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Figure A.3. Schematic drawing of incoming irradiance of the inside pan rim. A Cartesian coordinate system is established in (a), and (b) is the projection of the solution of (A.10) and (A.11) on the xov plane.
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Pan evaporation (Epan) measurements are used for gauging the atmospheric evaporative demand (AED), and Epan observation networks were established and maintained for a long history. However, due to the replacement of D20 pans with 601B pans across China in 2000, Epan observation networks in China, which is the largest networks in East Asia, became d...
Contexts in source publication
Context 1
... a partially filled pan, Ad,rim' is calculated as a fraction of Ad,rim, i.e., πDhe. The fraction can be expressed by θ5/π (illustrated in figure A.1 ...Context 2
... pan bottom receives the entire hemispherical irradiance from the surrounding ground surface (see figure C.1). Sgnd,bot(i) and Sgnd,bot(o) are calculated as: ...Context 3
... rest of the second part of the outgoing long-wave irradiance will be reflected once by the pan water surface and at least once by the inside pan rim (see figure D.1). The minimum absorption rate of this part of irradiance can be estimated by 1-(1-εrim')(1-εw) (e.g., for D20 and Class A, 1-(1-εrim')(1-εw)≈1-0.18×0.11=98%; ...Similar publications
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Citations
... For atmospheric water demand, we selected net radiation (Rn) and near-surface wind speed (Ws). They are the most important energetic and dynamic drivers of atmospheric water demand (Wang et al., 2018). For vegetation regulation, we chose leaf area index (LAI) and water use efficiency (WUE). ...
Rising atmospheric CO2 is anticipated to influence global runoff through its radiative effect and physiological effect, thereby resulting in profound impacts on water availability and security. While existing literature has explored the two effects on global total runoff, there is still a lack of attention to changes in runoff components (surface and subsurface runoff). Here, based on idealized 1% yr⁻¹ CO2 increase experiments and 14 Earth system models, we decouple the two effects on changes in runoff components and disentangle the contributions of three influencing factors, namely water supply, atmospheric water demand, and vegetation regulation, which are closely intertwined with the two effects. Global total runoff is expected to increase with rising CO2, and this increase mainly comes from subsurface runoff, leading to an elevated subsurface runoff ratio. Vegetation regulation emerges as the most important factor for the increase in subsurface runoff ratio, with the contribution of 49.3%, followed by water supply (41.7%) and atmospheric water demand (8.9%). Increased total runoff implies potentially more flood risk, while the increase in subsurface runoff ratio could decrease some of the risk. The results indicate the necessity of emphasizing changes in subsurface runoff under climate change.
... where N is sample size, E est,i is model estimated value at sample i, E obs,i is observed value at sample i, is mean observed value. The PBIAS indicates the tendency of the model to overestimate or underestimate the pan evaporation, and by how much, expressed as a percentage of the observations (Wang et al., 2018). Positive PBIAS values 6 of 17 indicate model underestimation bias, and negative values indicate model overestimation bias (Gupta et al., 1999;Moriasi et al., 2007). ...
In response to a decline in pan evaporation over the last 60 years under global warming of water bodies, we designed an experiment with water bodies heated naturally to different temperatures to investigate the physics of pan evaporation and explore the effect of water temperature. In this study, we developed a new aerodynamic model for pan evaporation by combining a free convention sub‐model that couples Fick's First Law of Diffusion with boundary layer theory and a forced convection sub‐model based on convection mass transfer. Both the improved aerodynamic model and the two sub‐models have a higher accuracy and stability (|PBIAS| < 6%, root mean squared Error [RMSE] < 0.65 mm d⁻¹, and Nash‐Sutcliffe efficiency [NSE] > 0.8). Sensitivity analysis shows that water temperature is the most sensitive parameter to evaporation (S1 = 0.58, ST = 0.78). The mechanism of rising water temperature on evaporation is not only due to the strengthening in mass diffusion (under windless conditions), but also in the promotion in mass convection (under windy conditions). The integrated effects of mass diffusion and mass convection could result in an increase in evaporation of 0.8 mm d⁻¹, as mean water temperature rises by 1°C. These results would be useful for evaporation estimation of the warming global water.
... PenPan model is initially a physical-based model to estimate monthly evaporation of the US Class A pan with a depth of 25.4 cm and a diameter of 120.7 cm (Rotstayn et al., 2006). Substantial studies suggested that the Pen-Pan model was also a powerful tool for explaining and simulating the evaporation of D20 pan widely used in China Li et al., 2013;Wang et al., 2018). In this model, E PenPan (mm) is composed of radiative (E PenPan,R , mm) and aerodynamic components (E PenPan , A , mm) as follows (Rotstayn et al., 2006): ...
