Forecasting Climate Variability in Nepal

Green plants play an important role in matter and energy transformations and are key messengers in the carbon and energy cycle. Vegetation net primary productivity (NPP) reflects the capability of plants to convert solar energy into photosynthate (fixed carbon). It can be used as a direct indicator of current status and future trend of ecological processes affected by human activities and climate variability, especially in fragile ecological environments such as karst areas where rock-desertification increasingly occurs. Using remote sensing and geographic information system technologies, research on spatio-temporal variations of NPP and its response to changes in climate variables (temperature, rainfall and solar radiation) in Guizhou province of China was conducted. Karst areas within the study area were delineated to explore the relationship between NPP and climate variables. The NPP model developed by Wang et al. (2010) was used. This model is a modification of the light use efficiency (LUE) model of Running et al. (2000), validated for this region in China. As input to the model, three climate scenarios were designed - one representing current conditions and two for hypothetical future conditions. Both future scenarios have a 2˚C increase in temperature and 6% increase in rainfall, applied spatially across the region using a constant scaling factor on the current condition scenario. The first future scenario has unchanged solar radiation; and the second has a 10% increase in solar radiation for investigating the effect of solar radiation on NPP. The model was run on a monthly time step for one year. Under the current condition scenario, the spatio- temporal patterns of monthly NPP were analysed using MODIS imagery, with the climate variables as model inputs. The relationships between NPP and temperature, and NPP and rainfall, were quantified using single and partial correlation. The hypothetical future condition scenarios were then used to explore the response of NPP to changes in these variables. This paper reports on the results of this modelling under all scenarios, with particular interest in the relationship between solar radiation and karst areas. Model results show that, under the current condition scenario, the mean annual NPP of terrestrial vegetation is 421gCm -2 with the NPP in karst areas being 11.9% lower than in non-karst areas. Maximum and minimum NPP values occur in July and January, respectively. The results show strong correlations between NPP and climatic variables: (1) Temperature is a key factor which significantly limits vegetation growth in the northwest. The correlation between NPP and temperature decreases from north to south, and is stronger in karst (than non-karst) areas. (2) The correlation between NPP and rainfall is most significant in the southeast and west in comparison with other areas, and is lower in karst (than non-karst) areas. Under the first future scenario, the total annual NPP decreases from 74×10 6 tC to 73×10 6 tC; whereas under the second future scenario, annual NPP increases from 74×10 6 tC to 79×10 6 tC. The results from both future scenarios indicate that the largest variation in NPP occurs in karst areas with severe rock-desertification, indicating solar radiation is a dominant factor affecting NPP in karst areas. There are obvious regional differences on spatio-temporal patterns of NPP between karst areas and non-karst areas. The analysis of the interactions among three climate variables - temperature, rainfall and solar radiation has contributed to our understanding of their contributions to NPP, especially in karst areas.

Understanding the capacity for different species to reduce their susceptibility to climate change via phenotypic plasticity is essential for accurately predicting species extinction risk. The climatic variability hypothesis suggests that spatial and temporal variation in climatic variables should select for more plastic phenotypes. However, empirical support for this hypothesis is limited. Here, we examine the capacity for ten Drosophila species to increase their critical thermal maxima (CTMAX ) through developmental acclimation and/or adult heat hardening. Using four fluctuating developmental temperature regimes, ranging from 13 to 33°C, we find that most species can increase their CTMAX via developmental acclimation and adult hardening, but found no relationship between climatic variables and absolute measures of plasticity. However, when plasticity was dissected across developmental temperatures, a positive association between plasticity and one measure of climatic variability (temperature seasonality) was found when development took place between 26 and 28°C, whereas a negative relationship was found when development took place between 20 and 23°C. In addition, a decline in CTMAX and egg-to-adult viability, a proxy for fitness, was observed in tropical species at the warmer developmental temperatures (26-28°C); this suggests that tropical species may be at even greater risk from climate change than currently predicted. The combined effects of developmental acclimation and adult hardening on CTMAX were small, contributing to a <0.60°C shift in CTMAX . Although small shifts in CTMAX may increase population persistence in the shorter term, the degree to which they can contribute to meaningful responses in the long term is unclear.

