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- Author or Editor: Fubao Sun x
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Abstract
Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application.
Abstract
Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application.
Abstract
Atmospheric evaporative demand plays a pivotal role in global water and energy budgets, and its change is very important for drought monitoring, irrigation scheduling, and water resource management under a changing environment. Here, future changes of pan evaporation E pan, a measurable indicator for atmospheric evaporative demand, are first projected and attributed over China through a physically based approach, namely, the PenPan model, forced with outputs from 12 state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project. An equidistant quantile mapping method was also used to correct the biases in GCMs outputs to reduce uncertainty in E pan projection. The results indicated that E pan would increase during the periods 2021–50 and 2071–2100 relative to the baseline period 1971–2000 under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios, which can mainly be attributed to the projected increase in air temperature and vapor pressure deficit over China. The percentage increase of E pan is relatively larger in eastern China than in western China, which is due to the spatially inconsistent increases in air temperature, net radiation, wind speed, and vapor pressure deficit over China. The widely reported “pan evaporation paradox” was not well reproduced for the period 1961–2000 in the climate models, before or after bias correction, suggesting discrepancy between observed and modeled trends. With that caveat, it was found that the pan evaporation has been projected to increase at a rate of 117–167 mm yr−1 K−1 (72–80 mm yr−1 K−1) over China using the multiple GCMs under the RCP 4.5 (RCP 8.5) scenario with increased greenhouse gases and the associated warming of the climate system.
Abstract
Atmospheric evaporative demand plays a pivotal role in global water and energy budgets, and its change is very important for drought monitoring, irrigation scheduling, and water resource management under a changing environment. Here, future changes of pan evaporation E pan, a measurable indicator for atmospheric evaporative demand, are first projected and attributed over China through a physically based approach, namely, the PenPan model, forced with outputs from 12 state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project. An equidistant quantile mapping method was also used to correct the biases in GCMs outputs to reduce uncertainty in E pan projection. The results indicated that E pan would increase during the periods 2021–50 and 2071–2100 relative to the baseline period 1971–2000 under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios, which can mainly be attributed to the projected increase in air temperature and vapor pressure deficit over China. The percentage increase of E pan is relatively larger in eastern China than in western China, which is due to the spatially inconsistent increases in air temperature, net radiation, wind speed, and vapor pressure deficit over China. The widely reported “pan evaporation paradox” was not well reproduced for the period 1961–2000 in the climate models, before or after bias correction, suggesting discrepancy between observed and modeled trends. With that caveat, it was found that the pan evaporation has been projected to increase at a rate of 117–167 mm yr−1 K−1 (72–80 mm yr−1 K−1) over China using the multiple GCMs under the RCP 4.5 (RCP 8.5) scenario with increased greenhouse gases and the associated warming of the climate system.
Abstract
Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation. However, this scaling behavior is inappropriate for high temperatures and short-duration precipitation extremes. Here, daily data from 702 stations during 1951–2014 and hourly data from 8 stations during 2000–15 are used to examine and explain this behavior in China. Both daily and hourly precipitation extremes exhibit an increase in temperature dependency at lower temperatures. The CC scaling transitions from positive to negative rates with temperatures greater than 25°C. Unlike the increase in daily data, which is similar to single-CC (1CC) scaling, the increase in hourly data resembles super-CC (2CC) scaling for temperatures greater than 13°C. Results show that the precipitation extremes are controlled by water vapor for a given temperature. At lower temperatures, precipitation extremes exhibit a positive linear dependence on daily actual vapor pressure whose value is almost equal to the saturated vapor pressure at a given temperature. At higher temperatures, actual vapor pressure has difficulty maintaining a consistent increasing rate because of the exponential increasing of the saturated vapor pressure. Higher temperatures result in larger vapor pressure deficits, which lead to sharp decreases in precipitation extremes. Similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature increases from 17°C along the northwest inland area to 25°C along the southeast coast. These behaviors demonstrate that precipitation extremes are firmly linked to temperature when there is sufficient moisture at lower temperatures and limited by insufficient moisture at higher temperatures. Overall, precipitation extreme events require more attention in a warming climate.
