The research presented here informs the confidence that can be placed in the simulations of land-surface models (LSMs). After introducing a method for simplifying a complex, heterogeneous land-cover dataset for use in LSMs, I show that LSMs can realistically represent the spatial distribution of heterogeneous land-cover processes (e.g., biogenic emission of volatile organic compounds) in Texas. LSM-derived estimates of biogenic emissions are sensitive (varying up to a factor of 3) to land-cover data, which is not well constrained by observations. Simulated emissions are most sensitive to land-cover data in eastern and central Texas, where tropospheric ozone pollution is a concern. I further demonstrate that interannual variation in leaf mass is at least as important to variation in biogenic emissions as is interannual variation in shortwave radiation and temperature. Model estimates show that more-humid regions with less year-to-year variation in precipitation have lower year-to-year variation in biogenic emissions: as modeled mean emissions increase, their mean-normalized standard deviation decreases. I evaluate three parameterizations of subsurface hydrology in LSMs (with (1) a shallow, 10-layer soil; (2) a deeper, many-layered soil; and (3) a lumped aquifer model) under increasing parameter uncertainty. When given their optimal parameter sets, all three versions perform equivalently well when simulating monthly change in terrestrial water storage. The most conceptually realistic model is least sensitive to errant parameter values. However, even when using the most conceptually realistic model, parameter interaction ensures that knowing ranges for individual parameters is insufficient to guarantee realistic simulation. LSMs are often developed and evaluated at data-rich sites but are then applied in regions where data are sparse or unavailable. I present a framework for model evaluation that explicitly acknowledges perennial sources of uncertainty in LSM simulations (e.g., parameter uncertainty, meteorological forcing-data uncertainty, evaluation-data uncertainty) and that evaluates LSMs in a way that is consistent with models’ typical application. The model performance score quantifies the likelihood that a representative ensemble of model performance will bracket observations with high skill and low spread. The robustness score quantifies the sensitivity of model performance to parameter error or data error. The fitness score ranks models’ suitability for broad application. / text

Groundwater is a major source of water in the western US. However, there are limited recharge estimates in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US. Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01% to 15% for Mosaic, 3.2% to 42% for Noah, and 6.7% to 31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge in data limited regions.

The land surface is a key component of the climate system and exchanges energy, water and carbon with the overlying atmosphere. It is the location of the terrestrial carbon sink and changes in the land surface can impact weather and climate at various time and spatial scales. It's ability to act as a source or a sink can influence atmospheric CO2 concentrations. Both models and observations have shown the reduced ability of the land surface to absorb increased anthropogenic CO2 emissions with results from the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) and phase 5 of the Coupled Model Intercomparison Project (CMIP5) have shown that the terrestrial carbon cycle is a major source of model uncertainty. Land surface models (LSMs) represent the interaction between the biosphere and atmosphere in earth system models (ESMs) and are important for simulating the terrestrial carbon cycle. In the context of land surface modelling, uncertainty arises from an incomplete understanding of land surface processes and the inability to model these processes correctly. As LSMs become more advanced, there is a need to understand their accuracy. In this thesis, the ability of the Joint UK Land Environment Simulator (JULES), the land surface scheme of the UK Met Office United Model, to simulate Gross Primary Productivity (GPP) fluxes is evaluated at various spatial scales (point, regional and global) in order to identify and quantify sources of uncertainty in the model. This thesis has three main objectives. Firstly, JULES is evaluated at the point scale across a range of biomes and climatic conditions using local (site-specific), global and satellite datasets. It was found that JULES is biased with total annual GPP underestimated by 16% and 30% across all sites compared to observations when using local and global data, respectively. The model's phenology module was tested by comparing results from simulations using the default phenology model to those forced with leaf area index (LAI) from the MODIS sensor. Model parameters were found to be a minor source of uncertainty compared to the meteorological driving data at the point scale as was the default phenology module in JULES. Secondly, in addition to evaluating simulated GPP fluxes at the point scale, the ability of JULES to simulate GPP at the global and regional scale for 2000-2010 was investigated with being able to simulate interannual variability and simulated global GPP estimates were found to be greater than the observation-based estimates, FLUXNET-MTE and MODIS, by 8% and 25%, respectively. At the regional scale, differences in GPP between JULES, FLUXNET-MTE and MODIS were observed mostly in the tropics and this was the reason for differences at the global scale. Simulating tropical GPP was found to be a major source of uncertainty in JULES. JULES was found to be insensitive to spatial resolution and when driven with the PRINCETON meteorological dataset, differences between model simulations driven using WFDEI-GPCC and PRINCETON occurred in the tropics (at 5°N-5°S) and extratropics (at 30°N-60°N). Finally, the response of JULES to changes in climate (surface air temperature, precipitation, atmospheric CO2 concentrations) was explored at the global and regional scale. Simulated GPP was found to have greater sensitivity to changes in precipitation and CO2 concentrations than air temperature at the global scale while LAI was sensitive only to changes in temperature and insensitive to changes in precipitation and CO2 concentrations. It was found that model sensitivity to climate at the global scale was determined by its behaviour at the regional scale.

