Search results

Weather forecasting is the trending topic around the world as it is the way to predict the threats posed by extreme rainfall conditions that lead to damage the human life and properties. These issues can be managed only when the occurrence of the worse weather is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the rainfall prediction system, the purpose of this paper is to propose an effective rainfall prediction model using the nonlinear auto-regressive with external input (NARX) model.

The paper proposes a rainfall prediction model using the time-series prediction that is enabled using the NARX model. The time-series prediction ensures the effective prediction of the rainfall in a particular area or the locality based on the rainfall data in the previous term or month or year. The proposed NARX model serves as an adaptive prediction model, for which the rainfall data of the previous period is the input, and the optimal computation is based on the proposed algorithm. The adaptive prediction using the proposed algorithm is exhibited in the NARX, and the proposed algorithm is developed based on the Grey Wolf Optimization and the Levenberg–Marqueret (LM) algorithm. The proposed algorithm inherits the advantages of both the algorithms with better computational time and accuracy.

The analysis using two databases enables the better understanding of the proposed rainfall detection methods and proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy is found to be better compared with the other existing methods and the mean square error and percentage root mean square difference of the proposed method are found to be around 0.0093 and 0.207.

The rainfall prediction is enabled adaptively using the proposed Grey Wolf Levenberg–Marquardt (GWLM)-based NARX, wherein an algorithm, named GWLM, is proposed by the integration of Grey Wolf Optimizer and LM algorithm.

This paper aims to describe the indigenous and innovative practices adopted by the small-scale farmers to cope with the impacts of climate change hazards on subsistence farming.

The data were collected through focus group discussions with 72 small-scale farmers from a rural community in Limpopo Province, South Africa. The discussions were analysed through verbatim transcripts and content analysis.

The study results show the farmers’ understanding of climate change variability and its hazards in the form of rainfall scarcity and excessively increased temperature, which are responsible for a declining production of indigenous crops. It has also been found that in the face of these hazards, the farmers experience low crop yields, which cannot provide the household food requirements. However, the small-scale farmers use a combination of local and innovative knowledge and skills to improve their crop production. They have adopted the indigenous adaptation mechanisms, which include rainfall prediction, preparation of the gardens, change of crops and the planting season to ensure better crop yields. The farmers also adopted innovative adaptation practices such as the use of fertilisers, growing of exotic crops and use of extension officers’ guidance and skills to minimise the risks and maximise the chances of resilient crop production.

This paper describes the farmers’ ability to use the indigenous and innovative adaptation practices. It is only focused on the farmers’ knowledge and skills other than the extension officers’ skills.

The adaptation practices reported in the study fall within the adaptation and mitigation systems stipulated in the South African National Climate Change Strategy to assist the small-scale farmers grow and maintain the crops to improve production and minimise the risks, thus ensuring food security under observable harsh climate hazards.

The purpose of this paper is to provide a detailed analysis of the trends in temperature and rainfall over the period 1967–2016 (50 years) for the Kingdom of Saudi Arabia and estimate the effect of these climatic changes on major crop production.

To set up an empirical association between crop yields and climatic variables, the study uses a fixed effect regression framework. This approach makes it possible to capture the effects of time-invariant indicators and farmers' independent adaptation strategies in reaction to year-to-year variations in precipitation and temperature.

The study observes a significant increase in average temperature by 1.9 degrees Celsius in the last 50 years and the greatest increase is noted in the summer. However, there is no significant change in rainfall. The results indicate that a one-degree Celsius increase in temperature reduces crop yields by 7–25%. The results also indicate that rainfall has a positive effect on all the crops. But, rainfall could not offset much of the adverse effects of temperature.

Future research can focus on the analysis of the climate change impact assessment for different regions in the Kingdom of Saudi Arabia and develop a place-based policy.

The recent initiative to phase out crop production makes the Kingdom of Saudi Arabia entirely rely on imports. This may have little or no impact presently. However, in the future, it is possible that any global shocks on agriculture due to climate change or geopolitical instability will make the situation worse off. It will threaten both food and nutrition security in the Kingdom of Saudi Arabia. Therefore, it is important to study these in the present context to prepare a road map for future food, water and nutrition security.

This paper aims to document indigenous knowledge systems (IKS) used for short- and long-range rainfall prediction by small holder farmers in three communities of Guruve District, in north-eastern Zimbabwe. The study also investigated farmers’ perceptions of contemporary forecasts and the reliability of both IKS and contemporary forecasts.

