Rainfall In Flood Season Research Articles

The Compound Response Relationship between Hydro-Sedimentary Variations and Dominant Driving Factors: A Case Study of the Huangfuchuan basin

The Huangfuchuan basin is one of the major sources of coarse sediment in the Yellow River and has long been a focal point and challenge for the conservation of soil and water in the Yellow River Basin. In this study, we analyzed the phase differentiation characteristics of water–sediment variations during the flood season in the Huangfuchuan basin using a long-term dataset. We elucidated the complex response relationship between water–sediment variations and meteorological factors and human activities, which is of great significance for revealing the mechanisms of water–sediment variations in the region and establishing a scientific water–sediment regulation system in the basin. Statistical methods such as the Mann–Kendall trend test, Sen’s slope estimation, Pettitt nonparametric test, and principal component analysis were employed to identify and analyze the trends and dominant driving factors before and after the water–sediment variations and abrupt changes in parameters such as rainfall and temperature in the Huangfuchuan basin. Additionally, multiple regression analysis was used to determine the extent of the contribution of climate and human activities to water–sediment variations in the Huangfuchuan basin. The study revealed that the year 2000 was a turning point for water–sediment variations, with decreases of 11.3%, 76.7%, and 85.1% in flood season rainfall, flood season runoff, and flood season sediment transport, respectively. Despite significant changes in the underlying surface conditions of the Huangfuchuan basin, the relationship between flood season sediment transport and flood season runoff remained a power–law relationship. In the absence of obvious abrupt changes in temperature, rainfall, and other meteorological factors, the changes in the underlying surface caused by human activities are the main cause of the changes in runoff and sediment yield in the Huangfuchuan basin. The current level of vegetation restoration in the Huangfuchuan basin is still relatively low, making it difficult to exert stronger control on sediment yield during the flood season. Meanwhile, human activities, primarily based on engineering measures, play a more significant role in the control of soil and water loss in the basin.

Response of erosion reduction effect of typical soil and water conservation measures in cropland to rainfall and slope gradient changes and their applicable range in the Chinese Mollisols Region, Northeast China

Mollisols are rich in organic matter, which makes them suitable for cultivation and for enhancing global food security. Mollisols are experiencing severe soil erosion due to overfarming and a lack of maintenance. Thus, suitable soil and water conservation measures (SWCMs) are needed to protect Mollisols. However, how SWCMs respond to changes in slope gradient and rainfall and their effective application area remain ambiguous. Using a long-term field observation dataset, meta-analysis, and statistical test methods, we reveal the typical erosion reduction effect of SWCMs and their responses to changes in slope gradient and rainfall. Next, we calculated a coupling factor, P·S, by slope gradient and rainfall in flood season to determine the effective SWCMs application threshold. Compared with bare land, no-tillage (NT), contour ridge tillage (CT), ridge hedgerows (RH), and terraced fields (TF) had an average erosion reduction coefficient of 91.5%. There was a significant exponential increase in the correlation between P·S and the soil erosion amount in plots with typical measures. According to this correlation and soil loss tolerance (200 t/km2), the upper limits of P·S (NT: 564 mm; CT: 885 mm; RH: 1135 mm) were determined and utilized to determine the effective application areas (NT: 311.40 103 km2; CT: 320.86 103 km2; RH: 323.72 103 km2) at the plot scale. In wet years, the applicable area of SWCMs declined toward the low-elevation foothills and flat terrain. All the results are applicable when slope length within 20 m. These results provide a basis for the precise allocation of SWCMs in Mollisols area and promote the scientific utilization of Mollisols resources.

A comprehensive framework model for the trend, period and evaluation of the precipitation enhancement effect: TPEM

Abstract In this study, a framework model (TPEM) for evaluating the temporal and spatial variation of urban precipitation is established. TPEM includes seven calculation methods at the same time. Taking the annual precipitation and flood season precipitation data of eight meteorological stations in Zhengzhou, China, from 1960 to 2020 as an example, the trend and period of rain island effect in Zhengzhou are analyzed from the annual and flood season rainfall scale, and the distance method is introduced to quantitatively evaluate the rain island effect. The results show that the rain island effect in the central urban area of Zhengzhou has a sudden increase trend in 1997 and 2004 respectively, and the increasing trend of rain island effect on the scale of annual and flood season rainfall lasts for four and ten years respectively. At the same time, it has fluctuation periods of 8–10a, 16–18a, 3–5a, 17–20a, 5–7a, and 2–3a respectively. The rain island effect in Zhengzhou is concentrated in the flood season. The research can provide a scientific basis for cities to deal with the rain island effect in the future.

