Search Results (19)

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases—stop, start, and acceleration—aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk’s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation’s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model’s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation’s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework. Full article

Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region. Full article

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future. Full article

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and the selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimal number of climate clusters. The k-means clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture, and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. This study also provided a methodological framework that can be used in the other river basins of Ethiopia and, indeed, elsewhere. Full article

Seasonal forecasting of the rainfall characteristics in Sahel is of crucial interest in determining crop variability in these countries. This study aims to provide further characterization of nine rainfall metrics over Senegal (Onset, cessation, LRS, CDD, CDD7, CDD15, NR90p, NR95p, NR99p) and their response to global SST patterns from 1981 to 2018. The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset and the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) were used. The results showed strong spatio-temporal variability with a pronounced south–north gradient for all metrics. The earliest onset was observed in the south of the country from 4 July and the latest onset in the north from 9 August. Since 2012, a new regime is observed with an increase in both long dry spells and extreme wet events. Furthermore, SST forcing has shown that the North tropical Atlantic and the East Equatorial Pacific are better able to explain the interannual variability of the intraseasonal descriptors. However, the prediction of metrics is earlier for the most remote basin (Pacific) compared to the most local basin (Atlantic). These results have implications for the seasonal forecasting of Sahel’s intraseasonal variability based on SST predictors, as significant predictability is found far from the beginning of the season. Full article

Changes in rainfall onset and cessation dates are critical for improving decision making and adaptation strategies in numerous socio-economic sectors. An objective method of determining onset and cessation date is employed over Southern West Africa (SWA) in this study. The method is applied over 34 years of the quasi-global rainfall dataset from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and five High Resolution Model Intercomparison Project (HighResMIP) model datasets under the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment. Generally, a strong agreement exists between CHIRPS and the HighResMIP models in capturing the behaviour of seasonal rainfall over SWA, with models able to capture the bimodal rainfall season. The ability of models in capturing onset and cessation dates as observed in CHIRPS shows the strength of these models in representing the short break between the two wet seasons that is otherwise known as the ‘Little Dry Season’. Patterns observed in the onset and cessation dates over the SWA region are consistent with the northward and southward displacement of the Intertropical Convergence Zone (ITCZ). The seasonal timing of the models shows good agreement with observations such that most mean onset/cessation dates agree within 26 days. While IPSL-CM6A-ATM-HR, a model among the five HighResMIPs used in the study, best agrees with CHIRPS in representing onset and cessation dates during the unimodal rainfall season, no one model best agrees with CHIRPS during the bimodal season, with models outperforming each other in representing onset/cessation dates with little variation. Full article

This paper examines the causes of widespread maize production failure in Ghana during the 2020 minor growing season. A mixed-methods approach was used to study smallholder maize farmers in the Ejura-Sekyedumase Municipality to provide a holistic understanding of the factors behind the maize production failure and to inform policy interventions. The results show that the decline in maize grain yield was caused by the failure of the minor season rains and, more importantly, the destruction of maize plants by fall armyworms. Other factors including poor soils and inadequate farm inputs contributed minimally to the observed maize failures. The agronomic practices adopted by the farmers to mitigate crop failures were undermined by their inability to master the onset and cessation of rainfall, the ineffectiveness of pesticides to control the fall armyworms and financial challenges. It is recommended that the government promotes and supports rainwater harvesting to address the impacts of drought and pests on food crop production. Furthermore, to ensure sustainable food production, a combination of indigenous knowledge and scientific farm practices are crucial to accurately forecast the weather and to control the fall armyworms. Full article

Rainfall onset and cessation date greatly influence cropping calendar decisions in rain-fed agricultural systems. This paper examined trends of onsets, cessation, and the length of growing season over Northern Ghana using CHIRPS-v2, gauge, and farmers’ perceptions data between 1981 and 2019. Results from CHIRPS-v2 revealed that the three seasonal rainfall indices have substantial latitudinal variability. Significant late and early onsets were observed at the West and East of 1.5° W longitude, respectively. Significant late cessations and longer growing periods occurred across Northern Ghana. The ability of farmers’ perceptions and CHIRPS-v2 to capture rainfall onsets are time and location-dependent. A total of 71% of farmers rely on traditional knowledge to forecast rainfall onsets. Adaptation measures applied were not always consistent with the rainfall seasonality. More investment in modern climate information services is required to complement the existing local knowledge of forecasting rainfall seasonality. Full article

