Temporal Trend Analysis of Meteorological Variables and Reference Evapotranspiration in the Inter-mountain Region of Wyoming

Abstract

Long-term trends in reference evapotranspiration (ETref) and its controlling factors are critical pieces of information in understanding how agricultural water requirements and water resources respond to a variable and changing climate. In this study, ETref, along with climate variables that directly and indirectly impact it, such as air temperature (T), incoming solar radiation (Rs), wind speed (u), relative humidity (RH), and precipitation (P), are discussed. All variables are analyzed for four weather stations located in irrigated agricultural regions of inter-mountain Wyoming: Pinedale, Torrington, Powell, and Worland. Non-parametric Mann−Kendall (MK) trend test and Theil–Sen’s slope estimator were used to determine the statistical significance of positive or negative trends in climate variables and ETref. Three non-parametric methods—(i) Pettitt Test (PT), (ii) Alexandersson’s Standard Normal Homogeneity Test (SNHT), and (iii) Buishand’s Range Test (BRT)—were used to check the data homogeneity and to detect any significant Trend Change Point (TCP) in the measured data time-series. For the data influenced by serial correlation, a modified version of the MK test (pre-whitening) were applied. Over the study duration, a statistically significant positive trend in maximum, minimum, and average annual temperature (Tmax, Tmin, and Tavg, respectively) was observed at all stations, except for Torrington in the southeast part of Wyoming, where these temperature measures had negative trends. The study indicated that the recent warming trends are much more pronounced than during the 1930s Dust Bowl Era. For all the stations, no TCPs were observed for P; however, significant changes in trends were observed for Tmax and Tmin on both annual and seasonal timescales. Both grass and alfalfa reference evapotranspiration (ETo and ETr) had statistically significant positive trends in at least one season (in particular, the spring months of March, April, and May (MAM) or summer months of June, July, and August (JJA) at all stations, except the station located in southeast Wyoming (Torrington) where no statistically significant positive trends were observed. Torrington instead experienced statistically significant negative trends in ETo and ETr, particularly in the fall months of SON and winter months of DJF. Over the period-of-record, an overall change of +26, +31, −48, and +34 mm in ETo and +28, +40, −80, and +39 mm in ETr was observed at Pinedale, Powell, Torrington, and Worland, respectively. Our analysis indicated that both ETo (−3.4 mm year−1) and ETr (−5.3 mm year−1) are decreasing at a much faster rate in recent years at Torrington compared to other stations. Relationships between climate variables and ETo and ETr on an annual time-step reveal that ETo and ETr were significantly and positively correlated to Tavg, Tmax, Rs, Rn, and VPD, as well as significantly and negatively correlated to RH.

Variation in Reference Evapotranspiration over the Tibetan Plateau during 1961–2017: Spatiotemporal Variations, Future Trends and Links to Other Climatic Factors

“…Because actual evapotranspiration is difficult to predict directly, researchers typically calculate the reference evapotranspiration (ET 0 ) first when calculating the crop-water demand [3][4][5]. It is important for agricultural management departments to study the long-term variation in trends of ET 0 and their relationship to climate factors [6,7].…”

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model

“…In recent year, the LSTM and CNN are probably the most popular, efficient and widely used deep techniques for time series forecasting. LSTMs were introduced to overcome the problems of vanishing gradients of RNNs with capability of storing important information containing long sequences, which were used in a number of applications, including speech recognition, stock price prediction, image text recognition, traffic flow forecasting, agriculture rainfall forecast, and grammar learning [ 19 , 20 ]. However, the application of LSTM in the field of hydrology has not been widely reported in the literature, but can be used in the estimation of hydrologic variables since several climatic variables used in hydrology exhibit time series behavior [ 21 ].…”

Quantification and Mapping of Satellite Driven Surface Energy Balance Fluxes in Semi-Arid to Arid Inter-Mountain Region

“…The availability of freshwater resources for agroecosystems has been an important issue for the sustainability of agricultural production in the U.S. and around the world. This is particularly important to the arid to semi-arid regions of the western U.S.A, including Wyoming, where climatic variability, drought, and water scarcity are becoming a persistent challenge [1]. In the inter-mountain region of Wyoming, irrigated agriculture is the backbone of the state's economy, representing an annual income of approximately $1.8 billion [2].…”