UNDERSTANDING ENVIRONMENTAL FACTORS DRIVING WILDLAND FIRE IGNITIONS IN ALASKAN TUNDRA

Wildland fire is a dominant disturbance agent that drives ecosystem change, climate forcing, and carbon cycle in the boreal forest and tundra ecosystems of the High Northern Latitudes (HNL). Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change through biogeochemical and biogeophysical effects. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. This dissertation addresses the overarching scientific question of “What environmental factors and mechanisms drive wildfire ignition in Alaskan tundra?” Environmental factors from multiple aspects are considered including fuel type and state, fire weather, topography, and ignition source. First, to understand the spatial distribution of fuel types in the tundra, multi- year satellite observations and field data were used to develop the first fractional coverage product of major fuel type components across the entire Alaskan tundra at 30 m resolution. Second, to account for the primary ignition source of fires in the HNL, an empirical-dynamical modeling framework was developed to predict the probability of cloud-to-ground (CG) lightning across Alaskan tundra, through the integration of Weather Research and Forecast (WRF) model and machine learning algorithm. Finally, environmental factors including fuel type distribution, fuel moisture state, WRF simulated ignition source and fire weather, and topographical features, were combined with empirical modeling methods to understand their roles in driving wildland fire ignitions across Alaskan tundra from 2001 to 2019. This work demonstrates the strong capability for accurate prediction of CG lightning and wildland fire probabilities, by incorporating dynamic weather models, empirical methods, and satellite observations in data-scarce regions like the HNL. The developed models present a novel component of fire danger modeling that can considerably strengthen the current capability to forecast fire occurrence and support operational fire management agencies in the HNL. In addition, the insights gained from this research will allow for more accurate representation of wildfire ignition probabilities in studies focused on assessing the impact of the projected climate change in HNL tundra which has largely absent in previous modeling efforts

Remote Sensing And Regional Climate Modeling Of Impacts Of Land Cover Changes On The Climate Of The Marmara Region Of Turkey

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada, arazi örtüsünde meydana gelen değişimlerin Marmara Bölgesi yaz iklimi üzerindeki etkisi, Landsat görüntülerinin iklim modelleme için kullanılabilirliği ve iklim modellemede kullanılan arazi örtüsü verilerinin doğruluğu araştırılmıştır. Bu amaçla, 1975 ve 2005 yılları için Landsat uydu görüntüleri kullanılarak Marmara Bölgesi arazi örtüsü verileri oluşturulmuştur. 2005 yılı arazi örtüsü verisi, bölgesel iklim modellerinde kullanılan global arazi örtüsü verisi ile kıyaslanıp, model arazi örtüsü verisindeki eksiklikler tespit edilmiştir. 1975 ve 2005 yılı arazi örtüsü verileri Weather Research and Forecasting (WRF) modelleme sistemine girdi olarak sunulup, bu verilerle model çalıştırılmıştır. Ayrıca modelin içindeki arazi kullanımı verisi kullanılarak kontrol simülasyonu gerçekleştirilmiştir. 2005 arazi örtüsü verisi ile gerçekleştirilen simülasyon sonuçları, kontrol simülasyonundan daha iyi sonuç vermiştir. Arazi kullanımı verisinin kalitesinin arttırılması daha doğru iklim simülasyon sonuçlarının alınmasına yardımcı olmuştur. Ayrıca. 1975 ve 2005 yılı arazi kullanımı ile yapılan simülasyon sonuçları karşılaştırılıp, Marmara Bölgesinde meydana gelen arazi kullanımı değişimlerinin lokal iklim üzerindeki etkisi incelenmiştir. Karşılaştırmalar sonucunda, Marmara Bölgesinde özellikle şehirleşmenin arttığı İstanbul, Bursa ve Adapazarı illerinde yaz ayı minimum ve ortalama sıcaklıklarının arttığı, rüzgar doğrultu ve şiddetlerinin değiştiği gözlemlenmiştir. Model sonuçları, arazi örtüsü verileri ve diğer ilgili tüm veriler Coğrafi Bilgi Sisteminde ortak bir çatı altında toplanarak, arazi kullanımı değişiminin iklim üzerindeki etkisi detaylı bir şekilde incelenmiştir. Çalışma sonuçları, Landsat uydu görüntülerinden üretilen arazi örtüsü verilerinin bölgesel iklim modelleme de başarıyla kullanabileceği ve bu verilerle daha doğru iklim simülasyon sonuçlarının elde edilebileceği gösterilmiştir.In this research, investigation of land cover change impact on summer climate of the Marmara Region, utilization of Landsat images in regional climate modeling and assessment the accuracy of global land cover data sets used in were employed. Land cover data of 1975 and 2005 were produced using Landsat satellite images. 2005 land cover data was compared with global land cover data used in regional climate models and deficiencies and inaccuracies in model land cover were determined. 1975 and 2005 land cover data then implemented to Weather Research and Forecasting modeling system and two experiments were conducted with these data. Besides, a control run was employed using model land cover data. The experiment conducted with 2005 land cover gave better results then control experiment. Improving the land cover data improved the climate simulation results. Another comparison was made between the results of 1975 and 2005 land cover data runs to analyze the impact of land cover change on local climate of the region. Comparison results show that minimum and average temperatures increased and wind directions and magnitudes changed as a result of urbanization increase in the Marmara Region especially in İstanbul, Bursa and Adapazari. Climate model results, land cover data and other ancillary data were collected in a Geographic Information System to determine the impact of land cover change on climate in detail. The results of this study showed that land cover data produced from Landsat images can be successfully used in regional climate modeling and more accurate climate simulation results can be obtained with these improved data.DoktoraPh

