Changes In Rainfall Intensity Research Articles

Evaluating the effect of geocell with vegetation and gravel on changes in the effective parameters of runoff using a rainfall simulator (case study: Iran)

Permeable pavements can reduce the negative effects of flooding in urban areas. This research seeks to study and compare the performance of the impervious surface as a control (O), sandy loam (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in reducing runoff volume (RV), time to start runoff (TR), runoff coefficient (C), time to end runoff (TER), peak flowrate (PF), time to peak (TP), and time base (TB) under six different rainfall intensities (i) and two different slopes (s) using a rainfall simulator. For this purpose, 96 physical models were considered at the laboratory level. For the dependent variables TR, TER, PF, TP, and TB, there was a significant difference between the data at the level of 5% in all cases. Based on the results, changes in rainfall intensity, slope, and type of treatment could make a significant difference in the results of the C parameter. All treatments were able to reduce runoff volume in all test conditions. Rosemary had a better performance in controlling runoff and parameters affecting it compared to turf. The maximum and minimum cumulative runoff volumes observed for treatment GGE are 85.4 and 2 L, respectively. Finally, the results revealed that GGE could perform better in reducing runoff and cumulative volume in all cases compared to the rest of the test groups. The highest percentage of runoff volume reduction in GGE treatment was 96.5%.

Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia

An increase in population increases the rate of urbanization. This results in changes in land cover from vegetation to artificial material. As a result, much of the land surface reflects the sun's energy. Consequently, this increases the surface temperature of the land. An increase in land surface temperature (LST) will increase the intensity of rainfall. Therefore, the present study aimed to investigate the relationship between the increase in LST and rainfall intensity. Changes in land cover can be detected by normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) parameters. Landsat satellite imagery was used to detect NDVI, NDBI, and LST. Image processing was done for imageries scanned in 1995, 2015, 2017, and 2021. Two areas in the East Java Province of Indonesia, namely Malang City and Pasuruan Area, were selected. The daily rainfall intensity data were collected from related rainfall stations in the same year. The Mononobe method was applied to analyze hourly and minute rainfall intensity. IDF curves were drawn from the analyzed results. The relationship between both parameters was analyzed by comparing the LST and hourly rainfall intensity from the IDF curve. The studied results showed that the maximum temperature increase from 1995 to 2021 for the Malang City and Pasuruan Area was 2.60 C and 7.60 C, respectively. For rain, the maximum rainfall intensity increased by 58 mm for Malang City and 18 mm for the Pasuruan Area. LST and rainfall intensity change trends of the two areas had a positive coefficient of regression. The findings can be used to predict the rainfall intensity and floods based on the LST data.  

Using integrated GIS and hydrological analysis for sizing culverts of multiple channel crossings at the flooded section of the Daboya-Mankarigu Road (IR10) in Ghana

A culvert is an important structure in Road construction to allow the conveyance of Channels crossing the road. Culverts are sized for a road to accommodate the volume of water crossing the road network to avoid flooding. Ghana Highways has a standard manual for culvert sizing at channel crossing. This manual serves as a guide for the proper sizing of culverts, however, lots of culverts have been found to have failed. Among the reasons for the failure of culverts could be under-sizing, urbanization, climate change, lack of maintenance, etc. The Daboya-Mankarigu Road is situated in the Savanah Region of Ghana in the North Gonja District. The section of the road from Chainage 9 + 075 to 10 + 200 has been experiencing flooding from 2020 to 2021 with a flood depth of 3.315 m in 2020 and 2.00 m in 2021. This study seeks to use integrated GIS and hydrological-based methods to propose new culverts to supplement the existing culverts to control flooding at section (9 + 075 to 10 + 200) of Daboya-Mankarigu Road (IR10). Geographic Information system (GIS) model (SWAT), Hydrological and hydraulic models were used to determine the peak flow at the catchment to Propose new culverts to supplement the existing culverts. Using a design period of 25 years for culverts, the modified rational method was used to determine the Peak flow of the catchment. A 25-year peak flow of 367.155 m3/s was determined and used for hydraulic analysis of the existing culverts. From the study, the existing culvert structures at the section had a hydraulic capacity of 78.732 m3/s which could not accommodate the remaining flow of 288.423 m3/s in the catchment. An observation was made that the changes in the rainfall can cause a change in rainfall intensity. An increase in built-up areas in the catchment can also increase the runoff coefficient which can result in higher peak flow in the catchment. Climate change, change in slope, and Land use in the catchment were also determined to have a huge influence on the adequacy of culverts since the peak flow is dependent on these parameters in the catchment as the years go by. A 4No. 4 m × 4 m box culvert, 3No. 3.5 m × 3.5 m box culvert, and 13 No. 1200 mm pipe culverts with 2 each at different chainages were proposed at suitable locations to supplement the existing culverts using the HDS-5 equations in AutoCAD Civil 3D. A recommendation is made to consider the installation of these new culverts at the flood section to control flooding and avoid overtopping of water on the IR10 road section (Daboya-Mankarigu) in the north Gonja District.

