Performance of the Xinanjiang Model

Ahirwar, Aruna Devi; Jain, Manoj; Perumal, Muthiah

doi:10.1007/978-981-10-5801-1_49

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…The Xinanjiang hydrologic model is a conceptual rainfall-runoff model that has been widely used in the humid and semi-humid areas worldwide [14]. There are four main components in the model: evapotranspiration calculation (a generalize three-layer evapotranspiration model), runoff calculation (a saturated runoff model), three water source partition (surface runoff, interflow and underground runoff) and flow concentration calculation (unit hydrograph and Muskingum method).…”

Section: Hydrologic Modelmentioning

confidence: 99%

Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons

Fang

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Statistical post-processing of ensemble forecasts could effectively improve their accuracy and reliability. In this study, three typical post-processing methods including equal weight (EW), model output statistics (MOS) and Bayesian model averaging (BMA) were applied to the raw multi-model runoff forecasts during the flood period (from 1 June to 30 September) of 2010-2013, and the processed results were compared and analyzed. It is shown that BMA is a promising post-processing method with highest accuracy, but this becomes completely different at lead times of 126-240 h in 2013. The main problems for the BMA performance instability were found to be outliers and low-correlation, which affect the linear regression model fitting in the bias correction procedure. Following this, a combination model of LR and EW was proposed to improve the bias correction procedure for lead times of 126-240 h. And the test results demonstrate that the combination model is effective and efficient, and it is able to lead to both accurate and reliable multi-model ensemble runoff forecasts for longer lead times.

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Section: Hydrologic Modelmentioning

confidence: 99%

Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons

Fang

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…The infiltration mechanism utilized in the Xinanjiang model [50] was adopted for use in the VIC model to generate the runoff from precipitation when it is higher than the available infiltration capacity. This scheme is commonly used in models that are used for flood forecasting, climate change studies, and water resource assessment in the humid and sub-humid regions of the world [51]. The model is capable of catchment response on any scale and can account for nonlinear spatial retention of soil moisture [52].…”

Section: Hydrological Modelsmentioning

confidence: 99%

Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin

Sridhar

Kang

Ali

2019

Water

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The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation <1 suggested no major difference in flows between the pre- and post-development of hydropower projects. The results suggested an increasing trend in streamflow without the effect of dams, while the inclusion of a few major dams resulted in decreased river streamflow of 6% to 15% possibly due to irrigation diversions and climate change. However, these estimates fall within the range of uncertainties in natural climate variability and hydrological parameter estimations. This study offers insights into the relationship between biophysical and anthropogenic factors and highlights that management of the Mekong River is critical to optimally manage increased wet season flows and decreased dry season flows and handle irrigation diversions to meet the demand for food and energy production.

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“…The Xinanjiang model, known for its structural simplicity and minimal data requirements, offers the advantage of easy integration with other models and demonstrates excellent scalability (Zhao, 1992). The model is widely applied to daily-scale streamflow prediction due to its adaptability and generalizability (Ahirwar et al, 2018;Rahman and Lu, 2015). Gong et al applied the Xinanjiang model to simulate hourly flood timing of small-to-medium watersheds in China's southern mountains (Gong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

Ke,

Wang,

Dong

et al. 2024

Preprint

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One indication of the impacts of climate change on the water cycle is growing streamflow uncertainty, which is especially evident in high and cold regions. An interval forecasting model is established, which couples a snowmelt module and an uncertainty module, based on the Xinanjiang model. The model can consider the climate change impacts by quantifying the streamflow variations in the form of interval forecasts. The model’s performance was assessed by applying it in the headwater region of the Yellow River Basin. Interval forecasts and uncertainty analyses were conducted. Results show that the model can accurately describe the daily streamflow process in the study area. Unlike the deterministic forecasting model, the interval forecasting model effectively addresses shortcomings in forecasting high-flow scenarios. Furthermore, outcomes from the uncertainty analysis indicate that the model parameter K (the ratio of potential evapotranspiration to pan evaporation) plays a crucial role in water balance computations; the model parameter B (exponent of distribution of soil tension water capacity curve) exhibits sensitivity, suggesting challenges in attaining complete soil saturation across the entire basin. In addition, the insensitivity of the snowmelt module parameters implies that the proportion of snowmelt streamflow is relatively low in the annual streamflow and remains stable. The study results can provide theoretical references for water resource planning and reservoir regulation in the Yellow River Basin.

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Performance of the Xinanjiang Model

Cited by 11 publications

References 35 publications

Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons

Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons

Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

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