Data were collected among small holder farmers in Guruve District using household interviews and focus group discussions in three wards in the district, grouped according to their agro-climate into high and low rainfall areas. To get an expert view of the issues, key informant interviews were held with key agricultural extension personnel and traditional leaders.

Results obtained showed show high dependence on IKS-based forecasts in the district. Over 80 per cent of the farmers used at least one form of IKS for short- and long-range forecasting, as they are easily understood and applicable to their local situations. Tree phenology, migration and behaviour of some bird species and insects, and observation of atmospheric phenomena were the common indicators used. Tree phenology was the most common with over 80 per cent of farmers using this indicator. While some respondents (60 per cent) viewed forecasts derived from IKS as more reliable than science-based forecasts, 69 per cent preferred an integration of the two methods.

The simplicity and location specificity of IKS-based forecasts makes them potentially useful to smallholder farmers, climate scientists and policymakers in tracking change in these areas for more effective climate change response strategies and policymaking.

This research paper is informed by a study to assess performance of local knowledge drought forecasts (LKDFs) in the Mzingwane catchment which is located in the Limpopo River Basin in Zimbabwe. The purpose of this paper is to validate local traditional knowledge (LTK) indicators being applied in Mzingwane catchment and verify their accuracy and reliability in drought forecasting and early warning.

LTK forecast data for 2012/2013 season were collected through structured questionnaires administered to 40 selected household heads and focus group discussions. Observations and key informant interviews with chiefs and the elderly (>55 years) were also used to collect additional LTK forecast data. Meteorological data on seasonal rainfall were collected from the meteorological Services Department of Zimbabwe (MSD). Two sets of comparisons were conducted namely the hind-cast comparison where the LKDF system results were evaluated against what the season turned out to be and forecast comparison where local LKDF system results were compared with downscaled meteorological forecasts.

The results showed that the majority of the LTK indicators used were accurate in forecasting weather and drought conditions when compared to the observed data of what the season turned out to be. LTK forecasts were found to be more accurate than meteorological forecast at local scale. This study has shown that the reliability of LTKs is high as demonstrated by the fact that the predicted event occurs.

Further validation be carried out for a number of seasons, in order to standardise the LTK indicators per geographical area.

The research creates platform for adoption of LTKs into formal forecasting systems. The research is useful to both meteorological researchers and resource constrained communities in Mzingwane catchment.

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.

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.

In the equatorial Pacific, rainfall is affected by global climate phenomena, such as El Niño Southern Oscillation (ENSO). However, current publicly available methodologies for valuing weather derivatives do not account for the influence of ENSO. The purpose of this paper is to develop a complete framework suitable for valuing rainfall derivatives in the equatorial Pacific.

In this paper, we implement a Markov chain for the occurrence of rain and a gamma model for the conditional quantities using vector generalized linear models (VGLM). The ENSO forecast probabilities reported by the International Research Institute for Climate and Society (IRI) are included as independent variables using different alternatives. We then employ the Esscher transform to price rainfall derivatives.

The methodology is applied and calibrated using the historical rainfall data collected at the El Dorado airport weather station in Bogotá. All the estimated coefficients turn out to be significant. The results prove more accurate than those of Markovian gamma models based on purely statistical descriptions of the daily rainfall probabilities.

This procedure introduces the novelty of incorporating variables related to the climatic phenomena, which are the forecast probabilities regularly published for the occurrence of El Niño and La Niña.

The study aims to identify the gaps and the potentialities of citizen-generated data in four axes of warning system: (1) risk knowledge, (2) flood forecasting and monitoring, (3) risk communication and (4) flood risk governance.

Research inputs for this work were gathered during an international virtual dialogue that engaged 40 public servants, practitioners, academics and policymakers from Brazilian and British hazard and risk monitoring agencies during the Covid-19 pandemic.

The common challenges identified were lack of local data, data integration systems, data visualisation tools and lack of communication between flood agencies.

This work instigates an interdisciplinary cross-country collaboration and knowledge exchange, focused on tools, methods and policies used in the Brazil and the UK in an attempt to develop trans-disciplinary innovative ideas and initiatives for informing and enhancing flood risk governance.

Spatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.

The study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.

The rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.

It will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.

The current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.