To adapt forest ecosystems and forest management to climate change, it is essential to know which forest regions and which tree species are resilient to climate variability and which ones are possibly affected most by past and anticipated future changes. In this contribution, for the main forest regions of Türkiye and six tree species, recent climate variability and trends were quantified and statistically correlated to record tree defoliation and vitality. Climate variables considered are maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and total precipitation (Prcp), which are compared to forest health responses recorded as part of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) on 277 plots across forests along the Black Sea and Mediterranean regions. In addition, long-term data on satellite measurements of the normalized difference vegetation index (NDVI) were extracted for the same 277 plots for the period 2008–2020. Firstly, 30 years (1991–2020) of reanalysis of climate variables from ECMWF were extracted for all plots; secondly, individual correlations and cross-correlations of climate variables and tree health and vitality were computed for the period 2008–2020 (significance level of 95%) for the four most dominant species from the Black Sea forests (F. orientalis, Q. cerris, P. sylvestris, P. orientalis) and two species from Mediterranean forests (P. brutia and C. libani). Temperature showed a stronger effect on most species than precipitation. Finally, time-lagged correlations were analyzed for seven-time lags (significance level of 95%) to evaluate legacy effect. The analysis revealed that different tree species from the two regions show different responses to climate variables. Species in the Mediterranean region are more resistant to droughts and climatic variations. Legacy effects of defoliation and NDVI have lasted for at least 2 years.

Climate change has been raising concerns about potential changes to crop yields and production systems. The focus of the present study is to assess the impact of climate variability on food crop yield in the Gicumbi district of Rwanda. Independent Climate variables like rainfall and temperature, and dependent beans and Irish potatoes production data over the past fifteen years i.e. a period from 2007 to 2022 have been used to investigate the evidence of climate variability in Gicumbi, changes in mentioned food crop production, and the likelihood of relationship between crop production of food crops and these climatic variables. Therefore, this research analyzed the effect of climate variability on the productivity of food crops (beans and Irish potatoes) in the Gicumbi district. The specific objectives are to analyze the climate variability in the Gicumbi district from 2007 up to 2022, to assess the productivity of beans and Irish potatoes in the same period, and to determine the extent to which climate variability influences the production of beans and Irish potatoes. The researcher used a descriptive research design. The total population of this study is from 3 selected sectors of Gicumbi totaling 91,054 residents and the sample size is 204 respondents selected using a convenient sampling technique. The primary data was collected using a questionnaire and key informant interviews. The regression analysis was used to analyze the relationship between climate variability and the changes in food crop production in study areas. Specifically, regression analysis was used to determine the relationship between temperature and rainfall variability and food crops (beans &amp; Irish potatoes) production in the focus District.