The present work focuses on the sources and transport of water vapor to the La Plata Basin (LPB), and the role of groundwater dynamics on the simulation of hydrometeorological conditions over the basin. In the first part of the study an extension to the Dynamic Recycling Model (DRM) is developed to estimate the water vapor transported to the LPB from different regions in South America and the nearby oceans, and the corresponding contribution to precipitation over the LPB. It is found that more than 23% of the precipitation over the LPB is from local origin, while nearly 20% originates from evapotranspiration from the southern Amazon. Most of the moisture comes from terrestrial sources, with the South American continent contributing more than 62% of the moisture for precipitation over the LPB. The Amazonian contribution increases during the positive phase of El Niño and the negative phase of the Antarctic Oscillation. In the second part of the study the effect of a groundwater scheme on the simulation of terrestrial water storage, soil moisture and evapotranspiration (ET) over the LPB is investigated. It is found that the groundwater scheme improves the simulation of fluctuations in the terrestrial water storage over parts of the southern Amazon. There is also an increase in the soil moisture in the root zone over those regions where the water table is closer to the surface, including parts of the western and southern Amazon, and of the central and southern LPB. ET increases in the central and southern LPB, where it is water limited. Over parts of the southeastern Amazon the effects of the groundwater scheme are only observed at higher resolution, when the convergence of lateral groundwater flow in local topographical depressions is resolved by the model. Finally, the effects of the groundwater scheme on near surface conditions and precipitation are explored. It is found that the increase in ET induced by the groundwater scheme over parts of the LPB induces an increase in near surface specific humidity, accompanied by a decrease in near surface temperature. During the dry season, downstream of the regions where ET increases, there is also a slight increase in precipitation, over a region where the model has a dry bias compared with observations. During the early rainy season, there is also an increase in the local convective available potential energy. Over the southern LPB, groundwater induces an increase in ET and precipitation of 13 and 10%, respectively. Over the LPB, the groundwater scheme tends to improve the warm and dry biases of the model. It is suggested that a more realistic simulation of the water table depth could further increase the simulated precipitation during the early rainy season.

