Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China

Xu, Haiming; Xu, Chong‐Yu; Chen, Hua; Zhang, Zengxin; Li, Lu

doi:10.1016/j.jhydrol.2013.09.004

Cited by 147 publications

(86 citation statements)

References 46 publications

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“…The station density was 0.35 stations per 1000 km 2 . Xu et al (2013) found that model simulations are influenced by rainfall station densities below 0.4 per 1000 km 2 . Under such conditions, runoff simulations may contain uncertainties due to poor representation of spatial precipitation variability, which is crucial in determining the runoff hydrograph (Singh, 1997).…”

Section: Uncertainty In Swat Model Simulationsmentioning

confidence: 96%

Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China

Yin

Xiong

et al. 2017

Hydrol. Earth Syst. Sci.

179

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Abstract. Water resources, which are considerably affected by land use/land cover (LULC) and climate changes, are a key limiting factor in highly vulnerable ecosystems in arid and semi-arid regions. The impacts of LULC and climate changes on water resources must be assessed in these areas. However, conflicting results regarding the effects of LULC and climate changes on runoff have been reported in relatively large basins, such as the Jinghe River basin (JRB), which is a typical catchment (> 45 000 km 2 ) located in a semi-humid and arid transition zone on the central Loess Plateau, northwest China. In this study, we focused on quantifying both the combined and isolated impacts of LULC and climate changes on surface runoff. We hypothesized that under climatic warming and drying conditions, LULC changes, which are primarily caused by intensive human activities such as the Grain for Green Program, will considerably alter runoff in the JRB. The Soil and Water Assessment Tool (SWAT) was adopted to perform simulations. The simulated results indicated that although runoff increased very little between the 1970s and the 2000s due to the combined effects of LULC and climate changes, LULC and climate changes affected surface runoff differently in each decade, e.g., runoff increased with increased precipitation between the 1970s and the 1980s (precipitation contributed to 88 % of the runoff increase). Thereafter, runoff decreased and was increasingly influenced by LULC changes, which contributed to 44 % of the runoff changes between the 1980s and 1990s and 71 % of the runoff changes between the 1990s and 2000s. Our findings revealed that large-scale LULC under the Grain for Green Program has had an important effect on the hydrological cycle since the late 1990s. Additionally, the conflicting findings regarding the effects of LULC and climate changes on runoff in relatively large basins are likely caused by uncertainties in hydrological simulations.

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Section: Uncertainty In Swat Model Simulationsmentioning

confidence: 96%

Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China

Yin

Xiong

et al. 2017

Hydrol. Earth Syst. Sci.

179

View full text Add to dashboard Cite

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“…Previous studies have confirmed that the spatial resolution of rainfall data substantially affects rainfall runoff simulations [5]. In particular, previous rainfall and hydrologic studies in urban basins, using high-density rain-gauge networks, have mentioned the necessity of accurately measuring intense heavy rainfall that is occurring in a specific area over a short period of time, and using spatially distributed rainfall for urban flood analysis [6,7]. However, it is not always realistic to install numerous rain gauges to measure spatially representative rainfall estimates over a metropolitan area for geographic and economic reasons [8].…”

Section: Introductionmentioning

confidence: 99%

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Yoon

Lee

2017

Water

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Flood prediction is difficult in urban areas because only sparse gauge data and radar data of low accuracy are usually used to analyze flooding and inundation. Sub-basins of urban areas are extremely small, so rainfall data of high spatial resolution are required for analyzing complex drainage systems with high spatial variability. This study aimed to produce three types of quantitative precipitation estimation (QPE) products using rainfall data that was derived from 190 gauges, including the new high-density rain-gauge network operated by the SK Planet company, and the automated weather stations of the Korea Meteorological Administration, along with weather radar data. This study also simulated urban runoff for the Gangnam District of Seoul, South Korea, using the obtained QPE products to evaluate hydraulic and hydrologic impacts according to three rainfall fields. The accuracy of this approach was assessed in terms of the amount and spatial distribution of rainfall in an urban area. The QPE products provided highly accurate results and simulations of peak runoff and overflow phenomena. They also accurately described the spatial variability of the rainfall fields. Overall, the integration of high-density gauge data with radar data proved beneficial for quantitative rainfall estimation.

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“…The minimum temperature is about 48-88C in January, and maximum temperature was about 298-308C in July. The mean annual precipitation is 1300-1700 mm, increasing from south to north, and average annual evapotranspiration (Xu et al 2013). Nearly 80% of the incidents of extreme precipitation in the Xiangjiang basin last less than a day, and most occur in June, July, and August.…”

Section: A Study Regionmentioning

confidence: 99%

Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

Hou

Chen

et al. 2017

Journal of Hydrometeorology

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Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change-induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an atsite statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

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Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China

Cited by 147 publications

References 46 publications

Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China

Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

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