Background: Providing persistent employment in the agricultural sector is one of the most significant economic and social challenges faced by many countries. The agricultural sector's high dependence on weather conditions makes it particularly vulnerable to climate change. Consequently, investigating the effects of climate change on the economy of this sector, especially concerning rural employment, is of paramount importance. This research aims to analyze the impact of climate change on citrus production in Mazandaran Province, utilizing the Ricardian approach. By examining the relationship between climatic variables and agricultural output, this study seeks to provide insights that can help enhance the resilience of the agricultural sector against climate fluctuations. Methods: The climatic variables studied in this research include temperature and rainfall data for Mazandaran Province from 2000 to 2017. These data were obtained from the Iran Meteorological Organization. The research focused on seven synoptic stations: Amol, Babolsar, Qarakhil, Nowshehr, Ramsar, Sari, and Siyabisheh. Among the various citrus products, orange and tangerine were selected due to their significant cultivated area and economic importance. The climatic variables analyzed include minimum temperature, maximum temperature, and monthly rainfall. These variables were downscaled for three distinct periods: the baseline period (2000 to 2017), as well as projected scenarios for 2020 and 2080, under three different greenhouse gas emission scenarios: optimistic (RCP2.6), medium (RCP4.5), and pessimistic (RCP8.5). The findings reveal that in orange production, changes in temperature and annual rainfall, compared to the average net income during the baseline period, will result in increased net income by 13.4%, 14.5%, and 25.2% under the three scenarios, respectively. For the years 2050 and 2080, the increases in net income are projected to be 14.3%, 27.8%, and 29.1%. In terms of tangerine production, the net income under the investigated scenarios is expected to rise by 5.2%, 6.7%, and 17.7%, with further increases of 6.2%, 15.0%, and 29.2% anticipated in 2050 and 2080, respectively. These results indicate a positive correlation between changing climatic conditions and the economic viability of citrus production in Mazandaran Province. In conclusion, the results obtained from examining the effects of climate change in 2050 and 2080 demonstrate a positive impact of changing climate variables on the net income derived from both orange and tangerine production. The findings suggest that climate change may lead to an upward trend in net income from citrus production, which could, in turn, foster employment development in this sector. This upward trend indicates that, while climate change poses significant challenges to agriculture, it may also create opportunities for increased productivity and economic growth in specific areas such as citrus farming. The implications of this research are significant for policymakers and agricultural stakeholders. Understanding how climate change can enhance or hinder agricultural productivity allows for better planning and resource allocation. It highlights the need for adaptive strategies that can help farmers maximize their yields while mitigating the adverse effects of climate variability. Moreover, the positive outlook regarding citrus production under changing climatic conditions emphasizes the importance of investing in agricultural research and development. By focusing on climate-resilient

Rainfed maize yield response to management and climate covariability at large spatial scales

Purpose: This study forecasts the agricultural productivity of five major crops—wheat, barley, maize, sunflower, and cotton—in Türkiye from 1962 to 2022, using climate variables alone and in combination with economic inputs. Design/Methodology/Approach: A panel dataset was constructed by matching annual crop yields with seasonal and annual temperature and precipitation variables, including lagged climate indicators. Two model configurations were tested: (i) climate-only and (ii) climate plus economic controls (fertilizer use, capital stock, labor). Three supervised learning models—Linear Regression, Random Forest, and Gradient Boosting—were evaluated using forward-chaining time-series cross-validation. Findings: Gradient Boosting with economic controls achieved the best out-of-sample performance (R² = 0.44, MAE = 547.8 kg/ha), followed by Random Forest. Climate-only versions of the same models yielded substantially lower accuracy (e.g., Gradient Boosting R² = 0.16), highlighting the added predictive value of structural inputs. Feature importance analysis identified growing season temperature as the most influential climate variable, while fertilizer, capital, and labor emerged as key predictors when included. Originality/Value: This study introduces a robust, time-aware machine learning framework for forecasting crop yields under climate variability. By integrating economic inputs, it enhances predictive accuracy and offers practical insights to support data-driven agricultural planning under climate uncertainty.