Assessing the degree of soil erosion in karst mountainous areas by extenics

The ecological environment of karst mountainous areas is fragile, and soil erosion has become an important factor restricting the social and economic development of these regions. How can the degree of soil erosion be scientifically assessed with a reasonable evaluation method? What is effect of the ecological environmental control measures? This study used extenics to assess the degree of soil erosion in karst mountainous areas. The typical karst mountainous area of Guizhou Province was used as the research object. In this study, a soil erosion monitoring indicator system for karst mountainous areas was developed, an evaluation model based on extenics was established, and an index correlation function and comprehensive correlation function were designed to assess the soil erosion of the karst mountainous areas. The key results were as follows. In the soil erosion monitoring index system, the soil nutrient indexes, soil erosion modulus, rock exposure, diversity index, flood season rainfall, population density and natural population growth rate had significant effects on soil erosion in the karst mountainous area. Additionally, we determined the grading standards for soil erosion intensity in typical karst mountains. Finally, the evaluation model was applied to a case study. The status of water and soil conservation was as follows: XW > ML > SB > ZS. These results provide new insights into the evaluation method, namely, extenics, which is helpful for conveniently, quickly and scientifically assessing soil erosion.

Effects of Waterlogging on Photosynthetic Characteristics and Yield of Summer Peanut

Yield of summer peanut is often reduced due to waterlogging caused by excessive rainfall in flood season. Exploring the effects of waterlogging duration under different cultivation pattern on photosynthetic characteristics and yield of peanut can provide scientific and technological support for dynamic monitoring, evaluation of waterlogging process, disaster prevention and mitigation. Field experiment is carried out in main peanut production area with clay soil. Peanut variety Yuhua 22 is used in field experiment under flat and ridge cultivation patterns. The yield formation process of peanut is divided into three stages: Early, middle and late, and four irrigation treatments (3 days, 5 days, 7 days and 9 days) are designed in these stages. In flat pattern, the depth of water in field is no less than 2 cm during irrigation, and the daily irrigation amount of ridged plots is consistent with that of flat plots. The results show that under the same irrigation amount, the waterlogging lasting days of ridge pattern are reduced by 1 to 5 days, and the flooding days are reduced by 3 to 5 days. The waterlogging duration is 4 to 5 days in 3 days irrigation treatment. Chlorophyll content and net photosynthetic rate show positive effects, resulting in the increase of dry matter accumulation in stems and leaves. The effects of waterlogging in all stages increase with waterlogging duration, but it has little effect in late stage because of the slow growth of peanut itself. For the 100-seed weight, the effects of heavy waterlogging are most significant in the middle period, followed by the early period, and then the late period. While for other factors, the effects are most significant in the early period. The waterlogging duration in all treatments is between 4 days and 16 days. The effects range from 1.3% to 64.2% on photosynthetic parameters and from 0.9% to -44.9% on biomass and yield. Compared with flat pattern, ridge pattern has less effects due to the decrease of waterlogging duration. The peanut yield loss of each treatment ranges from 3.4% to 11.6% in early stage, and from 1.2% to 6.9% in middle stage. The experiments take possible waterlogging lasting days in peanut production and two different cultivation patterns (flat and ridge) into consideration reasonably. The results are of great significance to enhance the capacity of disaster prevention and mitigation in peanut production.

The Environmental Capacity of Hun River, Liaoning Province, China

Based on the environmental functional area allocation, point and non-point sources, hydrology and water quality data of Hun River, SWAT model is built and used to calculate the hydrological, water quality and environmental capacity processes in the river channel. The results showed: (1) When the upstream water quality concentration was limited to the grade III of the national surface water quality standard, the environmental capacity of total phosphorus (TP) in Hun River is about 582.01 t/a. (2) According to the results of water environmental capacity in different water periods, the environmental capacity of Hun River is seriously overloaded in the flood season, although annual value is satisfied the standard. For the strong driving effect on the pollutants transport in the river by the intense rainfall in flood season, the concentration in flood season exceeds the water quality standard of grade III. (3) To strengthen the environmental management of Hun River, it is necessary to monitor non-point sources in the cultivated land, standardize the fertilization measures, and improve the rural sewage pipe networks and related facilities.

