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Abstract
A significant part of northern India is covered by the Himalayas, where a number of major Indian rivers originate. In the present study, a detailed analysis of rainstorms that affected the northwest region of the Himalayas has been made to assess the orographic effect of the Himalayas on precipitation in this region during the 135 years from 1875 to 2010. The study showed that the northwest Himalayas have experienced five most-severe rainstorms whose rain depths have not been surpassed so far. These severe rainstorms caused heavy to very heavy rainfall over the region that led to tremendous destruction to crops, communications, and livestock. Severe rainstorms have not occurred during the 2001–10 decade. It is also noticed that severe floods in this region have not occurred because of a “break monsoon situation” in the middle or eastern Himalayas.
Abstract
A significant part of northern India is covered by the Himalayas, where a number of major Indian rivers originate. In the present study, a detailed analysis of rainstorms that affected the northwest region of the Himalayas has been made to assess the orographic effect of the Himalayas on precipitation in this region during the 135 years from 1875 to 2010. The study showed that the northwest Himalayas have experienced five most-severe rainstorms whose rain depths have not been surpassed so far. These severe rainstorms caused heavy to very heavy rainfall over the region that led to tremendous destruction to crops, communications, and livestock. Severe rainstorms have not occurred during the 2001–10 decade. It is also noticed that severe floods in this region have not occurred because of a “break monsoon situation” in the middle or eastern Himalayas.
Abstract
To evaluate annual CO2 exchange rates in a wetland ecosystem, ecosystem respiration rate (Re ), net ecosystem productivity (NEP), and gross primary productivity (GPP) were investigated using the Closed Geosphere Experiment Facility (CGEF) located in northeastern Japan. The CGEF is highly airtight and equipped with a Geosphere Module (GM). The GM has a ground area of 5.8 × 8.7 m2 and an average height of 11.9 m, including a soil depth of 3.1 m. A wetland ecosystem dominated by Phragmites australis was introduced into the CGEF. Air temperature and CO2 concentration in the GM were controlled automatically. The hourly nighttime Re increased exponentially with the hourly average air temperature. Both hourly NEP and GPP depended on hourly photosynthetic photon flux density (PPFD). In addition, daily ecosystem CO2 exchange rates (Re , NEP, and GPP) were influenced by above-ground plant biomass. The annual NEP was found to be 64.2 ± 19.2 g C m−2 yr−1 and it resulted from the annual GPP of 555.8 ± 17.0 g C m−2 yr−1 and annual Re of −491.6 ± 15.6 g C m−2 yr−1. Therefore, the wetland ecosystem behaved as a CO2 sink for the entire year. The annual CO2 exchange rates obtained were reasonable values compared to the findings of published studies in P. australis–dominated wild wetlands using the eddy covariance technique and the combined method of internal gas pressures and flow measurements and harvesting.
Abstract
To evaluate annual CO2 exchange rates in a wetland ecosystem, ecosystem respiration rate (Re ), net ecosystem productivity (NEP), and gross primary productivity (GPP) were investigated using the Closed Geosphere Experiment Facility (CGEF) located in northeastern Japan. The CGEF is highly airtight and equipped with a Geosphere Module (GM). The GM has a ground area of 5.8 × 8.7 m2 and an average height of 11.9 m, including a soil depth of 3.1 m. A wetland ecosystem dominated by Phragmites australis was introduced into the CGEF. Air temperature and CO2 concentration in the GM were controlled automatically. The hourly nighttime Re increased exponentially with the hourly average air temperature. Both hourly NEP and GPP depended on hourly photosynthetic photon flux density (PPFD). In addition, daily ecosystem CO2 exchange rates (Re , NEP, and GPP) were influenced by above-ground plant biomass. The annual NEP was found to be 64.2 ± 19.2 g C m−2 yr−1 and it resulted from the annual GPP of 555.8 ± 17.0 g C m−2 yr−1 and annual Re of −491.6 ± 15.6 g C m−2 yr−1. Therefore, the wetland ecosystem behaved as a CO2 sink for the entire year. The annual CO2 exchange rates obtained were reasonable values compared to the findings of published studies in P. australis–dominated wild wetlands using the eddy covariance technique and the combined method of internal gas pressures and flow measurements and harvesting.