Abstract
Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation. However, this scaling behavior is inappropriate for high temperatures and short-duration precipitation extremes. Here, daily data from 702 stations during 1951–2014 and hourly data from 8 stations during 2000–15 are used to examine and explain this behavior in China. Both daily and hourly precipitation extremes exhibit an increase in temperature dependency at lower temperatures. The CC scaling transitions from positive to negative rates with temperatures greater than 25°C. Unlike the increase in daily data, which is similar to single-CC (1CC) scaling, the increase in hourly data resembles super-CC (2CC) scaling for temperatures greater than 13°C. Results show that the precipitation extremes are controlled by water vapor for a given temperature. At lower temperatures, precipitation extremes exhibit a positive linear dependence on daily actual vapor pressure whose value is almost equal to the saturated vapor pressure at a given temperature. At higher temperatures, actual vapor pressure has difficulty maintaining a consistent increasing rate because of the exponential increasing of the saturated vapor pressure. Higher temperatures result in larger vapor pressure deficits, which lead to sharp decreases in precipitation extremes. Similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature increases from 17°C along the northwest inland area to 25°C along the southeast coast. These behaviors demonstrate that precipitation extremes are firmly linked to temperature when there is sufficient moisture at lower temperatures and limited by insufficient moisture at higher temperatures. Overall, precipitation extreme events require more attention in a warming climate.
Abstract
Soil moisture (SM) during the vegetation growing season largely affects plant transpiration and photosynthesis, and further alters the land energy and water balance through its impact on the energy partition into latent and sensible heat fluxes. To highlight the impact of strong vegetation activity, we investigate global SM–climate interactions over the peak growing season (PGS) during 1982–2015 based on multisource datasets. Results suggest widespread positive SM–precipitation (P), SM–evapotranspiration (ET), and negative SM–temperature (T) interactions with non-negligible negative SM–P, SM–ET, and positive SM–T interactions over PGS. Relative to the influence of individual climate factors on SM, the compounding effect of climate factors strengthens SM–climate interactions. Simultaneously, variations of SM are dominated by precipitation from 50°N toward the south, by evapotranspiration from 50°N toward the north, and by temperature over the Sahara, western and central Asia, and the Tibetan Plateau. Importantly, the higher probability of concurrent SM wetness and climate extremes indicates the instant response of SM wetness to extreme climate. In contrast, the resistance of vegetation partially contributes to a consequent slower response of SM dryness to extreme climate. We highlight the significance of the compounding effects of climate factors in understanding SM–climate interaction in the context of strong vegetation activity, and the response of SM wetness and dryness to climate extremes.
Abstract
Soil moisture (SM) during the vegetation growing season largely affects plant transpiration and photosynthesis, and further alters the land energy and water balance through its impact on the energy partition into latent and sensible heat fluxes. To highlight the impact of strong vegetation activity, we investigate global SM–climate interactions over the peak growing season (PGS) during 1982–2015 based on multisource datasets. Results suggest widespread positive SM–precipitation (P), SM–evapotranspiration (ET), and negative SM–temperature (T) interactions with non-negligible negative SM–P, SM–ET, and positive SM–T interactions over PGS. Relative to the influence of individual climate factors on SM, the compounding effect of climate factors strengthens SM–climate interactions. Simultaneously, variations of SM are dominated by precipitation from 50°N toward the south, by evapotranspiration from 50°N toward the north, and by temperature over the Sahara, western and central Asia, and the Tibetan Plateau. Importantly, the higher probability of concurrent SM wetness and climate extremes indicates the instant response of SM wetness to extreme climate. In contrast, the resistance of vegetation partially contributes to a consequent slower response of SM dryness to extreme climate. We highlight the significance of the compounding effects of climate factors in understanding SM–climate interaction in the context of strong vegetation activity, and the response of SM wetness and dryness to climate extremes.
