深圳市极端降雨时空演变及其对福田河雨季水质的影响研究

极端降水事件的发生会引起区域径流、水质等地面水文要素发生改变，而温度、风速等气象水文要素的改变会导致极端水文事件频发。受地理环境以及人文环境的影响，水文气象要素具有一定的区域性分布特征，由此引发的极端降水事件在不同的区域具有不同的分布特征。在城市发展的过程中，水文气象要素的时空分布格局随之改变。极端降水事件对区域水文气象要素如径流和水质会产生显著影响，尤其在城市化迅速的地区，这一影响尤为突出。因此，探究城市发展过程中极端降水分布特征的变化以及极端降水对水质等的影响对区域暴雨风险评估、城市规划建设以及生态环境可持续发展具有重要意义。

本研究选定深圳市作为研究区域，基于中国区域地面气象要素驱动数据集（1979-2018 年）中的降水数据，结合极端降水指标，通过 Mann-Kendall 趋势分析法、Sen’s 坡度法和小波分析法等方法从极端降水量、暴雨频次等方面分析了深圳市近四十年极端降水的空间分布变化，并对深圳市的极端降水高风险区进行识别，进而掌握暴雨中心的变化情况以及极端降水在不同时间尺度上的变化特征。为进一步探究极端降水对区域径流水质的影响，基于极端降水高风险区的识别结果选定福田河流域作为径流水质研究对象，通过SWAT（Soil and Water Assessment Tool）模型对福田河流域水文水质进行建模，探究在不同极端降水事件中污染物浓度对极端降水的响应规律。本研究的主要结论如下：

（1）在 1979-2018 年间，深圳市极端降水频率及强度整体上南部强于北部，西部的上升趋势较东部更为显著；从极端降水量和暴雨频次两方面分析，深圳市极端降水高风险区位置随城市化进程从分散趋于集中，在福田和罗湖两区最为稳定。

（2）极端降水高风险区的月均暴雨日数和月最大日降水量在每个年代均呈上升趋势，尤其在 20 世纪 90 年代最为显著；极端降水高风险区的年降水量呈下降趋势，前后存在 6~8 年和 1~2 年的振荡周期；年暴雨日数稳定在 3~19 d 之间且呈下降趋势，而年最大日降水量呈上升趋势；季节降水量较为稳定，春、夏、秋季降水量均存在明显的振荡周期。

（3）以福田河流域为研究区域建立SWAT 模型，受可获得数据限制，利用博罗站实测径流数据对模型参数进行率定和验证，通过计算，决定系数 R2 和纳什系数 NS 均满足模型模拟精度的要求，模型可以很好地在东江流域开展径流模拟；代入参数最佳值，验证福田河模拟污染物浓度是否与实测污染物浓度相近。结果表明，福田河四个监测点的水质模拟值与实测值的 R2 和 NS 计算值均符合模型模拟精度要求，所建模型适用于福田河流域的污染物迁移变化模拟。

（4）通过分析同一年中不同场次极端降水事件中福田河径流对降水的响应时间以及降水对福田河氮磷的影响，发现不同极端降水事件中，降水引起径流量增加的时间不同，氨氮浓度在三次极端降水事件中对降水的响应各不相同，分别为不断上升、先上升后下降以及下降三种变化；而总磷浓度在三次极端降水事件中对降水的响应主要分为两种，在第一和第三次事件中不断上升，在事件二中先增加后减小。

Extreme precipitation events lead to changes in regional hydrological elements such as runoff and water quality, while changes in meteorological hydrological elements such as temperature and wind speed can lead to frequent extreme hydrological events. Influenced by geographical and cultural environments, hydro-meteorological elements exhibit regional distribution characteristics, which in turn lead to varying distribution characteristics of extreme precipitation events in different regions. In the process of urban development, the spatiotemporal distribution pattern of hydro-meteorological elements also changes. Moreover, extreme precipitation events significantly impact regional hydro-meteorological elements such as runoff and water quality, especially in areas of rapid urbanization, where the impact is particularly pronounced. Therefore, exploring the changes in the distribution characteristics of extreme precipitation during urban development and its impact on water quality is of great significance for regional storm risk assessment, urban planning and construction, and sustainable ecological development.

This study selected Shenzhen, one of the earliest special economic zones and a national economic center in China, as the research area. Using precipitation data from the Chinese Regional Ground Meteorological Element Drive Dataset (1979-2018) combined with extreme precipitation indices, the study analyzed the spatial distribution changes of extreme precipitation in Shenzhen over the past forty years from aspects such as extreme precipitation amounts and heavy rainfall frequency using methods such as the Mann-Kendall trend analysis, Sen's slope estimator, and wavelet analysis. This analysis identified high-risk areas for extreme precipitation in Shenzhen, thereby understanding changes in the storm center and the characteristics of extreme precipitation across different time scales. To further explore the impact of extreme precipitation on regional runoff water quality, the Futian River Basin was selected as the study object for runoff water quality based on the identification results of high-risk areas of extreme precipitation. Using the SWAT model, the hydrology and water quality of the Futian River Basin were modeled to investigate the response patterns of pollutant concentrations to extreme precipitation during different extreme precipitation events. The main conclusions of this study are as follows:

1) Between 1979 and 2018, the frequency and intensity of extreme precipitation in Shenzhen were generally stronger in the south than in the north, with a more significant upward trend in the west compared to the east. Analyzing from the amount of extreme precipitation and heavy rainfall frequency, the location of the high-risk areas for extreme precipitation in Shenzhen has tended to concentrate from dispersed during the urbanization process, with the most stable locations in Futian and Luohu districts.

2) The monthly average number of stormy days and the maximum daily precipitation amount in the high-risk areas of extreme precipitation showed an upward trend in each decade, especially in the 1990s; the annual precipitation amount in the high-risk areas showed a downward trend, with oscillation periods of 6-8 years and 1-2 years respectively; the annual number of stormy days remained stable between 3-19 days and showed a downward trend, while the maximum daily precipitation amount showed an upward trend; seasonal precipitation amounts were relatively stable, with significant oscillation periods in spring, summer, and autumn.

3) The SWAT model was established using the Futian River Basin as the research area, and the model parameters were calibrated and verified using the measured runoff data from the Boluo Station. After calculation, the coefficient of determination R² and the Nash coefficient NS both met the accuracy requirements of the model simulation, indicating that the model could perform well in simulating runoff in the Dongjiang River Basin. By inputting the optimal values of the parameters and running the model, it was verified whether the simulated pollutant concentrations were close to the actual measured concentrations. The results showed that the calculated R² and NS values of water quality simulations at the four monitoring points of the Futian River meet the model simulation accuracy requirements, and the model built is applicable to the simulation of pollutant migration changes in the Futian River Basin.

4) By analyzing the response time of Futian River runoff to precipitation and the impact of precipitation on nitrogen and phosphorus in the river during different extreme precipitation events within the same year, it was found that in different extreme precipitation events, the time it took for precipitation to increase runoff varied. The response of ammonia nitrogen concentration to precipitation varied across the three extreme precipitation events, showing three different patterns: continuously rising, rising then falling, and falling. The response of total phosphorus concentration to precipitation in the three extreme precipitation events mainly exhibited two patterns: continuously rising during the first and third events, and rising then falling during the second event.

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