Pan evaporation (Epan) analysis in the Qinghai‐Tibet Plateau (QTP) is important for improving the understanding of the climatic and environmental changes in the QTP and China. However, uneven station coverage, sparse and inconsistent observations hamper the in‐depth understanding of the spatiotemporal Epan patterns throughout the QTP over long time periods. Based on the PenPan model driven by the monthly gridded China meteorological forcing dataset, this study attempted to estimate monthly and annual grid pan evaporation (EPenPan) for the pan with a depth of 10 cm and a diameter of 20 cm at 0.1° resolution throughout the QTP and its surrounding areas during 1979–2017, then the spatiotemporal variations in EPenPan as well as the potential causative climatic variables were thoroughly examined. Results showed that the spatiotemporal patterns of EPenPan were in reasonably good agreement with the observations. The high values of mean annual EPenPan tended to distribute in the areas with water limitation or strong solar radiation like the southwest QTP. The wide decreases in annual EPenPan have reversed in the study area in around 1993, with some exceptions such as the continuous increase in the southwest QTP and monotonous decrease in the areas to the west and east of the Qaidam Basin. Comparatively, the trends in annual EPenPan in the QTP were less than the surrounding areas in the different periods, and overall accelerating annual EPenPan appeared in the both areas after 1993. With the Budyko curve, it was expected that drought severity would increase in the south of the surrounding areas in future, but the warming and wetting in the other areas would be kept. The wind speed (WS) was the primary contributor to decreasing annual EPenPan in the study area before 1993. Nevertheless, the most dominant factor for increasing EPenPan was vapour pressure deficit in the QTP and WS in the surrounding areas during 1994–2017.
... Two emission scenarios RCP4.5 and 8.5 were selected. The future projections of E pan from 2021 to 2100 were performed using the PenPan-V3 model (Wang et al., 2018). ...
Evaporation from open water surfaces is often estimated based on the pan evaporation (Epan), which is an essential measure for estimating atmospheric evaporative demand. Within the Central European region, Epan appears to be slightly underestimated in the case of the hydrological balance of water bodies. In the context of the recent multi-year period of drought, significant losses of surface water deposits were observed in countries of Central Europe. In spite of the ‘evaporation paradox’ phenomenon, Epan is not generally decreasing as expected by many studies from past decades. Recorded observations from the Czech Republic show an increase in Epan, which is associated with an increase in global radiation and vapor pressure deficit. The vast majority of meteorological stations show a strong or very strong increase in Epan during April, June, July and August. During the 1971–2018 period, the annual mean Epan has been increasing by an average of 2.97 mm yr⁻¹. For the period 2001–2018, the mean Epan was 18% higher (519 mm) than the 1971–2000 average (440 mm). Our simulations of future scenarios, using regional climate models, predicted a growth in Epan of up to 27–54%. Such an increase in evaporation would cause serious consequences for surface water availability and agricultural production during the periods of drought in the Czech Republic, as the drought period 2014-2018 has clearly demonstrated.
... The US Class A pan is the first type of evaporation pan to have robust E pan assimilation methods that can simulate the physical processes of a pan, i.e., the vapor transfer process between the water surface and atmosphere, the heat transfer process in boundary layers, and the radiation exchange processes on the water body and the pan wall (Lim et al., 2013;Wang et al., 2018). The development of E pan assimilation methods for US Class A pans relies on a Penman-style model proposed by Thom et al. (1981), the Penpan model derived by Linacre (1994), and a Penman-Monteith-style model developed by Pereira et al. (1995). ...
... The pan wall affects E pan through the radiation absorption from the atmosphere and the surrounding environment, and through heat conduction between the pan wall and the water body (Wang et al., 2019a). To simulate the vapor transfer and radiation processes of the water body and the pan wall, fully physical models, further versions of the PenPan model, were developed recently (Lim et al., 2016;Wang et al., 2018). Lim et al. (2016) derived the PenPan-V2 model for the US Class A pan and successfully applied in Australia E pan simulations. ...
... Lim et al. (2016) derived the PenPan-V2 model for the US Class A pan and successfully applied in Australia E pan simulations. Wang et al. (2018) derived the PenPan-V3 model and showed good performance in E pan simulations of D20 and 601B pans in China. ...
