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- Author or Editor: Taikan Oki x
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Abstract
A dense tipping-bucket rain gauge network was established in the Mae Chaem watershed in the mountains of northwestern Thailand as part of the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment-Tropics (GAME-T). Investigations of rainfall amounts, intensities, durations, and frequencies in the rainy season revealed strong orographic rainfall enhancement in the region. The larger amount of high-altitude rainfall was attributed to duration and frequency rather than intensity. Despite large rainfall variations, similar patterns were found in the two study years, 1998 and 1999.
Abstract
A dense tipping-bucket rain gauge network was established in the Mae Chaem watershed in the mountains of northwestern Thailand as part of the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment-Tropics (GAME-T). Investigations of rainfall amounts, intensities, durations, and frequencies in the rainy season revealed strong orographic rainfall enhancement in the region. The larger amount of high-altitude rainfall was attributed to duration and frequency rather than intensity. Despite large rainfall variations, similar patterns were found in the two study years, 1998 and 1999.
Abstract
It is now widely recognized that tropical deforestation can change the regional climate significantly. The increasing population and the spreading deforestation in the Indochina Peninsula, especially in Thailand, make it urgent to assess the effects of deforestation on the regional climate. Most of the previous numerical experiments generally have shown that decreases in precipitation occur as a result of deforestation. However, in most cases, these hydrometeorological changes have not been detected in observations. In this study, the nonparametric Mann–Kendall rank test and linear regression analysis were applied to analyze precipitation data obtained over a period of more than 40 yr, for each month, at each meteorological station in Thailand. Significant decreases in precipitation over Thailand were detected only in the time series of monthly precipitation in September. Amounts of precipitation recorded at many meteorological stations in September have decreased by approximately 100 mm month−1 (approximately 30% relative change) over the past three or four decades. Numerical experiments with a regional climate model based on the Regional Atmospheric Modeling System with a simple land surface scheme were carried out for the Indochina Peninsula. In these experiments, the type of vegetation in the northeastern part of Thailand was specified as either short vegetation (the current vegetation type) or forest (the former vegetation type). The experiments were carried out using the initial and boundary meteorological conditions of August and September in 1992–94. The initial and boundary conditions were interpolated from the data of the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis. In these numerical experiments, a decrease in precipitation over the deforested area was obtained for September, but not for August. The magnitude of the mean decrease in precipitation over the whole deforested area in these experiments was 26 mm month−1 (7% relative change), and the local maximum decrease was 88 mm month−1 (29% relative change). Precipitation in the wet season over the Indochina Peninsula basically occurs under the influence of the Southeast Asian summer monsoon system. The strong summer monsoon westerlies bring abundant moisture to the Indochina Peninsula as a source of precipitation. The monsoon westerlies are the predominant external force influencing the regional climate. However, the strong westerlies over the Indochina Peninsula disappear in September, although that is typically the month of maximum precipitation. Accordingly, it is inferred that the effect of local deforestation appears significantly only in September because of the absence of this strong external force.
Abstract
It is now widely recognized that tropical deforestation can change the regional climate significantly. The increasing population and the spreading deforestation in the Indochina Peninsula, especially in Thailand, make it urgent to assess the effects of deforestation on the regional climate. Most of the previous numerical experiments generally have shown that decreases in precipitation occur as a result of deforestation. However, in most cases, these hydrometeorological changes have not been detected in observations. In this study, the nonparametric Mann–Kendall rank test and linear regression analysis were applied to analyze precipitation data obtained over a period of more than 40 yr, for each month, at each meteorological station in Thailand. Significant decreases in precipitation over Thailand were detected only in the time series of monthly precipitation in September. Amounts of precipitation recorded at many meteorological stations in September have decreased by approximately 100 mm month−1 (approximately 30% relative change) over the past three or four decades. Numerical experiments with a regional climate model based on the Regional Atmospheric Modeling System with a simple land surface scheme were carried out for the Indochina Peninsula. In these experiments, the type of vegetation in the northeastern part of Thailand was specified as either short vegetation (the current vegetation type) or forest (the former vegetation type). The experiments were carried out using the initial and boundary meteorological conditions of August and September in 1992–94. The initial and boundary conditions were interpolated from the data of the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis. In these numerical experiments, a decrease in precipitation over the deforested area was obtained for September, but not for August. The magnitude of the mean decrease in precipitation over the whole deforested area in these experiments was 26 mm month−1 (7% relative change), and the local maximum decrease was 88 mm month−1 (29% relative change). Precipitation in the wet season over the Indochina Peninsula basically occurs under the influence of the Southeast Asian summer monsoon system. The strong summer monsoon westerlies bring abundant moisture to the Indochina Peninsula as a source of precipitation. The monsoon westerlies are the predominant external force influencing the regional climate. However, the strong westerlies over the Indochina Peninsula disappear in September, although that is typically the month of maximum precipitation. Accordingly, it is inferred that the effect of local deforestation appears significantly only in September because of the absence of this strong external force.