Weather and climate information services (WCIS) are gaining recognition among scientists and governments as an essential adaptation tool for agriculture, especially in the drylands of Africa. In Senegal, the widespread production and dissemination of WCIS was initiated in 2015 to cover the agricultural, pastoral and fishing sectors. This paper analyzes the types of decisions made by WCIS users, their preferences and level of satisfaction, and explores the triggers of agricultural WCIS adoption. We collected data during the onset and cessation of the rainy seasons to understand the utility and reliability of WCIS by farmers across all stages of the growing season. Data were analyzed using descriptive statistics. A binary logistic regression was tested to understand the socio-economic triggers in uptake of WCIS. Results showed that rainfall forecast is the most preferred WCIS (49% of the respondents) followed by extreme wind forecast. At the beginning of the rainy season, nearly 80% of the respondents have chosen the sowing date and about 60% have chosen crop varieties based on disseminated WCIS. In the middle of the growing season, about 70% of the respondents used WCIS to decide on fertilizer application dates. Results also showed that age and level of education, being trained on WCIS use, membership to farmers’ organizations, owning a radio have a significant effect on WCIS-based decision-making. These factors are essential for triggering the uptake of WCIS, and therefore are required to improve the implementation of existing weather climate services in Africa. Full article

Rice production in Nigeria is vulnerable to climate risks and rice farmers over time have experienced the risks and their respective impacts on rice farming. Rice farmers have also responded to perceived climate risks with strategies believed to be climate-smart. Farmers’ perception of climate risks is an important first step of determining any action to be taken to counteract the negative effects of climate change on agriculture. Studies on the link between perceived climate risks and farmers’ response strategies are increasing. However, there are limited studies on the determinants of rice farmers’ perception of climate events. The paper therefore examined climate change perception and uptake of climate-smart agriculture in rice production in Ebonyi State, Nigeria using cross-sectional data from 347 rice farmers in an important rice-producing area in Nigeria. Principal component analysis, multivariate probit regression model and descriptive statistics were adopted for data analysis. Perceived climate events include increased rainfall intensity, prolonged dry seasons, frequent floods, rising temperature, severe windstorms, unpredictable rainfall pattern and distribution, late onset rain, and early cessation of rain. Farmers’ socioeconomic, farm and institutional characteristics influenced their perception of climate change. Additionally, rice farmers used a variety of climate-smart practices and technologies to respond to the perceived climate events. Such climate-smart practices include planting improved rice varieties, insurance, planting different crops, livelihood diversification, soil and water conservation techniques, adjusting planting and harvesting dates, irrigation, reliance on climate information and forecasts, planting on the nursery, appropriate application of fertilizer and efficient and effective use of pesticides. These climate-smart agricultural measures were further delineated into five broad packages using principal component analysis. These packages include crop and land management practices, climate-based services and irrigation, livelihood diversification and soil fertility management, efficient and effective use of pesticide and planting on the nursery. High fertilizer costs, lack of access to inputs, insufficient land, insufficient capital, pests and diseases, floods, scorching sun, high labour cost, insufficient climate information, and poor extension services were the barriers to uptake of climate-smart agriculture in rice production. Rice farmers should be supported to implement climate-smart agriculture in rice production in order to achieve the objectives of increased rice productivity and income, food security, climate resilience and mitigation. Full article

