Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

Downscaling Coarse Resolution Satellite Passive Microwave SWE Estimates

The spatio-temporal heterogeneity of seasonal snow and its impact on socio-economic and environmental functionality make accurate, real-time estimates of snow water equivalent (SWE) important for hydrological and climatological predictions. Passive microwave remote sensing offers a cost effective, temporally and spatially consistent approach to SWE monitoring at the global to regional scale. However, local scale estimates are subject to large errors given the coarse spatial resolution of passive microwave observations (25 x 25 km). Regression downscaling techniques can be implemented to increase the spatial resolution of gridded datasets with the use of related auxiliary datasets at a finer spatial resolution. These techniques have been successfully implemented to remote sensing datasets such as soil moisture estimates, however, limited work has applied such techniques to snow-related datasets. This thesis focuses on assessing the feasibility of using regression downscaling to increase the spatial resolution of the European Space Agency’s (ESA) Globsnow SWE product in the Red River basin, an agriculturally important region of the northern United States that is widely recognized as a suitable location for passive microwave remote sensing research. Multiple Linear (MLR), Random Forest (RFR) and Geographically Weighted (GWR) regression downscaling techniques were assessed in a closed loop experiment using Snow Data Assimilation System (SNODAS) SWE estimates at a 1 x 1 km spatial resolution. SNODAS SWE data for a 5-year period between 2013-2018 was aggregated to a 25 x 25 km spatial resolution to match Globsnow. The three regression techniques were applied using correlative datasets to downscale the aggregated SNODAS data back to the original 1 x 1 km spatial resolution. By comparing the downscaled SNODAS estimates to the original SNODAS data, it was found that RFR downscaling produced much less variation in downscaled results, and lower RMSE values throughout the study period when compared to MLR and GWR downscaling techniques, indicating it was the optimal downscaling method. RFR downscaling was then implemented on daily Globsnow SWE estimates for the same time period. The downscaled SWE results were evaluated using SNODAS SWE as well as in situ derived SWE estimates from weather stations within the study region. Spatial and temporal errors were assessed using both the SNODAS and in situ reference datasets and overall RMSEs of 21 mm and 37 mm were found, respectively. It was observed that the southern regions of the basin and seasons with higher downscaled SWE estimates were associated with higher errors with overestimation being the most common bias throughout the region. A major contribution of this study is the illustration that RFR downscaling of Globsnow SWE estimates is a feasible approach to understanding the seasonal dynamics of SWE in the Red River basin. This is extremely beneficial for local communities within the basin for flood management and mitigation and water resource management

Development and Testing of Methods to Assess the Impact of Climate Change on Flood and Drought Risk at the European Scale

During the last 100 years global climate has warmed by an average of 0.6ºC, owing in part to human induced greenhouse gas emissions. Based on different scenarios of future greenhouse gas emissions projections of climate models indicate another 1.4 to 5.8 ºC of warming over the next century (IPCC, 2001a). The projected change in climate will significantly impact the hydrological cycle. A warmer climate will increase evaporation, the intensity of water cycling, and result in greater amounts of moisture in the air. It is expected that the magnitude and frequency of extreme weather events will increase, and that hydrological extremes such as floods and droughts will likely be more frequent and severe. The Joint Research Centre aims to develop knowledge and tools in support of the EU Climate Change Strategy that was recently put forward in the Commission’s Communication “Winning the Battle Against Global Climate Change” (COM(2005) 35). In view of this, an important research topic of the Land Management Unit of the IES is to assess the impact of climate change on the occurrence of hydrological extremes such as floods and droughts. This will be accomplished by developing an integrated modelling framework that combines regional climate predictions for Europe with the LISFLOOD model. LISFLOOD is a distributed, partially physically-based rainfall-runoff model that has been devised to simulate the hydrological behaviour in large European catchments (De Roo et al., 2000), with emphasis on predicting floods and droughts. Owing to its general nature, LISFLOOD is optimally suited for simulating the different hydrological regimes across Europe. Predicted climate for current conditions and for different scenarios of greenhouse gas emissions by the end of the 21st century will be used as input to LISFLOOD, after taking due account of any systematic bias in the climate forcing data obtained from climate models. Runoff statistics for the two periods will provide a means to estimate changes in the frequency and severity of hydrological extremes under different scenarios of future greenhouse gas emissions. Projections of future climate change are typically obtained from coupled Atmosphere-Ocean General Circulation Models (AOGCM). Because they require time steps of minutes but are used to predict climate change on time scales of months to centuries, their horizontal resolution is typically at least 100 km and hence their treatment of physical processes is approximate. Due to their coarse spatial resolution AOGCMs fail to explicitly capture fine-scale climatic structures needed for climate change impact studies and policy planning at the regional or sub-regional scale (e.g., catchment or basin scale). To resolve this problem, regionalization or downscaling methods have been developed that enhance regional detail and provide climatic information at smaller scales. The aim of this document is to provide an overview on existing methods for downscaling global climate information. Also, this document gives an overview of existing regional climate data sets for Europe, and details on how to use regional climate data for impact studies at the European and regional scale. The document is organised as follows. Section 2 presents a general overview of existing downscaling methods, with details of the underlying principles to generate regional climate information. In Section 3 an overview is given of regional climate data that are currently available to be used for impact studies at the European scale. In Section 4 some details are provided about the integrated modelling framework that couples the regional climate model data with the hydrological model LISFLOOD. Conclusions and an overview of current and further work are presented in Section 5.JRC.H.7-Land management and natural hazard

Future climate of Brussels and Paris for the 2050s under the A1B scenario

A

Impact of climate change on agricultural and natural ecosystems

This book illustrates the main results deriving from fourteen studies, dealing with the impact of climate change on different agricultural and natural ecosystems, carried out within the Impact of Climate change On agricultural and Natural Ecosystems (ICONE) project funded by the ALFA Programme of the European Commission. During this project, a common methodology on several Global Change-related matters was developed and shared among members of scientific communities coming from Latin America and Europe. In order to facilitate this interdisciplinary approach, specific mobility programmes, addressed to post-graduate, Master and PhD students, have been organized. The research, led by the research groups, was focused on the study of the impact of climate change on various environmental features (i.e. runoff in hydrological basins, soil erosion and moisture, forest canopy, sugarcane crop, land use, drought, precipitation, etc). Integrated and shared methodologies of atmospheric physics, remote sensing, eco-physiology and modelling have been applied

Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

A large scale mismatch exists between our understanding and quantification of ecosystem atmosphere exchange of carbon dioxide at local scale and continental scales. This paper will focus on the carbon exchange on the regional scale to address the following 5 question: What are the main controlling factors determining atmospheric carbon dioxide content at a regional scale? We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also sub models for urban and marine fluxes, which in principle include the main controlling mechanisms and capture the relevant dynamics 10 of the system. To validate the model, observations are used which were taken during an intensive observational campaign in the central Netherlands in summer 2002. These included flux-site observations, vertical profiles at tall towers and spatial fluxes of various variables taken by aircraft. The coupled regional model (RAMS-SWAPS-C) generally does a good job in sim15 ulating results close to reality. The validation of the model demonstrates that surface fluxes of heat, water and CO2 are reasonably well simulated. The comparison against aircraft data shows that the regional meteorology is captured by the model. Comparing spatially explicit simulated and observed fluxes we conclude that in general simulated latent heat fluxes are underestimated by the model to the observations which exhibit 20 large standard deviation for all flights. Sensitivity experiments demonstrated the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same test also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations

Remote Sensing Supported Sea Surface pCO(2) Estimation and Variable Analysis in the Baltic Sea

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO(2)) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO(2) estimation in the Baltic Sea and derived monthly pCO(2) maps for the marginal sea during the period of July 2002-October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO(2) estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO(2) estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (a(CDOM)), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO(2) estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO(2)-relevant variables (e.g., a(CDOM)), particularly in the summer months. In addition, the variables' importance for pCO(2) estimation varied between seasons and sub-basins. For example, the importance of a(CDOM) were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO(2) estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 mu atm. The pCO(2) maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO(2) in the Baltic Sea. The spatially and seasonally varying variables' importance for the pCO(2) estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO(2) estimation in marginal seas using remote sensing techniques. The pCO(2) maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Past and Future Hydrology Near the Arctic Treeline

The Arctic has warmed rapidly, increasing shrub cover and density, and thawing permafrost. Understanding, quantifying and predicting the impact of these environmental changes on the hydrological regime of Arctic headwater basins represents a great scientific challenge, particularly due to the sparse monitoring network, limited understanding of governing physical processes and their interaction, and the uncertainty in future climate projections. The purpose of this research is to better understand the impact of climate and vegetation change on the hydrology of Arctic basins near the treeline. This thesis is divided into four sections with the following objectives: (1) to test the coupling of a ground freeze/thaw algorithm with a hydrological model at two research sites in northern Yukon; (2) to diagnose the hydrology of a small Arctic basin near the treeline using a physically based hydrological model; (3) to quantify its historical long-term changes and investigate the individual and combined effect of changing climate and vegetation on its hydrology; and (4) to use high-resolution climate simulations under a high gas concentration scenario along with expected vegetation changes, to investigate changes to hydrological processes and regime. Results revealed the importance of including vegetation dynamics such as changes in shrub extension and density in hydrological models, to capture their impact on blowing snow redistribution and sublimation, and canopy interception and sublimation of snow, something neglected by current studies. This study demonstrated that increasing shrub extension and density near the Arctic treeline slightly compensates the historical decrease in mean annual discharge produced by the decreasing precipitation, providing a small degree of hydrological resiliency. Historical change analysis revealed that hydrological processes are decelerating near the Arctic treeline, such as decreasing evapotranspiration, soil moisture, sublimation and streamflow, mostly driven by climate change. However, under sufficient climate change (38% and 6.1 °C increase in mean annual precipitation and temperature, respectively) significant hydrological changes are expected, reversing the simulated historical changes. Projections show a significant increase in mean annual streamflow discharge, shortening of the snowcover seasons, deepening of the active layer thickness, increasing peak snow accumulation and earlier and larger peak streamflow. Whilst specific to one basin, they indicate the nature of hydrological changes facing Arctic hydrology. These changes will have significant impacts on ecosystems, infrastructure, landscape evolution and atmospheric feedbacks, which are required to be properly understood and quantified to design sustainable and effective mitigation and adaptation plans. The analyses and discussions presented in this study to diagnose the past and predict future Arctic hydrology are relevant for the scientific community of hydrologists, engineers, water managers and policy makers, particularly those interested in cold regions