Effect of microrelief features of tillage methods under different rainfall intensities on runoff and soil erosion in slopes

Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion. This study utilized rainfall simulation experiments to investigate the impact of four tillage methods (manual digging (MD), manual hoeing (MH), traditional ploughing (TP), and ridged ploughing (RP)) on runoff and soil erosion at the plot scale. The smooth slope (SS) was used as a benchmark. Rainfall intensities of 30, 60, 90, and 120 mm h−1 were considered. The study revealed that tillage altered rainwater distribution into depression storage, infiltration, and runoff. Tillage reduces runoff and increases infiltration. The four tillage methods (30–73%) increased the proportion of rainwater converted to infiltration to varying degrees compared to the SS (22–53%). Microrelief features influenced the role of tillage methods in soil erosion. Surface roughness and depression storage accounted for 79% of the variation in sediment yield. The four tillage methods reduced runoff by 2.1–64.7% and sediment yield by 2.5–77.2%. Moreover, increased rainfall intensity weakens the ability of tillage to control soil erosion. When rainfall intensity increased to 120 mm h−1, there was no significant difference in runoff yield among RP, TP, MH, and SS. Therefore, assessing the effectiveness of tillage in reducing soil erosion should consider changes in rainfall intensity. Additionally, the cover management (C) factor of the RUSLE was used to assess the effects of different tillage methods on soil loss. Overall, the C factor values for tilled slopes are in the order MH > TP > RP > MD with a range of 0.23–0.97. As the surface roughness increases, the C factor tends to decrease, and the two are exponential functions (R2 = 0.86). These studies contribute to our understanding of how different tillage methods impact runoff and soil erosion in sloped farmland and provide guidance for selecting appropriate local manual tillage methods.

Soil erosion and nutrient loss due to changes in rainfall intensity under different wind directions

AbstractIn nature, wind often occurs simultaneously with rainfall, causing nutrient loss in soil and water on slopes. However, the influence of different rainfall intensities combined with different wind directions on soil erosion and nutrient loss remains unclear. This study utilized wind‐driven rainfall laboratory flume experiments to analyse the influence of different wind directions (windward, leeward and crosswind) and rainfall intensities (30, 60 and 90 mm h−1) with constant wind speed (5 m s−1) on slope runoff, sediment yield and the associated losses of different forms of N ( , ) and P ( ‐P). The response of soil erosion, N and P loss to rainfall changes with different wind directions. When the rainfall intensity was constant, runoff, sediment yield and sediment‐associated total nitrogen (TN) and total phosphorus (TP) losses on slopes showed a trend of leeward > crosswind > windless > windward, whereas runoff‐associated TN and TP losses showed a trend of windward > crosswind > windless > leeward. Runoff, sediment yield and sediment‐associated TN and TP losses on leeward slopes were 1.09–1.17, 1.25–1.44, 1.33–1.62, and 1.40–1.66 times than windless slopes. Moreover, with constant wind direction, the runoff, sediment yield and associated N and P losses increased significantly with increasing rainfall intensity. When the rainfall intensity increased from 30 to 60 and 90 mm h−1, the runoff, sediment yield and associated TN and TP losses increased 2.02–2.28, 3.65–5.35, 3.96–34.39 and 4.28–8.45 times, respectively. The effects of rainfall on soil erosion and nutrient loss were significantly greater than those of wind. The loss of different forms of N and P associated with runoff and sediment was consistent with that of TN and TP losses. However, with an increase in rainfall intensity, the growth rates of runoff, sediment yield and associated N and P losses exhibited a gradually weakening trend for all wind directions. An increase in rainfall intensity enhanced the vertical vector force of wind‐driven rainfall, which increasingly decreased the angle between the rain incidence and the normal line of the horizon; therefore, the direction of the rain erosion force gradually approached that of windless conditions.

