Spatial variability of annual and monthly Potential Evapotranspiration (PET) over India

In this study, meteorological data for well distributed 140 locations in India for the period (1971-2005) have been utilized for estimation of potential evapotranspiration (PET) by Penman-Monteith equation. The highest average annual PET of 2342 mm was at Jalgaon and lowest of 921 mm at Ging. Range of average annual PET is 1421 mm. The mean annual PET averaged for all stations over India is 1547 mm with 12% contribution in winter, 34% in pre-monsoon, 35% in monsoon and 19% in post-monsoon seasons. The lowest centers with annual PET less than 1400 mm are mainly located above 30 degree N latitude. The high centers with annual PET more than 1800 mm are located in desert area and central India, with lowest values at hill stations during most of the months. The higher monthly PET values in excess of 200 mm are normally observed during pre-monsoon and monsoon over western and Central India. As the monsoon advances, the PET values over western India decrease gradually. The lower PET values are observed during winter and post-monsoon season. The lowest mean monthly PET of 82.1 mm is in December and highest mean monthly PET of 199.6 mm is in May. Mean annual and monthly PET over (2° × 2°) latitude/longitude grids have been developed and presented.

An investigation of reference evapotranspiration trends for crop water requirement estimation in Rajasthan

Trends in reference evapotranspiration (ETo) estimated using Penman-Monteith equation were analysed over arid, semi-arid and humid regions of northwest (NW) India during 1985–2018. The MannKendall is used to determine significance of trends. Theil-Sen’s estimator and least square linear fitting methods are adopted to find slopes of the trend lines. The results indicated a significant decrease in ETo on annual basis for most of the locations and NW India as a whole. However, the trend was not statisticallysignificant for seasonal ETo. The significant decrease in solar radiation and wind speed nullified the impact of increased temperature and resulted in slight decrease in ETo over arid and semi-arid regions of NW India which could probably be attributed to the increased dust hazy conditions prevailing. In NW India, water is a limiting resource the decrease in ETo may help researchers in decision makers to develop water assets and utilize the irrigation systems more effectively. There was also an increasing trend in production of major crops in the study region. Further, in near future, if this decreasing ETo trend were to remain, it would help in intensification of cropping system with the existing water resource.

Recent variations and trends in potential evapotranspiration (PET) over India

Thirty years meteorological time series data (1971-2000), for twenty two well distributed locations in India, have been utilized to compute potential evapotranspiration using FAO recommended Penman-Monteith equation. Annual, seasonal and monthly PET trends have been studied using linear trend analysis technique. Suitable graphs have been plotted to study the variations and changes in PET trends and to identify the specific periods as and when significant changes occur. The mean annual PET has been found to be lowest (1100 mm) at Buralikson and highest (2109 mm) at Bellary. Out of twenty two locations, significant decreasing trend in annual PET has been observed at seventeen locations and no significant trend at five locations. The mean annual dEo/dt over India has been found to be -9.36 mm/year. Linear relationship has been obtained to quantitatively estimate annual dEo/dt, at a given location, using annual PET range. Non linear relationships have been obtained (a) to quantitatively estimate the mean monthly dEo/dt values over India, (b) to quantitatively estimate the average cumulative dEo/dt values over India (mm/year) up to any particular month and (c) to quantitatively estimate the contribution (percent) towards average annual dEo/dt over India, up to any particular month.

Anticipatory responses to drought by plants: What are the environmental cues?

This study is an attempt to reconcile the physics-driven variation in reference evapotranspiration (ET0) and possible sensory-driven anticipatory acclimation that contributes to tolerance of dry weather spells and drought by plants growing in open fields. We use an original data set measured at high temporal resolution. These data include the standard meteorological observations plus detailed observations of different bands of sunlight: UV-B, UV-A, photosynthetically active and global down-welling short-wave radiation, blue, red and far-red light from two growth seasons at Helsinki, Finland. We also report ET0 computed with the FAO formulation of the Penman-Monteith equation. We assessed the correlations among variables at different time scales and their performance as predictors of ET0. We conclude that all studied bands of sunlight are consistently good predictors of ET0. UV radiation is a specially good predictor of the daily course of ET0 while longer wavelengths function better in the prediction of day to day variation in ET0. In most cases sunlight bands that plants are known to sense through specific photoreceptors can explain more than 95% of the variation in ET0, making them as cues carrying information on the demand side of the water budget of vegetation. Sunlight as sensed by plants is consequently a good candidate as driver of anticipatory acclimation to likely future drought events.

Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors

Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management. The hybrid ET model that combines the physical framework, namely the Penman-Monteith equation and machine learning (ML) algorithms, have proven to be effective in ET estimates. However, few studies compared the performances in estimating ET between multiple hybrid model versions using different ML algorithms. In this study, we constructed six different hybrid ET models based on six classical ML algorithms, namely the K nearest neighbor algorithm, random forest, support vector machine, extreme gradient boosting algorithm, artificial neural network (ANN) and long short-term memory (LSTM), using observed data of 17 eddy covariance flux sites of cropland over the globe. Each hybrid model was assessed to estimate ET with ten different input data combinations. In each hybrid model, the ML algorithm was used to model the stomatal conductance (Gs), and then ET was estimated using the Penman-Monteith equation, along with the ML-based Gs. The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors. The results also showed that although including RS factors can remarkably contribute to improving ET estimates, hybrid models except for LSTM using three or more RS factors were only marginally better than those using two RS factors. We also evidenced that the ANN-based model exhibits the optimal performance among all ML-based models in modeling daily ET, as indicated by the lower root-mean-square error (RMSE, 18.67–21.23 W m−2) and higher correlations coefficient (r, 0.90–0.94). ANN are more suitable for modeling Gs as compared to other ML algorithms under investigation, being able to provide methodological support for accurate estimation of cropland ET on a regional scale.