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

Mountain snowpacks are key components of surface water resources, as snowmelt significantly contributes to streamflow and serves as an essential indicator of climate change and variability. Accurate estimation of snowmelt-induced streamflow and assessment of its potential response to climate change are therefore essential for effective water resources planning and management. This study presents a new coupled modeling framework that integrates a cellular automata model (CAM) to simulate snow cover area (SCA) with the Snowmelt Runoff Model (SRM) to simulate daily streamflow. The model was applied to assess the impacts of climate change and associated uncertainty on SCA and streamflow in a Mediterranean mountainous catchment. Four local climate scenarios were developed from an ensemble of nine downscaled Regional Climate Models (RCMs), assuming two warming levels for Spain (1.5 °C and 3.0 °C). The CAM–SRM framework was calibrated and validated using precipitation and temperature data from the AEMET 5 km gridded product for the period 2000–2020. Under future climate conditions, the model projected a substantial decrease in SCA and a two-month shorter snow season in warmer scenarios. An earlier onset of snowmelt peak flows is also expected, along with a projected decline in mean annual streamflow of 19.4–32.9%. These results indicate significant changes in the hydrological regime driven by climate change. The proposed modeling framework proves effective for reproducing snowmelt-driven streamflow dynamics in snow-dominated catchments and offers valuable insights for future water resources planning and management in mountainous regions. Graphical Abstract Graphical abstract description : This study presents a novel coupled modelling framework for assessing climate change impacts on streamflow using the Snowmelt-Runoff Model (SRM). The new framework integrates a cellular automata model with the SRM, enabling streamflow simulations under non-historical conditions even in the absence of snow cover observations, which are required as input along with climate variables (precipitation and temperature) in the SRM formulation. The cellular automata model provides daily future snow cover simulations, which are incorporated into the SRM to generate streamflow projections. We evaluated the impacts of climate change on climate variables under four climate scenarios considering two warming levels for Spain (1.5 °C and 3.0 °C). These scenarios were derived from an ensemble of nine Regional Climate Models (RCMs) and downscaled using two different approaches: bias correction and delta change. This study identified the significant impacts of climate change in a Mediterranean snow-dominated basin (Canales basin, southern Spain). The results revealed increased temperatures and reduced precipitation, which led to substantial declines in snow cover and streamflow. This framework offers a valuable tool for water resource management and the development of adaptive climate strategies in snow-dominated mountain regions.

In-depth knowledge of smallholder farmers’ perception of changing climate variables such as recurrent and protracted droughts, late onset of rainfall, early cessation of rainfall and their coping adaptation strategies are very significant in designing climate resilient agriculture among smallholder food crop farmers in sub-Saharan Africa (SSA). This paper examines smallholder farmers’ perceptions of climate variability vis-á-vis meteorological and satellite remote sensing data and their implications for climate smart agriculture technologies. Integration of meteorological, satellite remote sensing and farm-level data were used. Multistage sampling procedure was used to select four towns, eight communities and 398 smallholder food crop farmers. Spearmans’ rank correlation coefficient and Standardized Precipitation Index were used to assess the distribution of climate variables. In addition, three vegetation drought characteristic indices, Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Water Supply Vegetation Index (WSVI) were used to examine drought conditions within the basin. The results indicated that smallholder farmers in the Offin river basin perceived recurrent and prolonged droughts, rising temperatures, late onset of rainfall, early cessation of rainfall, increasing dry spells, reduction in the length of rainfall season and shorten cropping season as a main indicators of climate variability. The findings further revealed that farmers’ perceptions on climate variability strongly agrees with meteorological and satellite remote sensing data which not only demonstrated rising temperature and frequent and prolonged droughts but also late onset and early cessation of rainfall and reduction in growing season rainfall. Smallholder food crop farmers in the Offin river basin have a high awareness of variation in climate condition and have taken coping strategies to reduce the effects of climate change and climate variability. Smallholder food crop farmers in the basin have also adopted climate smart agriculture technologies such as crop management techniques, integrated soil and nutrient management practices, tillage and residue management, small scale irrigation systems, inland valleys cropping and renewable energy systems to increase agricultural productivity and build resilience to climate variability. The policy implication is that, smallholder food crop farmers’ knowledge on climate variability should be considered as a practical input in designing and planning climate variability coping adaptation and mitigation strategies.

