Transpiration Model(蒸腾模型)研究综述
Transpiration Model 蒸腾模型 - Hydrological models used to estimate groundwater reserves use evapotranspiration models that are mainly determined by climate demand. [1] Evaporation models use the Penman, Priestley, Bruin, and Valiantzas models while evapotranspiration models use the Penman, Hargreaves, Jensen-Haise, Penman-Monteith, Radiation, Turc, and Makkink models, where all of these methods use climate data, such as are the minimum temperature (Tn), maximum temperature (Tx), air temperature (Ta), average humidity (RH), rainfall (R), duration of sun exposure (SS), and maximum wind speed (U) in calculations using Visual basic program in Microsoft Excel in the form of code. [2] A validation of the current TRNSYS simulation and evapotranspiration model was made using previous studies from the literature, and the comparison showed fair agreement. [3] Our approach integrates two independent measures of crop water availability – Normalized Difference Water Index, and Evapotranspiration modelled using remotely sensed land surface temperature – with k-means clustering to delineate zones in an automated fashion. [4] For that reason, several studies have addressed the restraining of mechanical failures and advantages of evapotranspiration model in soil–plant-slope stability continuum. [5] Transpiration can directly reflect the response of the crop growth and development, therefore irrigation design based on a transpiration model is an important factor towards establishing an efficient irrigation strategy. [6] In this study, an EvapoTranspiration Model for Arid Land (ETMAL) was proposed to estimate water loss in Hotan River basin with MODIS imagery and ERA5 climate reanalysis products. [7] These optimization strategies are based on a multi-input multi-output (MIMO) climate model and a modified evapotranspiration model. [8] Due to the energy imbalance problem that the sum of latent and sensible heat fluxes is not equivalent to available energy, there exists a structural error in energy balance-based evapotranspiration models. [9] The predictability of water vapor cycle, thermal comfort and surface energy budgets in urban environments will be enhanced by incorporating the transpiration model into urban canopy model. [10] ABSTRACT The remote sensing approach of Mapping Evapotranspiration with the Internalized Calibration (METRIC) model is one of the accurate evapotranspiration models but the its complexity motivated us to seek for a simplified version of the original model. [11] This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and its associated components to obtain a reference-based SPAC model of reference evapotranspiration (R-SPAC), and it applied the model to an agricultural ecosystem. [12] In this study, the community evapotranspiration model was initially constructed using field data, which was then expanded for applicability to the Xilin River Basin based on Geographic Information System technology and spatial heterogeneity characteristics of remote sensing data; both models were significant at the 0. [13] The main parameters affecting evapotranspiration rates in greenhouse settings including aerodynamic resistance, stomatal resistance and intercepted radiation are thoroughly discussed for accurate measurement and consideration in evapotranspiration models. [14] In this work, a combined