An Event-Based Resilience Index to Assess the Impacts of Land Imperviousness and Climate Changes on Flooding Risks in Urban Drainage Systems

Assessing the resilience of urban drainage systems requires the consideration of future disturbances that will disrupt the system’s performance and trigger urban flooding failures. However, most existing resilience assessments of urban drainage systems rarely consider the uncertain threats from future urban redevelopment and climate change, which leads to the underestimation of future disturbances toward system performance. This paper fills in the gap of assessing the combined and relative impacts of future impervious land cover and rainfall changes on flooding resilience in the context of a densely infilled urban catchment served by an urban drainage system in Salt Lake City, Utah, USA. An event-based resilience index is proposed to measure climate change and urbanization impacts on urban floods. Compared with the traditional resilience metric, the event-based resilience index can consider climatic and urbanized impacts on each urban flooding event; the new resilience index assist engineers in harvesting high-resolution infrastructure adaptation strategies at vulnerable spots from the system level to the junction level. Impact comparison for the case study shows that impervious urban surface changes induce greater effects on the system performance curves by magnifying the maximum failure level, lengthening the recovery duration, and aggravating the flooding severity than rainfall intensity changes. A nonlinear logarithmic resilience correlation is found; this finding shows that flooding resilience is more sensitive to the land imperviousness change due to urban redevelopment than rainfall intensity changes in the case study. This research work predicts the system response to the disturbances induced by climate change and urban redevelopment, improving the understanding of impact analysis, and contributes to the advancement of resilient urban drainage systems in changing environments.

Precession and obliquity forcing of the South African monsoon revealed by sub-tropical fires

While the influence of precession on monsoon at low latitudes through insolation forcing is well-known, the role of obliquity is still debated since its influence on the distribution of incoming solar radiation is small in these regions. In southern Africa, long marine and terrestrial sedimentary records attest of a precessional influence on the South African monsoon at orbital time scale. The obliquity signal is occasionally observed in the geological records although modeling results suggest an influence of precession and obliquity on summer monsoon. Here, we present a record of microscopic charcoal from core MD96-2098 located off Namibia covering the past 184,000 years. Our record of fire activity reveals cyclic changes at frequencies of 23, 58 and 12 kyr−1 and lacks the obliquity signal at 41 kyr−1. Changes in fire over southern Africa are interpreted as shifts in large and intense fires spreading in open-grassland savanna as a result of orbitally-driven changes in rainfall intensity associated with the South African monsoon. We show that, despite the absence of a 41 kyr obliquity imprint, the presence of 23, 58 and 12 kyr−1 frequencies likely stems from a nonlinear response of fire to precipitation controlled by a combination of precession and obliquity frequencies, supporting the influence of obliquity on the South African monsoon.

Influence of Land Surface Temperature and Rainfall on Surface Water Change: An Innovative Machine Learning Approach

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June’2005 to June’2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world.

Self‐Feedback Sensing Properties of Self‐Powered Multi‐Parameter Meteorological Sensor with Omnidirectional Array Structure

AbstractFrequent meteorological disasters pose more and more extensive threats to human life and property. However, the traditional meteorological sensors based on external power supply, for natural disaster monitoring and early warning, are severely limited in remote and unattended areas. Herein, a self‐powered multi‐parameter meteorological sensor (SMPM‐sensor) for various weather monitoring is proposed, which can monitor the weather through the feedback of electrical energy output while collecting natural energy. The designed 360° omnidirectional array structure facilitates efficient collection of all‐weather solar energy and rain energy in different directions and monitoring the light intensity without the influence of solar azimuth. Based on the four‐segmented disc‐shaped structure and the introduced Ecoflex transition layer, the frequency and amplitude of the wind electrical output can be effectively improved. Owing to structure and material optimizations, a good linearity of 0.9746 between the current and the solar illuminance is exhibited, a wide monitoring range of rainfall intensity changes from 0.1 to 11.9 mm min−1, and the linearity between wind speed and voltage frequency can be as high as 0.9986. Therefore, based on excellent sensing performances, good stability, and real‐time characteristic, the fabricated SMPM sensor provides a potential technological strategy for the field of meteorological disaster monitoring.