A long-term continuous and consistent pan evaporation (Epan) reanalysis dataset will augment the analysis of Epan distributions when the observation network is discontinuous or inconsistent, and assist in the evaluation of the outputs of General Circulation Models (GCMs) and Land Surface Models (LSMs). From the 1950s to early 2000s, China had a continuous observation of the D20 pan, but this was replaced by the 601B pan across China around 2002, and thus the Epan observation network became discontinuous and inconsistent. This study developed a long-term monthly, 0.05°, continuous and consistent reanalysis dataset for both D20 and 601B pans covering mainland China throughout 1960-2014, based on meteorological data homogenization and interpolation and Epan assimilation. The PenPan-V3 model used in Epan assimilation was successfully validated by observations at 767 and 591 stations for D20 and 601B pans, respectively. Comprehensively considering the physical influence of elevation, radiation, wind speed, humidity, and air temperature, the average annual and seasonal gridded Epan reanalyses show significant spatial dependent on proximity to the ocean and latitude, consistent with previous studies. The reanalysis dataset can be used to analyze Epan distributions across China, including the areas without observations, and to estimate the representativeness of Epan to atmospheric evaporative demand. The dataset has been released in two cloud servers in China and the United States, and it will continue to be maintained and updated.
... The key physical drivers of E pan are temperature, irradiance, vapor pressure deficit and wind speed (Stephens et al., 2018). Since a pan consists of both water body and pan wall, which includes side wall, inside and outside pan rim and (if any) pan bottom (Wang et al., 2018), the above physical drivers influence E pan by determining the physical processes of water body and pan wall. As an instrument for estimating the evaporation of open water (E ow ), the ratio E ow /E pan of ideal pans would be best to be as close as possible to 1 . ...
... To distinguish the influence of pan wall on E pan , some physical E pan models calculate all the physical processes for a pan and the surrounding environment, i.e., the vapor transfer process between water surface and atmosphere, the heat transfer process in boundary layers, and the radiation processes on each part of a pan. These physical models could estimate the E pan contributed by water body (E pan,wb ) and pan wall (E pan,pw ) (Lim et al., 2016;Wang et al., 2018). Pan wall affects E pan by the radiation absorption from atmosphere and surrounding environment, and by the heat conduction between pan wall and water body. ...
... The nationwide replacement leads to the inconsistent and discontinuous E pan observations in China. Therefore, in this study, continuous climatic data were used to simulate continuous E pan from 1960 to 2016 using the PenPan-V3 model (Wang et al., 2018). ...
Open water evaporation (Eow), such as evaporation of lake and reservoir, is typically estimated by observations of different pans. The observation networks of pan evaporation (Epan) were established and maintained worldwide for a long history. All the pans in the world consist of water body and pan wall, which includes side wall, pan rim and (if any) pan bottom. Since the pan wall will affect Epan by radiation absorption and heat conduction, once pan wall absorbs and conducts more heat for vaporizing than water body in a pan, observed Epan dynamics will greatly deviate Eow causing uncertainties and errors in estimating Eow. Thus, this study calculated Epan at 767 meteorological stations in China and quantified the contributions of water body and pan wall on Epan trends. For China as a whole, Epan decreased at -3.75 mm/a2 and increased at 3.68 mm/a2 during 1960-1993 and 1993-2016, respectively. 84% of Epan trends were contributed by water body. For 767 stations, Epan trends of 84 and 96 stations were dominated by pan wall during 1960-1993 and 1993-2016, respectively. Since pan wall contributed more than half of Epan trends for ~23% of the stations in China, the impacts of pan wall on Epan dynamics cannot be ignored.
... Although different types of pans, used by different countries and regions, generally have different geometry and materials (Table 1 in Wang et al., 2018), K p values of these pans can be measured by some local scale experiments. For example, the observed annual average K p of US Class A (with diameter 1.21 m), Russian GGI-3000 (with diameter 0.618 m) and China D20 pans (with diameter 0.2 m) (for photos, see Li et al. (2016)) are around 0.7, 0.8 and 0.6, respectively (Stanhill, 2002;Fu et al., 2004). ...
... Penman model has been successfully used in E ow calculation worldwide (McMahon et al., 2013). Different versions of PenPan models have been widely and successfully used in E pan calculation for different types of pans (Rotstayn et al., 2006;Yang and Yang, 2012;Lim et al., 2016;Wang et al., 2018). Therefore, to quantitatively evaluate the impact of environment variables on K p , Penman and PenPan models can be used to carry on a sensitivity analysis. ...
