Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration

Allen, Richard G.; Dhungel, Ramesh; Dhungana, Bibha; Huntington, Justin L.; Kiliç, Ayşe; Morton, Charles

doi:10.1016/j.agwat.2020.106531

Cited by 31 publications

(19 citation statements)

References 47 publications

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“…However, there is insufficient information about weather stations management and related local dryness, aridity or advection impacts, which justifies that T dew cannot be assumed equal to T min (Temesgen et al, 1999;Todorovic et al, 2013;Ren et al, 2016a;Paredes and Pereira, 2019), thus requiring specific estimation procedures when computing ET o PMT as described in the following. As demonstrated in two other papers in this Special Issue (Allen et al, 2020;Blankenau et al, 2020), despite observations could be performed in a reference site, the observed temperature could be larger, up to 5°C, than that observable for the same site when under irrigation, which would call for a generalized correction of observed air temperatures and humidity. Nevertheless, the approaches proposed herein provide for a correction of station data to approach reference site conditions, in line with more refined propositions by Allen et al (2020) and Blankenau et al (2020).…”

Section: Methodsmentioning

confidence: 84%

“…As demonstrated in two other papers in this Special Issue (Allen et al, 2020;Blankenau et al, 2020), despite observations could be performed in a reference site, the observed temperature could be larger, up to 5°C, than that observable for the same site when under irrigation, which would call for a generalized correction of observed air temperatures and humidity. Nevertheless, the approaches proposed herein provide for a correction of station data to approach reference site conditions, in line with more refined propositions by Allen et al (2020) and Blankenau et al (2020).…”

Section: Methodsmentioning

confidence: 84%

“…This approach holds when applied to a reference site where temperature and humidity are measured over an extensive and actively growing grass crop completely shading the ground and not short of water, which corresponds to the conditions assumed when defining the PM-ET o . Confirming this assertion, studies have shown that this approach does not hold when observations are performed in non-reference sites and when sites are affected by dryness and local advection, which cause that T min >T dew (Allen, 1996;Kimball et al, 1997;Temesgen et al, 1999;Paredes and Pereira, 2019;Allen et al, 2020). T dew = T min was adopted in various applications performed under conditions assumed as reference sites (e.g., Liu and Pereira, 2001;Pereira et al, 2003;Trajkovic, 2005;Popova et al, 2006;Jabloun and Sahli, 2008;Córdoba et al, 2015;Wable et al, 2019), or not affected by dryness (Cai et al, 2007;Martinez and Thepadia, 2010).…”

Section: Grass Reference Evapotranspiration and Its Computation With Reduced Datasetsmentioning

confidence: 99%

“…However, the majority of worldwide weather data are likely reported from sites affected by dryness and advection, thus adopting T dew = T min may cause less accurate estimates of ET o . Recently, De Bruin and Trigo (2019) discussed inaccuracies related with non-reference sites and Allen et al (2020) and Blankenau et al (2020) discussed about station aridity impacts. In addition, but often not mentioned in literature, data quality also plays a role in the accuracy of estimates (Allen et al, 2011;Kwon and Choi, 2011).…”

Section: Background and Computational Procedures For Pmtmentioning

confidence: 99%

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Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Paredes

Pereira

Almorox

et al. 2020

Agricultural Water Management

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The computation of the grass reference evapotranspiration with the FAO56 Penman-Monteith equation (PM-ET o ) requires data on maximum and minimum air temperatures (T max , T min ), actual vapour pressure (e a ), shortwave solar radiation (R s ), and wind speed at 2 m height (u 2 ). Nonetheless, related datasets are often not available, are incomplete, or have uncertain quality. To overcome these limitations, several alternatives were considered in FAO56, while many other procedures were tested and proposed in very numerous papers. The present study reviews the computational procedures relative to predicting the missing variables from temperature, i.e., the PM temperature approach (PMT), and estimating ET o with the Hargreaves-Samani (HS) equation. For the PMT approach, procedures refer to predicting: (a) the dew point temperature (T dew ) from the minimum or the mean air temperature; (b) shortwave solar radiation (R s ) from the air temperature difference (TD = T max -T min ) combined with a calibrated radiation adjustment coefficient (k Rs ); and (c) wind speed (u 2 ) using a default value or a regional or local average. The adequateness of computing T dew from air temperature was reassessed and the preference for using an average u 2 has been defined. To ease the estimation of R s, for the PMT approach and the coefficient of the HS equation, multiple linear regression equations for predicting k Rs were developed using local averages of the temperature difference (TD), relative humidity (RH) and wind speed as independent variables. All variables were obtained from the Mediterranean set of CLIMWAT climatic data. Two types of k Rs equations were developed: climate-focused equations specific to four climate types -humid, sub-humid, semi-arid, and hyper-arid and arid -, and a global one, applicable to any type of climate. The usability of the k Rs equations for the PMT and HS methods was assessed with independent data sets from Bolivia, Inner Mongolia, Iran, Portugal and Spain, covering a variety of climates, from hyper-arid to humid. With this purpose, ET o estimated with PMT and HS (ET o PMT and ET o HS ) were compared with PM-ET o computed with full data sets to evaluate the usability of the k Rs equations. Adopting the climate-focused k Rs equations with ET o PMT , the RMSE averaged 0.59, 0.64, 0.65 and 0.72 mm d −1 for humid, sub-humid, semi-arid, and arid and hyper-arid climates, respectively, while the RMSE values relative to ET o HS when using the respective climate-focused k Rs equations averaged 0.58, 0.60, 0.60 and 0.69 mm d −1 for the same climates. These results are similar to those obtained with the k Rs global equation. The accuracy of the PMT approach when using the k Rs equations was also evaluated when one, two, or all three T dew , R s and u 2 variables are missing and the resulting goodness-of-fit indicators demonstrated the advantage of the combined use of observed and estimated weather variables. The usability of the k Rs equations for an efficient parameterization of both the PMT approach and ...

