Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Meng, Shanshan; Xie, Xianhong; Yu, Xiao

doi:10.3390/w8010019

Cited by 13 publications

(8 citation statements)

References 58 publications

(101 reference statements)

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“…However, it is no longer appropriate to use constant parameters, especially for hydrological modeling under the changing environment. A temporal variability of the model parameters exists due to the influence of catchment condition changes including climate condition and catchment characteristics (e.g., land use and land cover) [12][13][14][15][16]. For example, Wang and Tang [17] found that rainfall and vegetation were the dominant controlling factors on the parameter of a Budyko equation, which is derived for mean annual water balance and is independent of temporal scale.…”

Section: Introductionmentioning

confidence: 99%

“…The first is to calibrate parameters in consecutive subsets of the historical data and attempts to obtain their values separately in each period using optimization algorithms [20,21]. The second method is to estimate model parameters via data assimilation techniques based on the available observations [12,15]. Another method of estimating time-variant parameters is to establish the parameter functions as dependent on covariates that can reflect the variability of catchment conditions [22].…”

Section: Introductionmentioning

confidence: 99%

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

Deng

Wang

2019

Sustainability

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Deng

Wang

2019

Sustainability

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“…The Xinanjiang (XAJ) model, a rainfall-runoff model developed by (Zhao 1992), has been widely used in China and many countries in the world for flood simulation in humid and semi-humid regions (Huo and Liu 2020;Liu et al 2016;Meng et al 2016;Yang et al 2020;Zhang et al 2019;Zhuo et al 2016). It is based on the concept of saturation excess runoff mechanism, which means that runoff is not produced until the soil moisture content of the aeration zone reaches field capacity.…”

Section: Xinanjiang Modelmentioning

confidence: 99%

Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes

Sheng

Chen

Guo³

et al. 2020

Water Resour Manage

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Practical application of hydrological models requires parameter transfer, both temporally and spatially, to compensate for the lack of data. In this study, the transferability of parameters is evaluated using a lumped hydrological model called the Xinanjiang model to simulate runoff at different spatiotemporal scales in the Jianxi basin in south-east China and its four sub-basins. The functional relationships are built based on the posterior distribution derived by the Differential Evolution Adaptive Metropolis (DREAM) algorithm to mitigate the effect of parameter uncertainty. The results show that (1) the sensitivity of parameters KE, SM, KI and KG shows obvious temporal characteristics, and the sensitivity of NK and CG shows strong spatial characteristics;(2) most relationships between sensitive parameters and scales are remarkable with goodness-of-fit coefficient higher than 0.9, which has been verified to Hua Chen

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“…The generated runoff is divided into the surface flow, interflow, and groundwater using steady infiltration. The total runoff can be routed by a linear system before arriving at the outlet of the catchment [37]. Flow routing uses the Muskingum or piecewise continuous algorithm.…”

Section: Precipitation-runoffmentioning

confidence: 99%

Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

Chen

Zhang

Gippel

et al. 2016

Advances in Meteorology

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Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classifiedZ-Rrelationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

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Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Cited by 13 publications

References 58 publications

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

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

Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes

Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

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