Abstract
The effects of natural and anthropogenic heterogeneity on a hydrological simulation are evaluated using a distributed biosphere hydrological model (DBHM) system. The DBHM embeds a biosphere model into a distributed hydrological scheme, representing both topography and vegetation in a mesoscale hydrological simulation, and the model system includes an irrigation scheme. The authors investigated the effects of two kinds of variability, precipitation variability and the variability of irrigation redistributing runoff, representing natural and anthropogenic heterogeneity, respectively, on hydrological processes. Runoff was underestimated if rainfall was placed spatially uniformly over large grid cells. Accounting for precipitation heterogeneity improved the runoff simulation. However, the negative runoff contribution, namely, the situation that mean annual precipitation is less than evapotranspiration, cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by accounting for anthropogenic heterogeneity caused by irrigation water withdrawals. Irrigation leads to increased evapotranspiration and decreased runoff, and surface soil moisture in irrigated areas increases because of irrigation. Simulations performed for the Yellow River basin of China indicated streamflow decreases of 41% due to irrigation effects. The latent heat flux in the peak irrigation season [June–August (JJA)] increased 3.3 W m−2 with a decrease in the ground surface temperature of 0.1 K for the river basin. The maximum simulated increase in the latent heat flux was 43 W m−2, and the ground temperature decrease was 1.6 K in the peak irrigation season.
Abstract
The effects of natural and anthropogenic heterogeneity on a hydrological simulation are evaluated using a distributed biosphere hydrological model (DBHM) system. The DBHM embeds a biosphere model into a distributed hydrological scheme, representing both topography and vegetation in a mesoscale hydrological simulation, and the model system includes an irrigation scheme. The authors investigated the effects of two kinds of variability, precipitation variability and the variability of irrigation redistributing runoff, representing natural and anthropogenic heterogeneity, respectively, on hydrological processes. Runoff was underestimated if rainfall was placed spatially uniformly over large grid cells. Accounting for precipitation heterogeneity improved the runoff simulation. However, the negative runoff contribution, namely, the situation that mean annual precipitation is less than evapotranspiration, cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by accounting for anthropogenic heterogeneity caused by irrigation water withdrawals. Irrigation leads to increased evapotranspiration and decreased runoff, and surface soil moisture in irrigated areas increases because of irrigation. Simulations performed for the Yellow River basin of China indicated streamflow decreases of 41% due to irrigation effects. The latent heat flux in the peak irrigation season [June–August (JJA)] increased 3.3 W m−2 with a decrease in the ground surface temperature of 0.1 K for the river basin. The maximum simulated increase in the latent heat flux was 43 W m−2, and the ground temperature decrease was 1.6 K in the peak irrigation season.
Abstract
This study investigates the projections of river discharge for 24 major rivers in the world during the twenty-first century simulated by 19 coupled atmosphere–ocean general circulation models based on the Special Report on Emissions Scenarios A1B scenario. To reduce model bias and uncertainty, a weighted ensemble mean (WEM) is used for multimodel projections. Although it is difficult to reproduce the present river discharge in any single model, the WEM results produce more accurate reproduction for most rivers, except those affected by anthropogenic water usage. At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa. In contrast, they decrease in the Mediterranean region, southern Africa, southern North America, and Central America. Although the geographical distribution of the changes in precipitation and runoff tends to coincide with that in the river discharge, it should be emphasized that the change in runoff at the upstream region affects the river flow in the downstream region. In high-latitude rivers (Amur, Lena, MacKenzie, Ob, Yenisei, and Yukon), the discharge increases, and the peak timing shifts earlier because of an earlier snowmelt caused by global warming. Discharge tends to decrease for the rivers in Europe to the Mediterranean region (Danube, Euphrates, and Rhine), and southern United Sates (Rio Grande).
Abstract
This study investigates the projections of river discharge for 24 major rivers in the world during the twenty-first century simulated by 19 coupled atmosphere–ocean general circulation models based on the Special Report on Emissions Scenarios A1B scenario. To reduce model bias and uncertainty, a weighted ensemble mean (WEM) is used for multimodel projections. Although it is difficult to reproduce the present river discharge in any single model, the WEM results produce more accurate reproduction for most rivers, except those affected by anthropogenic water usage. At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa. In contrast, they decrease in the Mediterranean region, southern Africa, southern North America, and Central America. Although the geographical distribution of the changes in precipitation and runoff tends to coincide with that in the river discharge, it should be emphasized that the change in runoff at the upstream region affects the river flow in the downstream region. In high-latitude rivers (Amur, Lena, MacKenzie, Ob, Yenisei, and Yukon), the discharge increases, and the peak timing shifts earlier because of an earlier snowmelt caused by global warming. Discharge tends to decrease for the rivers in Europe to the Mediterranean region (Danube, Euphrates, and Rhine), and southern United Sates (Rio Grande).
