Impact of canopy representations on regional modeling of evapotranspiration using the WRF-ACASA coupled model

Xu, Li; Pyles, R. D.; U, Kyaw Tha Paw; Snyder, R. L.; Monier, Erwan; Falk, Matthias; Chen, Shu Hua

doi:10.1016/j.agrformet.2017.07.003

Cited by 14 publications

(9 citation statements)

References 32 publications

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“…Many studies have investigated the impact of different parameterization, initialization, and inputs used for LSMs on regional atmospheric conditions (e.g., Avissar & Pielke, 1991; Case et al, 2008; Chen & Zhang, 2009; Chen et al, 2010; Jin et al, 2010; Niu et al, 2011; Ozdogan et al, ; Xu et al, 2017; Zeng et al, 2016; Zhu et al, ), but to our knowledge, this is a first study that specifically quantified and constrained the effect of excess moisture from crop transpiration on a heat wave event in the Midwest U.S. Our analyses highlight the interaction between excess moisture from cropland and atmospheric conditions. However, we are aware of the limitation of our study, which is not accounting for possible changes in soil thermodynamics, plant structure, soil moisture, and emissivity under the NOCROP experiment, which requires a follow‐up long‐term study to elucidate to what extent those parameters can balance out the deficit in transpiration.…”

Section: Discussionmentioning

confidence: 99%

“…In general, the resistance is dependent on solar radiation, air temperature, soil moisture, and vapor pressure deficit. The canopy resistance formulation in this model introduces some structural uncertainties exacerbated by feedback uncertainties resulting from the moisture flux parameters used in the resistance calculation (e.g., Xu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top‐Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

et al. 2020

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The primary focus of this study is to understand the contribution from excess moisture from crop transpiration to the severity of a heat wave episode that hit the Midwestern U.S. from 16 to 20 July 2011. To elucidate this, we first provide an optimal estimate of the transpiration water vapor flux using satellite total column water vapor retrievals whose accuracy and precision are characterized using independent observations. The posterior transpiration flux is estimated using a local ensemble transform Kalman filter that employs a mesoscale weather model as the forward model. The new estimation suggests that the prior values of transpiration flux from crops are biased high by 15%. We further use the constrained flux to examine the sensitivity of meteorology to the contributions from crops. Over the agricultural areas during daytime, elevated moisture (up to 40%) from crops not only increases humidity (thus the heat index) but also provides a positive radiative forcing by increasing downward longwave radiation (13 ± 4 W m−2) that results in even higher surface air temperature (+0.4 °C). Consequently, we find that the elevated moisture generally provides positive feedback to aggravate the heat wave, with daytime enhancements of heat index by as large as 3.3 ± 0.8 °C. Due to a strong diurnal cycle in the transpiration, the feedback tends to be stronger in the afternoon (up to 5 °C) and weaker at night. Results offer a potential basis for designing mitigation strategies for the effect of transpiration from agriculture in the future, in addition to improving the estimation of canopy transpiration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top‐Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

et al. 2020

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“…DiGiovanni (2013) showed that regional estimations of ET 0 do not accurately predict ET 0 computed from on‐site meteorological data measured at a green roof site in NYC. Therefore, a more accurate estimation of ET 0 is critically significant for cities located in an arid and semi‐arid climate, where is facing water scarcity and optimum use of water resources are a primary issue (Chow et al, 2014; Pataki et al, 2011; Xu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessment of reference evapotranspiration across an arid urban environment having poor data monitoring system

Shafieiyoun

Gheysari

Khiadani

et al. 2020

Hydrological Processes

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Estimation of reference evapotranspiration (ET0) in urban areas is challenging but essential in arid urban climates. To evaluate ET0 in an urban environment and non‐urban areas, air temperature and relative humidity were measured at five different sites across the arid city of Isfahan, Iran, over 4 years. Wind speed and sunshine hours were obtained from an urban surrounding weather station over the same period and used to estimate ET0. Calculated ET0 was compared with satellite‐based ET0 retrieved from the MOD16A2 PET product. Although MODIS PET was strongly correlated with the Valiantzas equation, it overestimated ET0 and showed average accuracy (r = 0.93–0.94, RMSE = 1.18–1.28 mm/day, MBE = 0.73–0.84 mm/day). The highest ET0 differences between an urban green space and a non‐urban area were 1.1 and 0.87 mm/day, which were estimated by ground measurements and MODIS PET, respectively. The sensitivity of ET0 to wind speed and sunshine hours indicated a significant effect on cumulative ET0 at urban sites compared to the non‐urban site, which has a considerable impact on the amount of irrigation required in those areas. Although MODIS PET requires improvement to accurately reflect field level microclimate conditions affecting ET0, it is beneficial to hydrological applications and water resource managers especially in areas where data is limited. In addition, our results indicated that using limited data methods or meteorological data from regional weather stations, leads to incorrect estimation of ET0 in urban areas. Therefore, decision‐makers and urban planners should consider the importance of precisely estimating ET0 to optimize management of urban green space irrigation, especially in arid and semi‐arid climates such as the city of Isfahan.

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“…The role of vegetation parameters (FVC and LAI) is relevant for weather forecasting and climate change assessments [11]. Their impact on land surface processes has been studied in the Eta operational model [12][13][14] and the Weather Research and Forecasting model (WRF) [15][16][17][18][19][20]. The relevance of using realistic information of the vegetation state on RCM performance has been analyzed in several works.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, most of the standard configurations of NWPM and RCM use climatological values of vegetation parameters. However, the vegetation has a strong inter-annual variability [18,21,31], leading this to a non-suitable characterization of surface properties. A known limitation is that surface properties can vary at several time scales depending on climate conditions and other processes such as urbanization, forest fires or changes in crops [32].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

et al. 2019

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The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

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Impact of canopy representations on regional modeling of evapotranspiration using the WRF-ACASA coupled model

Cited by 14 publications

References 32 publications

Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top‐Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top‐Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

Assessment of reference evapotranspiration across an arid urban environment having poor data monitoring system

Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

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