[1] Budyko's framework has been widely used to study basin-scale water and energy balances and one of the formulations of the Budyko curve is Fu's equation. The curve shape parameter ϖ in Fu's equation controls how much of the available water will be evaporated given the available energy. Previous studies have found that land surface characteristics significantly affect variations in the parameter ϖ. In this study, we focus on the vegetation impact and examine the conditions under which vegetation plays a major role in controlling the variability of ϖ. Using data from 26 major global river basins that are larger than 300,000 km2, the basin-specific ϖ parameter is found to be linearly correlated with the long-term averaged annual vegetation coverage. A simple parameterization for the ϖ parameter based solely on remotely sensed vegetation information is proposed, which improves predictions of annual actual evapotranspiration by reducing the root mean square error (RMSE) from 76 mm to 47 mm as compared to the default ϖ value used in the Budyko curve method. The controlling impact of vegetation on the basin-specific ϖ parameter is diminished in small catchments with areas less than 50,000 km2, which suggests a scale-dependence of the role of vegetation in affecting water and energy balances. In small catchments, other key ecohydrological processes need to be taken into account in order to fully capture the variability of the ϖ parameter in Fu's equation.

Vegetation control on water and energy balance within the Budyko framework

(1) How to predict the year-to-year variation of the east Asian summer monsoon (EASM) is one of the most challenging and important tasks in climate prediction. It has been recognized that the EASM variations are intimately but not exclusively linked to the development and decay of El Nino or La Nina. Here we present observed evidence and numerical experiment results to show that anomalous North Atlantic Oscillation (NAO) in spring (April-May) can induce a tripole sea surface temperature pattern in the North Atlantic that persists into ensuing summer and excite downstream development of subpolar teleconnections across the northern Eurasia, which raises (or lowers) the pressure over the Ural Mountain and the Okhotsk Sea. The latter strengthens (or weakens) the east Asian subtropical front (Meiyu-Baiu-Changma), leading to a strong (or weak) EASM. An empirical model is established to predict the EASM strength by combination of the El Nino-Southern Oscillation (ENSO) and spring NAO. Hindcast is performed for the 1979-2006 period, which shows a hindcast prediction skill that is comparable to the 14 state-of-the-art multimodel ensemble hindcast. Since all these predictors can be readily monitored in real time, this empirical model provides a real time forecast tool.

An Empirical Seasonal Prediction Model of the East Asian Summer Monsoon Using ENSO and NAO

Spatial-temporal changes in vegetation cover in a typical semi-humid and semi-arid region in China: Changing patterns, causes and implications

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data.

Droughts are among the most damaging environmental disasters that may have destructive damages on societal properties and lives. Generally, socio-economic drought occurs when water resource...

Assessing socio-economic drought evolution characteristics and their possible meteorological driving force

Variations in annual water-energy balance and their correlations with vegetation and soil moisture dynamics: A case study in the Wei River Basin, China

Water resources systems are often characterized by multiple objectives. Typically, there is no single optimal solution which can simultaneously satisfy all the objectives but rather a set of technologically efficient non-inferior or Pareto optimal solutions exists. Another point regarding multi-objective optimization is that interdependence and contradictions are common among one or more objectives. Therefore, understanding the competition mechanism of the multiple objectives plays a significant role in achieving an optimal solution. This study examines cascade reservoirs in the Heihe River Basin of China, with a focus on exploring the multi-objective competition mechanism among irrigation water shortage, ecological water shortage and the power generation of cascade hydropower stations. Our results can be summarized as follows: (1) the three-dimensional and two-dimensional spatial distributions of a Pareto set reveal that these three objectives, that is, irrigation water shortage, ecological water shortage and power generation of cascade hydropower stations cannot reach the theoretical optimal solution at the same time, implying the existence of mutual restrictions; (2) to avoid subjectivity in choosing limited representative solutions from the Pareto set, the long series of non-inferior solutions are adopted to study the competition mechanism. The premise of sufficient optimization suggests a macro-rule of ‘one falls and another rises,’ that is, when one objective value is inferior, the other two objectives show stronger and superior correlation; (3) the joint copula function of two variables is firstly employed to explore the multi-objective competition mechanism in this study. It is found that the competition between power generation and the other objectives is minimal. Furthermore, the recommended annual average water shortage are 1492 × 104 m3 for irrigation and 4951 × 104 m3 for ecological, respectively. This study is expected to provide a foundation for selective preference of a Pareto set and insights for other multi-objective research.

https://www.mdpi.com/2073-4441/11/5/995/pdf

Copula-Based Research on the Multi-Objective Competition Mechanism in Cascade Reservoirs Optimal Operation

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

Menglong Zhao

Papers

Assessing socio-economic drought evolution characteristics and their possible meteorological driving force

Variations in annual water-energy balance and their correlations with vegetation and soil moisture dynamics: A case study in the Wei River Basin, China

Copula-Based Research on the Multi-Objective Competition Mechanism in Cascade Reservoirs Optimal Operation

Data-driven model for divertor plasma detachment prediction