Assessing Climate Change Impacts on Water Resources and Colorado Agriculture Using an Equilibrium Displacement Mathematical Programming Model

Abstract

This research models selected impacts of climate change on Colorado agriculture several decades in the future, using an Economic Displacement Mathematical Programming model. The agricultural economy in Colorado is dominated by livestock, which accounts for 67% of total receipts. Crops, including feed grains and forages, account for the remainder. Most agriculture is based on irrigated production, which depends on both groundwater, especially from the Ogallala aquifer, and surface water that comes from runoff derived from snowpack in the Rocky Mountains. The analysis is composed of a Base simulation, designed to represent selected features of the agricultural economy several decades in the future, and then three alternative climatic scenarios are run. The Base starts with a reduction in agricultural water by 10.3% from increased municipal and industrial water demand, and assumes a 75% increase in corn extracted-ethanol production. From this, the first simulation (S1) reduces agricultural water availability by a further 14.0%, for a combined decrease of 24.3%, due to climatic factors and related groundwater depletion. The second simulation (S2-WET) describes wet year conditions, which negatively affect yields of irrigated corn and milking cows, but improves yields for OPEN ACCESSWater 2014, 6 1746 important crops such as non-irrigated wheat and forages. In contrast, the third simulation (S3-DRY) describes a drought year, which leads to reduced dairy output and reduced corn and wheat. Consumer and producer surplus losses are approximately $10 million in this simulation. The simulation results also demonstrate the importance of the modeling trade when studying climate change in a small open economy, and of linking crop and livestock activities to quantify overall sector effects. This model has not taken into account farmers' adaptation strategies, which would reduce the climate impact on yields, nor has it reflected climate-induced shifts in planting decisions and production practices that have environmental impacts or higher costs. It also focuses on a comparative statics approach to the analysis in order to identify several key effects of changes in water availability and yields, without having a large number of perhaps confounding assumptions.

Climate Change and Its Influence on the Karst Groundwater Recharge in the Jinci Spring Region, Northern China

“…Thus, the research on the recharge of precipitation and the Fenhe River seepage of the zone from the Fenhe Reservoir to Zhaishang to the karst groundwater in the Jinci Spring region is highly important to the research on the karst groundwater recharge in the entire spring region. The water balance in the basin from the Fenhe Reservoir to Zhaishang can be expressed as the following formula [26]:…”

Application of Holt-Winters Time Series Models for Predicting Climatic Parameters (Case Study: Robat Garah-Bil Station, Iran)

“…In recent years, researchers have studied the interaction of climate change on water resource issues in different parameters such as agriculture [47][48], water resources [49][50], urban water management [51][52], erosion control [53], ecosystems and environmental issues [54][55], and water supply and demand [56].…”

Modelización económica, hídrica y energética en producciones agrícolas bajo riego de la cuenca del río Colorado en La Pampa

“…Plantean un escenario "Go-Green" y otro alternativo en los cuales evalúan biodiversidad. Fathelrahman et al (2014) analizan los modelos de optimización respecto a los escenarios del CC. Explican que la programación matemática posee una larga historia en las ciencias económicas y de ingeniería ya que el enfoque maximiza una función objetivo frente a limitaciones físicas en los recursos.…”

“…Los modelos son métodos eficientes para reducir y entender la complejidad de los sistemas agroalimentarios (Candelaria Martínez et al, 2011). Dentro de los modelos económicos que utilizan la programación lineal (Irureta, 1964;Candler et al, 1981;Bowen & Young, 1985;Bazzani, 2005) o la PMP (Howitt, 1995;Henseler et al, 2005Henseler et al, , 2009Fathelrahman et al, 2014;Maneta & Howitt, 2014;Fernández et al, 2016) una característica importante es que se relaciona la producción agrícola (p. ej. mezcla de cultivos, superficie de cultivo, agua consumida) con variables económicas (MB, MN, VAN, etc.)…”

“…Existe una primera clasificación entre modelos económicos orientados al sector agroalimentario: a) modelos económicos de simulación (Scheierling et al, 2006;Candelaria Martínez et al, 2011). b) modelos económicos de optimización (Irureta, 1964;Candler et al, 1981;Bowen & Young, 1985;Mendoza et al, 1987;Mohamed et al, 2000;Bazzani, 2005;Cittadini et al, 2008;Zimmermann, 2008;Aurbacher & Dabbert, 2011) y; c) modelos económicos de equilibrios parciales y generales (Michetti, 2012;Fathelrahman et al, 2014). Los modelos económicos muchas veces se encuentran en EI con modelos hidrológicos denominados modelos hidroeconómicos (Ahrends et al, 2008;Blanco-Gutiérrez et al, 2013;Carmona et al, 2011;Esteve et al, 2015;Fernández et al, 2016; y con agentes integrados (Drobnik et al, 2017).…”