IMPACT OF LAND SURFACE VEGETATION CHANGE OVER THE LA PLATA BASIN ON THE REGIONAL CLIMATIC ENVIRONMENT: A STUDY USING CONVENTIONAL LAND-COVER/LAND-USE AND NEWLY DEVELOPED ECOSYSTEM FUNCTIONAL TYPES

Naturally occurring or human induced changes in land surface vegetation have been recognized as one of the important factors influencing climate change. The La Plata Basin in South America has experienced significant changes in structural land-cover/land-use types, and those changes can involve changes in the surface physical properties such as albedo and roughness length, evapotranspiration, infiltration, and water storage eventually affecting the development of precipita-tion and the hydroclimate of the basin. In this study, the Weather Research and Forecast (WRF) modeling system was employed to investigate the role of changing land surface conditions in the La Plata Basin. For this purpose, ensembles of seasonal simulations were prepared for a control case and two extreme land cover scenarios: the first one assumes an expansion of the agricultural activities and the second one assumes a "natural" vegetation cover where no croplands are present. An extreme anthropogenic land-cover change -simulating an extensive agricultural practice- implies that the northern part of the basin, where croplands replace forests and savannah, would experience an overall increase in albedo and reduced surface friction. The two changes lead to a reduction of sensible heat and surface temperature, and a somewhat higher evapotranspiration due to decreased stomatal resistance and stronger near-surface winds. The effect on sensible heat seems to dominate and leads to a reduction in convective instability. The stronger low level winds due to reduced friction also imply a larger amount of moisture advected out of the basin, and thus resulting in reduced moisture flux convergence (MFC) within the basin. The two effects, increased stability and reduced MFC, result in a reduction of precipitation. On the other hand, the southern part of the basin exhibits the opposite behavior, as crops would replace grasslands, resulting in reduced albedo, a slight increase of surface temperature and increased precipitation. Notably, the results are not strictly local, as advective processes tend to modify the circulation and precipitation patterns downstream over the South Atlantic Ocean. A newly developed land surface classification, so-called Ecosystem Functional Types (EFTs, systems that share homogeneous energy and mass exchanges with the atmosphere), is implemented in the WRF model to explore its usefulness in regional climate simulations of surface and atmospheric variables. Results show that use of the EFT data improves the climate simulation of 2-m temperature and precipitation, making EFTs a good alternative to land cover types in numerical climate models. An additional advantage of EFTs is that they can be calculated on a yearly basis, thus representing the interannual variability of the surface states. During dry years the 2-m temperature and 10-m wind are more sensitive to changes in EFTs, while during wet years the sensitivity is larger for the 2-m water vapor mixing ratio, convective available potential energy, vertically-integrated moisture fluxes and surface precipitation. This indicates that the impact of land-cover and land-use changes on the climate of the LPB is dependent not only on the wetness of the year, but also on the meteorological or climate variables. Comparisons with observations show that the simulated precipitation difference induced by EFT changes resembles the overall pattern of observed precipitation changes for those same years over the LPB. In the case of the 2-m temperature, the simulated changes due to EFT changes are similar to the observed changes in the eastern part and the southern part of the basin (especially in Uruguay), where t he strongest EFT changes occurred