Impact of Rainfall Intensity Changes on Water Storage of Subsurface Dam

Impact of Rainfall Intensity Changes on Water Storage of Subsurface Dam

Projected mid-century rainfall erosivity under climate change over the southeastern United States

Recent observations and climate change projections indicate that changes in rainfall energy, intensity, duration, and frequency, which determine the erosive power of rainfall, will amplify erosion rates around the world. However, the magnitude and scope of these future changes in erosive power of rainfall remain largely unknown, particularly at finer-resolutions and local scales. Due to a lack of available projected future sub-hourly climate data, previous studies relied on aggregates (hourly, daily) rainfall data. The erosivity for the southeastern United States in this study was calculated using the RUSLE2 erosivity calculation method without data limitation and a recently published 15-min precipitation dataset. This precipitation data was derived from five NA-CORDEX climate models' precipitation products under the Representative Concentration Pathway (RCP) 8.5 scenario. In this dataset, hourly climate projections of precipitation were bias-corrected and temporally downscaled to 15-min resolution for 187 locations with collocated 15-min precipitation observations. Precipitation, erosivity (R-factor), and erosivity density (ED) estimations were provided for historical (1970–1999) and future (2030–2059) time periods. Ensemble results for projected values (as compared to historical values) showed increase in precipitation, erosivity, and erosivity density by 14 %, 47 %, and 29 %, respectively. The future ensemble model showed an average annual R-factor of 11,237±1299 MJ mm ha−1h−1yr−1. These findings suggest that changes in rainfall intensity, rather than precipitation amount, may be driving the change in erosivity. However, the bias correction and downscaling limitations inherent in the original precipitation dataset and this study's analyses obscured this particular result. In general, coastal and mountainous regions are expected to experience the greatest absolute increase in erosivity, while other inland areas are expected to experience the greatest relative change. This study offers a novel examination of projected future precipitation characteristics in terms of erosivity and potential future erosion.

Automatic sequential rain sampling to study atmospheric particulate and dissolved wet deposition

Wet deposition is a key mechanism influencing lifetime of particles in the atmosphere. It allows the deposition of particles via two distinct processes, washout and rainout. Wet deposition of particles is highly variable in space and time depending both on atmospheric aerosol loads and precipitation. This variability can be observed from an event to another as well as within an event. Studying the chemical composition of wet deposition can help us to understand the origin of the aerosols as well as the way they are deposited. Indeed, depending on their intrinsic properties, aerosols have specific chemical signatures. Sequential rain sampling provides a means to monitor the chemical composition of wet deposition throughout a rain event, trace its origin, and discuss the relative contribution of rainout and washout during a single event. In order to address these issues, an automatic sequential rain sampler was specifically developed to collect the rain in consecutive fractions. The sampler has a large sampling area (1 m2) and an automatic distribution system allowing the sampling of wet deposition with high temporal resolution to document intra-event deposition variability and a sufficient volume to analyze the dissolved and particulate phases of the rain. Two rain events collected in July 2021 and February 2022 in an urban site in the surroundings of the Paris agglomeration were studied. Our results allowed us to quantify how the total mass concentration decreases with the precipitation amount during a rain event until reaching a constant level. By combining elemental and ionic analysis, the evolution of particulate and dissolved major and trace element concentrations is documented for these two rainfall events. The particulate phase shows a higher decreasing trend than the dissolved one. Major elements such as Fe, Si and Al are predominantly in the particulate phase while trace metals (TMs), like Mn and Zn, are shared in both dissolved and particulate phases. However, each element shows an increasing ratio of its dissolved to total (dissolved + particulate) fraction as rainfall progresses, especially for TM. The study of dissolved compounds (SO42−, NH4+ and NO3−) confirms that concentrations are decreasing with rainfall depth but that the variations are influenced by changes in rainfall intensity. The rainout and washout relative contributions to wet deposition are discussed regarding the studied chemical species and the rain events.

