Based on multiple remote-sensing image interpretation and classification, and economic and social data, this study focused on rural settlement and land use change amidst rapid urbanization. Rural settlements, spatial and temporal patterns of land use and influencing factors in the Bohai Rim were explored within 5×5 km grid cells, as per GIS spatial analysis and geostatistical analysis. Results show that the spatial distribution of rural settlements in the Bohai Rim is remarkably varied. The number of rural settlement sites in a 5×5 km grid cell exceeding 5.0 are distributed in a six-area pattern in the Bohai Rim; rural settlement dispersion is particularly high in agricultural regions in south Hebei and southwest Shandong, suggesting rural settlement density keeps increasing from northeast to southwest, characterized by high density and dispersed spatial distribution in traditional agricultural regions. Furthermore, rural settlements show dramatic spatial differences in terms of distribution and dynamic change degrees in the Bohai Rim. In terms of spatial distribution, rural residential land is always extensive in plains, with a high density of rural settlements, on the North China Plain in particular, and rural residential land in the south of Shandong province is also extensive, with most rural settlement land use areas in the 5×5 km grid cells exceeding 3 km 2 . However, traditional agricultural regions have underdeveloped economies, industrialization and tertiary industries, characterized by low urbanization rates, with farmers not feeling assimilated in rural or urban areas. In terms of the temporal sequence, urban expansion rapidly promotes the transformation of rural residential lands in rural-urban transitional belts of provincial capital or prefecture-level city into urban lands, and in traditional rural areas, residential lands are growing. The natural environment, transportation conditions, economic development and farmers' incomes all have effects on type of land use change and pattern of rural settlements. It is a core objective for future rural development to reconstruct a rational spatial pattern of villages or towns and well-organized village-town systems, build central villages, key towns or central towns, optimize or reconstruct production, living and eco-space of rural areas. It is of significance for rural geographical research to further interpret and explore spatial reconstruction theory.
Abstract:The latest Version-7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products were released by the National OPEN ACCESSWater 2014, 6 33Aeronautics and Space Administration (NASA) in December of 2012. Their performance on different climatology, locations, and precipitation types is of great interest to the satellite-based precipitation community. This paper presents a study of TMPA precipitation products (3B42RT and 3B42V7) for an extreme precipitation event in Beijing and its adjacent regions (from 00:00 UTC 21 July 2012 to 00:00 UTC 22 July 2012). Measurements from a dense rain gauge network were used as the ground truth to evaluate the latest TMPA products. Results are summarized as follows. Compared to rain gauge measurements, both 3B42RT and 3B42V7 generally captured the rainfall spatial and temporal pattern, having a moderate spatial correlation coefficient (CC, 0.6) and high CC values (0.88) over the broader Hebei, Beijing and Tianjin (HBT) regions, but the rainfall peak is 6 h ahead of gauge observations. Overall, 3B42RT showed higher estimation than 3B42V7 over both HBT and Beijing. At the storm center, both 3B42RT and 3B42V7 presented a relatively large deviation from the temporal variation of rainfall and underestimated the storm by 29.02% and 36.07%, respectively. The current study suggests that the latest TMPA products still have limitations in terms of resolution and accuracy, especially for this type of extreme event within a latitude area on the edge of coverage of TRMM precipitation radar and microwave imager. Therefore, TMPA users should be cautious when 3B42RT and 3B42V7 are used to model, monitor, and forecast both flooding hazards in the Beijing urban area and landslides in the mountainous west and north of Beijing.
Woody plant encroachment into semiarid and subhumid rangelands is a global phenomenon with important hydrological implications. Observational and experimental results reported both increases and decreases in annual runoff for encroached watersheds and little is known regarding the underlying runoff generation mechanisms. To systematically study the effect of woody plant encroachment on runoff generation processes, seven experimental watersheds were instrumented in 2010, three on grassland sites and four on adjacent sites that were heavily encroached by eastern redcedar (Juniperus virginiana) in the southern Great Plains, USA. Results showed that the runoff coefficient was 1.4 6 0.6% in eastern redcedar encroached watersheds, significantly lower than 4.4 6 0.7% in grassland watersheds for the four water years from 2011 to 2014. Eastern redcedar encroachment resulted in reduction of both surface and subsurface flows and the magnitude of reduction depended on annual precipitation. While there were nearly equal contributions between overland flow and subsurface flow, 87% of the total runoff from grassland watersheds occurred under saturated or nearly saturated soil condition, while 86% of runoff under encroached watersheds was generated under unsaturated soil condition, suggesting a shift from saturation excess overland flow to infiltration excess overland flow. These results permitted reconciliation of observed difference of streamflow responses associated with Juniperus spp. encroachment in the region and provided insights to better predict change in water resources under vegetation changes in subhumid regions of the south-central USA.
Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm‐season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic‐scale circulations are faithfully reproduced while still allowing WRF to develop small‐scale dynamics, thus effectively suppressing the large‐scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980–2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans‐state Oologah watershed is also achieved.
This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10 )2 from previous much underestimated level (<10 )3 ). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning's coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.