Hydrological assessment and biogeochemical advancement of the Noah-MP land surface model

textLand surface models (LSMs) simulate the energy, momentum, water, and carbon balance of the soil-vegetation-atmosphere system. As a key component of weather and climate models, LSMs play an important role in weather prediction and climate projections. Rapid growth in LSM development has resulted in both the improvement of existing process representation and the addition of new processes and functionalities. However, it is a challenge to evaluate the accuracy of energy, water, and nutrient fluxes simulated by LSMs, due to the lack of observational data and the complexity of interactions and feedback among different processes. Additionally, climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading nutrient for causing eutrophication in water bodies. However, few LSMs include nitrogen dynamics and nitrogen leaching is usually not well parameterized; hence these LSMs are not feasible for applications-based modeling, particularly for land management and agricultural impacts. Therefore, this dissertation uses the Noah-MP LSM to study the following three questions. (1) How do recent developments in Noah-MP improve its performance in hydrological modeling, based on a case study for the Mississippi River Basin? (2) Compared to other similar LSMs, what are the advantages and disadvantages of Noah-MP in assessing the water balance over the conterminous U.S.? (3) After coupling the Fixation and Uptake of Nitrogen plant model and the Soil and Water Assessment Tool soil nitrogen dynamics into Noah-MP, can this coupled model characterize the major nitrogen fluxes and how the nitrogen dynamics affect the carbon and water simulations? The main scientific findings are as follows. (1) Noah-MP shows significant improvement in modeling the major hydrological variables such as runoff, groundwater, evapotranspiration, soil moisture, and terrestrial water storage (TWS), which is very likely due to the incorporation of some major improvements into Noah-MP, particularly an unconfined aquifer storage layer for groundwater dynamics and an interactive vegetation canopy for dynamic leaf phenology. (2) Compared to other three LSMs, Noah-MP provides the best performance in simulating soil moisture and is among the best in simulating TWS. (3) The new Noah-MP with nitrogen dynamics performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). (4) The addition of nitrogen dynamics in Noah-MP improves the modeling of the carbon and water cycles (e.g., net primary productivity and evapotranspiration).Geological Science

Optimized Ensemble Generation for Probabilistic Chemistry Transport Modeling by Coupled Parameter Perturbation

