Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1 m) scale evapotranspiration (ET) monitoring. In this study, high-resolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (T_R) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the T_R data, while the DATTUTDUT model was insensitive to systematic errors in T_R as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.

Global freshwaters are severely depleted. Provision of improved water infrastructure technologies and innovation can address challenges posed by water shortages to environmental sustainability. China’s South-to-North Water Diversion Project has generated extensive debates over sustainability of water resources system in the northern drier region, which faces severe water scarcity hindering ecosystems, agriculture, industries and livelihoods. Some arguments extend the views that large infrastructure projects can have negative implications for biodiversity conservation and ecosystem goods and services. However, this study strengthens the opposite view, as such projects would resolve increasing environmental challenges northern China has been facing over many decades due to severe water shortages. The project empowers connectivity among individuals, community, and organizations that the sustainability of goods and services such as energy, irrigation and water supply are perceived, and livelihoods and the standard of peoples’ living is improved. A resilient, robust and adaptive water infrastructure framework can overcome the challenges of water shortages by meeting a long term social, economic and environmental goals for water resources systems in northern China. Such framework can also identify the thresholds of change and the threats associated with environmental sustainability.

With the increasing pressure from population growth and economic development, northern China (NC) faces a grand challenge of water scarcity, which can be further exacerbated by climatic and societal changes. The South‐to‐North Water Diversion (SNWD) project is designed to mitigate the water scarcity in NC. However, few studies have quantified the impact of the SNWD on water scarcity within the context of climatic and societal changes and its potential effects on economic and agricultural food in the region. We used water supply stress index (WaSSI) to quantify water scarcity within the context of environmental change in NC and developed a method to estimate the economic and agricultural impacts of the SNWD. Focuses were put on alleviating the water supply shortage and economic and agricultural benefits for the water‐receiving NC. We find that societal changes, especially economic growth, are the major contributors to water scarcity in NC during 2009–2099. To completely mitigate the water scarcity of NC, at least an additional water supply of 13 billion m³/year (comparable to the annual diversion water by SNWD Central Route) will be necessary. Although SNWD alone cannot provide the full solution to NC's water shortage in next few decades, it can significantly alleviate the water supply stress in NC (particularly Beijing), considerably increasing the agricultural production (more than 115 Tcal/year) and bringing economic benefits (more than 51 billion RMB/year) through supplying industrial and domestic water use. Additionally, the transfer project could have impacts on the ecological environment in the exporting regions.

The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

The surface water resources of a typical semi‐arid watershed (Huangfuchuan) in the middle reaches of the Yellow River have drastically decreased over the past decade, which has affected the governance strategies of the entire Yellow River. The causes of the decrease in surface water are generally attributed to climate fluctuations and human activities. In this study, a distributed biosphere hydrological model the Water and Energy Budget‐based Distributed Hydrological Model (WEB‐DHM) and a Contribution Assessment method were jointly applied to diagnose and quantify climate and human impacts on the streamflow change. Long‐term hydrometeorological trends were analysed first and one major change‐point (in 1998) in the annual streamflow series was identified through the nonparametric Mann–Kendall test and the annual precipitation‐streamflow double cumulative curve method. The WEB‐DHM model was calibrated and validated over the baseline period of 1985–1998; the natural streamflow was reconstructed for the impacted period of 1999–2006. Then, the contributions of climate fluctuations and human activities to streamflow change were determined quantitatively by comparing the natural streamflow with the observed value. The mean annual streamflow significantly decreased from 34.05 mm year⁻¹ to 13.72 mm year⁻¹ in the baseline and impacted periods, respectively, showing a reduction of 60%. Climate fluctuations accounted for a decrease in mean annual streamflow of approximately 10.38 mm year⁻¹ (51.03%), whereas human activities (including soil–water conservation measures, artificial water intakes and man‐made water storage infrastructure) caused a decrease of approximately 9.96 mm year⁻¹ (48.97%). These findings are potentially helpful to support the water resources planning and management in the middle reaches of the Yellow River.