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Abstract
Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.
Abstract
Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.
Abstract
Since World War II, the expansion of irrigation throughout the Great Plains has resulted in a significant decline in the water table of the Ogallala Aquifer, threatening its long-term sustainability. The addition of near-surface water for irrigation has previously been shown to impact the surface energy and water budgets by modifying the partitioning of latent and sensible heating. A strong increase in latent heating drives near-surface cooling and an increase in humidity, which has opposing impacts on convective precipitation. In this study, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation on precipitation. Using a satellite-derived fractional irrigation dataset, grid cells were divided into irrigated and nonirrigated segments and the near-surface soil layer within irrigated segments was held at saturation. Nine April–October periods (three drought, three normal, and three pluvial) were simulated over the Great Plains. Averaging over all simulations, May–September precipitation increased by 4.97 mm (0.91%), with localized increases of up to 20%. The largest precipitation increases occurred during pluvial years (6.14 mm; 0.98%) and the smallest increases occurred during drought years (2.85 mm; 0.63%). Precipitation increased by 7.86 mm (1.61%) over irrigated areas from the enhancement of elevated nocturnal convection. Significant precipitation increases occurred over irrigated areas during normal and pluvial years, with decreases during drought years. This suggests that a soil moisture threshold likely exists whereby irrigation suppresses convection over irrigated areas when soil moisture is extremely low and enhances convection when antecedent soil moisture is relatively high.
Abstract
Since World War II, the expansion of irrigation throughout the Great Plains has resulted in a significant decline in the water table of the Ogallala Aquifer, threatening its long-term sustainability. The addition of near-surface water for irrigation has previously been shown to impact the surface energy and water budgets by modifying the partitioning of latent and sensible heating. A strong increase in latent heating drives near-surface cooling and an increase in humidity, which has opposing impacts on convective precipitation. In this study, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation on precipitation. Using a satellite-derived fractional irrigation dataset, grid cells were divided into irrigated and nonirrigated segments and the near-surface soil layer within irrigated segments was held at saturation. Nine April–October periods (three drought, three normal, and three pluvial) were simulated over the Great Plains. Averaging over all simulations, May–September precipitation increased by 4.97 mm (0.91%), with localized increases of up to 20%. The largest precipitation increases occurred during pluvial years (6.14 mm; 0.98%) and the smallest increases occurred during drought years (2.85 mm; 0.63%). Precipitation increased by 7.86 mm (1.61%) over irrigated areas from the enhancement of elevated nocturnal convection. Significant precipitation increases occurred over irrigated areas during normal and pluvial years, with decreases during drought years. This suggests that a soil moisture threshold likely exists whereby irrigation suppresses convection over irrigated areas when soil moisture is extremely low and enhances convection when antecedent soil moisture is relatively high.
Abstract
The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April–October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May–September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May–September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains.
Abstract
The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April–October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May–September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May–September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) established a method for quantifying and comparing the influence of soil moisture on the atmosphere in AGCMs. The models included in the GLACE intercomparison displayed a wide range in the strength of this influence, with the Met Office Hadley Centre (MOHC) Atmosphere Model, version 3 (HadAM3), being one of the weakest. Applying the GLACE method to a much developed version of the MOHC model, the atmospheric component of the Hadley Centre Global Environmental Model version 3 (HadGEM3-A), it is demonstrated that this new model has a stronger coupling signal than its predecessor. Although this increase in the coupling strength cannot be attributed to changes in the land surface representation, the existence of the stronger signal enables an investigation of the signal’s dependence on key land surface parameters. The GLACE method is applied to four HadGEM3-A experiment cases, with soil hydraulic parameters specified using two methods of calculation from two different underlying soil texture datasets. These cases show differences in their volumetric soil moisture and their level of moisture availability for transpiration. A change in moisture availability produces a change in evaporation variability in the same direction, which is a key factor affecting the overall land–atmosphere coupling strength. For HadGEM3-A the parameter changes therefore produce a clear change in the GLACE diagnostic.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) established a method for quantifying and comparing the influence of soil moisture on the atmosphere in AGCMs. The models included in the GLACE intercomparison displayed a wide range in the strength of this influence, with the Met Office Hadley Centre (MOHC) Atmosphere Model, version 3 (HadAM3), being one of the weakest. Applying the GLACE method to a much developed version of the MOHC model, the atmospheric component of the Hadley Centre Global Environmental Model version 3 (HadGEM3-A), it is demonstrated that this new model has a stronger coupling signal than its predecessor. Although this increase in the coupling strength cannot be attributed to changes in the land surface representation, the existence of the stronger signal enables an investigation of the signal’s dependence on key land surface parameters. The GLACE method is applied to four HadGEM3-A experiment cases, with soil hydraulic parameters specified using two methods of calculation from two different underlying soil texture datasets. These cases show differences in their volumetric soil moisture and their level of moisture availability for transpiration. A change in moisture availability produces a change in evaporation variability in the same direction, which is a key factor affecting the overall land–atmosphere coupling strength. For HadGEM3-A the parameter changes therefore produce a clear change in the GLACE diagnostic.