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region. Full article

Future global warming may result in extreme precipitation events leading to crop, environment and infrastructure damage. Rainfall is a major input for the livelihood of peasant farmers in the Aswa catchment where the future rainfall variability, onset and cessation are also likely to be affected. The Aswa catchment has limited rainfall data; therefore, use of secondary datasets from Tropical Rainfall Measuring Mission (TRMM) is considered in this study, based on the close correlation of the recorded and TRMM rainfall. The latter was used to calibrate the statistical downscaling model for downscaling of two general circulation models to simulate future changes in rainfall. These data were analyzed for trends, wet and dry conditions/variability; onset and cessations of rain using the Mann–Kendall test, Standardized Precipitation Index (SPI) and the cumulative percentage mean rainfall method, respectively. Results show future rainfall is likely to increase, accompanied by increasing variability reaching as high as 118.5%. The frequency of SPI values above 2 (extreme wetness) is to increase above current level during mid and end of the century. The highest rainfall variability is expected especially during the onset and cessation months, which are generally expected to come earlier and later, by up to four and five weeks, respectively. The reliability worsens from the midterm (2036–2065) to long term (2066–2099). These likely changes in rainfall quantities, variability, onset and cessation months are some of the key rainfall dynamics that have implications for future arable agriculture, environment and water resource availability and planning over the Aswa catchment, as is increasingly the case elsewhere. Full article

Rainwater is considered a promising alternative water source for coastal areas where freshwater resources are increasingly under pressure. This study evaluates rain regime characteristics that influence the ability to exploit rainwater in the coastal provinces of the Vietnamese Mekong Delta (VMD). In particular, it considers the impact of climate change and El Niño–Southern Oscillation (ENSO) processes. We analyzed rainfall data from 102 monitoring stations across the VMD from 1989 to 2017. Using statistical methods, we explored (1) characteristics of the rainy season, including the dates of onset and cessation and season length; (2) average rainfall volumes; and (3) the maximum number of consecutive nonrainy days during the rainy season and over the year. We also analyzed changes in these characteristics over time, in parallel with ENSO processes and climate change. Trend lines were determined using nonparametric methods, utilizing Sen’s slope estimation and the Mann–Kendall test. Results showed a tendency for the rainy season to start earlier and end later in the western coastal zone of the study area, with season length gradually decreasing towards the East Sea and inland. The shortest rainy season was found in the estuary zone (in the northeast of the VMD). Rainfall was abundant on the west coast, again diminishing gradually towards the East Sea and inland. Rain was also quite plentiful during four dry season observation months in the study area, but it lacked the predictability needed for effective exploitation. The number of consecutive days without rain averaged 96 annually, with a difference of 29 days between the largest and smallest observations. The difference between the provinces with the longest and shortest periods without rain averaged 41 days. Although the impact of climate change on the rain regime is complex, we can say that the rainy season now tends to start earlier, end later, and be lengthier, though without exhibiting clear trends. ENSO processes significantly impacted rainfall regime characteristics, especially the dates of onset and cessation, and season length. Full article

We analyse uncertainties associated with the main features of the annual cycle of West African rainfall (amplitude, timing, duration) in 15 CMIP5 simulations over the Sahelian and Guinean regions with satellite daily precipitation estimates. The annual cycle of indices based on daily rainfall such as the frequency and the intensity of wet days, the consecutive dry (CDD) and wet (CWD) days, the 95th percentile of daily rainfall (R95), have been assessed. Over both regions, satellite datasets provide more consistent results on the annual cycle of monthly precipitation than on higher-frequency rainfall indices, especially over the Guinean region. By contrast, CMIP5 simulations display much higher uncertainties in both the mean precipitation climatology and higher-frequency indices. Over both regions, most of them overestimate the frequency of wet days. Over the Guinean region, the difficulty of models to represent the bimodality of the annual cycle of precipitation involves systematic biases in the frequency of wet days. Likewise, we found strong uncertainties in the simulation of the CWD and the CDD over both areas. Finally, models generally provide too early (late) onset dates over the Sahel (the Guinean region) and overestimate rainfall during the early and late monsoon phases. These errors are strongly coupled with errors in the latitudinal position of the ITCZ and do not compensate at the annual scale or when considering West Africa as a whole. Full article