Future Rainfall Erosivity over Iran Based on CMIP5 Climate Models

Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran’s climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of more frequent occurrences of heavy and torrential rains. Results also showed that the transformation of annual rainfall was not related to the spatial change of erosion. In the central and southern parts, the intensity of rainfall would increase. Therefore, erosion would be more in the south and central areas.

Changes in Rainfall Intensity, Rising Air Temperature, Wind Speed, and Its Relationship with Land Use in Makassar City

Makassar City is one of the big cities that is growing very rapidly in Indonesia which has a large coastal area. This city is very vulnerable to the impact of changes in climate variables such as rainfall, temperature and wind speed, especially when there are indications of massive land use changes. The aim of the research is to identify changes in the trend of high-intensity rainfall, changes in air temperature, wind speed, and their relationship to changes in land use in Makassar City which have an impact on climate change. Observation data at Maritime Meteorological Station of Paotere, Makassar for 30 years is used to detect changes in climate variables by using slope calculations on linear equations, line graphs, and boxplots. The results showed that the air temperature in Makassar has a lower increasing (0.06oC/year) than Indonesian region which is around 0.3 oC. The slope values at 07.00 WITA, 13.00 WITA, and 18.00 WITA representing temperatures in the morning, afternoon, and evening are 0.0387, 0.0476, and 0.0417. While the average slope of air temperature is 0.042. However, Rising of air temperature is followed by a decrease in the accumulation of annual rainfall to below 3000 mm/year. In addition, heavy rains that cause flooding, increasing the maximum wind speed also need to be observed because wind speed is one of the causes of hydrometeorological disasters that often occur.

Projection of rainfall intensity-duration-frequency curves at ungauged location under climate change scenarios

It is vital to quantify the changes in the IDF relationship due to climate change for designing climate-resilient urban hydraulic structures. Such projections are also important for ungauged locations due to the possible urban expansion or human settlements. This work proposed a method for IDF curve construction with associated uncertainty at ungauged locations under climate change scenarios, considering peninsular Malaysia as a case study. The bias in Global Satellite Mapping of Precipitation Gauge Calibrated (GSMaP_GC) data was estimated by comparing its IDF curve with the observed IDF curve. Daily rainfall simulations of four global climate models (GCMs), most suitable for the study area, were employed to approximate the possible alterations in future rainfall distribution for four radiative concentration pathways (RCPs). The results revealed changes in rainfall intensity by -3.4 − 26.7% during 2010−2039, -0.1 − 34.5% in 2040−2069 and -4.3 − 96.8% during 2070−2099 compared to base period 1971−2000 for different RCPs. Overall, the changes were pronounced for short-duration compared to high-duration rainfall. Also, higher emission scenarios showed a greater change than the lower scenarios. The climate change uncertainty range was wider for high-duration and smaller for short-duration. The uncertainty was also higher in the far future.

How drought tolerant are tropical woody crop species – turgor loss points for the five most common species in the emerging landscapes on the Malaysian Peninsular

Drought events are increasing with climate change and threaten the future of industrial plantations in Southeast Asia. Drought tolerance has been well investigated for oil palm plantations. A comparative study on the tolerance of different woody crop species is still missing. We assessed the leaf turgor loss point of five common woody crop species using osmometry. The aim was to assess possible future risks in terms of the species´ drought tolerance. The research was carried out in Simpang Pertang, Malaysia. We collected botanical material from five species, namely Acacia mangium, Hevea brasiliensis, Tectona grandis, Mangifera indica, and Elaeis guineensis. To assess the adaptation of the local tree species pool, we measured the turgor loss point of the 65 most abundant tree species in the Pasoh forest reserve for comparison. Measured species showed a wide range of drought tolerance, ranging from -2.56 MPa to -1.15 MPa and -2.46 MPa to -0.88 MPa for crop species and forest species, respectively. Under the current precipitation regime, there is less concern about the cultivation success of these species in the study area. With the predicted changes in rainfall intensity and frequency, a reduction in the productivity of woody crop plantations can be expected.