In data-poor regions around the world, particularly in less-privileged countries, hydrologists cannot always take advantage of available hydrological models to simulate a hydrological system due to the lack of reliable measurements of hydrological variables, in particular rainfall and streamflows, needed to implement and evaluate these models. Rainfall estimates obtained with remotely deployed sensors constitute an excellent source of precipitation for these basins, however they are prone to errors that can potentially affect hydrologic simulations. Concurrently, limited access to streamflow measurements does not allow a detailed representation of the system's structure through parameter estimation techniques. This dissertation presents multiple studies that evaluate the usefulness of remotely sensed products for different hydrological applications and the sensitivity of simulated streamflow to parameter uncertainty across basins with different hydroclimatic characteristics with the ultimate goal of increasing the applicability of land surface models in ungauged basins, particularly in South America. Paper 1 presents a sensitivity analysis of daily simulated streamflows to changes in model parameters along a hydroclimatic gradient. Parameters controlling the generation of surface and subsurface flow were targeted for the study. Results indicate that the sensitivity is strongly controlled by climate and that a more parsimonious version of the model could be implemented. Paper 2 explores how errors in satellite-estimated precipitation, due to infrequent satellite measurements, propagate through the simulation of a basin's hydrological cycle and impact the characteristics of peak streamflows within the basin. Findings indicate that nonlinearities in the hydrological cycle can introduce bias in simulated streamflows with error-corrupted precipitation. They also show that some characteristics of peak discharges are not conditioned by errors in satellite-estimated precipitation at a daily time step. Paper 3 evaluates the dominant sources of error in three satellite products when representing convective storms and how shifts in the location of the storm affect simulated peak streamflows in the basin. Results indicate that satellite products show some deficiencies retrieving convective processes and that a ground bias correction can mitigate these deficiencies but without sacrificing the potential for real-time hydrological applications. Finally, spatially shifted precipitation fields affect the magnitude of the peaks, however, its impact on the timing of the peaks is dampened out by the system's response at a daily time scale.

Reliable prediction of climate change and its impact on and feedbacks from terrestrial carbon cycles requires realistic representation of physiological and ecological processes in coupled climate-carbon models. This is hampered by various deficiencies in model structures and parameters. The goal of my study is to improve model realism by incorporating latest advances of fundamental eco-physiological processes and further to use such improved models to investigate climate-carbon interactions at regional to global scales. I focus on the CO₂ diffusion within leaves (a key plant physiological process) and large-scale disturbances (a fundamental ecological process) as extremely important but not yet in current models. The CO₂ diffusion within plant leaves is characterized by mesophyll conductance (g[subscript m]), which strongly influences photosynthesis. I developed a g[subscript m] model by synthesizing new advances in plant-physiological studies and incorporated this model into the Community Land Model (CLM), a state-of-art climate-carbon model. I updated associated photosynthetic parameters based on a large dataset of leaf gas exchange measurements. Major findings are: (1) omission of g[subscript m] underestimates the maximum carboxylation rate and distorts its relationships with other parameters, leading to an incomplete understanding of leaf-level photosynthesis machinery; (2) proper representation of g[subscript m] is necessary for climate-carbon models to realistically predict carbon fluxes and their responsiveness to CO₂ fertilization; (3) fine tuning of parameters may compensate for model structural errors in contemporary simulations but introduce large biases in future predictions. Further, I have corrected a numerical deficiency of CLM in its calculation of carbon/water fluxes, which otherwise can bias model simulations. Large-scale disturbances of terrestrial ecosystems strongly affect their carbon sink strength. To provide insights for modeling these processes, I used satellite products to examine the temporal-spatial patterns of greenness after a massive ice storm. I found that the greenness of impacted vegetation recovered rapidly, especially in lightly and severely impacted regions. The slowest rebound occurred over moderately impacted areas. This nonlinear pattern was caused by an integrated effect of natural regrowth and human interventions. My results demonstrate mechanisms by which terrestrial carbon sinks could be significantly affected and help determine how these sinks will behave and so affect future climate. / text

