Rainfall Duration Research Articles

Effects of microtopography shaping on characteristics of eroded sediment particles of a dump slope in an opencast coal mine in China

AbstractThe properties of sediment particles are crucial research areas for understanding soil erosion on dump slopes. In this study, the slope of the southern dump of the ATB opencast coal mine in the Pingshuo mining area in Shanxi Province, China, was selected to conduct microtopographical shaping and artificial rainfall simulation experiments. Three different gradients of rainfall intensity, 20, 50, and 80 mm·h−1, were set. The main component of rainwater was water (chemistry H2O) by a rainfall duration of 30 min. A Microtrac S3500 laser particle size analyzer was used to measure the collected erosion sediment particles. This study conducted level benches and fish‐scale pits microtopography shaping on the slope of the waste dump. The dimensions of the level bench design were 1, 2, and 1 m for the step width, for the step length, and between each level bench, respectively. The fish‐scale pits were 60 cm (length), 40 cm (width), and 30 cm (depth). The horizontal and row spacing of each fish‐scale pit was 1 m, and an unshaped terrain was used as the control group. Through dynamic monitoring and statistical analysis of the data, the impacts of rainfall intensity and microtopography shaping mode on the features of the eroded sediment particles were analyzed. Fine silt particles (53%) were lost as a result of the surface erosion of the slope. The mean starting periods of sediment particle loss decreased with the increase in rainfall intensity (589 s > 402 s > 305 s). Additionally, the clay and silt particle enrichment ratios decreased (ER >1), whereas the sand particle enrichment ratios increased (ER <1). The level benches had a stronger interception effect on fine sediment, whereas the fish‐scale pits had a weaker interception effect on sediment particles under heavy rain. The distribution properties of erosion sediment particles on a slope can be assessed using fractal dimension as an index. This study is significant for understanding the erosion mechanism of eroded sediment particles on dump slopes and provides a scientific foundation for soil erosion control on opencast coal mine dump slopes in the Loess Plateau. These results highlight the potential for the level bench to effectively delay the occurrence of runoff and greatly enhance the interception of sediment particles.

Efficacy of rain barrels and rain gardens to reduce urban pluvial flooding in densely built-up residential areas: a case study on Miyahara-Cho in Saitama City, Japan

In recent years, flooding in densely built-up residential areas has tended to increase because of the increased frequency of heavy rain and climate change. Urban pluvial flooding mitigation is thus needed by reducing the runoff of sewer systems using green infrastructure solutions. In this regard, rain barrels (RBs) and rain gardens (RGs) are considered effective stormwater management methods. This study aimed to verify the effectiveness of RBs and RGs and their combined use in reducing urban pluvial flooding in densely built-up residential areas. This research undertook a case study in Miyahara-Cho, Saitama City, Japan. It involved the analysis of the total runoff of sewer systems and the flood condition in the sewer to verify the rainwater runoff control effect of RBs and RGs through simulations using the US Environmental Protection Agency’s stormwater management model. This study simulates two main types of rainfall events: short duration and long duration. The results of the short duration rainfall showed that the combination of RBs and RGs is highly effective and can reduce the impact of excess runoff. In the case of the long duration rainfall, the volume and speed of stormwater runoff of the first peak rainfall were reduced.

Implications of rainfall variability on groundwater recharge and sustainable management in South Asian capitals: An in-depth analysis using Mann Kendall tests, continuous wavelet coherence, and innovative trend analysis