Progressing developments in atmospheric modeling increase the complexity of modeling systems to improve forecast skills. At the same time, this augmented complexity hampers a reliable and efficient estimation of forecast uncertainties from a limited ensemble of forecasts. Especially chemistry transport models are highly sensitive to uncertainties in model parameters like emissions. Current algorithms for estimating related uncertainties suffer from the high dimensionality of the system. But multiple interactions of chemical compounds also induce multi-variational couplings in model states and uncertainties. This study introduces an optimized ensemble generation approach in which model parameters are efficiently perturbed according to their coupling. The approach applies the Karhunen-Loève expansion which approximates covariances of the model parameters by a limited set of leading eigenmodes. These modes represent the coupled leading uncertainties from which random perturbations can be sampled efficiently. For correlated model parameters, it is shown that leading uncertainties can be represented by a low number of perturbations driven by a few eigenmodes. Focusing on model parameters which depend on local atmospheric and terrestrial conditions, state-dependent covariances are approximated from various related sensitivities. As the simulation of all combined sensitivities is computationally demanding, independent input sensitivities are introduced in this study. Assuming tangent linearity, multiple combined sensitivities can be represented by a low number of independent sensitivities. Besides the reduction of computational resources, this setup allows for the integration of different kinds of uncertainties in a convenient way. The Karhunen-Loève ensemble algorithm is applied to biogenic emissions, dynamical boundary layer parameters and dry deposition in order to account for various uncertainties affecting concentrations of biogenic gases in the atmosphere. A case study in the Po valley in July 2012 indicates exceptionally high sensitivity of biogenic emissions on land surface properties. These sensitivities induce large perturbations of biogenic emissions by the ensemble algorithm. Resulting forecast uncertainties are at least as large as mean concentrations, which is in accordance to high-resolution Zeppelin observations. Results from the case study demonstrate a sufficient uncertainty estimation for selected model parameters by the Karhunen-Loève ensemble with about 10 members. As total leading uncertainties arise from sensitivities to land surface properties, forecast uncertainties of biogenic trace gases appear to be almost time-invariant. Thus, this study shows that the predictability of biogenic gases is more dependent on regional characteristics than on forecast time

Investigating the Eco-Hydrological Impact of Tropical Cyclones in the Southeastern United States

<p>Tropical Cyclones (TCs) intensity and frequency are expected to be impacted by climate change. Despite their destructive potential, these phenomena, which can produce heavy precipitation, are also an important source of freshwater. Therefore any change in frequency, seasonal timing and intensity of TCs is expected to strongly impact the regional water cycle and consequently the freshwater availability and distribution. This is critical, due to the fact that freshwater resources in the US are under stress due to the population growth and economic development that increasingly create more demands from agricultural, municipal and industrial uses, resulting in frequent over-allocation of water resources. </p><p>In this study we concentrate on monitoring the impact of hurricanes and tropical storms on vegetation activity along their terrestrial tracks and investigate the underlying physical processes. To characterize and monitor the spatial organization and time of recovery of vegetation disturbance in the aftermath of major hurricanes over the entire southeastern US, a remote sensed framework based on MODIS enhanced vegetation index (EVI) was developed. At the SE scale, this framework was complemented by a water balance approach to estimate the variability in hurricane groundwater recharge capacity spatially and between events. Then we investigate the contribution of TCs (season totals and event by event) to the SE US annual precipitation totals from 2002 to 2011. A water budget approach applied at the drainage basins scale is used to investigate the partitioning of TCs' precipitation into surface runoff and groundwater system in the direct aftermath of major TCs. This framework allows exploring the contribution of TCs to annual precipitation totals and the consequent recharge of groundwater reservoirs across different physiographic regions (mountains, coastal and alluvial plains) versus the fraction that is quickly evacuated through the river network and surface runoff. </p><p>Then a Land surface Eco-Hydrological Model (LEHM), combining water and energy budgets with photosynthesis activity, is used to estimate Gross Primary Production (GPP) over the SE US The obtained data is compared to AmeriFlux and MODIS GPP data over the SE United States in order to establish the model's ability to capture vegetation dynamics for the different biomes of the SE US. Then, a suite of numerical experiments is conducted to evaluate the impact of Tropical Cyclones (TCs) precipitation over the SE US. The numerical experiments consist of with and without TC precipitation simulations by replacing the signature of TC forcing by NARR-derived climatology of atmospheric forcing ahead of landfall during the TC terrestrial path. The comparison of these GPP estimates with those obtained with the normal forcing result in areas of discrepancies where the GPP was significantly modulated by TC activity. These areas show up to 10% variability over the last decade.</p>Dissertatio

A Model for Continental-Scale Water Erosion and Sediment Transport and Its Application to the Yellow River Basin