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23855号 / 工博第4942号 / 新制||工||1772(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 中北 英一, 講師 萬 和明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Arid and semi-arid basins in the Western United States (US) have been significantly impacted by human alterations to the water cycle and are among the most susceptible to water stress from urbanization and climate change. The climate of the Western US is projected to change in response to rising greenhouse gas concentrations. Combined with land use/land cover (LULC) change, it can influence both surface and groundwater resources, both of which are a significant source of water in the US. Responding to this challenge requires an improved understanding of how we are vulnerable and the development of strategies for managing future risk. In this dissertation, I explored how hydrology of semi-arid regions responds to LULC and climate change and how hydrologic projections are influenced by the choice and calibration of models. The three main questions I addressed with this dissertation are: 1. Is it important to calibrate models for forecasting absolute/relative changes in streamflow from LULC and climate changes? 2. Do LSMs make reasonable estimates of groundwater recharge in the western US? 3. How might recharge change under projected climate change in the western US? Results from this study suggested that it is important to calibrate the model spatially to analyze the effect of LULC change but not as important for analyzing the relative change in streamflow due to climate change. Our results also highlighted that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating current and future recharge in data limited regions. Average annual recharge is projected to increase in about 62% of the region and decrease in about 38% of the western US in future and varies significantly based on location (-50% - +94 for near future and -90% to >100% for far future). Recharge is expected to decrease significantly (-13%) in the South region in the far future. The Northern Rockies region is expected to get more recharge in both in the near (+5.1%) and far (+9.0%) future. Overall, this study suggested that land use/land cover (LULC) change and climate change significantly impacts hydrology in semi-arid regions. Model choice and model calibrations also influence the hydrological predictions. Hydrological projections from models have associated uncertainty, but still provide valuable information for water managers with long term water management planning.

The increasing urbanization of the greater Salt Lake City area (GSLA) has contributed to the development of an urban canopy over this area. This canopy refers to the effects of building profiles, varying land surface properties and anthropogenic heating on local meteorological conditions including temperature, humidity, and wind velocity. Urban Canopy Models (UCMs) can be used to represent these characteristics on a mesoscale without needing to develop models accounting for effects of individual buildings. One method used to classify urban areas are Local Climate Zones (LCZs), which assign different properties to different types of urban areas. A baseline model that represents current GSLA conditions was developed using a series of sensitivity studies, which focused on the effects of mesh resolution, land surface models, UCMs, anthropogenic heating rates and LCZ urban classifications. The baseline model was validated using measured meteorological data. Four urban growth scenarios were compared to this baseline model to evaluate the effects of future growth on local 2-meter air temperatures, 2-meter relative humidity, and 10-meter wind speed. Results showed increased urban density did not affect daytime temperatures within the GSLA, but did significantly increase local nighttime temperatures. The effects of anthropogenic heating rates were most noticeable during early nighttime hours. Also, increased urbanization affected local temperatures, but did not appear to have "downwind" effects on other areas. A User Guide documenting the modeling approach was developed to support additional studies.

Predicting the interactions between climate change and ecosystems remains a core problem in global change research; tropical forest ecosystems are of particular importance because of their disproportionate role in global carbon and water cycling. Amazonia is unique among tropical forest ecosystems, exhibiting a high degree of coupling with its regional hydrometeorology, such that the stability of the entire forest-climate system is dependent on the functioning of its component parts. Belowground ecohydrological interactions between soil moisture environments and the roots which permeate them initiate the water transport pathway to leaf stomata, yet despite the disproportionate role they play in vegetation-atmosphere coupling in Amazonian forest ecosystems, the impacts of climate variability on the belowground environment remain understudied. The research which follows is designed to address critical knowledge gaps in our understanding of root functioning in Amazonian tropical forests as it relates to seasonality and extremes in belowground moisture regime as well as discerning which ecohydrological mechanisms govern ecosystem-level processes of carbon and water flux. A secondary research theme is the evaluation and use of models of ecosystem function as applied to Amazonia - these models are the "knowledge boxes" which build in the ecohydrological hypotheses (some testable than others) deemed to be most important for the forest ecosystems of Amazonia. In what follows, I investigate (i) which mechanisms of water supply (from the soil environment) and water demand (by vegetation) regulate the magnitude and seasonality of evapotranspiration across broad environmental gradients of Amazonia, (ii) how specific hypotheses of root function are or are not corroborated by soil moisture measurements conducted under normal seasonal and experimentally-induced extreme drought conditions, and (iii) the linkage between an extreme drought event with associated impacts on root zone soil moisture, the inferred response of root water uptake, and the observed impacts on ecosystem carbon and water flux in an east central Amazonian forest.