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To solve the instability problem of Zhangjiayao landslide caused by rainfall, the internal mechanism of slope instability and the supporting effect of anti-slide piles are studied. The research results can provide theoretical basis for the prevention and control of loess landslides.

A three-dimensional finite element model of Zhangjiayao landslide is established by field geological survey, laboratory test and numerical simulation.

The results show that Zhangjiayao landslide is a loess-mudstone contact surface landslide, and rainfall leads to slope instability and traction landslide. The greater the rainfall intensity, the faster the pore water pressure of the slope increases and the faster the matrix suction decreases. The longer the rainfall duration, the greater the pore water pressure of the slope and the smaller the matrix suction. Anti-slide pile treatment can significantly improve slope stability. The slope safety factor increases with the increase of embedded depth of anti-slide pile and decreases with the increase of pile spacing.

Based on the unsaturated soil seepage theory and finite element strength reduction method, the failure mechanism of Zhangjiayao landslide was revealed, and the anti-slide pile structure was optimized and designed based on the pile-soil interaction principle. The research results can provide theoretical basis for the treatment of loess landslides.

1. A three-dimensional finite element model of Zhangjiayao landslide is established.

2. Zhangjiayao landslide is a loess-mudstone contact surface landslide.

3. The toe of Zhangjiayao slope is first damaged by heavy rainfall, resulting in traction landslide.

4. The deformation of Zhangjiayao slope is highly dependent on rainfall intensity and duration.

5. The anti-slide pile can effectively control the continuous sliding of Zhangjiayao slope.

A three-dimensional finite element model of Zhangjiayao landslide is established.

Zhangjiayao landslide is a loess-mudstone contact surface landslide.

The toe of Zhangjiayao slope is first damaged by heavy rainfall, resulting in traction landslide.

The deformation of Zhangjiayao slope is highly dependent on rainfall intensity and duration.

The anti-slide pile can effectively control the continuous sliding of Zhangjiayao slope.

Heavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.

The three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.

GSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.

A workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

This study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.

The method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.

Pumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.

Because the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.

This paper examines the impact of extreme rainfall shocks on the performance in test scores of students living near at-risk urban areas in Brazil.

To identify the causal effect, we consider the exogenous variation of rainfall at the municipal level conditioned on the distance from the school to risk areas and the rainfall intensity in the school months.

The results suggest that extreme precipitation shocks, defined as a shock of at least three months of high-intensity rainfall, have an adverse impact on both math and language performance. Through a heterogeneous effects analysis, we find that the impact varies by student gender, with girls being more affected. In addition, among students who study near at-risk areas, those with better previous school performance and higher socioeconomic status are more negatively affected.

Our results suggest that extreme weather events can increase the differences in human capital accumulation between the population living near risk areas and those living more distant from these areas.

Tropical wetland ecosystems are threatened by climate change but also play a key role in its mitigation and adaptation through management of land use and other drivers. Local-level assessments are needed to support evidence-based wetland management in the face of climate change. This study aims to examine the local communities’ knowledge and perception of climate change in Yala wetland, Kenya, and compare them with observed data on climate trends. Such comparisons are useful to inform context-specific climate change adaptation actions.

The study used a mixed methods approach that combined analysis of climate data with perceptions from the local community. Gridded data on temperature and rainfall for the period from 1981 to 2018 were compared with data on climate change perceptions from semi-structured questionnaires with 286 key informants and community members.

Majority of the respondents had observed changes in climate parameters – severe drought (88.5%), increased frequency of floods (86.0%) and irregular onset and termination of rains (90.9%) in the past 20 years. The perceptions corresponded with climate trends that showed a significant increasing trend in the short rains and the average maximum temperature, high incidence of very wet years and variability in onset and termination of rainfall between 1981 and 2018. Gender, age and education had little influence on knowledge and awareness of climate change, except for frequency of floods and self-reported understanding of climate change. The community perceived the wetland to be important for climate change adaptation, particularly the provision of resources such as grazing grounds during drought.

The study faced challenges of low sample size, use of gridded climate data and reproducibility in other contexts. The results of this study apply to local communities in a tropical wetland in Western Kenya, which has a bi-modal pattern of rainfall. The sample of the study was regional and may therefore not be representative of the whole of Kenya, which has diverse socioeconomic and ecological contexts. Potential problems have been identified with the use of gridded data (for example, regional biases in models), although their usefulness in data scarce contexts is well established. Moreover, the sample size has been found to be a less important factor in research of highly complex socio-ecological systems where there is an attempt to bridge natural and social sciences.

This study addresses the paucity of studies on climate change trends in papyrus wetlands of sub-Saharan Africa and the role of local knowledge and perceptions in influencing the management of such wetlands. Perceptions largely influence local stakeholders’ decisions, and a study that compares perceptions vs “reality” provides evidence for engagement with the stakeholders in managing these highly vulnerable ecosystems. The study showed that the local community’s perceptions corresponded with the climate record and that adaptation measures are already ongoing in the area.

This study presents a case for the understanding of community perceptions and knowledge of climate change in a tropical wetland under threat from climate change and land use change, to inform management under a changing climate.

The purpose of this paper was to examine smallholder farmers’ perceptions of climate change risks, adaptation responses and the links between adaptation strategies and perceived/experienced climate change risks in South Gondar, Ethiopia.

This paper used a convergent mixed methods design, which enables us to concurrently collect quantitative and qualitative data. Survey data was collected from 352 households, stratified into Lay Gayint 138 (39%), Tach Gayint 117 (33%) and Simada district 97 (28%). A four-point Likert scale was used to produce a standardised risk perception index for 14 climate events. Moreover, using a one-way analysis of variance, statistical differences in selecting adaptation strategies between the three districts were measured. A post hoc analysis was also carried out to identify the source of the variation. The findings of this paper are supplemented by qualitative data gathered through focus group discussions and key informant interviews of households who were chosen at random.

The standardised climate change risk perception index suggests that persistent drought, delayed onset of rainfall, early termination of rainfall and food insecurity were the major potentially dangerous climate change risks perceived by households in the study area. In response to climate change risks, households used several adaptation strategies such as adjusting crop planting dates, crop diversification, terracing, tree planting, cultivating drought-tolerant crop varieties and off-farm activities. A Tukey’s post hoc test revealed a significant difference in off-farm activities, crop diversification and planting drought-tolerant crop types among the adaptation strategies in the study area between Lay Gayint and Simada districts (p < 0.05). This difference reconfirms that adaptation strategies are location-specific.

Although many studies are available on coping and adaptation strategies to climate change, this paper is one of the few studies focusing on the linkages between climate change risk perceptions and adaptation responses of households in the study area. The findings of this paper could be helpful for policymakers and development practitioners in designing locally specific, actual adaptation options that shape adaptation to recent and future climate change risks.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.

Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.

The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.

Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.