Abstract
Land surface temperature (LST) is an essential variable for high-temperature prediction, drought monitoring, climate, and ecological environment research. Several recent studies reported that LST observations in China warmed much faster than surface air temperature (SAT), especially after 2002. Here we found that the abrupt change in daily LST was mainly due to the overestimation of LST values from the automatic recording thermometer under snow cover conditions. These inhomogeneity issues in LST data could result in wrong conclusions without appropriate correction. To address these issues, we proposed three machine learning models—multivariate adaptive regression spline (MARS), random forest (RF), and a novel simple tree-based method named extreme gradient boosting (XGBoost)—for accurate prediction of daily LST using conventional meteorological data. Daily air temperature (maximum, minimum, mean), sunshine duration, precipitation, wind speed, relative humidity, daily solar radiation, and diurnal temperature range of 2185 stations over 1971–2002 from four regions of China were used to train and test the models. The results showed that the machine learning models, particularly XGBoost, outperformed other models in estimating daily LST. Based on LST data corrected by the XGBoost model, the dramatic increase in LST disappeared. The long-term trend for the new LST was estimated to be 0.32° ± 0.03°C decade−1 over 1971–2019, which is close to the trend in SAT (0.30° ± 0.03°C decade−1). This study corrected the inhomogeneities of daily LST in China, indicating the strong potential of machine learning models for improving estimation of LST and other surface climatic factors.
Abstract
Land surface temperature (LST) is an essential variable for high-temperature prediction, drought monitoring, climate, and ecological environment research. Several recent studies reported that LST observations in China warmed much faster than surface air temperature (SAT), especially after 2002. Here we found that the abrupt change in daily LST was mainly due to the overestimation of LST values from the automatic recording thermometer under snow cover conditions. These inhomogeneity issues in LST data could result in wrong conclusions without appropriate correction. To address these issues, we proposed three machine learning models—multivariate adaptive regression spline (MARS), random forest (RF), and a novel simple tree-based method named extreme gradient boosting (XGBoost)—for accurate prediction of daily LST using conventional meteorological data. Daily air temperature (maximum, minimum, mean), sunshine duration, precipitation, wind speed, relative humidity, daily solar radiation, and diurnal temperature range of 2185 stations over 1971–2002 from four regions of China were used to train and test the models. The results showed that the machine learning models, particularly XGBoost, outperformed other models in estimating daily LST. Based on LST data corrected by the XGBoost model, the dramatic increase in LST disappeared. The long-term trend for the new LST was estimated to be 0.32° ± 0.03°C decade−1 over 1971–2019, which is close to the trend in SAT (0.30° ± 0.03°C decade−1). This study corrected the inhomogeneities of daily LST in China, indicating the strong potential of machine learning models for improving estimation of LST and other surface climatic factors.
Abstract
Climate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited. Based on spatially distributed catchments in China, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annual ET is predictable in nonhumid regions but less predictable in humid regions, and annual Q is predictable in humid regions but less reliable in nonhumid regions. The main cause of the varied predictability lies in the variation of water storage change ΔS in the water balance equation. It affects both the estimation and the variability of Q in nonhumid catchments more than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and nonhumid catchments. Moreover, the differences between the controlling factors of ET variability in different regions add more differences in their predictability. The dominant control of precipitation makes it easy to predict annual ET in nonhumid regions. By contrast, precipitation, potential evaporation, and their covariance take considerable effort to determine annual ET variations, which leads to less reliable ET estimation and predictability in humid catchments. Therefore, one can accurately predict annual ET in nonhumid catchments and Q in humid catchments based on commonly used hydrological models. With proper consideration of ΔS, the predictability of annual ET and Q in both humid and nonhumid catchments can be improved.