Abstract
Continuous and direct measurement of evapotranspiration (ET) by the eddy covariance (EC) technique is still a challenge under monsoon climate because of a considerable amount of missing data during the long rainy periods and the consequential gap-filling process. Under such wet canopy conditions, especially in forests, evaporation of the intercepted precipitation (E WC) contributes significantly to the total ET. To quantify the role of E WC, leaf wetness has been measured at multiple levels in the canopy simultaneously with eddy covariance measurements at the KoFlux Gwangneung deciduous and coniferous forests for the entire year from September 2007 to August 2008. In this study, the measured EWC and the controlling mechanism during the wet canopy conditions have been scrutinized. Based on the evaluation of the four different algorithms of E WC estimation, that of the variable infiltration capacity (VIC) land surface model (LSM) has been adopted. All the missing E WC data are then recalculated by using the algorithm of VIC LSM and compared against the traditionally gap-filled E WC data based on the modified lookup table (MLT) method. The latter consistently underestimated E WC on average by 39% in deciduous forest and by 28% in coniferous forest. Major causes of such differences were due to the failure of considering aerodynamic coupling, advection of sensible heat, and heat storage in the MLT-based gap-filling method. Accordingly, a new gap-filling strategy for E WC is proposed that takes proper controlling mechanisms into account.
Abstract
Continuous and direct measurement of evapotranspiration (ET) by the eddy covariance (EC) technique is still a challenge under monsoon climate because of a considerable amount of missing data during the long rainy periods and the consequential gap-filling process. Under such wet canopy conditions, especially in forests, evaporation of the intercepted precipitation (E WC) contributes significantly to the total ET. To quantify the role of E WC, leaf wetness has been measured at multiple levels in the canopy simultaneously with eddy covariance measurements at the KoFlux Gwangneung deciduous and coniferous forests for the entire year from September 2007 to August 2008. In this study, the measured EWC and the controlling mechanism during the wet canopy conditions have been scrutinized. Based on the evaluation of the four different algorithms of E WC estimation, that of the variable infiltration capacity (VIC) land surface model (LSM) has been adopted. All the missing E WC data are then recalculated by using the algorithm of VIC LSM and compared against the traditionally gap-filled E WC data based on the modified lookup table (MLT) method. The latter consistently underestimated E WC on average by 39% in deciduous forest and by 28% in coniferous forest. Major causes of such differences were due to the failure of considering aerodynamic coupling, advection of sensible heat, and heat storage in the MLT-based gap-filling method. Accordingly, a new gap-filling strategy for E WC is proposed that takes proper controlling mechanisms into account.
Abstract
Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.
Abstract
Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.
Abstract
The likelihood and magnitude of the impacts of climate change on potential vegetation and the water cycle in Mesoamerica is evaluated. Mesoamerica is a global biodiversity hotspot with highly diverse topographic and climatic conditions and is among the tropical regions with the highest expected changes in precipitation and temperature under future climate scenarios. The biogeographic soil–vegetation–atmosphere model Mapped Atmosphere Plant Soil System (MAPSS) was used for simulating the integrated changes in leaf area index (LAI), vegetation types (grass, shrubs, and trees), evapotranspiration, and runoff at the end of the twenty-first century. Uncertainty was estimated as the likelihood of changes in vegetation and water cycle under three ensembles of model runs, one for each of the groups of greenhouse gas emission scenarios (low, intermediate, and high emissions), for a total of 136 runs generated with 23 general circulation models (GCMs). LAI is likely to decrease over 77%–89% of the region, depending on climate scenario groups, showing that potential vegetation will likely shift from humid to dry types. Accounting for potential effects of CO2 on water use efficiency significantly decreased impacts on LAI. Runoff will decrease across the region even in areas where precipitation increases (even under increased water use efficiency), as temperature change will increase evapotranspiration. Higher emission scenarios show lower uncertainty (higher likelihood) in modeled impacts. Although the projection spread is high for future precipitation, the impacts of climate change on vegetation and water cycle are predicted with relatively low uncertainty.
Abstract
The likelihood and magnitude of the impacts of climate change on potential vegetation and the water cycle in Mesoamerica is evaluated. Mesoamerica is a global biodiversity hotspot with highly diverse topographic and climatic conditions and is among the tropical regions with the highest expected changes in precipitation and temperature under future climate scenarios. The biogeographic soil–vegetation–atmosphere model Mapped Atmosphere Plant Soil System (MAPSS) was used for simulating the integrated changes in leaf area index (LAI), vegetation types (grass, shrubs, and trees), evapotranspiration, and runoff at the end of the twenty-first century. Uncertainty was estimated as the likelihood of changes in vegetation and water cycle under three ensembles of model runs, one for each of the groups of greenhouse gas emission scenarios (low, intermediate, and high emissions), for a total of 136 runs generated with 23 general circulation models (GCMs). LAI is likely to decrease over 77%–89% of the region, depending on climate scenario groups, showing that potential vegetation will likely shift from humid to dry types. Accounting for potential effects of CO2 on water use efficiency significantly decreased impacts on LAI. Runoff will decrease across the region even in areas where precipitation increases (even under increased water use efficiency), as temperature change will increase evapotranspiration. Higher emission scenarios show lower uncertainty (higher likelihood) in modeled impacts. Although the projection spread is high for future precipitation, the impacts of climate change on vegetation and water cycle are predicted with relatively low uncertainty.