Abstract
A simple algorithm is presented for transferring root-zone soil moisture from surface soil moisture data on a global scale. Analysis of offline soil moisture data shows that the climatological relationship between surface and root-zone soil moisture becomes linear when appropriate time lags are applied. The climatological relationship of root-zone soil moisture among different land surface models (LSMs) is also linear; therefore, the root-zone and surface soil moisture obtained from one LSM can be applied to another. The algorithm is then applied to the surface soil moisture observations made by the precipitation radar on board the Tropical Rainfall Measuring Mission precipitation radar (TRMM/PR), and the transferred root-zone soil moisture is input to a general circulation model (GCM) summer—June, July, August—precipitation simulation as the boundary condition. The approach is computationally efficient, and the simulation using the root-zone soil moisture by the transfer method is much better than a simulation using root-zone soil moisture without the transfer method, assuming that the volumetric percentage of TRMM/PR is representative of the root zone. The result indicates that the simple transfer process will increase the utility of surface soil moisture data for a GCM.
Abstract
A simple algorithm is presented for transferring root-zone soil moisture from surface soil moisture data on a global scale. Analysis of offline soil moisture data shows that the climatological relationship between surface and root-zone soil moisture becomes linear when appropriate time lags are applied. The climatological relationship of root-zone soil moisture among different land surface models (LSMs) is also linear; therefore, the root-zone and surface soil moisture obtained from one LSM can be applied to another. The algorithm is then applied to the surface soil moisture observations made by the precipitation radar on board the Tropical Rainfall Measuring Mission precipitation radar (TRMM/PR), and the transferred root-zone soil moisture is input to a general circulation model (GCM) summer—June, July, August—precipitation simulation as the boundary condition. The approach is computationally efficient, and the simulation using the root-zone soil moisture by the transfer method is much better than a simulation using root-zone soil moisture without the transfer method, assuming that the volumetric percentage of TRMM/PR is representative of the root zone. The result indicates that the simple transfer process will increase the utility of surface soil moisture data for a GCM.
Abstract
Terrestrial water storage (TWS) is a fundamental component of the water cycle. On a regional scale, measurements of terrestrial water storage change (TWSC) are extremely scarce at any time scale. This study investigates the feasibility of estimating monthly-to-seasonal variations of regional TWSC from modeling and a combination of satellite and in situ surface observations based on water balance computations that use ground-based precipitation observations in both cases. The study area is the Klamath and Sacramento River drainage basins in the western United States (total area of about 110 000 km2). The TWSC from the satellite/surface observation–based estimates is compared with model results and land water storage from the Gravity Recovery and Climate Experiment (GRACE) data. The results show that long-term evapotranspiration estimates and runoff measurements generally balance with observed precipitation, suggesting that the evapotranspiration estimates have relatively small bias for long averaging times. Observations show that storage change in water management reservoirs is about 12% of the seasonal amplitude of the TWSC cycle, but it can be up to 30% at the subbasin scale. Comparing with predevelopment conditions, the satellite/surface observation–based estimates show larger evapotranspiration and smaller runoff than do modeling estimates, suggesting extensive anthropogenic alteration of TWSC in the study area. Comparison of satellite/surface observation–based and GRACE TWSC shows that the seasonal cycle of terrestrial water storage is substantially underestimated by GRACE.
Abstract
Terrestrial water storage (TWS) is a fundamental component of the water cycle. On a regional scale, measurements of terrestrial water storage change (TWSC) are extremely scarce at any time scale. This study investigates the feasibility of estimating monthly-to-seasonal variations of regional TWSC from modeling and a combination of satellite and in situ surface observations based on water balance computations that use ground-based precipitation observations in both cases. The study area is the Klamath and Sacramento River drainage basins in the western United States (total area of about 110 000 km2). The TWSC from the satellite/surface observation–based estimates is compared with model results and land water storage from the Gravity Recovery and Climate Experiment (GRACE) data. The results show that long-term evapotranspiration estimates and runoff measurements generally balance with observed precipitation, suggesting that the evapotranspiration estimates have relatively small bias for long averaging times. Observations show that storage change in water management reservoirs is about 12% of the seasonal amplitude of the TWSC cycle, but it can be up to 30% at the subbasin scale. Comparing with predevelopment conditions, the satellite/surface observation–based estimates show larger evapotranspiration and smaller runoff than do modeling estimates, suggesting extensive anthropogenic alteration of TWSC in the study area. Comparison of satellite/surface observation–based and GRACE TWSC shows that the seasonal cycle of terrestrial water storage is substantially underestimated by GRACE.
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 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
Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).
Abstract
Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).
