Monthly ET0 Research Articles

Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6

One of the primary factors in the hydrological cycle is reference evapotranspiration (ET0). The prediction of ET0 is crucial to manage irrigation water in agriculture under climate change; however, little research has been conducted on the trends of ET0 changes in Shandong Province. In this study, to estimate ET0 in the entire Shandong Province, 245 sites were chosen, and the monthly ET0 values during 1901–2020 were computed using the Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized to forecast the monthly ET0 during 2021–2100, and the predictions were compared to two CMIP6 climate scenarios, SSP2-4.5 and SSP5-8.5. The hierarchical clustering results revealed that Shandong Province encompassed three homogeneous regions. The ET0 values of Clusters H1 and H2, which were situated in inland regions and major agricultural areas, were the highest. The SAO-CNN-BiGRU-Attention and SSP5-8.5 forecasting results generally displayed a monotonically growing trend during the forecast period in the three regions; however, the SAO-CNN-BiGRU-Attention model displayed a declining tendency at a few points. According to the SAO-CNN-BiGRU-Attention and SSP5-8.5 results, during 2091–2100, H1, H2, and H3 will reach their peaks; the SSP2-4.5 results show that H1, H2, and H3 will peak in 2031–2040. At the end of the forecast period, for Clusters H1, H2, and H3, the prediction rate of SAO-CNN-BiGRU-Attention increased by 1.31, 1.56%, and 1.80%, respectively, whereas SSP2-4.5’s prediction rate increased by 0.31%, 0.95%, and 1.57%, respectively, and SSP5-8.5’s prediction rate increased by 10.88%, 10.76%, and 10.69%, respectively. The prediction results of SAO-CNN-BiGRU-Attention were similar to those of SSP2-4.5 (R2 > 0.96). The SAO-CNN-BiGRU-Attention deep learning model can be used to forecast future ET0.

A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles

ABSTRACT The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ET c and ET a on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (K s) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET 0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ET a showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.

Performance evaluation of simpler reference crop evapotranspiration estimation equations with and without local calibration

The present study was taken up to demonstrate an overall methodology for local calibration of simpler reference crop evapotranspiration (ET0) estimation equations relative to the physically-based, albeit more data-intensive Penman-Monteith (PM) method. Climate records for the historical period 2006-2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used for this purpose. The numerical coefficients in the original forms of the simpler equations namely, Blaney-Criddle (BC), Priestley-Taylor (PT), Radiation (RAD), and Turc (TC) were treated as unknown parameters and optimized for each station using PM ET0 estimates as reference. Results of local calibration indicated significant improvement in prediction accuracies and bias reduction for all the equations with the calibrated PT equation exhibiting the best performance. Spatial maps depicting the variations in the optimal values of parameters were developed to aid the accurate estimation of monthly ET0 values in the study area using only minimal climate inputs.

Yield prediction in banana (Musa sp.) using STELLA model

To overcome the challenges encountered in banana cultivation, such as the high cost of production due to high water consumption by the banana plant, efficient management practices are being adopted. The use of agricultural forecasting techniques is an alternative that has been gaining attention in rural areas. One way to manage and improve agricultural productivity is the use of technologies that allow the monitoring of production. The implementation of computational tools as software to aid processes, such as irrigation management, is gradually taking up space in the agricultural sector. In this light, herein, the present study aimed to develop a model using STELLA 8.0 software to estimate the growth and productivity of irrigated banana (Musa sp.). For this, the physiological processes and water demand were calculated using reference evapotranspiration (ET0) and culture evapotranspiration (ETc) in the first banana cycle for the climatic conditions of the Jaíba Project (Jaíba, Minas Gerais State, Brazil). The data of the climatic conditions were obtained from the National Institute of Meteorology. It was verified that the average monthly ET0 was 5.78 mm day-1. In addition, the water requirement of the plant corresponded to a blade equivalent to 65% of ET0. The verified productivity was 8.93 t ha-1, which is considered adequate for the simulated conditions. The model responded efficiently to the proposed application and was characterized as a prognostic tool of reality through simplified representation.