Groundwater resources, a critical component of hydrogeological systems, are under increasing pressure due to climate change and anthropogenic activities. South Asia, a region highly vulnerable to climate shocks, has seen over half of its population affected by climate-related disasters in the past two decades. These climate changes, characterized by intensifying heat waves, cyclones, droughts, and flooding, pose significant risks to groundwater resources management. This study aimed to address the recent variability, intensity, and depth of rainfall patterns in the capitals of south Asian countries on a daily, monthly, and annual basis. Mann-Kendal tests (MKT), Innovative Trend Analysis (ITA), and Continuous Wavelet Coherence were conducted to identify trend patterns. The Gumbel distribution of Intensity Depth Frequency (IDF) analysis was performed to analyze the depth and intensity of rainfall patterns for rainfall durations of 5, 10, 15, 30 min, 1–hr, 2–hr, 12–hr and 24–hr for 2, 5, 10, 25, 50, 75 and 100 years return periods. Results of ITA and MKT showed similar trend results for all cities and time scales. Findings suggest that Kotte receives the highest annual rainfall of 1745.28 mm and Kabul the lowest (380.76 mm). MKT showed both positive and negative monthly trends in most of the months, while annual rainfall exhibited positive trends in all cities with a change rate between +0.56 and + 2.34 mm/year. Seasonal trend analysis confirms positive trends for all seasons, except in Delhi, Malé, and Kathmandu in winter. IDF analysis reveals that current rainfall trends will slightly affect the depth and intensity of rainfall events for the various return periods. The study provides valuable insights into the water-energy-food nexus and contributes to the broader discourse on water policy and social aspects of groundwater in the face of climate change.

Maximum rainfall and erosivity during the rainy season (2011-2020) in the municipality of Pau dos Ferros, semi-arid region of Rio Grande do Norte, Brazil

The intensity and volume of rainfall are determining factors in erosive processes, especially in the semi-arid regions where this phenomenon occurs concentrated during a specific period of the year. In Pau dos Ferros, Rio Grande do Norte, challenges such as floods and soil loss are evident, highlighting the lack of recent studies on erosivity. Thus, considering that this gap compromises the necessary foundation for conservation practices and a thorough understanding of these processes, this work aims to analyze the variation in the monthly erosivity pattern during the rainy season in the municipality, between the years 2011 and 2020. To achieve this, it uses daily rainfall data, application of equations for rainfall coefficient, intensity, duration, and frequency, as well as historical and estimated erosivity indices, and classification of the type of monthly rainfall erosivity. The results showed that the rainy season in Pau dos Ferros was concentrated from February to May, with the first two months being the most significant in annual accumulated precipitation (1,358.5 mm and 1,410.6 mm) and rainfall coefficients, as well as que the rains of, no more that fifteen minutes, represent a bigger immediate erosivity if compared to the ones of thirty-minute-long, which indicates a greater cumulative power. Furthermore, the estimated monthly erosivity for February and March exceeded the historical average, while April and May remained close to expectations. Finally, it is expected that this study can support territorial planning actions, in addition to encouraging additional research on the relationship between erosivity, soil types and land use.

Assessment of Rainfall-Induced Shallow Landslides in Kavre District, Nepal

Steep slopes and fragile geology make Nepal a landslide-prone country. Kavre district experiences a large number of shallow landslides in the monsoon season. This research aims to establish rainfall intensity relation with rainfall duration along with rainfall threshold for the initiation of shallow landslides in the Kavre district, Nepal. The six rain gauge stations' rainfall data in the Kavre district from 1987-2016 and landslide data from 1987-2013 were used for establishing rainfall thresholds. The rainfall that risks the landslide event was sorted and the regression analysis of the rainfall and landslide yielded a relationship I =86.738 *D-1.066 having a coefficient of determination R2 = 0.1257. The analysis also incorporates the drawing of the lower boundary which is the 48-h rainfall depth that indicates the rainfall threshold for the initiation of landslide in the Kavre district. The 48-hour rainfall intensity and rainfall depth at which the risk of landslide initiation is high in the Kavre district is 1.4 mm/h. and 67.18mm. Analysis of the antecedent rainfall and the landslide occurrence event indicated that the daily rainfall at failure conditions is better correlated with 3 days of cumulative rainfall at failure than with 5, 7, 10, 15, and 30 days. The study is significant for rainfall-induced landslide disaster management.

Time-varying characteristics of saturated hydraulic conductivity in grassed swales based on the ensemble Kalman filter algorithm —A case study of two long-running swales in Netherlands

Saturated hydraulic conductivity (Ks) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, Ks is assumed as constant or decrease with a clogging factor. However, the Ks measured on site cannot be the input of the hydrological model directly. Therefore, in this study, an Ensemble Kalman Filter (EnKF) based approach was carried out to estimate the Ks of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between Ks and possible influencing factors (antecedent dry period, temperature, rainfall, rainfall duration, total rainfall and seasonal factors) were studied and a Multivariate nonlinear function was established to optimize the hydrological model. The results revealed that the EnKF method was satisfying in the Ks estimation, which showed a notable decrease after long-term operation, but revealed a recovery in summer and winter. After the addition of Multivariate nonlinear function of the Ks into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant Ks. A sensitivity analysis revealed that the effect of each influencing factors on the Ks varies depending on the type of grassed swale. However, these findings require further investigation and data support.

