio-temporal statistical analysis of some water balance variables. In order to understand past climate variations, a statistical analysis must be made for representative time series both temporally and spatially. These data sets, however, often have irregularities and gaps in the measurements which hinder their direct use. For this reason, the statistical analysis used in this study was based on three steps: i) filling gaps of missing data using two imputation methods, namely multiple imputation by chained equations (MICE) and the k-nearest neighbour method (k-NN); ii) the detection within time series of significant changes that occurred in the past. Dates of these changes are characterized by breaks at the level of the mean of the studied climate variables. These breaks were confirmed by three statistical tests (Pettitt, Buishand, and Normal Homogeneity SNH); iii) the investigation of potential climate data trends by using statistical tests (Sen and Mann-Kendall) as well as with a projection technique for the future. As an application, we processed climate data from five weather stations in the Gharb perimeter, located in northern Morocco, for the periods 1974-2016 and 2021-2050. The results obtained showed that MICE imputation is the most efficient for all stations. For trends, while the series of temperatures, potential evapotranspiration, and water deficit showed significant upward trends over all time steps, the trends were non-significant for the precipitation series. The projections for 2021-2050 showed that we could witness a slight shift in the wettest season of the year and that the effect of warming would be greater from west to east in the Gharb perimeter due to the effect of continentality.