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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.

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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.