Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Abstract

Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index ∆ was developed by combining the Nash-Sutcliffe efficiency, the volume error, the Box-Cox transformed root-mean-square error, and the Kling-Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a ∆ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021-2050) when compared to the runoff simulations in the baseline period , where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.

Evaluating the impact of climate change on surface water resources in the upper Ganjiang River Basin, China

“…The variations of the projected multi-year mean precipitation and temperature over the UGRB during the period 2021-2050 are relatively close to the conclusions proposed by Deng & Wang (2019), and both are precipitation declines and potential evaporation increases; however, in terms of annual runoff, the opposite conclusions are drawn. In this paper, the annual mean runoff is expected to increase by 8.4, 6.5 and 5.0% under three RCPs in the next 30 years, while the latter reported that a decreasing trend about À9.9, À19.5 and À11.4%.…”

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

“…Annual streamflow prediction is equally important for reservoir operation and water resource management in the Ganjiang River Basin, because it is the prerequisite and foundation for compiling a water resource allocation plan and carrying out reservoir operation. As an important control station in the upper reaches of the Ganjiang River Basin and a runoff monitoring station at the dam site of the Wan'an Reservoir, the Dongbei Hydrological Station is the basin outlet of the study area, controlling a drainage area of 40,231 km 2 [39]. Influenced by the plum rain, rainfall of the Dongbei Hydrological Station concentrates from March to June, accounting for about 54% of annual rainfall.…”

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

“…It overcomes the problem of long-term dependencies and is more suitable for dealing with time series forecasting problems. The computation process of the LSTM unit is described in Equations ( 4)- (8):…”

“…Currently, runoff prediction models can be divided into two types: process-driven models [8][9][10] and data-driven models [11][12][13]. Process-driven models are modeled to simulate complex non-linear physical hydrological process through a series of mathematical equations based on an understanding and simplification of the principles of the natural water system [14,15].…”