Estimation and evaluation of trend analysis of the Penman-Monteith reference evapotranspiration of Raipur region, Chhattisgarh central India

Understanding evapotranspiration and its long-term trends is essential for water cycle studies, modeling and for water uses. Temporal analysis of evapotranspiration is therefore important for the management of water resources, particularly in the context of climate change. The objective of this study is to evaluate reference evapotranspiration using the AquaCrop model developed by FAO and analyze the trend of reference evapotranspiration (ET0) in the Raipur Region. Mann-Kendall test and Sen’s slope were used to detect trends and amplitude changes in ET0. The average monthly ET0 was found maximum in May (8.91 mm/day), whereas the average minimum ET0 was estimated in December (1.91 mm/day). The mean daily reference evapotranspiration ranges from 2.49 mm per day to 7.61 mm per day in the study area. Results show on the monthly time scale, decreasing trends were identified in ET0 data in most of the months. A significant negative trend magnitude was found for March, April and May. This study provides the necessary information on atmospheric demand on water requirements for growing different crops in different seasons. This information will be very important for researchers and farmers in planning irrigation scheduling, irrigation water management studies in different seasons.

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China.

The accurate estimation of reference evapotranspiration (ET0) is crucial for water resource management and crop water requirements. This study aims to develop an efficient and accurate model to estimate the monthly ET0 in the Jialing River Basin, China. For this purpose, a relevance vector machine, complex extreme learning machine (C-ELM), extremely randomized trees, and four empirical equations were developed. Monthly climatic data including mean air temperature, solar radiation, relative humidity, and wind speed from 1964 to 2014 were used as inputs for modeling. A total comparison was made between all constructed models using four statistical indicators, i.e., the coefficient of determination (R2), Nash efficiency coefficient (NSE), root mean square error (RMSE) and mean absolute error (MAE). The outcome of this study revealed that the Hargreaves equation (R2 = 0.982, NSE = 0.957, RMSE = 7.047 mm month−1, MAE = 5.946 mm month−1) had better performance than the other empirical equations. All machine learning models generally outperformed the studied empirical equations. The C-ELM model (R2 = 0.995, NSE = 0.995, RMSE = 2.517 mm month−1, MAE = 1.966 mm month−1) had the most accurate estimates among all generated models and can be recommended for monthly ET0 estimation in the Jialing River Basin, China.

Modifying the SWAT Model to Simulate Eco-Hydrological Processes in an Arid Grassland Dominated Watershed

Grasslands are the main land cover type and one of the most important ecosystems in arid and alpine endorheic basins. The vegetation coverage of grasslands is spatially heterogeneous in arid and alpine areas and it may lead to variations in water allocation. The Soil and Water Assessment Tool (SWAT) is one of the most widely used semi-distributed catchment-scale eco-hydrological models. The leaf area index (LAI) is one of the vegetation coverage indexes and is incorporated in the SWAT model. However, in SWAT, the LAI accumulation is controlled by heat, and neglects other relevant factors such as precipitation and terrain. To address the drawbacks of the SWAT in simulating vegetation coverage and plant patterns, several studies have focused on improving LAI estimation. However, they still have been limited to arid and alpine grasslands with different vegetation coverages. In this study, we modified the SWAT model using remotely sensed LAI data with high temporal and spatial resolution. We used this to better simulate eco-hydrological processes in grassland basins with different vegetation coverages in the upper reaches of the Bayin River Basin. Results showed that for the original SWAT model, the simulated LAI was homogeneous within each land use/cover type, whereas the remotely sensed LAI was spatially heterogeneous and better captured the vegetation coverage of the entire basin. The proper estimation of the LAI was reflected in the improved simulation of the monthly streamflow and sediment yield at the basin outlet and the monthly ET. These findings indicate that the modified SWAT could better simulate hydrological processes in arid and alpine grasslands with different vegetation coverages.

Application of the US-EPA’s HAWQS model to predict future climate impacts on hydrological processes

Climate change could add a variety of uncertainties to hydrological processes and water resources. Very limited efforts have been devoted to applying the US-EPA (Environmental Protection Agency)’s Hydrologic and Water Quality System (HAWQS) model for predicting climate change impacts on hydrological processes at basin scale. Here we applied the model to project the next 50 years’ (from 2021 to 2070) hydrological processes at the Yazoo River basin in Mississippi, USA. Simulations showed that over the next 50 years, there are no significant trends in monthly precipitation, ET, runoff, and discharge; only 2% of the annual precipitation percolated into the deep aquifer; and dry seasons become dryer and wet seasons become wetter. These findings provide very useful information to stakeholders for water resource management. Our study further suggests that the HAWQS model is a user friendly and time-saving tool for basin scale hydrological modeling.

Assessment of spatiotemporal variability and trend analysis of reference crop evapotranspiration for the southern region of Peninsular India.

Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. In this study, the Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 to 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal variation, indicating major difference in ET0 values was with respect to the stations rather than years under study. The nonparametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed a statistically significant increasing trend for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity, and solar radiation signifies their influence on the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen's slope indicated that increasing values of mean temperature, solar radiation, and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited a positive ET0 trend with the highest and lowest annual values among ten stations.