Abstract
Thermal inertia retrieval using a thermal infrared remote sensing technique has been examined as a possible method for estimating soil moisture. This method is an application of the theory that thermal inertia highly correlates with soil water content. This study shows a method for retrieving thermal inertia from a heat budget model of the earth’s surface using radiative surface temperatures, insolation, and meteorological data observed in field experiments. In bare to sparsely vegetated areas, this method has the potential to estimate subsurface soil moisture with a precision of ±3%–4% of the daily volumetric soil moisture content at a significance level of 5%, which is enough to roughly classify thermal inertia estimates into a few levels of soil moisture (e.g., wet, middle, and dry). The analysis also includes an examination of the practical performance of the thermal inertia estimation according to the temporal resolution of the data, assuming the use of satellite and routine meteorological data. It is found that the following combination of data can achieve the precision given above: radiative surface temperature from geostationary/multiple polar orbiting satellites, insolation retrieved from geostationary satellite data, and routine meteorological data.
Abstract
Thermal inertia retrieval using a thermal infrared remote sensing technique has been examined as a possible method for estimating soil moisture. This method is an application of the theory that thermal inertia highly correlates with soil water content. This study shows a method for retrieving thermal inertia from a heat budget model of the earth’s surface using radiative surface temperatures, insolation, and meteorological data observed in field experiments. In bare to sparsely vegetated areas, this method has the potential to estimate subsurface soil moisture with a precision of ±3%–4% of the daily volumetric soil moisture content at a significance level of 5%, which is enough to roughly classify thermal inertia estimates into a few levels of soil moisture (e.g., wet, middle, and dry). The analysis also includes an examination of the practical performance of the thermal inertia estimation according to the temporal resolution of the data, assuming the use of satellite and routine meteorological data. It is found that the following combination of data can achieve the precision given above: radiative surface temperature from geostationary/multiple polar orbiting satellites, insolation retrieved from geostationary satellite data, and routine meteorological data.
Abstract
Carbon and water cycles are intimately coupled in terrestrial ecosystems, and water-use efficiency (WUE; carbon gain at the expense of unit water loss) is one of the key parameters of ecohydrology and ecosystem management. In this study, the carbon cycle and water budget of terrestrial ecosystems were simulated using a process-based ecosystem model called Vegetation Integrative Simulator for Trace Gases (VISIT), and WUE was evaluated: WUEC, defined as gross primary production (GPP) divided by transpiration; and WUES, defined as net primary production (NPP) divided by actual evapotranspiration. Total annual WUEC and WUES of the terrestrial biosphere were estimated as 8.0 and 0.92 g C kg−1 H2O, respectively, for the period 1995–2004. Spatially, WUEC and WUES were only weakly correlated. WUES ranged from <0.2 g C kg−1 H2O in arid ecosystems to >1.5 g C kg−1 H2O in boreal and alpine ecosystems. The historical simulation implied that biospheric WUE increased from 1901 to 2005 (WUEC, +7%; WUES, +12%) mainly as a result of the augmentation of productivity in parallel with the atmospheric carbon dioxide increase. Country-based analyses indicated that total NPP is largely determined by water availability, and human appropriation of NPP is also related to water resources to a considerable extent. These results have implications for 1) responses of the carbon cycle to the anticipated global hydrological changes, 2) responses of the water budget to changes in the terrestrial carbon cycle, and 3) ecosystem management based on optimized resource use.
Abstract
Carbon and water cycles are intimately coupled in terrestrial ecosystems, and water-use efficiency (WUE; carbon gain at the expense of unit water loss) is one of the key parameters of ecohydrology and ecosystem management. In this study, the carbon cycle and water budget of terrestrial ecosystems were simulated using a process-based ecosystem model called Vegetation Integrative Simulator for Trace Gases (VISIT), and WUE was evaluated: WUEC, defined as gross primary production (GPP) divided by transpiration; and WUES, defined as net primary production (NPP) divided by actual evapotranspiration. Total annual WUEC and WUES of the terrestrial biosphere were estimated as 8.0 and 0.92 g C kg−1 H2O, respectively, for the period 1995–2004. Spatially, WUEC and WUES were only weakly correlated. WUES ranged from <0.2 g C kg−1 H2O in arid ecosystems to >1.5 g C kg−1 H2O in boreal and alpine ecosystems. The historical simulation implied that biospheric WUE increased from 1901 to 2005 (WUEC, +7%; WUES, +12%) mainly as a result of the augmentation of productivity in parallel with the atmospheric carbon dioxide increase. Country-based analyses indicated that total NPP is largely determined by water availability, and human appropriation of NPP is also related to water resources to a considerable extent. These results have implications for 1) responses of the carbon cycle to the anticipated global hydrological changes, 2) responses of the water budget to changes in the terrestrial carbon cycle, and 3) ecosystem management based on optimized resource use.
Abstract
Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.
Abstract
Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.
Abstract
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
Abstract
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
Abstract
The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.
Abstract
The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.