&amp;lt;p&amp;gt;The Tree Ring Width (TRW) records are one of the main paleoclimate proxies that estimate the past climate variability. TRW measurements pave the way for scientists to produce sequences from various kinds of trees and reconstruct climate variables over the past years. Understanding the relation between TRW and climate variables in the past would help us analyze climate change events. This study has applied multi-gridded datasets to find the relations and model TRW data with different climate variables in South Korea's northeast. We utilized TRW data related to our case study that is available on the NOAA website; furthermore, we have checked three primary gauges, namely Agmerra (The Modern-Era Retrospective Analysis for Research and Applications), CRU TS4.03 (Climatic Research Unit Time-Series version 4.03), and APHRODITE's (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) for climate variables. In the first step, we have checked the relation between the gauges' precipitation data and observation TRW. According to the obtained efficiency criteria, CRU performed the best consequences. In the second step, we have tried to model observation TRW as a dependent variable and four climate variables of CRU (precipitation, minimum temperature, potential evapotranspiration, and diurnal temperature range) as independent ones over 1969-1998. We have created a linear regression model and determined the accurate coefficients for each climate variable. Besides, we have examined the observation TRW and modeled TRW data. The results showed that with &amp;lt;em&amp;gt;R&amp;lt;sup&amp;gt;2 &amp;lt;/sup&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;sup&amp;gt;&amp;amp;#160;&amp;lt;/sup&amp;gt;&amp;amp;#8776; 0.40 and a &amp;lt;em&amp;gt;p-value&amp;lt;/em&amp;gt; of 0.0323, the regression line was linearly significant at the 95 percent significance level. It represents that our model is acceptable. We will extend our model with Artificial Intelligence methods and try to apply other TRW stations in the future step. In this way, we may produce highly accurate models and fill the gaps for future researches.&amp;lt;/p&amp;gt;

Climate change and variability has hampered global agricultural growth and is anticipated to have a notable impact on crop production owing to the high reliance of agricultural activities on weather. The study examined impact of climate variability on production of major food crops (maize, beans and Irish potatoes) in Kieni West sub-County, Kenya. Mixed methods research design was used. Pearson’s coefficient of correlation (r) was used to assess the relationship between climate variables and the major food crops. Multiple Linear Regression (MLR) was employed to assess the effect of rainfall and temperature variability on major food crops. Analysis of quantitative data was done using descriptive and inferential statistics while analysis of qualitative data was done thematically. Data presentation was by use of figures, tables and verbatim quotes. Correlation results revealed a positive relationship between rainfall and bean (r = 0.46) and maize production (r = 0.33) and a negative relationship between rainfall and Irish potatoes production (r = -0.056). A negative relationship between maximum temperatures and production of bean (r = -0.89) and maize (r = -0.56) was observed while a positive relationship between maximum temperatures and production of Irish potatoes (r = 0.02) was reported. Also, correlation results revealed a negative relationship between minimum temperatures and production of maize (r = -0.35), beans (r = -0.43) and Irish potatoes (r = -0.38). Coefficient of Determination (R2) value of 0.11, 0.18, 0.003 for maize, beans and Irish potatoes respectively against rainfall was reported. R2 values of 0.32, 0.80 and 0.00 was reported for maize, beans and Irish potatoes respectively against maximum temperatures while R2 values of 0.12, 0.18 and 0.14 was reported for maize, beans and Irish potatoes respectively against minimum temperatures. The study concludes that climate variability had differential impacts on production of maize, beans and Irish potatoes in the study area.

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM’s seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R &lt; 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources.

I discuss three aspects of research necessary for investigating possible effects of changes in climatic variability on crop yields. Additional information on changed variability effects is needed to further elucidate uncertainties in our knowledge of possible impacts of climate change on agriculture. First, sensitivity analyses of crop responses to shifting change in variability must be performed. Second, investigations of how climatic variability may change under perturbed (e.g., greenhouse gas-warmed) climate conditions should be undertaken. If one has some confidence in estimates of how variability may change, then a third research task is the formation of climate change scenarios that incorporate changes in climatic variability and their application to crop-climate models to determine crop responses. In this chapter, these research tasks are discussed regarding one climate variable, precipitation. I summarize two research projects that have been undertaken to investigate the sensitivity of the CERES-wheat (Triticum aestivum L.) crop model to changes in climatic variability, on daily to annual time scales, for sites in the central Great Plains. I also provide an example of determining possible changes in daily variability of precipitation through analysis of results from two regional climate model experiments, and then go on to describe an example of forming a climate change scenario that incorporates changes in daily precipitation variability estimated from the regional model runs. The CERES-wheat model exhibits considerable sensitivity to changes in precipitation variability, and variability of yields is positively correlated with variability in precipitation. Consideration of changes on both daily and interannual time scales is important. Estimates of changes in wheat yields simulated from a scenario that includes changes in variability are very different from changes in yield determined from a scenario that considers only mean changes in precipitation.

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.