Non-stationary modeling of wet-season precipitation over the Inner Mongolia section of the Yellow River basin

Wang, Yixuan; Duan, Limin; Tong, Xin; Liu, Tingxi; Li, Dongfang; Li, Wei

doi:10.1007/s00704-022-04279-y

Cited by 5 publications

(4 citation statements)

References 71 publications

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“…The results show that there is a significant positive correlation between precipitation and temperature in the northwestern part of Yellow River Basin of Inner Mongolia, where there is a lack of rain, and the water demand for big herbs is mostly from rainwater, so it is more sensitive to rainfall 44 . The southeast Inner Mongolia portion of Yellow River Basin is not subject to water constraints as it has abundant rainfall, so it has a higher dependence on temperature 45 .…”

Section: Discussionmentioning

confidence: 96%

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Wang,

Xing,

et al. 2024

Sci Rep

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Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000–2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

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Section: Discussionmentioning

confidence: 96%

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Wang,

Xing,

et al. 2024

Sci Rep

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“…In addition, this study employs the mean, variance, skewness coefficient, kurtosis coefficient, and Filliben correlation coefficient as statistical markers of the residual sequence. The mean is closer to 0, the variance is closer to 1, the skewness coefficient is closer to 0, the kurtosis coefficient is closer to 3, and the Filliben coefficient is closer to 1, indicating that the model fits better [31].…”

Section: Gamlssmentioning

confidence: 88%

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Zhang,

Zhi,

Zhang

et al. 2024

Water

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The hydrological series in the Loess Plateau region has exhibited shifts in trend, mean, and/or variance as the environmental conditions have changed, indicating a departure from the assumption of stationarity. As the variations accumulate, the compound effects caused by the driving variables on runoff variations grow complex and interactive, posing a substantial risk to water security and the promotion of high-quality development in regions or river basins. This study focuses on the Tuwei River Basin in the Loess Plateau, which experiences significant changes in vegetation coverage and minimal human disturbance, and examines the cross-driving relationship between the runoff change and its driving variables (including hydrometeorological and environmental variables). A quantitative statistical analysis method based on the GAMLSS is then developed to estimate the interacting effects of changes in the driving variables and their contribution to runoff changes. Finally, various anticipated scenarios are used to simulate the changes in driving variables and runoff disturbances. The findings indicate the following: (1) The developed GU, LO, and NO distribution-based GAMLSSs provide a notable advantage in effectively capturing the variations in groundwater storage variables, actual evapotranspiration, and underlying surface parameters, as well as accurately estimating the impacts of other relevant variables. (2) The precipitation and groundwater storage variables showed predominantly positive contributions to the runoff change, but actual evapotranspiration had an adverse effect. The changes in underlying surface parameters, particularly since 2000, increase actual evapotranspiration, while decreasing groundwater storage, resulting in a progressive decrease in runoff as their contribution grows. (3) The scenario simulation results reveal that alterations to the underlying surface have a substantial influence on the evolution of runoff in the Tuwei River Basin. Additionally, there are cross-effects between the impact of various driving variables on runoff, potentially compounding the complexity of inconsistent changes in runoff sequences.

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“…This accounts for about 18.63% of the total area of the Yellow River Basin, with elevations ranging from 843 to 2,315 m (Figure 1). The study area is located in an arid and semi-arid region, characterized by drought and scarce rainfall, strong evaporation, large diurnal temperature variations, and is typical of a midtemperate continental climate (Wang et al, 2023). It has an annual average precipitation of 305 mm and an average annual temperature of 6.5 °C (Zhang et al, 2023).…”

Section: Study Areamentioning

confidence: 99%

“…Currently, there are relatively many studies on downscaling of GPM IMERG precipitation datasets in humid areas (Ma et al, 2018;Min et al, 2020;Yan et al, 2021a), but relatively few in arid and semi-arid areas. The Inner Mongolia Reach of the Yellow River Basin is located in an arid and semi-arid area, where the terrain is diverse and complex, and precipitation exhibits distinct regional and seasonal variations (Wang et al, 2023). In this study, we focused on the Inner Mongolia Reach of the Yellow River Basin.…”

Section: Introductionmentioning

confidence: 99%

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

Tu,

Duan

2024

Front. Environ. Sci.

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The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

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Non-stationary modeling of wet-season precipitation over the Inner Mongolia section of the Yellow River basin

Cited by 5 publications

References 71 publications

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

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