Global hydro-climatological indicators and changes in the global hydrological cycle and rainfall patterns

There are few commonly used indicators that describe the state of Earth’s global hydrological cycle and here we propose three indicators to capture how an increased greenhouse effect influences the global hydrological cycle and the associated rainfall patterns. They are: i) the 24-hr global total rainfall, ii) the global surface area with daily precipitation, and iii) the global mean precipitation intensity. With a recent progress in both global satellite observations and reanalyses, we can now estimate the global rainfall surface area to provide new insights into how rainfall intensity changes over time. Based on the ERA5 reanalysis, we find that the global area of daily precipitation decreased from 43 to 41% of the global area between 1950 and 2020, whereas the total daily global rainfall increased from 1440 Gt to 1510 Gt per day. However, the estimated 24-hr global precipitation surface area varies when estimated from different reanalyses and the estimates are still uncertain. To further investigate historical variations in the precipitation surface area, we carried out a wavelet analysis of 24-hr precipitation from the ERA5 reanalysis that indicated how the rainfall patterns have changed over time. Our results suggest that individual precipitation systems over the globe have shrunk in terms of their spatial extent while becoming more intense throughout the period 1950–2020. Hence, the wavelet results are in line with an acceleration of the rate of the global hydrological cycle, combined with a diminishing global area of rainfall.

Detection of Climate Change in the Kedungsoko Irrigation Area – Nganjuk, Indonesia

Rainfall is one of the main components in discussing climate where there is a complex process in it. Rainfall has a dynamic characteristic, and it is not fixed along the season period. The change in rainfall intensity has commonly happened. However, if the change has extremely different, it will indicate climate change overall. The rainfall change can impact the agricultural sector because dropped rainfall will affect the crops. Too many rainfalls will cause flooding and rotting plants. However, too little water also causes nutritional deficiency in the crop and causes crop failure. The crop failure in the Kedungsoko Irrigation Area-Nganjuk-Indonesia is due to climate change, such as the extreme rainfall dropping there. In this research, the climate change is mainly related to the season shift for arranging the cropping pattern in the research area. The potency of events like that can reoccur in the future. This research intends to analyze the climate change in the Kedungsuko Irrigation Area statistically. In addition, this research aims to investigate the season shift based on climate change. The methodology uses parametric and non-parametric statistics. Based on the extreme analysis by using the parametric method (F-test and T-test) and non-parametric method (Mann Kendall test), it is obtained the prediction of the extreme rainfall that is likely to happen in the future is in January and December. It is hoped to become the consideration for anticipating the same undesirable event in the Kedungsoko Irrigation Area.

Temporal changes in rainfall intensity–duration thresholds for post-wildfire flash floods in southern California

Abstract. Rainfall intensity–duration (ID) thresholds are commonly used to assess flash flood potential downstream of burned watersheds. High-intensity and/or long-duration rainfall is required to generate flash floods as landscapes recover from fire, but there is little guidance on how thresholds change as a function of time since fire. Here, we force a hydrological model with radar-derived precipitation to estimate ID thresholds for post-fire flash floods in a 41.5 km2 watershed in southern California, USA. Prior work in this study area constrains temporal changes in hydrological model parameters, allowing us to estimate temporal changes in ID thresholds. The results indicate that ID thresholds increase by more than a factor of 2 from post-fire year 1 to post-fire year 5. Thresholds based on averaging rainfall intensity over durations of 15–60 min perform better than those that average rainfall intensity over shorter time intervals. Moreover, thresholds based on the 75th percentile of radar-derived rainfall intensity over the watershed perform better than thresholds based on the 25th or 50th percentile of rainfall intensity. Results demonstrate how hydrological models can be used to estimate changes in ID thresholds following disturbance and provide guidance on the rainfall metrics that are best suited for predicting post-fire flash floods.

