No description available.

The land (or ‘terrestrial’) biosphere strongly influences the exchange of carbon, energy and water between the land surface and the atmosphere. The size of the land carbon store and the magnitude of the interannual variability of the carbon exchange make models of the land surface a vital component in climate models. This thesis addresses two aspects of land surface modelling: soil respiration and phenology modelling, using different techniques with the goal of improving model representation of land-atmosphere interaction. The release of heat associated with soil respiration is neglected in the vast majority of large-scale models but may be critically important under certain circumstances. In this thesis, the effect of this heat release is considered in two ways. Firstly, a deliberately simple model for soil temperature and soil carbon, including biological heating, is constructed to investigate the effect of thermal energy generated by microbial respiration on soil temperature and soil carbon stocks, specifically in organic soils. Secondly, the mechanism for biological self-heating is implemented in the Joint UK Land Environment Simulator (JULES), in order to investigate the impacts of the extra feedback in a complex model. With the intention of improving estimates of the parameters governing modelled land surface processes, a data assimilation system based on the JULES land surface model is presented. The ADJULES data assimilation system uses information from the derivative of JULES (or adjoint) to search for a locally optimum parameter set by calibrating against observations. In this thesis, ADJULES is used with satellite-derived vegetation indices to improve the modelling of phenology in JULES.

The scale-bound view of landsurfaces, being an assemblage of certain landforms, occurring within limited scale ranges, has been challenged by the scale-free characteristics of fractal geometry. This thesis assesses the fractal model by examining the irregularity of landsurface form, for the self-affine behaviour present in fractional Brownian surfaces. Different methods for detecting self-affine behaviour in surfaces are considered and of these the variogram technique is shown to be the most effective. It produces the best results of two methods tested on simulated surfaces, with known fractal properties. The algorithm used has been adapted to consider log (altitude variance) over a sample of log (distances) for: complete surfaces; subareas within surfaces; separate directions within surfaces. Twenty seven digital elevation models of landsurfaces arc re-examined for self- affine behaviour. The variogram results for complete surfaces show that none of these are self-affine over the scale range considered. This is because of dominant slope lengths and regular valley, spacing within areas. For similar reasons subarea analysis produces the non-fractal behaviour of markedly different variograms for separate subareas. The linearity of landforms in many areas, is detected by the variograms for separate directions. This indicates that the roughness of landsurfaces is anisotropic, unlike that of fractal surfaces. Because of difficulties in extracting particular landforms from their landsurfaces, no clear links between fractal behaviour, and landform size distribution could be established. A comparative study shows the geomorphometric parameters of fractal surfaces to vary with fractal dimension, while the geomorphometry of landsurfaces varies with the landforms present. Fractal dimensions estimated from landsurfaces do not correlate with geomorphometric parameters. From the results of this study, real landsurfaces would not appear to be scale- free. Therefore, a scale-bound approach towards landsurfaces would seem to be more appropriate to geomorphology than the fractal alternative.

The main goal of this research is to contribute to the understanding of nutrient transport and transformations in soil and its impact on groundwater on a large scale. This thesis specifically integrates the physical, chemical and biochemical nitrogen transport processes with a spatial and temporal Land Surface Scheme (LSS). Since the nitrogen biotransformation kinetics highly depends on soil moisture and soil temperature, a vertical soil nitrogen transport and transformations model was coupled with SABAE-HW. The model provides an improved interface for groundwater modeling to simulate soil moisture and soil temperature for a wide range of soil and vegetation. It is assumed that the main source of organic N is from animal manure. A-single-pool nitrogen transformation is designed to simulate nitrogen dynamics. Thus, the complete mathematical model (SABAE-HWS) is able to investigate the effects of nitrogen biochemical reactions in all seasons. This thesis reports the first field comparison of SABAE-HW using an extensive ten-year data set from BOREAS/BERMS project located in Saskatchewan, Canada. The performance of SABAE-HWS is calibrated and verified using 3 years (2002-2004) data from Carberry site in Canada, Manitoba. The effects of three rates of hog manure application, 2500, 5000, and 7500 gal/acre, was investigated to study the distribution of soil ammonium and soil nitrate within the 120 cm of soil profile. The results clearly showed that there is a good agreement between observed and simulated soil ammonium and nitrate for all treatment at the first two years of study. However, it was found a significant difference between observations and simulations at lower depths for 7500 gal/acre by the end of growing season of 2004. Also, 10 years climate data from OJP site was used to evaluate the effect of manure rates on the distribution of soil nitrate at Carberry site. The results indicated that to minimize the risk of nitrate leaching, the rate of manure application, accumulated soil nitrogen from earlier applications and the atmospheric conditions should be all taken into account at the same time. Comparing the results of SABAE-HWS and SHAW model also showed the importance of the crop growth model in simulating soil NH4-N and NO3-N.