Ability of stochastic modelling to forecast crop reference evapotranspiration*

AbstractThe main objective of this study is to evaluate the ability to forecast seasonal and non‐seasonal monthly reference plant water consumption (ET0) with the Box–Jenkins approach (autoregressive integrated moving average), the abbreviation of which is ARIMA (indicated as SARIMA for seasonal series). This target was achieved by using the necessary climate parameters obtained from the Ankara meteorology station to calculate the monthly ET0 data sequences by using the Penman–Monteith relationship. Of the 12 months’ non‐seasonal ET0 data, only 4 months achieved all the conditions expected in simulation after satisfying the stationary condition. The ARIMA (SARIMA) (1,0,1) (3,1,0) model produced the best results among all models providing the necessary conditions for the seasonal series. When comparing the results of ET0 estimation from this model to that of the non‐seasonal series, it was determined that using the seasonal series rather than making an estimate based on the non‐seasonal series provided more successful results, and the model giving the most successful forecast for the study is the SARIMA model above.

Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

The reference evapotranspiration (ET0) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET0 using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET0 time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET0 prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET0 prediction in Serbia is the novelty of this study.

Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites

Abstract. Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.

Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimization algorithm, namely intelligent water drops (IWD) (i.e., SVR−IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognized by using two pre-processing techniques consisting of τ Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as utilizing the τ Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves−Samani (H−S) and Priestley−Taylor (P−T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.

Multimodel‐based analyses of evapotranspiration and its controls in China over the last three decades

AbstractEvapotranspiration (ET) plays a key role in linking water, energy, and carbon cycles. However, at regional scales, variations and controls of ET are still not well understood, due to the substantial uncertainties in ET estimates. Here, the variations in ET over China during the last three decades and their drivers and controls were analysed at monthly, seasonal, and annual scales, using well‐evaluated land surface model simulations. The results suggested that (a) the spatially averaged ET over China increased during 1979–2012 at each temporal scale. Overall, the ET significantly increased (p < .05) in most of western China and decreased in several regions in eastern China during most months and seasons. (a) The increases in spatially averaged ET during the nongrowing season were mainly determined by warming. The increased annual ET, seasonal ET, and monthly ET within the growing season were jointly caused by vegetation greening and climate warming. (c) During November–March, ET was primarily determined by temperature in most of central China and was determined by water availability in the remaining regions. The annual ET and the monthly ET within April–October were mainly controlled by water availability in most of northern and western China, and several regions in central China, by solar radiation in most of central and southern China and by temperature in some regions in the Tibetan Plateau. The study highlights the large effects of warming and greening on ET over China during the last three decades. The results may benefit water resource management under climate change.

Evaluation of evapotranspiration variations according to soil type using multivariate statistical analysis

It is known that the soil type and its characteristics are very important to the agriculture and to maintain the health of a forest. Thus, the objective of this work was to verify, through multivariate statistical analysis techniques, the variations of evapotranspiration values as a function of the soil type in three different vegetation cover areas: sugarcane, planted forest and native forest. For this, an area of 2231.926 km2 was chosen, using monthly Landsat 8 satellite imagery with Orbit/Path 220 and Point/Row 75, between latitudes 20°43′55.2″S and 22°36′28.8″S and longitudes 46°37′48,0″W and 48°50′16,8″W, in the region of Corumbataí, northeast of the State of São Paulo, Brazil, collected over 24 months between April 2013 and March 2015. The evapotranspiration evaluation was obtained by satellite imagery using a hybrid model of the Two Source Energy Balance (TSEB), with a proposed adjustment methodology to convert hourly ET to monthly and annually data. The multivariate statistical analysis of monthly ET patterns across soil type classified three evapotranspiration groups in the three studied land cover areas. In the areas with sugarcane, evapotranspiration separated statistical groups in the areas of the Cerrado biome with Ferralsol (LVA) and Lithic Leptosol (RL), another group in the areas with Ferralsol (LV) and Ferralic Arenosol (RQ), and a third group of Acrisol (PVA) in an Atlantic Forest biome area. In the areas with planted forest and native forest, evapotranspiration separated a first group with soils LVA, RQ, PVA, Dystric Gleysol (GX) and RL in areas of Cerrado, a second group with LV in Atlantic Forest area and a third group just with Fibric Histosol (OY).

An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration.

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.