Spatiotemporal Variability of Regional Rainfall Frequencies in South Korea for Different Periods

Understanding regional as well as temporal variations in probability rainfall is essential for addressing climate change-related hydrological issues. Few studies have conducted spatial analyses on probability rainfall using up-to-date rainfall data, which is crucial to comprehend regional rainfall variations for effective flood management and hydraulic structure design. In this study, we analyzed the spatiotemporal variations of probability rainfall factors in South Korea using 61 rainfall stations and four rainfall periods (years) (recent-10, 2011–2020; recent-20, 2001–2020; recent-30, 1991–2020; recent-40, 1981–2020). We mapped probability rainfall information, including probability rainfall intensities (20, 30, and 40 mm/h), return periods (10, 20, 50, and 100 years), rainfall durations (1, 2, 6, and 24 h), and rainfall depth. Results revealed wide variations in the northern and southwest inland regions based on rainfall periods. Decadal annual rainfall analysis revealed that the north and southwest inland regions indicated lower recent decadal rainfall than that in previous decades, while decadal annual rainfall in the southeast inland region remained constant. The generated spatial and temporal distribution maps offer valuable insights for comprehending the variation in probability rainfall factors across different time periods in South Korea, with practical implications for the planning and design of hydraulic structures.

Responsive characteristics of soil water regimes to rainfall events in a boreal larch forest in China: Dynamic processes and decoupling effects

Investigating the soil water dynamics in relation to rainfall events is of great importance for enhancing our understanding of eco-hydrological processes and improving soil hydrological models. However, the lack of long-term, in-situ observational data and the neglect of soil moisture decoupling have hindered the study of dynamic responses in soil water regimes to rainfall events in China’s boreal forests with permafrost. This study utilized soil water content monitoring and rainfall observational data from the Xing’an larch (Larix gmenilii) forest in Northeast China during the growing seasons of 2015–2021 to evaluate the dynamic response of soil water regimes to rainfall events. The results show a significant increase in the decoupling strength between soil water and rainfall with increasing soil depth. The thresholds of rainfall amount (RA), duration (RD), and intensity (RI) for triggering soil wetting events also increased. Soil water dynamic processes exhibited inconsistent and complex patterns in response to the hierarchical effects of RA, RD, and RI along the vertical soil profile. However, the dominant controls on soil water dynamic responses to RA, RD, and RI varied depending on specific response metrics, such as the accumulated soil water content increments (ASMI), duration time (DT), and the mean slope of the soil wetting curve (Sm). Interestingly, the dominant factor for affecting response time (RT) changed from RI to RD as soil depths increased. Importantly, the decoupling of soil moisture led to a substantial reduction in the responsiveness of these metrics of subsurface soil water to rainfall, and the role of the decoupling effect diminished with increasing soil depths. These findings highlight the complexity of the dynamic response of soil water to rainfall events and provide new insights into the relationship between soil water dynamics and rainfall features through the introduction of the decoupling of soil moisture in boreal forests.

Development of an integrated model for assessing landslide susceptibility on vegetated slopes under random rainfall scenarios