Abstract
A fundamental problem in ecohydrology is diagnosing impacts of vegetation dynamics on the catchment response. This study uses a distributed hydrologic model and remote sensing data to evaluate the effects of seasonal vegetation greening on the basin water balance and the partitioning of evapotranspiration ET into soil evaporation, transpiration, and evaporation of intercepted water. Using remotely sensed data, updates are made to model vegetation parameters related to radiation, interception, and transpiration as ecosystems respond to precipitation during the North American monsoon (NAM). Comparisons of simulations with static and seasonally varying vegetation parameters reveal lower ET but higher vegetation-mediated ET losses because of the greening. Sensitivity analyses indicate that vegetation fraction is the primary control on ET and its partitioning, while interception parameters play a secondary role. As a result, spatial patterns in ET partitioning in the catchment exhibit a strong signature of vegetation fraction, though fine (coarse)-scale influences of soil moisture (radiation) are also observed. Vegetation-mediated ET losses were significant in large fractions of the catchment and exhibited ecosystem-dependent seasonal evolutions. The numerical simulations presented here provide the first spatially explicit estimates of ET partitioning accounting for vegetation dynamics obtained from remotely sensed data at the catchment scale.
Abstract
A fundamental problem in ecohydrology is diagnosing impacts of vegetation dynamics on the catchment response. This study uses a distributed hydrologic model and remote sensing data to evaluate the effects of seasonal vegetation greening on the basin water balance and the partitioning of evapotranspiration ET into soil evaporation, transpiration, and evaporation of intercepted water. Using remotely sensed data, updates are made to model vegetation parameters related to radiation, interception, and transpiration as ecosystems respond to precipitation during the North American monsoon (NAM). Comparisons of simulations with static and seasonally varying vegetation parameters reveal lower ET but higher vegetation-mediated ET losses because of the greening. Sensitivity analyses indicate that vegetation fraction is the primary control on ET and its partitioning, while interception parameters play a secondary role. As a result, spatial patterns in ET partitioning in the catchment exhibit a strong signature of vegetation fraction, though fine (coarse)-scale influences of soil moisture (radiation) are also observed. Vegetation-mediated ET losses were significant in large fractions of the catchment and exhibited ecosystem-dependent seasonal evolutions. The numerical simulations presented here provide the first spatially explicit estimates of ET partitioning accounting for vegetation dynamics obtained from remotely sensed data at the catchment scale.