... To analyze the sensitivity of K p to these variables, we use the PenPan-V3 model, which can well simulate E pan by using all these meteorological variables. The PenPan-V3 model is the latest version of PenPan model (Rotstayn et al., 2006;Lim et al., 2016), which shows good performance in E pan simulation of D20 pans (Wang et al., 2018). The calculated monthly pan evaporation (E pan,cal ) in PenPan-V3 model follows ...
Data of open water evaporation (Eow), such as evaporation of lake and reservoir, have been widely used in hydraulic and hydrological engineering projects, and water resources planning and management in agriculture, forestry and ecology. Because of the low-cost and maneuverability, measuring the evaporation of a pan has been widely regarded as a reliable approach to estimate Eow through multiplying an appropriate pan coefficient (Kp). Kp is affected by geometry and materials of a pan, and complex surrounding environment variables. However, the relationship between Kp and different environment variables is unknown. Thus, this study chose China D20 pan as an example, used meteorological observations from 767 stations and introduced the latest PenPan model to analyze the sensitivity of Kp to different environment variables. The results show that, the distribution of annual Kp had a strong spatial gradient. For all the stations, annual Kp ranged from 0.31 to 0.89, and decreased gradually from southeast to northwest. The sensitivity analysis shows that for China as a whole, Kp was most sensitive to relative humidity, followed by air temperature, wind speed and sunshine duration. For 767 stations in China, Kp was most sensitive to relative humidity for almost all the stations. For stations north of Yellow River, wind speed and sunshine duration were the next sensitive variables; while for stations south of Yellow River, air temperature was the next sensitive variable. The method introduced in this study could benefit estimating and predicting Kp under future changing environment.
Pan evaporation (Epan) is a critical measure of the atmospheric evaporation demand. Analyzing meteorological data from the Tropical Rainforest Comprehensive Meteorological Observation Field in the Xishuangbanna Tropical Botanical Garden (XTBG Meteorological Observing Station) based on physical models is helpful to improve our understanding of the state of the hydrological cycle in the Xishuangbanna tropical rainforest region. In this study, we investigated the long-term trend in Epan with the aid of observation data from 1959 through 2021. Moreover, correlation analysis of Epan was performed, such as trend test, assessment of periodic properties and abrupt change analysis. Then, D20 Epan data and related meteorological data from 1979 to 2008 were used to drive Penman‒Monteith and PenPan models for simulating Epan. The partial derivative attribution method was used to analyze the dominant factors affecting Epan. The results showed that Epan exhibits obvious periodic changes, the 19a is the first primary period. In addition, there was a clear "evaporation paradox" phenomenon in Xishuangbanna. Epan showed decreasing trend during both 1959-2008 and 2009-2018, and the decreasing trend reached a significant level with a rate of -3.404 mm·a-2 during 1959-2008. Through comparative analysis, the PenPan model was considered more suitable for simulating Epan in Xishuangbanna. In order to identify the main meteorological factors influencing Epan, complete data from the D20 pan monitoring period, namely, 1979-2008, were selected for attribution calculations. The variations in the net radiation and saturated vapor pressure deficit are the main triggers that explain the "evaporation paradox" phenomenon in Xishuangbanna.
Global warming is expected to increase the atmospheric evaporative demand and make more surface water for evapotranspiration, aggerating water sources' social and ecological shortage. Pan evaporation, as a routine observation worldwide, is an excellent metric to indicate the response of terrestrial evaporation to global warming. However, several non-climatic effects, such as instrument upgrades, have destroyed the homogenization of pan evaporation and limited its applications. In China, 2400s meteorological stations have observed daily pan evaporation since 1951. The observed records became discontinuous and inconsistent due to the instrument upgrade from micro-pan D20 to large-pan E601. Here, combining the Penpan model (PM) and random forest model (RFM), we developed a hybrid model to assimilate different types of pan evaporation into a consistent dataset. Based on the cross-validation test, on a daily scale, the hybrid model has a lower bias (RMSE=0.41 mm day-1) and better stability (NSE=0.94) than the two sub-models and the conversion coefficient method. Finally, we produced a homogenized daily dataset of E601 across China from 1961 to 2018. Based on this dataset, we analyzed the long-term trend of pan evaporation. Pan evaporation showed a -1.23±0.57 mm a-2 downward trend from 1961-1993, primarily caused by decreased pan evaporation in warm seasons over North China. After 1993, the pan evaporation in South China increased significantly, resulting in a 1.83±0.87 mm a-2 upward trend across China. With better homogeneity and higher temporal resolution, the new dataset is expected to promote drought monitoring, hydrological modeling, and water resources management. Free access to the dataset can be found at https://figshare.com/s/0cdbd6b1dbf1e22d757e.
