Reference evapotranspiration (ET0) in the hydrological cycle is one of the processes that is significantly affected by climate change. The Qinghai–Tibet Plateau (QTP) is universally recognized as a region that is sensitive to climate change. In this study, an area elevation curve is used to divide the study area into three elevation zones: low (below 2800 m), medium (2800–3800 m) and high (3800–5000 m). The cumulative anomaly curve, Mann–Kendall test, moving t-test and Yamamoto test results show that a descending mutation occurred in the 1980s, and an ascending mutation occurred in 2005. Moreover, a delay effect on the descending mutation in addition to an enhancement effect on the ascending mutation of the annual ET0 were coincident with the increasing altitude below 5000 m. The annual ET0 series for the QTP and different elevation zones showed an increasing trend from 1961 to 2017 and increased more significantly with the increase in elevation. Path analysis showed that the climate-driven patterns in different elevation zones are quite different. However, after the ascending mutations occurred in 2005, the maximum air temperature (Tmax) became the common dominant driving factor for the whole region and the three elevation zones.

No abstract

A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

Reference evapotranspiration (ET0) is a key factor in the hydrological cycle and energy cycle. In the context of rapid climate change, studying the dynamic changes in ET0 in the Tibetan Plateau (TP) is of great significance for water resource management in Asian countries. This study uses the Penman–Monteith formula to calculate the daily ET0 of the TP and subsequently uses the Mann–Kendall (MK) test, cumulative anomaly curve, and sliding t-test to identify abrupt change points. Morlet wavelet analysis and the Hurst index based on rescaled range analysis (R/S) are utilized to predict the future trends of ET0. The Spearman correlation coefficient is used to explore the relationship between ET0 changes and other climate factors. The results show that the ET0 on the TP exhibited an increasing trend from 1961 to 2017, with the most significant increase occurring in winter; an abrupt change to a tendency to decrease occurred in 1988, and another abrupt change to a tendency to increase occurred in 2005. Spatially, the ET0 of the TP shows an increasing trend from east to west. The change trend of the ET0 on the TP will not be sustainable into the future. In addition, the mean temperature has the greatest impact on the ET0 changes in the TP.

As the most direct indicator of drought, the dynamic assessment and prediction of actual evapotranspiration (AET) is crucial to regional water resources management. This research aims to develop a framework for the regional AET evaluation and prediction based on multiple machine learning methods and multi-source remote sensing data, which combines Boruta algorithm, Random Forest (RF), and Support Vector Regression (SVR) models, employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and CMIP6, covering meteorological, vegetation, and hydrological variables. To verify the framework, it is applied to grids of South America (SA) as a case. The results meticulously demonstrate the tendency of AET and identify the decisive role of T, P, and NDVI on AET in SA. Regarding the projection, RF has better performance in different input strategies in SA. According to the accuracy of RF and SVR on the pixel scale, the AET prediction dataset is generated by integrating the optimal results of the two models. By using multiple parameter inputs and two models to jointly obtain the optimal output, the results become more reasonable and accurate. The framework can systematically and comprehensively evaluate and forecast AET; although prediction products generated in SA cannot calibrate relevant parameters, it provides a quite valuable reference for regional drought warning and water allocating.