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Section: Methodsmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 84%

Section: Grass Reference Evapotranspiration and Its Computation With Reduced Datasetsmentioning

confidence: 99%

Section: Background and Computational Procedures For Pmtmentioning

confidence: 99%

See 2 more Smart Citations

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Paredes

Pereira

Almorox

et al. 2020

Agricultural Water Management

View full text Add to dashboard Cite

show abstract

“…Advances in ICT provide great opportunities to make use of large volumes of data and sources (sensors, models, remote sensing, images, tweets, farmers' knowledge and experience, etc.). For using the K c -ET o approach, ET o may be estimated with reduced datasets using the PMT and HS-eq approaches which require, among other, ground observed data, gridded and reanalysis data, and/or Meteosat Second Generation products, as well as forecasted weather data (Allen et al, 2020;Paredes et al, 2020a, b). K c data sets refer to the updated tabulated standard K c and K cb values (Pereira et al, 2020a,b) and to the use of the A&P approach to compute K cb and K c from f c and height (Allen and Pereira, 2009;Pereira et al, 2020c,d).…”

Section: Tablementioning

confidence: 99%

Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach

Pereira

Paredes

Jovanović

2020

Agricultural Water Management

150

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This study reviews soil water balance (SWB) model approaches to determine crop irrigation requirements and scheduling irrigation adopting the FAO56 method. The K c -ET o approach is discussed with consideration of baseline concepts namely standard vs. actual K c concepts, as well as single and dual K c approaches. Requirements for accurate SWB and appropriate parameterization and calibration are introduced. The one-step vs. the two-step computational approaches is discussed before the review of the FAO56 method to compute and partition crop evapotranspiration and related soil water balance. A brief review on transient state models is also included. Baseline information is concluded with a discussion on yields prediction and performance indicators related to water productivity. The study is continued with an overview on models development and use after publication of FAO24, essentially single K c models, followed by a review on models following FAO56, particularly adopting the dual K c approach. Features of dual K c modeling approaches are analyzed through a few applications of the SWB model SIMDualKc, mainly for derivation of basal and single K c , extending the basal K c approach to relay intercrop cultivation, assessing alternative planting dates, determining beneficial and nonbeneficial uses of water by an irrigated crop, and assessing the groundwater contribution to crop ET in the presence of a shallow water table. The review finally discusses the challenges placed to SWB modeling for real time irrigation scheduling, particularly the new modeling approaches for large scale multi-users application, use of cloud computing and adopting the internet of things (IoT), as well as an improved wireless association of modeling with soil and plant sensors. Further challenges refer to the use of remote sensing energy balance and vegetation indices to map K c , ET and crop water and irrigation requirements. Trends are expected to change research issues relative to SWB modeling, with traditional models mainly used for research while new, fastresponding and multi-users models based on cloud and IoT technologies will develop into applications to the farm practice. Likely, the K c -ET o will continue to be used, with ET o from gridded networks, re-analysis and other sources, and K c data available in real time from large databases and remote sensing.

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Simulating sagebrush–cheatgrass plant community biomass production in the Great Basin using ALMANAC

Schantz,

Kiniry,

Williams

et al. 2024

Crop Science

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Cheatgrass (Bromus tectorum) is a widespread species of concern throughout the western US, as it dominates many low‐elevation rangelands and continues to spread annually. As a winter annual grass, however, cheatgrass can produce high‐quality and cheap protein forage for livestock early in the growing season. Estimating biomass can lead to better management in these western United States and Great Basin plant communities. The present study was designed to evaluate the accuracy of biomass simulations in Great Basin plant communities using the process‐based Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model as it simulates biomass production and competing species interactions. For this study, data were used from the Sagebrush Steppe Treatment Evaluation Project to simulate cheatgrass, perennial grass, forb, and sagebrush biomass across three community types: native, invaded, and a co‐dominated community of sagebrush, perennial grasses, cheatgrass, and forbs at six representative Great Basin sites from 2006 to 2018. Our results indicated a strong relationship between simulated and measured biomass of total cheatgrass and perennial grasses across the three plant communities. Sagebrush and forb biomass were poorly simulated across most plant community types. Model accuracy also varied by site, largely depending on elevation. We saw high variability in simulated biomass across years, likely because of the single point‐in‐time measurements at peak biomass and the low biomass values of cheatgrass and forbs. Collectively, ALMANAC shows potential for assessing biomass production and plant interactions but will require more data and model development to fully comprehend its utility.

show abstract

Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration

Cited by 31 publications

References 47 publications

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach

Simulating sagebrush–cheatgrass plant community biomass production in the Great Basin using ALMANAC

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