Variation of reference evapotranspiration and its teleconnection with multiple large‐scale climate oscillations in the Yangtze River Delta, China

Evapotranspiration is a key factor in regional hydrological processes and water resources management. Long‐term variation of evapotranspiration affects the regional climate wet/dry tendency and agricultural production profoundly. Thus, reference evapotranspiration (ET0) values for 46 meteorological stations in the Yangtze River Delta (YRD) were calculated for 1957–2014 using the FAO‐Penman–Monteith (FAO‐PM) method. The variation patterns of ET0 values were determined based on the principal component analysis (PCA) method. In addition, the methods of cross wavelet transform (CWT), Kendall tau‐b correlation coefficient determination and cross‐correlation method were applied in the assessment of the correlation between ET0 values and large‐scale climate oscillations, such as the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Oceanic Niño3.4 Sea Surface Temperature Index (NINO) and the Indian Ocean Dipole (IOD). Annual ET0 patterns for three dominant geographic subregions of the YRD (the southeastern, northwestern and mid‐eastern) were determined. There were only several discontinuous lower timescale bands between the three change patterns of annual ET0 and climate oscillations. In seasonal scale, the temporal patterns of ET0 changed simultaneously with the NAO, NINO and PDO in spring, the PDO in summer and the NAO in winter. The monthly ET0 was mostly influenced by the NAO and IOD in January, the IOD in February, the IOD and NINO in March, the NINO in June, the PDO in July to October, the NINO, IOD and NAO in October and the NINO in December. The lag times for the ET0 changes were about 0–5 months for the NAO and NINO, 1–2 months for the PDO and 4 months for the IOD.

Intra‐annual differences of 3‐month Standardized Precipitation‐Evapotranspiration Index dryness/wetness sensitivity over southwest China

The monthly 3‐month Standardized Precipitation‐Evapotranspiration Index (SPEI‐3) over southwest China (SWC) shows obvious inter‐annual and inter‐decadal variability during 1961–2012. By examining the isolated contributions of precipitation and reference evapotranspiration (ET0) alone to the monthly SPEI‐3 anomalies, we find out that although anomalous precipitation plays a predominant role, ET0 impacts cannot be neglected, particularly during February–May. The monthly dryness/wetness sensitivity to precipitation (Sp; Set0 for ET0), that is, SPEI‐3 changes by per millimetre of precipitation (ET0) anomalies, is quantified as the slope of a linear regression, which has precipitation (ET0) contributions and anomalies during 1961–2012 as the dependent and the independent variables, respectively. Both SWC Sp and Set0 have evident intra‐annual differences, decreasing during February–August and increasing afterwards. Moreover, their magnitudes are dependent on climatological precipitation and generally decrease with precipitation increase. To sum up, the estimated respective contributions of monthly precipitation and ET0 anomalies are favourable for thoroughly understanding SWC dry/wet condition evolutions during the past 52 years. More importantly, Sp and Set0 are of significance for drought forecasting and emergency system over SWC.

Impact of climate change on potential evapotranspiration (case study: west and NW of Iran)

Potential evapotranspiration (ET0) is one of the main elements when computing agricultural irrigation requirements and scheduling. All climatic parameters as well as ET0 are influenced by climate change. The aim of this study is trend analysis of monthly and annual ET0 time series in the west and NW of Iran. Values of ET0 are estimated at 36 selected stations, using the FAO-56 Penman-Monteith (FAO-56 PM) method. Then, the non-parametric Mann-Kendall (MK) method was used to detect trends. The slopes of trend lines are estimated using Sen’s estimator approach. Results showed that about 86%of the monthly ET0 time series had upward trends of which 35.6 and 43% were significant at 0.01 and 0.05 levels, respectively, 47.4% exhibited a significant upward trend at the 10% level. In contrast, less than 0.7% of the whole monthly ET0 time series showed a significant downward trend (α < 0.01). Only 1.1% of the monthly ET0 time series had significant downward trends (α < 0.10). The strongest positive upward trend (significant at the 0.01 level) was detected in August at the Kermanshah station. However, the strongest negative downward trend belonged to the Khodabandeh station. The steepest upward and downward monthly ET0 slopes were observed at Maragheh and Khodabandeh stations. The magnitude of trends for these two stations are estimated as 2.33 and − 2.01 mm/year, respectively. On an annual timescale, above 94% of the stations had upward trend slopes. About 67% of the total stations exhibited significant trends at the 10% level. Very few sites (2.7%) showed downward annual ET0 trends (α < 0.10). At the annual scale, the first three strongest upward trends belonged to the Kermanshah, Urmia, and Tabriz stations, respectively (α < 0.01). It can be concluded that ET0 in the west and NW of Iran have an increasing trend for most of the stations. Therefore, it is important to use water in a prudent manner in this area.