The Evaporation Estimation on Lake Most

After finishing the mining process, the best way to deal with the residual of open-cut coal mines in the north-western region of the Czech Republic has been proposed to be hydric recultivation. The area of our study is the first artificial Lake Most (formerly known as Ležáky-Most coal quarry) finished in 2014 and opened to the public in 2020 for recreational purposes. Since the lake is a closed system without natural inflow and outflow, the prediction of evaporation plays a crucial role in the securitization of long-term sustainability based on the capability of keeping the stable level of a dimension of the final water level. In this paper, we use the historical data consisting of the altitude of the lake level, its area, the perimeter of the shoreline, and especially the volume of refilled water. These data are compared against the computational methods; namely, the Penman-Monteith Equation and Hargreaves-Samani model calibrated by the method proposed in our previous work.

Estimation of irrigation water requirement of strawberry crop under polyhouse and shadenet house conditions

Strawberry is a commercial crop with high added value, which was extended to new cultivation zones of India.Therefore, it is important to know the suitability of climate condition for growing strawberry in Indian condition. Protected cultivation structures are used to cultivate crops under partial controlled climatic condition to get higher yield and better quality harvest. There are different kinds of protected cultivation structure normally adopted in India such as greenhouse, shadenet house and low tunnels. Exact of amount water and nutrients required to applied to get higher yield through minimizing loss of quality. The objective of the study is to assess the effect of protected cultivation structure on ambient temperature, relative humidity and crop water requirement of strawberry with drip irrigation system grown during winter season (November-February). Reference evapotranspiration was calculated using the FAO-56 Penman-Monteith equation considering the locally recorded weather parameters. Monthly average of daily reference evapotranspiration values are ranging between 1.3 to 3.3 mm day-1, 1.4 to 3.7 mm day-1 and 2.0 to 4.9 mm day-1 for polyhouse, shadenet house and open field, respectively. The total water requirement of drip irrigated straw berry in protected cultivation structure is reduced by about 35.2 % under polyhouse and 25.5 % under shade net house in comparison to open field cultivation.

Optimal Calibration of Evaporation Models against Penman–Monteith Equation

We present an approach for the calibration of simplified evaporation model parameters based on the optimization of parameters against the most complex model for evaporation estimation, i.e., the Penman–Monteith equation. This model computes the evaporation from several input quantities, such as air temperature, wind speed, heat storage, net radiation etc. However, sometimes all these values are not available, therefore we must use simplified models. Our interest in free water surface evaporation is given by the need for ongoing hydric reclamation of the former Ležáky–Most quarry, i.e., the ongoing restoration of the land that has been mined to a natural and economically usable state. For emerging pit lakes, the prediction of evaporation and the level of water plays a crucial role. We examine the methodology on several popular models and standard statistical measures. The presented approach can be applied in a general model calibration process subject to any theoretical or measured evaporation.

Comparison of FAO Crop Reference Estimates and Radiation based Estimates for Daily Reference Evapotranspiration Estimation

<p>Reference evapotranspiration (ETo), a hypothetical concept to estimate evapotranspiration from irrigated and large grass fields is crucial in finding the irrigation water demand in places with extensive agricultural practice. In general, the FAO method (based on the Penman-Monteith equation) is used to estimate ETo from stations that are placed in locations that violate the requirements for reference evapotranspiration. In this study we compare radiation-based methods used to estimate reference evapotranspiration such as ETo De Bruin and ETo Makkink with more conventional ETo approaches in FAO PM method and Priestley Taylor method using in-situ measurements from stations placed in two different settings: (1) Areas that are well-irrigated but surrounded by dry land, (2) Areas that are dry but extensive. We use two spatially dense networks of stations: 1) CIMIS stations of California located in irrigated and in-extensive fields, (2) MESONET stations of Oklahoma located on dry surfaces.  We analyze the differences in the ETo estimates and hypothesize that the radiation-based estimates give more accurate results in the conditions given above for irrigation advisory. We also assess the spatial variability of the different ETo estimates and attempt to investigate the reason behind the differences in these estimates due to the climatic factors.</p>

Rankings of extreme and widespread dry and wet events in the Iberian Peninsula between 1901 and 2016

Extensive, long-standing dry and wet episodes are two of the most frequent climatic extreme events in the Iberian Peninsula. Here, a method for ranking regional extremes of persistent, widespread drought and wet events is presented, considering different timescales. The method is based on the multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI) gridded dataset for the Iberian Peninsula. Climatic Research Unit (CRU) data are used to compute the SPEI between 1901 and 2016 by means of a log-logistic probability distribution function. The potential evapotranspiration (PET) is computed using the Penman–Monteith equation. The ranking classification method is based on the assessment of the magnitude of an event, which is obtained after considering both the area affected by the respective dryness or wetness – defined by SPEI values over a certain threshold – and its intensity in each grid point. A sensitivity analysis of the impact of different thresholds used to define dry and wet events is also performed. For both the dry and wet periods, this simple yet robust tool allows for the identification and ranking of well-known regional extremes of persistent, extensive dry and wet periods at different timescales. A comprehensive dataset of rankings of the most extreme, prolonged, widespread dry and wet periods in the Iberian Peninsula is presented for aggregated timescales of 6, 12, 18, and 24 months. Results show that no region in the Iberian Peninsula is more prone to the occurrence of any of these long-term (dry and/or wet) extreme events. Finally, it is highlighted that the application of this methodology to other domains and periods represents an important tool for extensive, long-standing, extreme event assessment worldwide.