International research team constructs new model for hydrological research

2019-03-07

Most of us know that water is in constant motion as it undergoes change in nature. Scientists in the study of hydrology are engaged with the distribution of the spatial and temporal features of water and its law of change in nature. It begs the question for core research of the terrestrial water cycle, “Where is the rain?”  

In the process of runoff generation, runoff simulation and accurate forecasting are of great significance to preventing floods and droughts while hydropower generation, shipping, ecological protection and other key areas related to the national economy also enable a higher quality of people's livelihood - safety, security and property of the masses.

In collaboration with scientists from the Netherlands, the United Kingdom, Germany and other countries, researchers from the Key Laboratory of Geographic Information Science of East China Normal University (ECNU) have been working on hydrological rainfall and runoff models. They have recently proposed a new topographic-based Storage Capacity curve (HSC). The breakthrough was published in the Top Hydrology and Earth System Sciences (HESS) where the simple topography and calibration-free runoff generation module is displayed for readers.  

Even though an accurate prediction of hydrology must be based on the proper scientific methods, reasoning and understanding according to the objective law of nature, the reservoir of rainfall-runoff is so expansive that it often covers over thousands of square kilometers, with strong heterogeneity in time and space, uneven distribution and extreme complexities.  

Hydrological processes are also affected by other factors, such as meteorological and climatic measures, including precipitation, rainfall intensity, temperature, radiation, etc, while underlying factors include topography, soil, geology, vegetation, etc. In consideration of these factors, the difficulties in accurately prediciting a hydrological forecast increase significantly. 

In addition, many traditional models rely on previously measured runoff data to calibrate model parameters, which greatly limits the application of the model in vast areas without such data. In the 1970s to 1980s, a group of the world's model (top model) emerged, such as the Xinanjiang model developed by Prof. Zhao Renjun from Hohai university (Beijing), TVGM model developed by Prof. Xia Jun from Wuhan university (Hubei), HBV model by Swedish institute of meteorology and hydrology, and TOPMODEL by Prof. Keith Beven from the university of Lancaster. However, the application of these models were still restricted in many ways so that the studies of hydrological modeling in the following decades didn’t see any breakthrough acheivements in the field.  

In the past six years, an international research team consisting of researcher Gao Hongkai from ECNU Key Laboratory of Geographic Information Sciences along with other researchers from China, the Netherlands, Britain and Germany, have tried dozens of new models based on structures and algorithms in which they jumped ahead of the former related research perspectives based on ecological hydrology, Newtonian physics and proposed models HSC (HAND - -based Storage Capacity curve).  

The HAND-based Storage Capacity curve (HSC) uses a topographic index (HAND, Height Above the Nearest Drainage) to establish nonlinear relationships between the runoff coefficient and soil moisture through the hypothesis of large sample ecological observation data support.

For a long time, topography has only been used to drive levels of water flow from high to low, but recent studies have found that topography also acts as an indicator to reflect the the more comprehensive features of the generation cycles (i.e., water, soil and gas). Writing in PNAS in 2017, Fan et al., a famous American hydrologist ( Chinese descent), used root-depth data from more than 2,200 sites around the world in finding that topography has a decisive influence on root depth; that is, in general, the bigger the HAND, the deeper the roots.

The construction of the HSC model is based on the objective laws of natures, discovered by its ecological big data and combined with the theory of full production flow. 


This is the first theoretical and applied innovation that directly links the deep spatial distribution of ecological roots with hydrological runoff processes. This model has been rigorously verified by multi-source heterogeneous data in more than 300 watersheds with different climates, vegetation, soil and topography in the United Kingdom and the United States. At the same time, through the comparison with the classic hydrological model HBV and TOPMODEL, it is found that the new model not only does not need to determine the yield flow parameters, but also can simulate the spatial and temporal changes of the yield flow area of the variable source, and the simulation effect is significantly improved compared with the traditional model.

This model is not only a breakthrough in the theory of ecological hydrology, but also has a broad prospect in practical application. The runoff yield calculated by rainfall runoff is closely related to almost all water-related problems, including more water (flood), less water (drought), dirty water (water pollution), muddy water (sediment) and dead water (water ecology). The new model can be widely used in disaster prevention and reduction, water resource management and dispatching, water pollution prevention and control, and water ecological restoration. Since the new model does not require parameter calibration, it can be applied to non-data areas in a large number of developing countries, which plays an important supporting role in infrastructure construction and ecological and environmental protection of countries along the One Belt And One Road. The research and development of this model reflects the academic goal of attaching importance to original innovation and provides an important support for the integrated innovation of the next research and development project results.


Edited by Siyuan Zhang     Proofread by Joshua Mayfield    Reviewed by Wenjun Guo


0

华东师范大学
East China Normal University