Accessing the Difference in the Climate Elasticity of Runoff across the Poyang Lake Basin, China

Abstract

Understanding the effects of climate and catchment properties' changes on water yield is a challenging component in assessments of future water resources. Here, we spatially applied the water-energy balance equation, based on the widely-used Budyko framework, to quantify the temporal and spatial differences of the climate elasticity of runoff in the Poyang Lake Basin (PYLB), highlighting the influence of the catchment properties' parameter n variation on the climate elasticity and runoff prediction. By using Sen's slope and the Mann-Kendall method, we found that, for the whole study period , annual temperature in PYLB significantly increased at a rate of 1.44% per decade. Basin-wide wind speed and net radiation had been declining at 0.17 m/s and 46.30 MJ/m 2 per decade. No significant trend was detected in precipitation and relative humidity. The moving average method was applied to evaluate the temporal pattern of n. The results showed that the calibrated catchment properties' parameter and the derived elasticities were not constant during the past 50 years. We found that in most sub-basins, the n values increased during 1970-1980, followed by a decreasing trend in the period from 1980 to 1990, whereas the n value in Fuhe sub-basin kept increasing for the almost the whole study period. In addition, the climate elasticity is highly correlated with the n value, indicating that the catchment properties' parameter was the dominant factor influencing climate elasticity in PYLB in the past 50 years. We also attempted to predict the runoff trend with the consideration of trends in n. However, in some sub-basins, there were still considerable differences between the predicted runoff trend and the observed one. The method used here to evaluate the temporal pattern of n should be an extension of the existing literature and will provide a better understanding of elasticity in the regional hydrological cycle.

Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation

“…Poyang Lake exchanges water with the Yangtze River through a narrow outlet located in Hukou [27], while receiving water from the five sub-basins of the Ganjiang River, the Fuhe River, the Xinjiang River, the Raohe River and the Xiushui River [25]. The PYLB belongs to a subtropical wet climate zone with an annual mean precipitation of 1680 mm and annual mean temperature of 17.5 • C [28]. In normal years, the water surface of Poyang Lake can expand to 4000 km 2 and the water volume is about 3.20 × 10 10 m 3 .…”

“…The meteorological data from 13 National Meteorological Observatory (NMO) stations inside the PYLB from 1960-2013 were obtained from the National Climate Center of China Meteorological Administration (CMA), including daily observations of precipitation, temperature, relative humidity and so on. Missing values in the meteorological data were further interpolated using data from the nearest 5 meteorological stations [28]. Standardization of datasets is a common requirement for many neural network frameworks [18].…”

Drought Characteristics and Its Response to the Global Climate Variability in the Yangtze River Basin, China

“…They have been provided by the National Climatic Centre (NCC) of CMA (the China Meteorological Administration). The daily records of meteorological variables provided by CMA had gone through a standard quality control process before delivery, and with no missing data on the variables used in this study [42]. For ENSO data, all the GCV indices are listed in Table 1.…”

Spatiotemporal Variations of Summer Precipitation and Their Correlations with the East Asian Summer Monsoon in the Poyang Lake Basin, China

“…The spatiotemporal distribution of precipitation in the PYLB is uneven with obvious seasonal and regional features. The PYLB has four distinct seasons with an average annual temperature of 17.5 • C and an average annual precipitation of 1680 mm [29][30][31]. The locations of the meteorological stations are shown in Figure 1.…”