Improved prediction model for flood-season rainfall based on a nonlinear dynamics-statistic combined method

Abstract Precipitation predictions during the flood season are critical and imperative on continents, especially in monsoon-impacted areas. However, majority of current dynamical models failed to predict the flood-season rainfall very well, although their simulations are high correct. In this study, based on the EOF decomposition of multi-factors field, we used a similar-error correction method to improve model prediction effect, which we call dynamic–statistic combined prediction method. Chinese Global atmosphere-ocean Coupled Model/Climate System Model was combined with dynamic–statistic combined prediction method as a case and the real-time prediction during 2009-2019 were implemented. The spatial anomaly correlation coefficient between predicted and observed values was used to assess the effectiveness of the improvement. The results show that the average anomaly correlation coefficient scores of dynamic–statistic combined prediction method (0.16) is 0.12 higher than that of Chinese Global atmosphere-ocean Coupled Model/Climate System Model (0.04), implying that dynamic–statistic combined prediction method has a broad application prospects in precipitation prediction. We suggest that dynamic–statistic combined prediction method should be promoted to other models for testing.

Variation and Trend Analysis of Rainfall in Qingshui River Basin of Ningxia in China

The study on the variation law and trend of rainfall characteristics of Qingshui River, a typical tributary of the upper Yellow River in China, is of great significance to reveal the cause of runoff and sediment change in the Yellow River. According to the daily rainfall data on 93 rainfall stations in the Qingshui River Basin from 1958 to 2015, the rainfall characteristics of the basin, such as annual rainfall, flood season rainfall, and main flood season rainfall were calculated. Using time-domain analogy, Mann-Kendall trend and abrupt change point test, coefficient of variation, etc., the variation and trend of rainfall characteristic values in the past 60 years were analyzed. The results showed that the distribution of rainfall during the periods from 1970 to 1979 and 2010 to 2015 was significantly different from other periods. There is no significant trend change in rainfall characteristic values from 1958 to 2015. From 1958 to 2015, the intensity of discrete changes in the rainfall characteristics of different series from small to large is the annual rainfall, flood season rainfall, and main flood season rainfall. The intensity of the discrete changes in main flood season rainfall gradually decreased with time.

Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method.

Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention.

Study on the Spatial-temporal Differentiation and Driving Mechanism of Ecological Vulnerability in Zhangye City

Based on the “Sensitivity-Pressure-Resilience” evaluation model, combined with the spatial principal component (SPA) analysis method of GIS, we evaluated the ecological vulnerability of Zhangye City in 2000, 2005 and 2010, then we used geographical detector model to analyse the main driving factors of the ecological vulnerability. The results showed that: 1. The ecological vulnerability of Zhangye City was higher in the West and lower in the East. The average value of ecological vulnerability index was 0.45, which was mild fragile, ecological vulnerability index was the highest in Linze county, and the lowest in Minle county. 2. The overall ecological vulnerability showed a trend of improvement from 2000 to 2010, due to the increase of precipitation in 2005, the ecological vulnerability index was higher than that of 2000 and 2010. 3. Geographical detector results showed that six factors could explain the ecological vulnerability, which ranked as: rainfall in flood season (0.1138) > temperature in high temperature season (0.1116) > elevation (0.0581) > vegetation coverage (0.0532) > topographic relief (0.0411) > biological abundance index (0.0384), and the interaction of the 6 factors had a stronger explanatory power to the ecological vulnerability.

A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China.

The Yangtze River Delta (YRD) is one of the most developed regions in China. This is also a flood-prone area where flood disasters are frequently experienced; the situations between the people–land nexus and the people–water nexus are very complicated. Therefore, the accurate assessment of flood risk is of great significance to regional development. The paper took the YRD urban agglomeration as the research case. The driving force, pressure, state, impact and response (DPSIR) conceptual framework was established to analyze the indexes of flood disasters. The random forest (RF) algorithm was used to screen important indexes of floods risk, and a risk assessment model based on the radial basis function (RBF) neural network was constructed to evaluate the flood risk level in this region from 2009 to 2018. The risk map showed the I-V level of flood risk in the YRD urban agglomeration from 2016 to 2018 by using the geographic information system (GIS). Further analysis indicated that the indexes such as flood season rainfall, urban impervious area ratio, gross domestic product (GDP) per square kilometer of land, water area ratio, population density and emergency rescue capacity of public administration departments have important influence on flood risk. The flood risk has been increasing in the YRD urban agglomeration during the past ten years under the urbanization background, and economic development status showed a significant positive correlation with flood risks. In addition, there were serious differences in the rising rate of flood risks and the status quo among provinces. There are still a few cities that have stabilized at a better flood-risk level through urban flood control measures from 2016 to 2018. These results were basically in line with the actual situation, which validated the effectiveness of the model. Finally, countermeasures and suggestions for reducing the urban flood risk in the YRD region were proposed, in order to provide decision support for flood control, disaster reduction and emergency management in the YRD region.

Study on multi-time scale variation rule of rainfall during flood season in Jilin Province in recent 65 years

A total of 65-year rainfall data collected from 30 meteorological stations in Jilin Province during 1951 to 2015 was analysed by sliding average method, anomaly analysis method and Mann-Kendall mutation test, combined with MapInfo, ArcGIS software and R language drawing tool. The interdecadal, interannual and annual variation characteristics of rainfall in flood season in Jilin Province were used to reveal the multi-time scale characteristics of rainfall variation and the periodic variation and mutation points of rainfall sequences at different time scales. The results show that: 1) the interdecadal and interannual rainfall in Jilin Province shows a trend of fluctuating downward, especially in the 21st century, the decline in rainfall is more significant, and the sudden change points of annual precipitation occur in 1983 and 1992; 2) the monthly rainfall in flood season shows a significant downward trend, all but a small decline in June; 3) the rainfall during the flood season is mainly concentrated in July and August, accounting for 65% of the total rainfall during the flood season. The average proportion in September is the lowest, only 13%. In addition, the proportion of rainfall in June in the whole flood season has an increasing trend, that is, the rainfall in flood season is ahead of schedule; 4) the average rainfall intensity of each month in flood season shows an upward trend, and the decreasing rate of rainfall days is greater than that of rainfall.

Sediment Yield Modeling Using SWAT Model: Case of Changjiang River Basin

Soil erosion is posing a severe challenge to the productivity of land throughout the word, this alarming situation requires urgent interventions in order to preserve water and soil resources. This study presents an application of the model Soil and Water Assessment Tool(SWAT) to simulate daily and monthly water flow and sediment load from 1980 to 2012 in the Changjiang River basin, which is a sub-basin of the Poyang Lake basin, China. The sensitivity analysis is carried out by using the LH-OAT method. The model was calibrated and validated based on a comparison of simulated and measured runoff and sediment data of the Tankou and Dufengken hydrological station in Changjiang River. Indeed, for the monthly time step application, the relative error of the model to the runoff simulation was less than 10.21% in the calibration period and the validation period. The correlation coefficient(R2) and Nash-Suttcliffe(Ens) were higher than or equal to 0.91, and The relative error of the model to the sediment simulation was less than 26.0% in the calibration period and the validation period, R2 and Ens were higher than 0.76. The research indicates that SWAT model can be used to simulate the runoff and sediment, water loss and soil erosion are mainly caused by rainfall in flood season, and the sediment is up to 99.26% of the total year, and it has a good correlation among rainfall, runoff and sediment in flood period.

Using a Hidden Markov Model to Analyze the Flood-Season Rainfall Pattern and Its Temporal Variation over East China

The homogeneous hidden Markov model (HMM), a statistical pattern recognition method, is introduced in this paper. Based on the HMM, a 53-yr record of daily precipitation during the flood season (April–September) at 389 stations in East China during 1961–2013 is classified into six patterns: the South China (SC) pattern, the southern Yangtze River (SY) pattern, the Yangtze–Huai River (YH) pattern, the North China (NC) pattern, the overall wetter (OW) pattern, and the overall drier (OD) pattern. Features of the transition probability matrix of the first four patterns reveal that 1) the NC pattern is the most persistent, followed by the YH, and the SY is the least one; and 2) there exists a SY–SC–SY–YH–NC propagation process for the rain belt over East China during the flood season. The intraseasonal variability in the occurrence frequency of each pattern determines its start and end time. Furthermore, analysis of interdecadal variability in the occurrence frequency of each pattern in recent six decades has identified three obvious interdecadal variations for the SC, YH, and NC patterns in the mid–late 1970s, the early 1990s, and the late 1990s. After 2000, the patterns concentrated in the southern region play a dominant role, and thus there maintains a “flooding in the south and drought in the north” rainfall distribution in eastern China. In summary, the HMM provides a unique approach for us to obtain both spatial distribution and temporal variation features of flood-season rainfall.

