Watershed water-energy balance dynamics and their association with diverse influencing factors at multiple time scales

Tibetan lake change linked to large-scale atmospheric oscillations via hydroclimatic trajectory

Lakes are known as sentinels of climate change, but their responses may differ from one to another leading to different strategies in lake protection. It is particularly the case in the Tibetan Plateau (TP) of multiple hydrological processes. We employed the Budyko framework to study Tibetan lakes from two lake-basins of contrasting climates for the period between 1980 and 2022: Taro Co Basin (TCB) in a sub-arid climate, and Ranwu Lake Basin (RLB) in a sub-humid climate. Our results showed that total lake area, surface air temperature, evapotranspiration, and potential evapotranspiration increased in both lake-basins, while precipitation and soil moisture increased in the TCB but decreased in the RLB. In the Budyko space, two basins had contrast hydroclimatic trajectories in terms of aridity and evaporative index. The TCB shifted from wetting to drying trend, while the RLB from drying to wetting in early 2000s. Notably, lake change was generally consistent with the drying/wetting phases in the TCB, but in contrast with that in the RLB, which can be attributed to warming-induced glacier melting. Despite of significant correlation with the large-scale atmospheric oscillations, it turned to be more plausible if lake area changes were substituted with basin's hydroclimatic trajectories. Among the large-scale oscillations, El Niño-Southern Oscillation (ENSO) is the most dominant control of lake trends and their drying/wetting shifts. Our findings offer a valuable insight into lake responses to climate change in the TP and other regions.

Runoff change controlled by combined effects of multiple environmental factors in a headwater catchment with cold and arid climate in northwest China

The limited runoff in cold and arid regions is sensitive to environmental changes, and it is thus urgent to explore the change and controlling factors of runoff under the background of global warming and intensified human activities. However, previous studies have rarely considered the combined effects of multiple controlling factors at varying scales over time. With the headwater region of the Manas River in northwest China as the study area, we investigated the change in runoff for the period of 1954-2016 and its relationship with regional environmental factors (e.g. precipitation PCP, temperature TMP, potential evapotranspiration ET₀, snow cover extent SCE, land use, and normalized difference vegetation index NDVI) and/or global atmospheric circulation (e.g. North Atlantic Oscillation NAO, Arctic Oscillation AO, Pacific Interdecadal Oscillation PDO, and El Nino Southern Oscillation ENSO). In particular, the combined effects of multiple environmental factors were determined at different scales by the multiple wavelet coherence. The annual runoff significantly increased at a rate of 0.508 × 10⁸ m³/decade, and the climate tended to be warmer and wetter. Among the regional and global environmental factors, NDVI and ENSO were the single factor mostly correlated with runoff, while NDVI-TMP and ENSO-PDO were the combined factors with the stronger relations on runoff, respectively. The regional environmental factors had larger impacts on runoff than the global environmental factors, and the natural factors outperformed human activities in controlling runoff. The accelerated melting of snow/glacier induced by the increasing temperature dominated runoff change, and the increasing water inputs from wetter climate may play a second role in runoff. The runoff characteristics in cold and arid regions seem to be different from those regions with little snow/glacier, which should be paid more attention. The employed multiple wavelet coherence is helpful in determining the processes dominating runoff change.

Future trends of water resources and influences on agriculture in China

Water resources are indispensable for all social-economic activities and ecosystem functions. In addition, changes in water resources have great significance for agricultural production. This paper uses five global climate models from CMIP5 to evaluate the future spatiotemporal variation in water resources in China under four RCP scenarios. The results show that the available precipitation significantly decreases due to evapotranspiration. Comparing the four RCP scenarios, the national average of the available precipitation is the highest under the RCP 2.6 and 4.5 scenarios, followed by that under the RCP 8.5 scenario. In terms of spatial distribution, the amount of available precipitation shows a decreasing trend from southeast to northwest. Regarding temporal changes, the available precipitation under RCP 8.5 exhibits a trend of first increasing and then decreasing, while the available precipitation under the RCP 6.0 scenario exhibits a trend of first decreasing and then increasing. Under the RCP 2.6 and 4.5 scenarios, the available precipitation increases, and the RCP 4.5 scenario has a higher rate of increase than that of RCP 2.6. In the context of climate change, changes in water resources and temperature cause widespread increases in potential agricultural productivity around Hu's line, especially in southwestern China. However, the potential agricultural productivity decreases in a large area of southeastern China. Hu's line has a partial breakthrough in the locking of agriculture, mainly in eastern Tibet, western Sichuan, northern Yunnan and northwestern Inner Mongolia. The results provide a reference for the management and deployment of future water resources and can aid in agricultural production in China.

A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors

One of the most important applications of remote imaging systems in agriculture, with the greatest impact on global sustainability, is the determination of optimal crop irrigation. The methodology proposed by the Food and Agriculture Organization (FAO) is based on estimating crop evapotranspiration (ETc), which is done by computing the reference crop evapotranspiration (ETo) multiplied by a crop coefficient (Kc). Some previous works proposed methods to compute Kc using remote crop images. The present research aims at complementing these systems, estimating ETo with the use of soil moisture sensors. A crop of kikuyu grass (Pennisetum clandestinum) was used as the reference crop. Four frequency-domain reflectometry sensors were installed, gathering moisture information during the study period from May 2015 to September 2016. Different machine learning regression algorithms were analyzed for the estimation of ETo using moisture and climatic data. The values were compared with respect to the ETo computed in an agroclimatic station using the Penman–Monteith method. The best method was the randomizable filtered classifier technique, based on the K* algorithm. This model achieved a correlation coefficient, R, of 0.9936, with a root-mean-squared error of 0.183 mm/day and 6.52% mean relative error; the second-best model used artificial neural networks, with an R of 0.9470 and 11% relative error. Thus, this new methodology allows obtaining accurate and cost-efficient prediction models for ETo, as well as for the water balance of the crops.