respiration and transpiration model is proposed for fresh cape gooseberry fruits, also considering changes in water activity (aw) and moisture content. [15] Actual evapotranspiration models based on remote sensing data from visible bands of Sentinel-2, including Penman-Monteith–Stewart (RS-PMS) and Penman-Monteith–Leuning (RS-PML), were evaluated at different temporal scales in a Cabernet Sauvignon vineyard (Vitis vinifera L. [16] To overcome these challenges, here we describe a coupled photosynthesis evapotranspiration model of intermediate complexity. [17] The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. [18] In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. [19] Employing evapotranspiration models is a widely used method to estimate reference evapotranspiration (ETREF) based on weather data. [20] The results showed that the monthly streamflows estimated based on any of the previous evapotranspiration models are almost the same. [21] Evapotranspiration modeling performance derived from the improved kc model integrating drought stress factor was superior to that derived by kc-FAO directly in semiarid area. [22] On the other hand, accuracy and reliability of simple reference evapotranspiration models vary widely according to regional climate conditions. [23] Three different evapotranspiration models have been compared in this study. [24] A Kalman filter using an evapotranspiration model in combination with experimental data was used to refine the measurements, serving as the input for a model predictive controller (MPC). [25] A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. [26] Based on reasonably accurate empirical data, the exergy budget shows that the overall exergy conversion efficiency is quite low in plants and is very sensitive to even small changes in the environmental condition and to the adopted evapotranspiration model, as it would have been expected. [27] Comprehensive weather data provided by NLDAS included 1/8° latitude/longitude resolution over a domain that covers the continental United States, part of Canada, and part of Mexico (125o W –67o W, 25o N –53o N) in Increased Bias in Evapotranspiration Modeling Due to Weather and Vegetation Indices Data Sources. [28] The field measurements were used to determine the appropriate input hydraulic parameters and evapotranspiration model that can be used for numerical modeling. [29] However, despite water and carbon cycle coupling, there are few diagnostic global evapotranspiration models that have complete carbon constraint on water flux run at a high spatial resolution. [30] Remote-Sensing (RS) based evapotranspiration models are presented as a feasible means in order to provide accurate spatially-distributed ET estimates over this region. [31] Based on the remote sensing monitoring techniques, region evapotranspiration model, multi-criteria evaluation model, the spatial distribution of winter wheat, actual evapotranspiration (ET), and soil fertility score (SF) was measured in sequence. [32] Based on the traditional evapotranspiration models for urban underlying surface and the proposed model for water dissipation in buildings, a new modelling system was built to simulate the total UWD. [33] For this purpose, several evapotranspiration models have been developed and presented, all based on the Penman–Monteith approach, the “big-leaf” model. [34] Optimization