Physically-based landslide susceptibility analysis has been extensively employed to predict the likelihood of landslides owing to its remarkable predictive capability. However, due to an insufficiently comprehensive analysis of the vegetation-disaster connection, this approach may face limitations in its application to regions where there is significant variation in vegetation diversity and topography. An integrated model was thus proposed for predicting landslide susceptibility with the consideration of the positive and negative effects of vegetation on slope stability. However, to accurately implement the slope failure process under the influence of vegetation, it is important to have a high temporal resolution rainfall scenario for simulating the interception process of vegetation crowns on rainfall. Analysis of vegetation hydrological processes is associated with difficulties in a regional area because of limited data availability. The stochastic simulation method for extreme rainfall scenarios, adopted in this study, is known to be effective in dealing with uncertainty caused by insufficient data. The method, which comprises the Gumbel model and Bounded Random Cascade Model (BRCM), was used to overcome the difficulties in integrating the comprehensive effects of vegetation, the results of which provide refined rainfall scenarios for landslide susceptibility prediction from the perspective of the failure mechanism of vegetated slopes. The application of the proposed method for landslide susceptibility zoning was validated using a confusion matrix and ROC curve analysis in a practical case. Results showed that the method performed effectively and reliably. Furthermore, this study investigated the influence of different extreme rainfall scenarios on slope stability. Results revealed that as the return period of extreme rainfall increased from 50 to 150 years, the overall proportion of susceptible areas for different rainfall durations exhibited an upward trend. Additionally, the study found that increasing vegetation biomass had a positive impact on slope stability in the study area. The findings of this study are significant for disaster prevention and mitigation in mountainous regions, providing scientific support for risk management strategies.

Trend in rainfall associated with tropical cyclones in Mexico attributed to climate change and variability

Abstract The aim of this study was to investigate the existence and the magnitude of trend in different areas and durations of rainfall associated with tropical cyclones (TCR). To achieve this objective, a mixed-method approach was employed using depth–area–duration (DAD) and areal reduction factor (ARFs) curves that can be described as a logarithm equation to generate time series that allows the application of statistical methods such as the Mann–Kendall (MK) and Spearman Rho (SR) to detect trends. Time series are generated by substituting different areas in the logarithmic equations. The evidence presented shows that in Mexico, the TCR lasting 24 h shows an increasing trend for maximum areas between 300 and 1,700 km2 according to the MK and SR tests, respectively; according to these same tests for durations of 48 h, upward trends were observed up to maximum areas between 5,700 and 6,900 km2. The Sen's slope reports annual increases between 0.76 and 1.32 mm and between 1.15 and 2.06 for a duration of 24 and 48 h, respectively. In contrast, no trends were observed in the time series obtained from the ARFs. Finally, the Pettitt test reports an abrupt jump from the year 1997 in all cases.

Hydrological Modeling of Urban Flooding in Nandigama: Land Use Dynamics and Climate Influence

Urban flooding affects many areas in the world, caused by heavy rainfall and global warming. Our focus is on Nandigama, a highly urbanized region in Andhra Pradesh, India. Flood maps created based on urbanisation in the study area. TREX model and spatial data used to evaluate the impact of land use changes on runoff. Our analysis showed that urban expansion has increased significantly over the past thirty years, increasing surface area and runoff, leading to more flooding events with lower-intensity rainfall. Climate change has also contributed to this problem, with shorter rainfall durations but higher intensities leading to more frequent small floods. This has resulted in a 21% rise in discharge, from 2.39 m3 /s in 2000 to 2.91 m3 /s in 2020. This study provides essential information for land use planning and flood control management projects.

Integrated hydrological modelling of two contrasting watersheds with a terminal reservoir in the Upper Tapi River basin, India

Abstract The present study developed an integrated hydrologic model for sustainable utilisation and water management in two complex watersheds with varying physioclimatic features and reservoirs. The soil and water assessment tool (SWAT) is used for predicting integrated inflows into the Hatnur reservoir from the Burhanpur and Purna watersheds of the Upper Tapi River basin, while outflows are simulated using a rule curve. The influence of watershed complexities on hydrological model parameters and the watershed processes are investigated using extensive multisite and multivariable calibration (1998–2007) and validation (2008–2013) approaches, including sensitivity and uncertainty analyses. The sensitive parameters are related to curve number (CN), groundwater, slope, and main channel characteristics. The annual streamflow (m3/km2/mm of rainfall) in the Burhanpur watershed is 568.7, which is 4.2 times higher than the Purna watershed's streamflow of 136.2. The hypsometric analysis, areal rainfall, and flow duration curves revealed a substantially different streamflow pattern and a larger coefficient of variation in the spatial distribution of water balance components over sub-watersheds in the Burhanpur watershed compared to the Purna watershed due to diverse topographic features. The developed model would be useful for planning controlled releases from the terminal reservoir to mitigate hazards in the downstream reaches of the Tapi River basin.