The evaluation of soil stability in loess hilly and gully region of Northern Shaanxi based on GIS

Based on the previous research in loess hilly region of Northern Shaanxi, this paper takes the soil erosion degree as the main measure of soil stability and the soil utility, annual average rainfall in flood season (from June to September), and topography, as the main measure indexes of soil stability. After that, the evaluation system of soil stability in loess hilly region of Northern Shaanxi can be constructed, which can be done by special analysis of GIS. The results illustrate that the soil stability showed a trend of high south‐east and low north‐west. Soil with good stability is mainly distributed in Ganquan County, and Yanchuan County, where vegetation coverage is high, vegetation types are mostly forest land and grassland, ecological environment is good, and precipitation erosion effect is not significant. Soil with intermediate stability is mainly distributed in Baota district and its surrounding areas, where the main vegetation types are bush fallow and grassland, and the terrain is flat and gently rolling. Soil with the worst stability is mainly distributed in Suide County, and Wuqi County. The area is mostly sandy and desert, the terrain is fragmented, soil is loose, vegetation cover is not high, making the soil the worst soil stability, and strong rainfall conditions are prone to soil erosion. The prerequisite of the implementation of soil consolidation projects is having evaluation on soil stability. The research results can be the theoretical evidence, and implement guarantee of regional soil exploitation and reorganization, and the reference to enhancing the assurance of ecological safety.

Risk Evaluation and Prediction of Rainfall Disaster in Flood Season Based on Pearson Fitness Test and Risk Function

Heavy rainfall during flood season causes great losses for the cities and the farmland. In order to better describe the flood season rainfall disaster events, the risk function combining risk probability and risk loss is put forward, and the risk degree value is obtained by combining the corresponding risk loss. The probability distribution of precipitation in flood season is found out through Pearson fitness test, and the probability of precipitation in each flood season is calculated using the hydrological frequency analysis method. In this paper, taking Tongzhou district of Beijing city as an example, the above method was used for risk analysis, and the result was close to the reality. The research results have deepened the understanding of the changing law of precipitation risk in Tongzhou district and have certain reference value for urban disaster prevention decision-making.

Rainfall-induced Landslide Hazard Analysis Based on GIS in Yanbian Region, China

This paper mainly studies landslide hazard in Yanbian region，8 landslide factors of soil moisture content, hydrogeology, land use type, slope, slope aspect, fault, river and rainfall are analyzed in the area of Yanbian region. Using the analytic hierarchy process (AHP), the relative weights of 8 landslide factors were obtained, and the internal weights of each factor were determined by the data; the rainfall factors were then divided into three categories such as average annual rainfall, average flood season rainfall, and the maximum average rainfall. And the comparison of the rainfall factors was made. Finally based on the results of these weights to the remote sensing technology(RS) and geographic information system(GIS) technology combined to evaluate its area, we are mapping the risk assessment area for landslide hazard in Yanbian region. According to the map analysis, figure in the dangerous hierarchies result in accordance with the historical landslides is better. At the same time. landslides and hazard area of statistical data analysis showed that the AHP method combined with GIS technology is suitable for promoting the Yanbian regional landslide hazard assessment.

Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Study on Sediment Reduction Effect by Soil Retaining Dam with Different Storage Capacity Allocation Proportion in Dali River Watershed

Analysis and study were carried out by means of mathematical statistics on sediment reduction effect by soil retaining dam with different storage capacity allocation proportion in Dali river watershed of the Yellow River. The results indicate that, sediment reduction by large, middle and small soil retaining dam account for 80.1%, 14.6%, 5.3% of the total reduction; the sediment reduction benefit has reached its maximum since 1990s, amounting to 30.2%, with corresponding storage allocation proportion of 1.84:2.37:5.79.Dam and reservoir have obvious influence on the correlation between rainfall and sediment in the river watershed, sediment transport is reduced by 10 million ton corresponding to annual average flood season rainfall of 350mm in the watershed. Within the sediment reduction capability of soil retaining dam, the sediment reduction volume increase with more rainfall in the flood season, having the feature of the more incoming sediment is, the more sediment reduction will be.

Dynamic change of net primary productivity and fractional vegetation cover in the yellow river Basin using multi-temporal AVHRR NDVI Data

An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.