Abstract
Over arid regions, two community land models [Noah and Community Land Model (CLM)] still have difficulty in realistically simulating the diurnal cycle of surface skin temperature. Based on theoretical arguments and synthesis of previous observational and modeling efforts, three revisions are developed here to address this issue. The revision of the coefficients in computing roughness length for heat significantly reduces the underestimate of daytime skin temperature but has a negligible effect on nighttime skin temperature. The constraints of the minimum friction velocity and soil thermal conductivity help improve nighttime skin temperature under weak wind and dry soil conditions. These results are robust in both Noah and CLM, as well as in Noah, with 4 versus 10 soil layers based on in situ data at the Desert Rock site in Nevada with a monthly averaged diurnal amplitude of 31.7 K and the Gaize site over Tibet, China, with an amplitude of 44.6 K. While these revisions can be directly applied to CLM or other land models with subgrid tiles (including bare soil), suggestions are also made on their application to Noah and other land models that treat bare soil and vegetated area together in a model grid cell. It is suggested that the challenging issue of measuring and simulating surface sensible heat flux under stable conditions should be treated as a land–atmosphere coupled issue, involving the interplay of ground and sensible heat fluxes in balancing the net radiation over arid regions, rather than as an atmospheric turbulence issue alone. The implications of such a coupling perspective are also discussed.
Abstract
Over arid regions, two community land models [Noah and Community Land Model (CLM)] still have difficulty in realistically simulating the diurnal cycle of surface skin temperature. Based on theoretical arguments and synthesis of previous observational and modeling efforts, three revisions are developed here to address this issue. The revision of the coefficients in computing roughness length for heat significantly reduces the underestimate of daytime skin temperature but has a negligible effect on nighttime skin temperature. The constraints of the minimum friction velocity and soil thermal conductivity help improve nighttime skin temperature under weak wind and dry soil conditions. These results are robust in both Noah and CLM, as well as in Noah, with 4 versus 10 soil layers based on in situ data at the Desert Rock site in Nevada with a monthly averaged diurnal amplitude of 31.7 K and the Gaize site over Tibet, China, with an amplitude of 44.6 K. While these revisions can be directly applied to CLM or other land models with subgrid tiles (including bare soil), suggestions are also made on their application to Noah and other land models that treat bare soil and vegetated area together in a model grid cell. It is suggested that the challenging issue of measuring and simulating surface sensible heat flux under stable conditions should be treated as a land–atmosphere coupled issue, involving the interplay of ground and sensible heat fluxes in balancing the net radiation over arid regions, rather than as an atmospheric turbulence issue alone. The implications of such a coupling perspective are also discussed.
Abstract
A distributed hydrologic model is used to evaluate how runoff mechanisms—including infiltration excess (RI ), saturation excess (RS ), and groundwater exfiltration (RG )—influence the generation of streamflow and evapotranspiration (ET) in a mountainous region under the influence of the North American monsoon (NAM). The study site, the upper Sonora River basin (~9350 km2) in Mexico, is characterized by a wide range of terrain, soil, and ecosystem conditions obtained from best available data sources. Three meteorological scenarios are compared to explore the impact of spatial and temporal variations of meteorological characteristics on land surface processes and to identify the value of North American Land Data Assimilation System (NLDAS) forcing products in the NAM region. The following scenarios are considered for a 1-yr period: 1) a sparse network of ground-based stations, 2) raw forcing products from NLDAS, and 3) NLDAS products adjusted using available station data. These scenarios are discussed in light of spatial distributions of precipitation, streamflow, and runoff mechanisms during annual, seasonal, and monthly periods. This study identified that the mode of runoff generation impacts seasonal relations between ET and soil moisture in the water-limited region. In addition, ET rates at annual and seasonal scales were related to the runoff mechanism proportions, with an increase in ET when RS was dominant and a decrease in ET when RI was more important. The partitioning of runoff mechanisms also helps explain the monthly progression of runoff ratios in these seasonally wet hydrologic systems. Understanding the complex interplay between seasonal responses of runoff mechanisms and evapotranspiration can yield information that is of interest to hydrologists and water managers.