Evaluation of future flood probability in agricultural reservoir watersheds using an integrated flood simulation system

In agricultural reservoir watersheds, the presence of various agricultural hydraulic structures combined with the influence of climate change necessitates effective flood risk analysis. This study aims to develop a flood risk evaluation technique for agricultural basins by incorporating the low temporal resolution of climate change scenarios. The approach utilizes flood probability as a key component and incorporates the Multiplicative Random Cascade model as a probability-based rainfall time decomposition model to capture the inherent uncertainty in climate change data. Reliability analysis with the limit state equation is employed for probability-based risk evaluation. Given that agricultural watersheds typically consist of agricultural reservoirs and farmland, the study develops a hydraulic and hydrological linkage model to comprehensively calculate flood probability, simulating from the agricultural reservoir in the upper side to the farmland located at the bottom edge of the watershed. The flood risk assessment is applied to the Yedang Reservoir watershed in Korea, using the Shared Socio-economic Pathways climate change scenario. Results indicate that flood probability is the highest in river levees during future precipitation events, converging to nearly 100% under 24-hour duration rainfall, irrespective of the rain frequency. Despite the hydraulic structures at the farmland being designed for a lower frequency of rain events, the average probability of flooding in the farmland have reached 50%. The agricultural reservoir exhibits a low risk of flooding even under climate change conditions. The proposed method not only enables the establishment of flood countermeasures but also facilitates the evaluation and prediction of future flood risk for each hydraulic structure located in rural areas affected by climate change.

Exploring biochar addition impacts on soil erosion under natural rainfall: A study based on four years of field observations on the Loess Plateau

Biochar’s potential impact on soil erosion has gained attention, but remained inconsistent, particularly lacks the results from continuous field monitoring under diverse rainfall conditions. This study aimed to explore the long-term variation in soil erosion induced by biochar addition under distinct natural rainfalls. Six runoff plots, treated with different amounts of biochar derived from apple tree branches (0, 1%, 2.5%, 4%, 5.5% and 7%), were established. Over a four-year period from 2017 to 2020, runoff and soil loss from these plots were continuously monitored and analyzed. Among 215 recorded rainfall events, 27 events resulted in runoff and sediment production were classified into three rainfall patterns (RI, RII and RIII) using K-means clustering. RIII characterized by high mean rainfall depth (138.80 mm), duration (3443 min), and erosivity (879 MJ.mm (ha. h)−1), but infrequent occurrence, followed by RII and RI with moderate and low mean depth, duration and erosivity, but higher frequency. Biochar addition, relative to control, resulted in an average annual runoff reduction, ranging from 9% to 36%, with a mean reduction of 28%, and reduced runoff for each rainfall, ranging from 4% to 28%, with a mean reduction of 12%. Similarly, biochar addition also reduced average annual soil loss, ranging from 43% to 79%, with a mean reduction of 61%, and reduced soil loss for each rainfall, ranging from 29% to 74%, with a mean reduction of 52%. The reduction of runoff and soil loss was positively correlated with biochar addition rates. Notably, the soil loss reduction was exceeded that of runoff. Soil erosion was negatively linked to surface area of soil particles (p < 0.05), total carbon, and total organic carbon (p < 0.01), but positively correlated with sand content (p < 0.01) and bulk density (BD) (p < 0.05) under biochar addition. Comparison of the mean reduction of runoff and soil loss among three rainfall patterns, the order was as follows: RII>RI>RIII. The optimal controlling of soil erosion was observed in 7% biochar addition under RII. Runoff-soil loss relationships well-fitted by power equation, showed soil incorporated with biochar treatments exhibited heightened susceptibility to erosion under RIII with high intensity compared to RII and RI with moderate or low rainfall intensity. Thus, the findings indicate biochar is a potential soil amendment to improve a degraded or degrading sloping farmland, however, the rainfall characteristics need to be considered when it is applied to the sloping farmland prone to erosion.