Quantifying suspended sediment discharge at large catchment scales has significant implications for various research fields such as water quality, global carbon and nutrient cycle, agriculture sustainability, and landscape evolution. There is growing evidence that climate warming is accelerating the water cycle, leading to changes in precipitation and runoff and increasing the frequency and intensity of extreme weather events, which could lead to intensive erosion and sediment discharge. However, suspended sediment discharge is still rarely represented in regional climate models because it depends not only on the sediment transport capacity based on streamflow characteristics but also on the sediment availability in the upstream basin. This thesis introduces a continental-scale Atmospheric and Hydrological-Sediment Modelling System (AHMS-SED), which overcomes the limitations of previous large-scale water erosion models. Specifically, AHMS-SED includes a complete representation of key hydrological, erosion and sediment transport processes such as runoff and sediment generation, flow and sediment routing, sediment deposition, gully erosion and river irrigation. In this thesis, we focus on developing and applying AHMS-SED in the Yellow River Basin of China, an arid and semi-arid region known for its wide distribution of loess and the highest soil erosion rate in the world. There are three key issues involving the model development and application: human perturbation (irrigation) of the water cycle, the uncertainty of precipitation forcing on the water discharge and the large-scale water erosion and sediment transport. This thesis addresses all these three issues in the following way. First, a new irrigation module is integrated into the Atmospheric and Hydrological Modelling System (AHMS). The model is calibrated and validated using in-situ and remote sensing observations. By incorporating the irrigation module into the simulation, a more realistic hydrological response was obtained near the outlet of the Yellow River Basin. Second, an evaluation of six precipitation-reanalysis products is performed based on observed precipitation and model-simulated river discharge by the AHMS for the Yellow River Basin. The hydrological model is driven with each of the precipitation-reanalysis products in two ways, one with the rainfall-runoff parameters recalibrated and the other without. Our analysis contributes to better quantifying the reliability of hydrological simulations and the improvement of future precipitation-reanalysis products. Third, a regional-scale water erosion and sediment transport model, referred to as AHMS-SED, is developed and applied to predicting continental-scale fluvial transport in the Yellow River Basin. This model couples the AHMS with the CASCade 2-Dimensional SEDiment (CASC2D-SED) and takes into account gully erosion, a process that strongly affects the sediment supply in the Chinese Loess Plateau. The AHMS-SED is then applied to simulate water erosion and sediment processes in the Yellow River Basin for a period of eight years, from 1979 to 1987. Overall, the results demonstrate the good performance of the AHMS-SED and the upland sediment discharge equation based on rainfall erosivity and gully area index. AHMS-SED is also used to predict the evolution of sediment transport in the Yellow River Basin under specific climate change scenarios. The model results indicate that changes in precipitation will have a significant impact on sediment discharge, while increased irrigation will reduce the sediment discharge from the Yellow River

A Socio-Hydrologic Assessment of Mountain Water Supply Vulnerability to Changing Snowmelt

Climate change is accelerating disconnects between snowmelt-driven water supply and downstream demand. Identifying what makes people and places vulnerable to these disconnects can improve understanding of present conditions and help anticipate future changes in water management. This dissertation seeks to understand the potential for increasing disconnects between downstream agriculturally productive regions and their primary water supply—higher elevation, mountainous (upland) environments. We do so by focusing on agriculturally productive regions in the western United States (US) that are heavily reliant on seasonal snowmelt-driven streamflow, and using interdisciplinary tools such as big data, conceptual modeling, social science, and computational hydrology to assess vulnerability from the source (mountains) to demand (agriculture) We find that a process-based framework isolating three dominant mechanisms linking snow to streamflow helps explain changes in snowmelt-driven streamflow in 537 upland catchments throughout the US. We then use a hydrogeological framework and optimized averaging in a subset of our initial 537 catchments, highlighting the critical and often overlooked role of groundwater contributions in high, arid, and deep mountain catchments. Equipped with a more robust understanding of surface water and groundwater supplies in the western US, we then quantify the benefits of adaptation to changing snow resources particularly in hay-dominated agriculturally productive systems with smaller declines in snow relative to reservoir storage. Finally, we derive a flexible approach for expanding vulnerability assessments beyond the mountains and show that robust consideration of multiple aspects of vulnerability requires better measures of the social value of water as well as demand

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept