Search for: All records

The winter-spring shoulder season, or vernal window, is a key period for ecosystem carbon, water, and energy cycling. Sometimes referred to as mud season, in temperate forests, this transitional season opens with the melting of snowpack in seasonally snow-covered forests and closes when the canopy fills out. Sunlight pours onto the forest floor, soils thaw and warm, and there is an uptick in soil respiration. Scientists hypothesize that this window of ecological opportunity will lengthen in the future; these changes could have implications across all levels of the ecosystem, including the availability of food and water in human systems. Yet, there remains a dearth of observations that track both winter and spring indicators at the same location. Here, we present an inquiry-based, low-cost approach for elementary to high school classrooms to track environmental changes in the winter-spring shoulder season. Engagement in hypothesis generation and the use of claim, evidence, and reasoning practices are coupled with field measurement protocols, which provides teachers and students an authentic research experience that allows for a place-based understanding of local ecosystems and their connection to climate change. 

Abstract Global food security can be threatened by short-term extreme events that negatively impact food production, food purchasing power, and agricultural economic activity. At the same time, environmental pollutants like greenhouse gases (GHGs) can be reduced due to the same short-term extreme stressors. Stress events include pandemics like COVID-19 and widespread droughts like those experienced in 2015. Here we consider the question: what if COVID-19 had co-occurred with a 2015-like drought year? Using a coupled biophysical-economic modeling framework, we evaluate how this compound stress would alter both agricultural sector GHG emissions and change the number of undernourished people worldwide. We further consider three interdependent adaptation options: local water use for crop production, regional shifts in cropland area, and global trade of agricultural products. We find that GHG emissions decline due to reduced economic activity in the agricultural sector, but this is paired with large increases in undernourished populations in developing nations. Local and regional adaptations that make use of natural resources enable global-scale reductions in impacted populations via increased global trade. 

Winters in northeastern North America have warmed faster than summers, with impacts on ecosystems and society. Global climate models (GCMs) indicate that winters will continue to warm and lose snow in the future, but uncertainty remains regarding the magnitude of warming. Here, we project future trends in winter indicators under lower and higher climate-warming scenarios based on emission levels across northeastern North America at a fine spatial scale (1/16°) relevant to climate-related decision making. Under both climate scenarios, winters continue to warm with coincident increases in days above freezing, decreases in days with snow cover, and fewer nights below freezing. Deep snowpacks become increasingly short-lived, decreasing from a historical baseline of 2 months of subnivium habitat to <1 month under the warmer, higher-emissions climate scenario. Warmer winter temperatures allow invasive pests such as Adelges tsugae (Hemlock Woolly Adelgid) and Dendroctonus frontalis (Southern Pine Beetle) to expand their range northward due to reduced overwinter mortality. The higher elevations remain more resilient to winter warming compared to more southerly and coastal regions. Decreases in natural snowpack and warmer temperatures point toward a need for adaptation and mitigation in the multi-million-dollar winter-recreation and forest-management economies. 

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBMwas first published in 1989; here, we describe the first fully open-sourceWBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of themodel functionality, utility, and evaluation of simulated global riverdischarge and irrigation water use. This new version adds a novel suite ofwater source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.