Biological Systems Engineering


Date of this Version



Published in Journal of Hydrology, Vol. 542, pp. 978–1001, 2016.


© 2016. This manuscript version is made available under the Elsevier user license


Detection of long-term changes in climate variables over large spatial scales is a very important prerequisite to the development of effective mitigation and adaption measures for the future potential climate change and for developing strategies for future hydrologic balance analyses under changing climate. Moreover, there is a need for effective approaches of providing information about these changes to decision makers, water managers and stakeholders to aid in efficient implementation of the developed strategies. This study involves computation, mapping and analyses of long-term (1968-2013) county-specific trends in annual, growing-season (1st May- 30th Sept.) and monthly air temperatures [(maximum (Tmax), minimum (Tmin) and average (Tavg)], daily temperature range (DTR), precipitation, grass reference evapotranspiration (ETo) and aridity index (AI) over the USA Great Plains region using datasets from over 800 weather station sites. Positive trends in annual Tavg, Tmax and Tmin, DTR, precipitation, ETo and AI were observed in 71, 89, 85, 31, 61, 38 and 66% of the counties in the region, respectively, whereas these proportions were 48, 89, 62, 20, 57, 28, and 63%, respectively, for the growing-season averages of the same variables. On a regional average basis, the positive trends in growing-season Tavg, Tmax and Tmin, DTR, precipitation, ETo and AI were 0.18°C decade-1, 0.19°C decade-1, 0.17°C decade-1, 0.09°C decade-1, 1.12 mm yr-1, 0.4 mm yr-1 and 0.02 decade-1, respectively, and the negative trends were 0.21°C decade-1, 0.06°C decade-1, 0.09°C decade-1, 0.22°C decade-1, 1.16 mm yr-1, 0.76 mm yr-1 and 0.02 decade-1, respectively. The temporal trends were highly variable in space and were appropriately represented using monthly, annual and growing-season maps developed using Geographic Information System (GIS) techniques. The long-term and spatial and temporal information and data for a large region provided in this study can be used to analyze county-level trends in important climatic/hydrologic variables in context of climate change, water resources, agricultural and natural resources response to climate change.