Abstract
A distributed hydrologic model is used to evaluate how runoff mechanisms—including infiltration excess (RI ), saturation excess (RS ), and groundwater exfiltration (RG )—influence the generation of streamflow and evapotranspiration (ET) in a mountainous region under the influence of the North American monsoon (NAM). The study site, the upper Sonora River basin (~9350 km2) in Mexico, is characterized by a wide range of terrain, soil, and ecosystem conditions obtained from best available data sources. Three meteorological scenarios are compared to explore the impact of spatial and temporal variations of meteorological characteristics on land surface processes and to identify the value of North American Land Data Assimilation System (NLDAS) forcing products in the NAM region. The following scenarios are considered for a 1-yr period: 1) a sparse network of ground-based stations, 2) raw forcing products from NLDAS, and 3) NLDAS products adjusted using available station data. These scenarios are discussed in light of spatial distributions of precipitation, streamflow, and runoff mechanisms during annual, seasonal, and monthly periods. This study identified that the mode of runoff generation impacts seasonal relations between ET and soil moisture in the water-limited region. In addition, ET rates at annual and seasonal scales were related to the runoff mechanism proportions, with an increase in ET when RS was dominant and a decrease in ET when RI was more important. The partitioning of runoff mechanisms also helps explain the monthly progression of runoff ratios in these seasonally wet hydrologic systems. Understanding the complex interplay between seasonal responses of runoff mechanisms and evapotranspiration can yield information that is of interest to hydrologists and water managers.
Abstract
Land–atmosphere coupling strength or the degree to which land surface anomalies influence boundary layer development—and in extreme cases, rainfall—is arguably the single most fundamental criterion for evaluating hydrological model performance. The Global Land–Atmosphere Coupling Experiment (GLACE) showed that strength of coupling and its representation can affect a model’s ability to simulate climate predictability at the seasonal time scale. And yet, the lack of sufficient observations of coupling at appropriate temporal and spatial scales has made achieving “true” coupling in models an elusive goal. This study uses Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) soil moisture (SM), multisensor remote sensing (RS) evaporative fraction (EF), and Atmospheric Infrared Sounder (AIRS) lifting condensation level (LCL) to evaluate the realism of coupling in the Global Land Data Assimilation System (GLDAS) suite of land surface models (LSMs), Princeton Global Forcing Variable Infiltration Capacity model (PGF–VIC), seven global reanalyses, and the North American Regional Reanalysis (NARR) over a 5-yr period (2003–07). First, RS and modeled estimates of SM, EF, and LCL are intercompared. Then, emphasis is placed on quantifying RS and modeled differences in convective-season daily correlations between SM–LCL, SM–EF, and EF–LCL for global, regional, and conditional samples. RS is found to yield a substantially weaker state of coupling than model products. However, the rank order of basins by coupling strength calculated from RS and models do roughly agree. Using a mixture of satellite and modeled variables, a map of hybrid coupling strength was produced, which supports the findings of GLACE that transitional zones tend to have the strongest coupling.
Abstract
Land–atmosphere coupling strength or the degree to which land surface anomalies influence boundary layer development—and in extreme cases, rainfall—is arguably the single most fundamental criterion for evaluating hydrological model performance. The Global Land–Atmosphere Coupling Experiment (GLACE) showed that strength of coupling and its representation can affect a model’s ability to simulate climate predictability at the seasonal time scale. And yet, the lack of sufficient observations of coupling at appropriate temporal and spatial scales has made achieving “true” coupling in models an elusive goal. This study uses Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) soil moisture (SM), multisensor remote sensing (RS) evaporative fraction (EF), and Atmospheric Infrared Sounder (AIRS) lifting condensation level (LCL) to evaluate the realism of coupling in the Global Land Data Assimilation System (GLDAS) suite of land surface models (LSMs), Princeton Global Forcing Variable Infiltration Capacity model (PGF–VIC), seven global reanalyses, and the North American Regional Reanalysis (NARR) over a 5-yr period (2003–07). First, RS and modeled estimates of SM, EF, and LCL are intercompared. Then, emphasis is placed on quantifying RS and modeled differences in convective-season daily correlations between SM–LCL, SM–EF, and EF–LCL for global, regional, and conditional samples. RS is found to yield a substantially weaker state of coupling than model products. However, the rank order of basins by coupling strength calculated from RS and models do roughly agree. Using a mixture of satellite and modeled variables, a map of hybrid coupling strength was produced, which supports the findings of GLACE that transitional zones tend to have the strongest coupling.