广东省水质现状及驱动因素

PDF HTML阅读 XML下载 导出引用 引用提醒 广东省水质现状及驱动因素 DOI: 10.5846/stxb202101120115 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 广东省科技计划项目（2018B030320002，2019A050506001）；广东省自然科学基金项目（2018B030311059，2021A1515012579） Analysis of water quality status and driving factors in Guangdong Province Author: Affiliation: Fund Project: Science & Technology Plan Project of Guangdong Province of China (2018B030320002, 2019A050506001);Natural Science Fund grant (2018B030311059, 2021A1515012579) of Guangdong Province, China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:水质现状评估及其驱动因素分析是实现水生态保护、水资源利用和水污染治理的关键，对于水生态系统的可持续发展具有重要意义。以广东省七大流域为研究区，基于2019-2020年间的溶解氧（DO）、透明度（SDD）、悬浮物（SPM）、叶绿素a （Chla）、氨氮（NH3N）、总氮（TN）、总磷（TP）7个指标的水质监测数据，综合运用单因子指数法（SI）和综合水质指数（WQI）评价方法，分丰水期（N=66）和枯水期（N=54）评估研究区的水质现状，并探讨水质参数与地形、气象、社会经济和土地覆被类型等驱动因素之间的相关关系。SI评估结果显示广东七大流域主要以工业污水、农业面源等造成的Chla和TN浓度超标、部分水体富营养化严重为主，同时伴有溶解氧浓度偏低的问题；WQI评估结果显示研究区有57%以上的采样点属于中等以下水质。Chla、SPM、NH3N和TP浓度具有显著的季节和驱动因素差异：丰水期的Chla和TP浓度低于枯水期，但SPM和NH3N浓度高于枯水期。枯水期DO、TN和WQI的显著性影响因子为丰水期的1/3左右；这种季节差异可能是流域内降雨、营养盐负荷和土地覆被类型导致的复杂地表径流及面源污染所致。珠江三角洲河网区、粤西诸河、韩江下游以及粤东诸河练江流域的水质问题突出。未来水生态系统的可持续发展研究可以借助长时间序列、多频次、高分辨率的遥感监测手段和多种数值模拟方法以及常规水质评估模型，探讨气候变化、河岸带产业结构和流域土地利用方式对面源污染的影响，以进一步厘清降雨强度、三产结构和土地利用方式转变对区域水质变化的影响。 Abstract:Water quality evaluation and driving forces analysis, two key factors to achieve ecological protection, resource utilization, and pollution control of water, are pivotal to the sustainable development of aquatic ecosystem. We utilized the in-situ experiment data of seven water quality parameters-dissolved oxygen (DO), transparency (Secchi disk depth, SDD), suspended particulate matters (SPM), chlorophyll a (Chla), ammonia nitrogen (NH3N), total nitrogen (TN), and total phosphorus (TP)-which were collected from seven major basins in Guangdong Province during 2019-2020. This paper evaluates the water quality status of Guangdong Province in the high-flow season (HFS, N=66) and low-flow season (LFS, N=54) using the single factor index method (SI) and the comprehensive water quality index (WQI). The correlation between water quality parameters and driving factors, such as topography, meteorology, socio-economic and land cover types, were analyzed using Pearson correlation coefficient. The SI-based evaluation results show that the seven basins in Guangdong province are mainly faced with problems such as exceeding standard of Chla and TN concentration, and low DO concentration, which were caused by industrial sewage and agricultural non-point sources. The WQI evaluation results show that 57% of the sampling points in the study area are below the moderate level. The concentrations of Chla, SPM, NH3N and TP have significant differences in water period and driving factors:the concentrations of Chla and TP in HFS are lower than that in LFS, but the concentrations of SPM and NH3N are opposite in the two periods. The number of significant factors affecting the evaluation results of DO, TN, and WQI in LFS is about 1/3 of those in HFS. The seasonal difference is caused by the changes of complicated surface runoff and non-point source pollution, such as precipitation, nutrient load and land cover types in the basins. Water quality problems are prominent in the river network area of the Pearl River Delta, the lower reaches of the Hanjiang River, the rivers in western Guangdong, and the Lianjiang Basin of the eastern Guangdong. The water quality problems in the Pearl River Delta and the West River are mainly exceeded concentrations of TN and SPM. The main water quality problem of North River and East River is high TN concentration. The rivers in western Guangdong have insufficient DO, low SDD, and high concentrations of Chla and NH3N. The conditions of TN and DO for some sampling points in the rivers of eastern Guangdong and the Hanjiang River are not optimistic. In the future, the integrated methods combining remote sensing monitoring, numerical simulation, and regular water quality evaluation models should be enhanced to explore the effects of non-point source pollution from climate change, riparian industrial structure, and adjustment of watershed land cover patterns. These integrated methods can help to further clarify the water quality impact of changes in rainfall intensity, production structure, and land cover patterns, which greatly promote the sustainable development of aquatic ecosystems. 参考文献 相似文献 引证文献