Flood Discharge For Ungauged Catchment At Teriang River, Pahang By Using HEC-HMS

Insufficiency of meteorological data during extreme rainfall events hinders simulation and forecasting analysis in flood mitigation systems. Hence, the existence of a reliable database in hydrology is crucial in flood modelling for ungauged catchments, especially for calibration and validation purposes. This paper focused on the generation of rainfall data by using Hydrological Procedure No. 1 (HP1) from the Department of Irrigation and Drainage (DID) Malaysia. Subsequently, analysis of flood hydrographs at Teriang River, Pahang, Malaysia, using HEC-HMS 4.8 for 50 and 100-year return periods was also obtained in this study. For validation purposes, the outcomes were compared to Hydrological Procedure No. 11 (HP11), designed for rural catchments. For precipitation data generation, the design rainfall was calculated based on generalised isopleth maps from HP1 for various rainfall durations ranging from 0.25 hours to 72 hours. Based on the results, the maximum peak discharge for 50 ARI was 354.7 m3/s, while for 100 ARI it was 410 m3/s before calibration. Comparing the results from the HEC-HMS model and the outputs from HP11, the analysis for 50-year and 100-year return periods in the Teriang River showed that the relative percentage differences for peak discharge were 19.99% and 17.64%, respectively. The calibration process managed to obtain a relative percentage difference of 2.94% for 50 ARI and 4.36% for 100 ARI. In conclusion, HEC-HMS can be used as a reliable tool to produce simulated streamflow for ungauged catchments with the help of the generated rainfall temporal patterns from HP1, which is a procedure that contains estimation of design rainfall intensity based on the rainfall intensity-during-frequency relationship (IDF relationship).

Estimating IDF curves under changing climate conditions for different climate regions

Abstract Although climate models can highlight potential shifts in intensity–duration–frequency (IDF) curves, their limited geographical and temporal resolutions limit their direct use in predicting sub-daily heavy precipitation. To use global or regional model outputs to predict urban short-term precipitation, approaches that give the requisite level of spatial and temporal downscaling are required, and these processes remain one of the difficulties that have demanded intensive effort in recent years. Although no novel methods are given in this work, there are few studies in the literature that investigate the impact of climate change on the analysis and design of infrastructure-related engineering structures. Therefore, the purpose of this research is to determine the potential changes in IDF curves because of climate change. The equidistance quantile matching method was used to turn future rainfall forecast data from global climate models (HadGEM2-ES, MPI-ESM-MR, and GFDL-ESM2M) corresponding to RCP4.5 and RCP8.5 scenarios into standard duration rainfall data, and new IDF curves were generated. These IDF curves corresponded very well with those generated from observed data (R2 ≈ 1). The HadGEM2-ES model predicts up to a 25% rise in rainfall intensity, whereas the other two models expect up to a 50% drop.

Toward Optimal Rainfall for Flood Prediction in Headwater Basins—Orographic QPE Error Modeling Using Machine Learning

AbstractQuantitative Precipitation Estimates (QPE) from merged rain gauge and radar measurements have become widely available in the last two decades. The errors associated with these products are yet to be fully understood, especially in complex terrain where ground clutter and overshooting artifacts are significant and vary in space and time depending on the storm and underlying synoptic conditions. The location and timing of precipitation in addition to rainfall intensity and duration are critical to the simulation of flood response in headwater basins. This work proposes a generalizable Physics‐guided Artificial Intelligence (PAI) framework for QPE error modeling. First, QPE error climatology derived from the hydrologic Inverse Rainfall Correction (Liao &amp; Barros, 2022, https://doi.org/10.1016/j.rse.2022.113107) to historical floods in selected headwater basins is analyzed to identify dominant precipitation regimes. Second, for each precipitation regime, a Multilayer Perceptron error prediction model is trained using event‐specific precipitation metrics at hourly scale as input, and subsequently used to predict estimation errors for various QPE products. The corrected QPE can then be used for hydrologic simulations and flood nowcasting. The PAI framework is demonstrated in the Southern Appalachian Mountains using the 57 largest floods over 2008–2017. The Probability Distribution Function of predicted precipitation errors follows a Gaussian‐like distribution but varies significantly between cold and warm season events, while the spatial distribution is inextricably connected to basin geomorphology. On average, large improvements on hourly Kling‐Gupta Efficiency from −0.5 to 0.4 are achieved, and the peak flood error is reduced by 70%, with distinctively better results for cold season events.