of the evapotranspiration model was increased by the inclusion of growing season length, suggesting that growing season length is partially responsible for variations in evapotranspiration over time. [35] BTA-θ provides a structure to incorporate other plant water stress data for transpiration modelling. [36] Five well‐known evapotranspiration models and five evaporation models with different wind functions were tested. [37] The purpose of this study is to determine the actual evapotranspiration model based on the volume of the irrigation to obtain an accurate evapotranspiration value on Alfisols with maize plantation. [38] Improving evapotranspiration modelling improves the soil water balance, pasture growth, tree-grass competition and safe carrying capacity, where animal numbers are matched to available pasture. [39]用于估算地下水储量的水文模型使用主要由气候需求决定的蒸发蒸腾模型。 [1] 蒸发模型使用 Penman、Priestley、Bruin 和 Valiantzas 模型，而蒸发蒸腾模型使用 Penman、Hargreaves、Jensen-Haise、Penman-Monteith、Radiation、Turc 和 Makkink 模型，所有这些方法都使用气候数据，例如使用 Visual basic 计算的最低温度 (Tn)、最高温度 (Tx)、空气温度 (Ta)、平均湿度 (RH)、降雨量 (R)、日照持续时间 (SS) 和最大风速 (U)以代码形式在 Microsoft Excel 中编写程序。 [2] 使用文献中的先前研究对当前的 TRNSYS 模拟和蒸散模型进行了验证，并且比较表明相当一致。 [3] 我们的方法整合了作物水分可用性的两个独立测量——标准化差异水分指数，以及使用遥感地表温度建模的蒸散量——通过 k-means 聚类以自动方式描绘区域。 [4] 出于这个原因，一些研究已经解决了土壤-植物-坡度稳定性连续体中机械故障的抑制和蒸发蒸腾模型的优势。 [5] 蒸腾可以直接反映作物生长发育的反应，因此基于蒸腾模型的灌溉设计是建立有效灌溉策略的重要因素。 [6] 在这项研究中，提出了旱地蒸发蒸腾模型（ETMAL），以利用 MODIS 图像和 ERA5 气候再分析产品估计和田河流域的水损失。 [7] 这些优化策略基于多输入多输出 (MIMO) 气候模型和改进的蒸发蒸腾模型。 [8] 由于潜热和感热通量之和不等于可用能量的能量不平衡问题，基于能量平衡的蒸发蒸腾模型存在结构性误差。 [9] 通过将蒸腾模型纳入城市冠层模型，可以提高城市环境中水汽循环、热舒适度和地表能量收支的可预测性。 [10] 摘要 用内化校准（METRIC）模型绘制蒸散量的遥感方法是准确的蒸散量模型之一，但其复杂性促使我们寻求原始模型的简化版本。 [11] 本研究更新了土壤-植物-大气连续体 (SPAC) 蒸发蒸腾模型及其相关组件，以获得基于参考的 SPAC 参考蒸发蒸腾模型 (R-SPAC)，并将该模型应用于农业生态系统。 [12] 本研究首先利用野外数据构建群落蒸散模型，然后基于地理信息系统技术和遥感数据的空间异质性特征，将其扩展到锡林河流域的适用性；两个模型都在 0 时显着。 [13] 对影响温室环境中蒸散速率的主要参数（包括空气动力阻力、气孔阻力和截获辐射）进行了深入讨论，以便在蒸散模型中进行准确测量和考虑。 [14] 在这项工作中，提出了新鲜海角醋栗果实的呼吸和蒸腾组合模型，同时考虑了水分活度 (aw) 和水分含量的变化。 [15] 基于 Sentinel-2 可见波段遥感数据的实际蒸散模型，包括 Penman-Monteith-Stewart (RS-PMS) 和 Penman-Monteith-Leuning (RS-PML)，在赤霞珠葡萄园的不同时间尺度上进行了评估(Vitis vinifera L. [16] 为了克服这些挑战，我们在这里描述了一个中等复杂度的耦合光合作用蒸散模型。 [17] 作物蒸散模型（ETc）和作物水分胁迫指数（CWSI）是从它们与VIs之间建立的关系中获得的。 [18] 在这项研究中，1961-2008 年从大扎布流域获得的每日降雨量和温度分别用于使用 LARS-WG 和多元线性回归建立降雨和蒸散模型。 [19] 使用蒸散发模型是一种广泛使用的方法，用于根据天气数据估计参考蒸散发 (ETREF)。 [20] 结果表明，基于以前的任何蒸发蒸腾模型估计的月流量几乎相同。 [21] 在半干旱地区，由改进的kc模型整合干旱胁迫因子得到的蒸散模拟性能优于直接由kc-FAO得到的模拟结果。 [22] 另一方面，简单参考蒸散模型的准确性和可靠性因区域气候条件而异。 [23] 本研究比较了三种不同的蒸发蒸腾模型。 [24] 使用蒸发蒸腾模型结合实验数据的卡尔曼滤波器用于细化测量，作为模型预测控制器 (MPC) 的输入。 [25] 使用 Landsat 图像实施了基于遥感的蒸发蒸腾模型，以管理位于内布拉斯加州中西部的配备 VRI 的中心枢纽灌溉田地的灌溉，该灌溉田于 2017 年种植玉米，2018 年种植大豆。 [26] 基于相当准确的经验数据，火用预算表明植物的整体火用转换效率非常低，并且对环境条件的微小变化和所采用的蒸发蒸腾模型非常敏感，正如预期的那样。 [27] NLDAS 提供的综合天气数据包括覆盖美国大陆、加拿大部分地区和墨西哥部分地区（125o W –67o W，25o N –53o N）的 1/8° 纬度/经度分辨率。基于天气和植被指数数据源的蒸散量建模。 [28] 现场测量用于确定可用于数值模拟的适当输入水力参数和蒸发蒸腾模型。 [29] 然而，尽管水和碳循环耦合，但很少有诊断性全球蒸发蒸腾模型对以高空间分辨率运行的水通量具有完全的碳约束。 [30] 提出基于遥感 (RS) 的蒸散模型作为一种可行的方法，以便在该区域提供准确的空间分布 ET 估计。 [31] 基于遥感监测技术，依次测量区域蒸散模型、多准则评价模型、冬小麦的空间分布、实际蒸散量（ET）和土壤肥力得分（SF）。 [32] 在传统的城市下垫面蒸散模型和提出的建筑物耗水模型的基础上，建立了一个新的模拟系统来模拟总UWD。 [33] 为此目的，已经开发并提出了几种蒸发蒸腾模型，均基于 Penman-Monteith 方法，即“大叶”模型。 [34] 蒸发蒸腾模型的优化通过包含生长季节长度而增加，这表明生长季节长度是蒸发蒸腾随时间变化的部分原因。 [35] BTA-θ 提供了一种结构，可以结合其他植物水分胁迫数据进行蒸腾建模。 [36] 测试了五个著名的蒸发蒸腾模型和五个具有不同风功能的蒸发模型。 [37] 本研究的目的是确定基于灌溉量的实际蒸散模型，以获得玉米种植园 Alfisols 的准确蒸散值。 [38] 改进蒸发蒸腾模型可改善土壤水分平衡、牧场生长、树草竞争和安全承载能力，其中动物数量与可用牧场相匹配。 [39]