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- Author or Editor: Yi Luo x
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Abstract
Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°–50°N, 72°–107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash–Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale.
Abstract
Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°–50°N, 72°–107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash–Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale.
Abstract
Seasonal variations in precipitation (P) and potential evapotranspiration (ET0) are critical for regional hydrometeorological studies and water resource management. The sinusoidal function is widely used to describe the seasonal pattern of P and ET0. However, high errors occur either in the arid places or in places with hyperseasonal precipitation. These limitations are intrinsic properties of the sinusoidal climate descriptor and remain a barrier to provide insight into regional water–energy partitions and hydrologic similarity and predictability. In this study, we used a Gaussian framework as an alternative to describe seasonal variations in P and ET0 regimes in the Yellow River basin (YRB). The results show that the Gaussian framework provides a good approximation to the seasonal pattern of P and has a strong regional applicability for reproducing the monthly P and ET0. This allows us to assess the climate seasonality characterizing the regional balance between water supply and energy availability using δP, δET0, and aridity index. The climate seasonality indicates that the balance between water supply and energy availability has a switch in about 32% of the grid cells during the seasonal cycle from 1982 to 2015. These grid cells are mostly located in regions with average annual precipitation above 550 mm. In the northwest region of the YRB, which has a dry climate, the amount of potential evapotranspiration always exceeds the precipitation. We argue that the Gaussian function provides a quantitative conceptual framework for accurate assessment of regional water supply and energy availability and offers a penetrating insight into hydrometeorology.
Abstract
Seasonal variations in precipitation (P) and potential evapotranspiration (ET0) are critical for regional hydrometeorological studies and water resource management. The sinusoidal function is widely used to describe the seasonal pattern of P and ET0. However, high errors occur either in the arid places or in places with hyperseasonal precipitation. These limitations are intrinsic properties of the sinusoidal climate descriptor and remain a barrier to provide insight into regional water–energy partitions and hydrologic similarity and predictability. In this study, we used a Gaussian framework as an alternative to describe seasonal variations in P and ET0 regimes in the Yellow River basin (YRB). The results show that the Gaussian framework provides a good approximation to the seasonal pattern of P and has a strong regional applicability for reproducing the monthly P and ET0. This allows us to assess the climate seasonality characterizing the regional balance between water supply and energy availability using δP, δET0, and aridity index. The climate seasonality indicates that the balance between water supply and energy availability has a switch in about 32% of the grid cells during the seasonal cycle from 1982 to 2015. These grid cells are mostly located in regions with average annual precipitation above 550 mm. In the northwest region of the YRB, which has a dry climate, the amount of potential evapotranspiration always exceeds the precipitation. We argue that the Gaussian function provides a quantitative conceptual framework for accurate assessment of regional water supply and energy availability and offers a penetrating insight into hydrometeorology.
Abstract
Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day interval for the 1995–2004 period over the U.S. southern Great Plains. The relative retrieval accuracy for the MODIS channels replicated from AVHRR or VEGETATION/SPOT data was typically better than 5%. Correlation coefficients between replicated and original data varied from 0.92 to 0.98 for all channels except MODIS channel 5, where it was lower (0.77–0.84). The developed method provides valuable information for parameterization of spectral albedo in global climate models and can be extended to generate global multispectral data compatible with MODIS from historical AVHRR and VEGETATION/SPOT observations for the pre-MODIS era.
Abstract
Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day interval for the 1995–2004 period over the U.S. southern Great Plains. The relative retrieval accuracy for the MODIS channels replicated from AVHRR or VEGETATION/SPOT data was typically better than 5%. Correlation coefficients between replicated and original data varied from 0.92 to 0.98 for all channels except MODIS channel 5, where it was lower (0.77–0.84). The developed method provides valuable information for parameterization of spectral albedo in global climate models and can be extended to generate global multispectral data compatible with MODIS from historical AVHRR and VEGETATION/SPOT observations for the pre-MODIS era.
Abstract
The quality and vertical distribution of precipitation are of utmost importance in hydrological modeling studies in glacierized catchments, which have been facing known challenges in the quality, distribution, and magnitude of observed as well as gridded precipitation due to complex topography. The purpose of the study is to reveal the vertical distribution of precipitation based on multiple datasets and evaluate the suitability of these datasets for hydrological applications and in the upper Indus basin (UIB). The performance of five gridded precipitation datasets, that is, APHRODITE, CFSR, Princeton Global Meteorological Forcing Dataset for Land Surface Modeling (PGMFD), TRMM, and High Asia Refined analysis (HAR), was evaluated against the observed precipitation (OBS) during 2001–07. A corrected reference precipitation dataset was constructed based on the water and mass balance using inverse modeling methods. TRMM was identified as the best dataset to represent the spatial and temporal distribution of OBS precipitation. The runoff coefficients for OBS, APHRODITE, TRMM, and PGMFD were greater than 1, indicating that these datasets were underestimated and unable to close the water balance in UIB. The mean annual corrected precipitation was estimated as 593 mm yr−1, while the main water-producing elevation zone was located between 3900 and 6600 m. CFSR had the highest correlation and lowest bias with corrected precipitation along the vertical profile. It is concluded that the OBS and most of the gridded precipitation are insufficient to sustain the water and mass balance in UIB. It is recommended to correct the precipitation based on mass balance at high altitudes (especially along the main water-producing zone) in glacierized catchments before its application in hydrological modeling studies.
Abstract
The quality and vertical distribution of precipitation are of utmost importance in hydrological modeling studies in glacierized catchments, which have been facing known challenges in the quality, distribution, and magnitude of observed as well as gridded precipitation due to complex topography. The purpose of the study is to reveal the vertical distribution of precipitation based on multiple datasets and evaluate the suitability of these datasets for hydrological applications and in the upper Indus basin (UIB). The performance of five gridded precipitation datasets, that is, APHRODITE, CFSR, Princeton Global Meteorological Forcing Dataset for Land Surface Modeling (PGMFD), TRMM, and High Asia Refined analysis (HAR), was evaluated against the observed precipitation (OBS) during 2001–07. A corrected reference precipitation dataset was constructed based on the water and mass balance using inverse modeling methods. TRMM was identified as the best dataset to represent the spatial and temporal distribution of OBS precipitation. The runoff coefficients for OBS, APHRODITE, TRMM, and PGMFD were greater than 1, indicating that these datasets were underestimated and unable to close the water balance in UIB. The mean annual corrected precipitation was estimated as 593 mm yr−1, while the main water-producing elevation zone was located between 3900 and 6600 m. CFSR had the highest correlation and lowest bias with corrected precipitation along the vertical profile. It is concluded that the OBS and most of the gridded precipitation are insufficient to sustain the water and mass balance in UIB. It is recommended to correct the precipitation based on mass balance at high altitudes (especially along the main water-producing zone) in glacierized catchments before its application in hydrological modeling studies.
A Study of Extrapolation Nowcasting Based on IVAP-Retrieved WindAbstract
In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.
Abstract
In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.
Abstract
Climate models project an enhancement in SST seasonal cycle over the midlatitude oceans under global warming. The underlying mechanisms are investigated using a set of partially coupled experiments, in which the contribution from direct CO2 effects (i.e., the response in the absence of wind change) and wind feedbacks can be isolated from each other. Results indicate that both the direct CO2 and wind effects contribute to the enhancement in the SST seasonal cycle, with the former (latter) being more important in the Northern Hemisphere (Southern Hemisphere). Further decomposition of the wind effect into the wind stress feedback and wind speed feedback reveals the importance of the wind stress–driven ocean response in the change of SST seasonal cycle, a result in contrast to a previous study that ascribed the midlatitude SST seasonal cycle change to the thermodynamic wind speed feedback. The direct CO2 effect regulates the SST seasonal cycle through the warming-induced shoaling in the annual mean mixed layer depth (MLD) as well as the MLD difference between winter and summer. Moreover, the surface wind seasonal cycle changes due solely to the direct CO2 effect are found to bear a great resemblance to the full wind response, suggesting that the root cause for the enhancement of the midlatitude SST seasonal cycle resides in the direct CO2 effect. This notion is further supported by an ocean-alone experiment that reproduces the SST seasonal cycle enhancement under a spatially and temporally homogeneous surface thermal forcing.
Abstract
Climate models project an enhancement in SST seasonal cycle over the midlatitude oceans under global warming. The underlying mechanisms are investigated using a set of partially coupled experiments, in which the contribution from direct CO2 effects (i.e., the response in the absence of wind change) and wind feedbacks can be isolated from each other. Results indicate that both the direct CO2 and wind effects contribute to the enhancement in the SST seasonal cycle, with the former (latter) being more important in the Northern Hemisphere (Southern Hemisphere). Further decomposition of the wind effect into the wind stress feedback and wind speed feedback reveals the importance of the wind stress–driven ocean response in the change of SST seasonal cycle, a result in contrast to a previous study that ascribed the midlatitude SST seasonal cycle change to the thermodynamic wind speed feedback. The direct CO2 effect regulates the SST seasonal cycle through the warming-induced shoaling in the annual mean mixed layer depth (MLD) as well as the MLD difference between winter and summer. Moreover, the surface wind seasonal cycle changes due solely to the direct CO2 effect are found to bear a great resemblance to the full wind response, suggesting that the root cause for the enhancement of the midlatitude SST seasonal cycle resides in the direct CO2 effect. This notion is further supported by an ocean-alone experiment that reproduces the SST seasonal cycle enhancement under a spatially and temporally homogeneous surface thermal forcing.
An IVAP-Based Dealiasing Method for Radar Velocity Data Quality ControlAbstract
Dealiasing is a common procedure in radar radial velocity quality control. Generally, there are two dealiasing steps: a continuity check and a reference check. In this paper, a modified version that uses azimuthal variance of radial velocity is introduced based on the integrating velocity–azimuth process (IVAP) method, referred to as the V-IVAP method. The new method can retrieve the averaged winds within a local area instead of averaged wind within a full range circle by the velocity–azimuth display (VAD) or the modified VAD method. The V-IVAP method is insensitive to the alias of the velocity, and provides a better way to produce reference velocities for a reference check. Instead of a continuity check, we use the IVAP method for a fine reference check because of its high-frequency filtering function. Then a dealiasing procedure with two steps of reference check is developed. The performance of the automatic dealiasing procedure is demonstrated by retrieving the wind field of a tornado. Using the dealiased radar velocities, the retrieved winds reveal a clear mesoscale vortex. A test based on radar network observations also has shown that the two-step dealiasing procedure based on V-IVAP and IVAP methods is reliable.
Abstract
Dealiasing is a common procedure in radar radial velocity quality control. Generally, there are two dealiasing steps: a continuity check and a reference check. In this paper, a modified version that uses azimuthal variance of radial velocity is introduced based on the integrating velocity–azimuth process (IVAP) method, referred to as the V-IVAP method. The new method can retrieve the averaged winds within a local area instead of averaged wind within a full range circle by the velocity–azimuth display (VAD) or the modified VAD method. The V-IVAP method is insensitive to the alias of the velocity, and provides a better way to produce reference velocities for a reference check. Instead of a continuity check, we use the IVAP method for a fine reference check because of its high-frequency filtering function. Then a dealiasing procedure with two steps of reference check is developed. The performance of the automatic dealiasing procedure is demonstrated by retrieving the wind field of a tornado. Using the dealiased radar velocities, the retrieved winds reveal a clear mesoscale vortex. A test based on radar network observations also has shown that the two-step dealiasing procedure based on V-IVAP and IVAP methods is reliable.
Abstract
Runoff in the Amu Darya River (ADR) in central Asia has been declining steadily since the 1950s. The reasons for this decline are ambiguous, requiring a complete analysis of glaciohydrological processes across the entire data-scarce source region. In this study, grid databases of precipitation from the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and temperature from Princeton’s Global Meteorological Forcing Dataset (PGMFD) are used to force the distributed, glacier-enhanced Soil and Water Assessment Tool (SWAT) model to simulate glaciohydrological processes for 1951–2007 so as to determine long-term streamflow changes and the primary driving factors in the source region of the ADR. The study suggests that the database was a suitable proxy for temperature and precipitation forcing in simulating glaciohydrological processes in the data-scarce alpine catchment region. The estimated annual streamflow of 72.6 km3 in the upper ADR had a decreasing trend for the period from 1951 to 2007. Change in precipitation, rather than in temperature, dominated the decline in streamflow in either the tributaries or mainstream of the ADR. The streamflow decreased by 15.5% because of the decline in precipitation but only increased by 0.2% as a result of the increase in temperature. Thus, warming temperature had much less effect than declining precipitation on streamflow decline in the ADR in central Asia in 1951–2007.
Abstract
Runoff in the Amu Darya River (ADR) in central Asia has been declining steadily since the 1950s. The reasons for this decline are ambiguous, requiring a complete analysis of glaciohydrological processes across the entire data-scarce source region. In this study, grid databases of precipitation from the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and temperature from Princeton’s Global Meteorological Forcing Dataset (PGMFD) are used to force the distributed, glacier-enhanced Soil and Water Assessment Tool (SWAT) model to simulate glaciohydrological processes for 1951–2007 so as to determine long-term streamflow changes and the primary driving factors in the source region of the ADR. The study suggests that the database was a suitable proxy for temperature and precipitation forcing in simulating glaciohydrological processes in the data-scarce alpine catchment region. The estimated annual streamflow of 72.6 km3 in the upper ADR had a decreasing trend for the period from 1951 to 2007. Change in precipitation, rather than in temperature, dominated the decline in streamflow in either the tributaries or mainstream of the ADR. The streamflow decreased by 15.5% because of the decline in precipitation but only increased by 0.2% as a result of the increase in temperature. Thus, warming temperature had much less effect than declining precipitation on streamflow decline in the ADR in central Asia in 1951–2007.
Abstract
Climate response is often assumed to be linear in climate sensitivity studies. However, by examining the surface temperature (TS) response to pairs of oceanic forcings of equal amplitude but opposite sign in a large set of local q-flux perturbation experiments with CAM5 coupled to a slab, we find strong asymmetry in TS responses to the heating and cooling forcings, indicating a strong nonlinearity intrinsic to the climate system examined. Regardless of where the symmetric forcing is placed, the cooling response to the negative forcing always exceeds the warming to the positive forcing, implying an intrinsic inclination toward cooling of our current climate. Thus, the ongoing global warming induced by increasing greenhouse gases may have already been alleviated by the asymmetric component of the response. The common asymmetry in TS response peaks in high latitudes, especially along sea ice edges, with notable seasonal dependence. Decomposition into different radiative feedbacks through a radiative kernel indicates that the asymmetry in the TS response is realized largely through lapse rate and albedo feedbacks. Further process interference experiments disabling the seasonal cycle and/or sea ice reveal that the asymmetry originates ultimately from the presence of the sea ice component and is further amplified by the seasonal cycle. The fact that a pair of opposite tropical q-flux forcings can excite very similar asymmetric response as a pair placed at 55°S strongly suggests the asymmetric response is a manifestation of an internal mode of the climate model system.
Abstract
Climate response is often assumed to be linear in climate sensitivity studies. However, by examining the surface temperature (TS) response to pairs of oceanic forcings of equal amplitude but opposite sign in a large set of local q-flux perturbation experiments with CAM5 coupled to a slab, we find strong asymmetry in TS responses to the heating and cooling forcings, indicating a strong nonlinearity intrinsic to the climate system examined. Regardless of where the symmetric forcing is placed, the cooling response to the negative forcing always exceeds the warming to the positive forcing, implying an intrinsic inclination toward cooling of our current climate. Thus, the ongoing global warming induced by increasing greenhouse gases may have already been alleviated by the asymmetric component of the response. The common asymmetry in TS response peaks in high latitudes, especially along sea ice edges, with notable seasonal dependence. Decomposition into different radiative feedbacks through a radiative kernel indicates that the asymmetry in the TS response is realized largely through lapse rate and albedo feedbacks. Further process interference experiments disabling the seasonal cycle and/or sea ice reveal that the asymmetry originates ultimately from the presence of the sea ice component and is further amplified by the seasonal cycle. The fact that a pair of opposite tropical q-flux forcings can excite very similar asymmetric response as a pair placed at 55°S strongly suggests the asymmetric response is a manifestation of an internal mode of the climate model system.
Abstract
Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.
Abstract
Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.