Effects of climate change variables on poultry production system and the need for strategic climate actions in Delta State, Nigeria

Poultry production system plays an important economic, nutritional and socio-cultural role in the livelihood of households in Delta State, Nigeria. Our present knowledge of the effect of climate change variables on poultry sub sector is significantly inadequate, unclear and thus attracting empirical investigation in recent times. This study drives its significance from the current global and national debates on climate change and its impact on livestock production system, particularly among poultry production systems. This study investigates the effects of climate change variables on poultry production system in Delta State, Nigeria (2008 -2018). A ten year (2008-2018) time series data were collected on climate variables (temperature, humidity rainfall and sunshine duration) were elicited from NIMET website. Also 10 year time series data on poultry egg and broiler production were collected from FAOSTAT. Collected data were analysed with the relevant statistical tools (descriptive statistics and inferential statistics). The result shows that poultry meat output range from 26,000 to 273000 tonnes with a mean of 200,738.63 tonnes. Poultry egg output ranges between 489,288 tonnes to 660,000 with a mean of 591,327.45 tonnes. Multiple regression result shows that climate variable s explained 60.2% of the variation in poultry production. Test of hypothesis reveals that temperature and rainfall are the most significant climate variable (P≤0.05) that influenced poultry production. It was recommended that strategic climate change actions such as regulating poultry house temperature, should be practiced by poultry farmers in order to generate better output of eggs and broiler.

A numerical simulation study on the evolutionary characteristics of the damage process of karst soil cavity under positive pressure effect

Positive pressure effect is the result of rapid urban development in recent years, which leads to over-exploitation of karst groundwater, coupled with the emergence of extreme weather (such as heavy rainfall, the increase in the duration of heavy rainfall), leading to the sharp rise of karst water level, the gas in the karst cavity can not be discharged in time to form a high positive pressure in the soil cavity and promote the destruction of the soil layer, which then induces a series of phenomena, such as karst subsidence. As a new collapse mechanism, the positive pressure effect causes geological disasters that seriously affect the safety of people's lives and properties in karst regions. In order to study the damage characteristics and evolution characteristics of karst soil cavities under positive pressure effect, this study is based on the ASCII text provided by FLAC itself, and the orthotropic simulation computation compilation program, which realizes the finite element analysis in FLAC3D, and focuses on the inhomogeneous change of soil displacement, redistribution of stresses, and plastic damage of karst soil cavities in different evolution stages under the action of positive pressure, and summarizes the characteristics and laws of stress, strain and damage in plastic zone of karst soil cave at different stages of evolution. The results will play a positive role in further investigating the potential mechanical effects, development mechanism and critical warning conditions during the evolution of covered karst soil caves, and also have important scientific research value in deepening the theory of prevention and control of collapse disasters in covered karst soil caves.

Hybrid Algorithm of Backpropagation and Relevance Vector Machine with Radial Basis Function Kernel for Hydro-Climatological Data Prediction

Hydro-climatological data serves a pivotal role in monitoring climatic alterations and facilitating agricultural planning, inclusive of evapotranspiration estimation, water management, and crop pattern design. The necessity to accurately and expeditiously model and forecast this data underscores the need for effective methodologies. This paper introduces a hybrid algorithm, integrating backpropagation and relevance vector machine (BP-RVM) with a radial basis function (RBF) kernel. A comparative analysis was conducted between RBF and Logsig activation functions in conjunction with resilient backpropagation (trainrp) and Levenberg-Marquardt backpropagation (trainlm). The algorithm was employed to predict and categorize rainfall, temperature, wind speed, humidity, and sunshine duration data. Through extensive trials, the architecture parameters in the training-testing process of the BP-RVM algorithm were meticulously determined. Mean squared error (MSE) and mean absolute percentage error (MAPE) values were classified as indicating high forecast accuracy (&lt;10%). Despite the RBF-trainlm kernel function combination exhibiting a faster epoch completion rate, the BP-RVM algorithm with the RBF-trainrp kernel function combination is recommended for future data prediction stages due to its lower error generation. The BP-RVM-RBF-trainrp algorithm outperformed BP-RVM-RBF-trainlm, with an average error difference of 1.39% in the training process and 2.28% in the testing process. The identified algorithms and architectures present potential for future applications in evapotranspiration calculation and crop pattern planning based on hydro-climatological data.