Reference evapotranspiration (ET ref) is a key parameter of hydro-meteorological studies as well as water resource planning. In this study, we adopted the Penman-Monteith FAO 56 model to estimate ET ref and through the differential equation and detrending method to determine sensitivities and the contributions of four meteorological parameters to ET ref based on daily weather data from 60 stations of Jiangsu province during 1961-2015. Results reveal that ET ref and its trends in the three sub-regions of the Jiangsu province had a significant spatial heterogeneity. A significant decreasing tendency of ET ref (p < 0.001) was observed in the Huaibei region, while a slightly increasing tendency was identified in the Jianghuai and Sunan regions. These changes of ET ref were caused by a significant increasing trend in air temperature (TA) and significant decreasing trends in wind speed (WS), sunshine duration (SD) as well as a non-significant change trend in actual vapor pressure (VP). However, the VP was the meteorological parameter to which ET ref was most sensitive, whereas ET ref was more sensitive to TA and SD in the summer but less so in the winter; the least sensitive factor, WS, had the opposite trend. Across the whole region, WS contributed most to ET ref , followed by SD, while the positive contribution of TA to ET ref could not offset the negative contributions of WS and SD. Although the effect of VP on changes in ET ref is small, it could not be ignored, especially in the winter. The reverse relationship between increasing TA and decreasing ET ref , namely the "evaporation paradox," occurred in Jiangsu province. Thus, the outcomes of this study will contribute to thorough insight into the response to changes in ET ref to the provincial water planning and management in eastern China.

Abstract:A sensitivity analysis of the responses of crops to the chosen production adaptation options under regional climate change was conducted in this study. Projections of winter wheat production for different sowing dates and cultivars were estimated for a major economic and agricultural province of China from 2021 to 2080 using the World Food Study model (WOFOST) under representative concentration pathways (RCPs) scenarios. A modeling chain was established and a correction method was proposed to reduce the bias of the resulting model-simulated climate data. The results indicated that adjusting the sowing dates and cultivars could mitigate the influences of climate change on winter wheat production in Jinagsu. The yield gains were projected from the chosen sowing date and cultivar. The following actions are recommended to ensure high and stable yields under future climate changes: (i) advance the latest sowing date in some areas of northern Jiangsu; and (ii) use heat-tolerant or heat-tolerant and drought-resistant varieties in most areas of Jiangsu rather than the currently used cultivar. Fewer of the common negative effects of using a single climate model occurred when using the sensitivity analysis because our bias correction method was effective for scenario data and because the WOFOST performed well for Jiangsu after calibration.

Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March-May season using the Mann-Kendall (MK) trend test and simple linear regression (SLR) over the period 2000-2015. Results showed an increasing trend of both dekadal rainfall amount and rain days (third and seventh dekads). The light rain days (SLR = 0.181; MK = 0.350) and wet days (SLR = 0.092; MK = 0.118) also depict an increasing trend. The rate of increase of light rain days and wet days during the third dekad (light rain days: SLR = 0.020; MK = 0.279 and wet days: SLR = 0.146; MK = 0.376) was slightly greater than during the seventh dekad (light rain days: SLR = 0.014; MK = 0.018 and wet days: SLR = 0.061; MK = 0.315) dekad. Seventy-four percent accounted for 2-4 consecutive dry days, but no significant trend was detected. The extreme rainfall was increasing over the third (MK = 0.363) and seventh (MK = 0.429) dekads. The rainfall amount and rain days were highly correlated (r: 0.43-0.72).

Apples (Malus pumila Mill.) are widely cultivated in 95 countries and regions around the globe. China is the world's largest producer of apples. Prediction of apple yield in the context of climate change has become an important topic of research. The study sites in this investigation include 28 apple-producing base counties located in the Shaanxi province of the northwest Loess Plateau. In this study, grey relational analysis was used to examine 88 climatic factors and to extract those factors that significantly influence the meteorological yield (MY) of apples. A support vector machine (SVM) was used to make a quantitative prediction of changes in MY in the apple-producing areas of Shaanxi province from the years 2000-2099 under 2 climate change scenarios, RCP 4.5 and RCP 8.5. In addition, fuzzy information granulation was used to analyze the variation trends and variation spaces of MY from 2020 to 2049 and 2050 to 2099, compared with the 1990-2019 reference period. The results showed that for the 10-day and monthly climatic factors affecting the MY of apples, climate resource factors are more influential than meteorological disaster factors and spring factors are significantly more influential than other seasonal factors. Overall, there are more and broader climate resource factors affecting MY, and spring climatic conditions are more important for it. In the RCP 4.5 scenario, 9 base counties showed slight decreases, 2 counties showed significant decreases, 15 counties maintained or had slightly increased, and 2 counties showed significant increases. The variation of unit yield was − 1.44-1.85 t/ha. In the RCP 8.5 scenario, 10 base counties showed slight decreases, 2 counties showed significant decreases, 12 counties maintained or had slightly increased, and 4 counties showed significant increases. The variation of unit yield was − 2.43-2.78 t/ha. For both future climate change scenarios, the uncertainty of MY increased with time.

Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.