The main goal of this research is to contribute to the understanding of nutrient transport and transformations in soil and its impact on groundwater on a large scale. This thesis specifically integrates the physical, chemical and biochemical nitrogen transport processes with a spatial and temporal Land Surface Scheme (LSS). Since the nitrogen biotransformation kinetics highly depends on soil moisture and soil temperature, a vertical soil nitrogen transport and transformations model was coupled with SABAE-HW. The model provides an improved interface for groundwater modeling to simulate soil moisture and soil temperature for a wide range of soil and vegetation. It is assumed that the main source of organic N is from animal manure. A-single-pool nitrogen transformation is designed to simulate nitrogen dynamics. Thus, the complete mathematical model (SABAE-HWS) is able to investigate the effects of nitrogen biochemical reactions in all seasons. This thesis reports the first field comparison of SABAE-HW using an extensive ten-year data set from BOREAS/BERMS project located in Saskatchewan, Canada. The performance of SABAE-HWS is calibrated and verified using 3 years (2002-2004) data from Carberry site in Canada, Manitoba. The effects of three rates of hog manure application, 2500, 5000, and 7500 gal/acre, was investigated to study the distribution of soil ammonium and soil nitrate within the 120 cm of soil profile. The results clearly showed that there is a good agreement between observed and simulated soil ammonium and nitrate for all treatment at the first two years of study. However, it was found a significant difference between observations and simulations at lower depths for 7500 gal/acre by the end of growing season of 2004. Also, 10 years climate data from OJP site was used to evaluate the effect of manure rates on the distribution of soil nitrate at Carberry site. The results indicated that to minimize the risk of nitrate leaching, the rate of manure application, accumulated soil nitrogen from earlier applications and the atmospheric conditions should be all taken into account at the same time. Comparing the results of SABAE-HWS and SHAW model also showed the importance of the crop growth model in simulating soil NH4-N and NO3-N.

Seventeen Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were evaluated, focusing on the seasonal sensitivities of net biome production (NBP), net primary production (NPP), and heterotrophic respiration (Rh) to interannual variations in temperature and precipitation during 1982-2005 and their changes over the twenty-first century. Temperature sensitivity of NPP in ESMs was generally consistent across northern high-latitude biomes but significantly more negative for tropical and subtropical biomes relative to satellite-derived estimates. The temperature sensitivity of NBP in both inversion-based and ESM estimates was generally consistent in March-May (MAM) and September-November (SON) for tropical forests, semiarid ecosystems, and boreal forests. By contrast, for inversion-based NBP estimates, temperature sensitivity of NBP was nonsignificant for June-August (JJA) for all biomes except boreal forest; whereas, for ESM NBP estimates, the temperature sensitivity for JJA was significantly negative for all biomes except shrublands and subarctic ecosystems. Both satellite-derivedNPP and inversion-based NBP are often decoupled from precipitation, whereas ESM NPP and NBP estimates are generally positively correlated with precipitation, suggesting that ESMs are oversensitive to precipitation. Over the twenty-first century, changes in temperature sensitivities of NPP, Rh, and NBP are consistent across all RCPs but stronger under more intensive scenarios. The temperature sensitivity of NBP was found to decrease in tropics and subtropics and increase in northern high latitudes in MAM due to an increased temperature sensitivity of NPP. Across all biomes, projected temperature sensitivity of NPP decreased in JJA and SON. Projected precipitation sensitivity of NBP did not change across biomes, except over grasslands in MAM.

The hydrological components of land surface climate models have increased greatly in complexity over the past decade, from simple bucket models to multilayer models including separate and distinct soil water and ground water components. While the parameterizations included in these models have also increased in complexity, the fundamental ability of the numerical solution for the vertical movement of soil water in the Community Land Model (or other land surface models) to simply maintain the hydrostatic solution of the original partial differential equation has yet to be determined.Also, the ability of current generation reanalysis products to simulate near surface quantities as gauged by flux tower measurements has yet to be determined.This study demonstrates that the numerical solution as used in CLM3.5 cannot maintain the hydrostatic state. An alternate form of the equation, titled the Modified Richards equation is presented so that the numerical solution maintains steady statesolutions. Also, an improved and simple bottom boundary condition is derived that itself doesn't destroy hydrostatic initial conditions. The new solution is demonstrated to be as accurate as proven numerical solutions while being one to three orders more computationally efficient. The Modified Richards equation together with the new bottom boundary condition is shown to improve the ability of CLM to simulate soil water, water table depth, and near surface turbulent fluxes.Comparison with flux tower observations shows that ERA-Interim better simulates near surface temperature and wind speed than other current generation reanalysis products. Reanalysis products are able to reproduce the flux tower observations on monthly timescales, and the errors between the products and the measurements are primarily due to biases. However, at six hourly timescales the errors are not only larger but also caused primarily by a lack of correlation with the observations.

No description available.

This paper presents two new schemes for interpolating missing samples in satellite diurnal temperature cycles (DTCs). The first scheme, referred to here as the cosine model, is an improvement of the model proposed in [2] and combines a cosine and exponential function for modelling the DTC. The second scheme uses the notion of a Reproducing Kernel Hilbert Space (RKHS) interpolator [1] for interpolating the missing samples. The application of RKHS interpolators to the DTC interpolation problem is novel. Results obtained by means of computer experiments are presented.

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.