Abstract
Climate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited. Based on spatially distributed catchments in China, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annual ET is predictable in nonhumid regions but less predictable in humid regions, and annual Q is predictable in humid regions but less reliable in nonhumid regions. The main cause of the varied predictability lies in the variation of water storage change ΔS in the water balance equation. It affects both the estimation and the variability of Q in nonhumid catchments more than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and nonhumid catchments. Moreover, the differences between the controlling factors of ET variability in different regions add more differences in their predictability. The dominant control of precipitation makes it easy to predict annual ET in nonhumid regions. By contrast, precipitation, potential evaporation, and their covariance take considerable effort to determine annual ET variations, which leads to less reliable ET estimation and predictability in humid catchments. Therefore, one can accurately predict annual ET in nonhumid catchments and Q in humid catchments based on commonly used hydrological models. With proper consideration of ΔS, the predictability of annual ET and Q in both humid and nonhumid catchments can be improved.
Abstract
The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (E pan) is an important indicator of climate change; however, current research on E pan has focused on its change in mean rather than its variability. The variability of monthly E pan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in E pan variance between space and time. The E pan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in E pan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of E pan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in E pan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring.
Significance Statement
The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.
Abstract
The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (E pan) is an important indicator of climate change; however, current research on E pan has focused on its change in mean rather than its variability. The variability of monthly E pan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in E pan variance between space and time. The E pan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in E pan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of E pan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in E pan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring.
Significance Statement
The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.
Abstract
Pan evaporation (E pan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly E pan was calculated over China during 1951–2021, resulting in an average R 2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of E pan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of E pan trends, the E pan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of E pan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted E pan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in E pan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in E pan. These results show the complex and dynamic nature of E pan and underscore the need for continued monitoring and in-depth analysis of its drivers.
Significance Statement
The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.
Abstract
Pan evaporation (E pan) serves as a monitorable method for estimating potential evaporation, evapotranspiration, and reference crop evapotranspiration, providing crucial data and information for fields such as water resource management and agricultural irrigation. Based on the PenPan model, the monthly E pan was calculated over China during 1951–2021, resulting in an average R 2 of 0.93 ± 0.045 and an RMSE of 21.48 ± 6.06 mm month−1. The trend of E pan over time was characterized by an initial increase before 1961, followed by a decrease from 1961 to 1993, and a subsequent increase from 1994 to 2021. However, the sustained duration and magnitude of the decreasing trend led to an overall decreasing trend in the long-term dataset. To better understand the drivers of E pan trends, the E pan process was decomposed into radiative and aerodynamic components. While radiation was found to be the dominant component, its trend remained relatively stable over time. In contrast, the aerodynamic component, although smaller in proportion, exhibited larger fluctuations and played a crucial role in the trend of E pan. The primary influencing factors of the aerodynamic component were found to be wind speed and vapor pressure deficit (VPD). Wind speed and VPD jointly promoted E pan before 1961, and the significant decrease in wind speed from 1961 to 1993 led to a decrease in E pan. From 1994 to 2021, the increase in VPD was found to be the main driver of the observed increase in E pan. These results show the complex and dynamic nature of E pan and underscore the need for continued monitoring and in-depth analysis of its drivers.
Significance Statement
The primary objective of this study is to explore the spatiotemporal patterns and potential driving factors of pan evaporation in China based on constructing a comprehensive dataset of pan evaporation. This is important because pan evaporation is an important indicator of the water cycle, which is currently undergoing modifications and is expected to become more pronounced as the climate continues to warm. Our findings showed that the patterns of pan evaporation were characterized by its drivers. As the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to the pattern and attribution of pan evaporation.
Abstract
Satellite and gridded meteorological data can be used to estimate evaporation (E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in “wet” regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments 
Abstract
Satellite and gridded meteorological data can be used to estimate evaporation (E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in “wet” regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments
