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- Author or Editor: Yongkang Xue x
- Journal of Hydrometeorology x
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Abstract
Proper simulation of soil freezing and thawing processes is an important issue in cold region climate studies. This paper reports on a frozen soil parameterization scheme for cold region studies that includes water flow and heat transfer in soil with water phase change. The mixed-form Richards’ equation is adopted to describe soil water flow affected by thermal processes in frozen soil. In addition, both liquid water and ice content have been taken into account in the frozen soil hydrologic and thermal property parameterization. To solve the complex nonlinear equation set and to ensure water conservation during simulation of complex phase change processes, efficient computational procedures have been designed and a new modified Picard iteration scheme is extended to solve the mixed-form Richards’ equation with phase change. The frozen soil model was evaluated using observational data from the field station at Rosemount, Minnesota, and the Tibet D66 site. The results show that the model is capable of providing good simulations of the evolution of temperature and liquid water content in frozen soil. Comparisons of simulation results from sensitivity studies indicate that there is a maximum difference of about 50 W m−2 in sensible and ground heat fluxes with and without the inclusion of the effect of ice content on matric potential and that using the exponential relationship between hydraulic conductivity and ice content produces realistic results.
Abstract
Proper simulation of soil freezing and thawing processes is an important issue in cold region climate studies. This paper reports on a frozen soil parameterization scheme for cold region studies that includes water flow and heat transfer in soil with water phase change. The mixed-form Richards’ equation is adopted to describe soil water flow affected by thermal processes in frozen soil. In addition, both liquid water and ice content have been taken into account in the frozen soil hydrologic and thermal property parameterization. To solve the complex nonlinear equation set and to ensure water conservation during simulation of complex phase change processes, efficient computational procedures have been designed and a new modified Picard iteration scheme is extended to solve the mixed-form Richards’ equation with phase change. The frozen soil model was evaluated using observational data from the field station at Rosemount, Minnesota, and the Tibet D66 site. The results show that the model is capable of providing good simulations of the evolution of temperature and liquid water content in frozen soil. Comparisons of simulation results from sensitivity studies indicate that there is a maximum difference of about 50 W m−2 in sensible and ground heat fluxes with and without the inclusion of the effect of ice content on matric potential and that using the exponential relationship between hydraulic conductivity and ice content produces realistic results.
Abstract
A statistical–dynamical study was performed on the role of hydrometeorological interactions in the midlatitudes and the semiarid Tropics. For this, observations from two field experiments, the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) and the Hydrological Atmospheric Pilot Experiment (HAPEX)–Sahel, representative of the midlatitudes and the semiarid tropical conditions, and simulated results from a land surface model, Simplified Simple Biosphere (SSiB) model were statistically analyzed for direct and interaction effects. The study objectives were to test the hypothesis that there are significant differences in the land surface processes in the semiarid tropical and midlatitudinal regimes and to identify the nature of the differences in the evapotranspiration exchanges for the two biogeographical domains. Results suggest there are similarities in the direct responses but the interactions or the indirect feedback pathways could be very different. The arid tropical regimes are dominated through vegetative pathways (via variables such leaf area index, stomatal resistance, and vegetal cover); the midlatitudes show soil wetness (moisture)–related feedback. In addition, for the midlatitudinal case, the vegetation and the soil surface acted in unison, leading to more interactive exchanges between the vegetation and the soil surface. The water-stressed semiarid tropical surface, on the other hand, showed response either directly between the vegetation and the atmosphere or between the soil and the atmosphere with very little interaction between the vegetation and the soil variables. Thus, the semiarid Tropics would require explicit bare ground and vegetation fluxes consideration, whereas the effective (combined vegetation and soil fluxes) surface representation used in various models may be more valid for the midlatitudinal case. This result also implied that with higher resource (water) availability the surface invested more in the surrounding environment. On the other hand, with poor resource availability (such as water stress in the tropical site), the surface components retain individual resources without sharing.
Abstract
A statistical–dynamical study was performed on the role of hydrometeorological interactions in the midlatitudes and the semiarid Tropics. For this, observations from two field experiments, the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) and the Hydrological Atmospheric Pilot Experiment (HAPEX)–Sahel, representative of the midlatitudes and the semiarid tropical conditions, and simulated results from a land surface model, Simplified Simple Biosphere (SSiB) model were statistically analyzed for direct and interaction effects. The study objectives were to test the hypothesis that there are significant differences in the land surface processes in the semiarid tropical and midlatitudinal regimes and to identify the nature of the differences in the evapotranspiration exchanges for the two biogeographical domains. Results suggest there are similarities in the direct responses but the interactions or the indirect feedback pathways could be very different. The arid tropical regimes are dominated through vegetative pathways (via variables such leaf area index, stomatal resistance, and vegetal cover); the midlatitudes show soil wetness (moisture)–related feedback. In addition, for the midlatitudinal case, the vegetation and the soil surface acted in unison, leading to more interactive exchanges between the vegetation and the soil surface. The water-stressed semiarid tropical surface, on the other hand, showed response either directly between the vegetation and the atmosphere or between the soil and the atmosphere with very little interaction between the vegetation and the soil variables. Thus, the semiarid Tropics would require explicit bare ground and vegetation fluxes consideration, whereas the effective (combined vegetation and soil fluxes) surface representation used in various models may be more valid for the midlatitudinal case. This result also implied that with higher resource (water) availability the surface invested more in the surrounding environment. On the other hand, with poor resource availability (such as water stress in the tropical site), the surface components retain individual resources without sharing.
Abstract
In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.
Abstract
In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.
Abstract
When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.
Abstract
When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.
Abstract
This is an exploratory study to investigate the spatial and temporal characteristics of east China’s (EC) river runoff and their relationship with precipitation and sea surface temperature (SST) at the continental scale. Monthly mean data from 72 runoff stations and 160 precipitation stations in EC, covering a period between 1951 and 1983, are used for this study. The station river runoff data have been spatially interpolated onto 1° grid boxes as runoff depth based on an extracted drainage network. Comparing runoff depth with precipitation shows that seasonal variation in runoff is consistent with the development of the summer monsoon, including the delayed response of runoff in several subregions. The dominant spatial scales and temporal patterns of summer runoff and precipitation are studied with empirical orthogonal function (EOF) analysis and wavelet analyses. The analyses show interannual, biennial, and longer-term variations in the EOF modes. South–north dipole anomaly patterns for the first two runoff EOF’s spatial distributions have been identified. The first/second runoff principal components (PCs) are highly correlated with the second/first precipitation PCs, respectively. The summer runoff’s EOF PCs also show significant correlations with the multivariate El Niño–Southern Oscillation index (MEI) of the summer and winter months, while the summer precipitation PCs do not. Statistic analysis shows that EOF1 of runoff and EOF2 of precipitation are related to El Niño, while EOF2 of runoff and EOF1 of precipitation are related to a dipole SST anomaly over the northwestern Pacific. The interdecadal relationship between summer runoff, precipitation, and SST variability is further studied by singular value decomposition (SVD) analysis. Pronounced warming (SST) and drying (runoff) trends in first SVD PCs have been identified. These SVDs are used to reconstruct a decadal anomaly pattern, which produces flooding in part of the Chang Jiang River basin and dryness in the northern EC, consistent with observations.
Abstract
This is an exploratory study to investigate the spatial and temporal characteristics of east China’s (EC) river runoff and their relationship with precipitation and sea surface temperature (SST) at the continental scale. Monthly mean data from 72 runoff stations and 160 precipitation stations in EC, covering a period between 1951 and 1983, are used for this study. The station river runoff data have been spatially interpolated onto 1° grid boxes as runoff depth based on an extracted drainage network. Comparing runoff depth with precipitation shows that seasonal variation in runoff is consistent with the development of the summer monsoon, including the delayed response of runoff in several subregions. The dominant spatial scales and temporal patterns of summer runoff and precipitation are studied with empirical orthogonal function (EOF) analysis and wavelet analyses. The analyses show interannual, biennial, and longer-term variations in the EOF modes. South–north dipole anomaly patterns for the first two runoff EOF’s spatial distributions have been identified. The first/second runoff principal components (PCs) are highly correlated with the second/first precipitation PCs, respectively. The summer runoff’s EOF PCs also show significant correlations with the multivariate El Niño–Southern Oscillation index (MEI) of the summer and winter months, while the summer precipitation PCs do not. Statistic analysis shows that EOF1 of runoff and EOF2 of precipitation are related to El Niño, while EOF2 of runoff and EOF1 of precipitation are related to a dipole SST anomaly over the northwestern Pacific. The interdecadal relationship between summer runoff, precipitation, and SST variability is further studied by singular value decomposition (SVD) analysis. Pronounced warming (SST) and drying (runoff) trends in first SVD PCs have been identified. These SVDs are used to reconstruct a decadal anomaly pattern, which produces flooding in part of the Chang Jiang River basin and dryness in the northern EC, consistent with observations.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.
Abstract
Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.
Abstract
The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated.
It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.
Abstract
The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated.
It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.
