Average Annual Minimum Temperature Research Articles

Variability and trends of temperature and rainfall over three agro-ecological zones in North Shewa, Central Ethiopia

This study was conducted to analyze rainfall and temperature variability and trends over three Agro-Ecological Zones (AEZs) in Central Ethiopia. Global weather data for Soil and Water Assessment Tool (SWAT) and global mean monthly sea surface temperature (SST) data series were used for analysis. Mann–Kendall (MK) test was utilized to analyze rainfall and temperature trends. Sen’s slope estimator was employed to find out the rate of change. The study detected an annual increase of 0.07 °C (p < 0.001) in mean maximum temperature at Kolla AEZ. It also showed a 0.06 °C rise per annum (p < 0.001) for both Dega and Woina Dega AEZs. The average annual minimum temperature has increased by 0.03 °C per year at Kolla (p < 0.001), Woina Dega (p < 0.05), and Dega (p < 0.01), which means an increase of 1.05 °C between 1979 and 2013. Results from precipitation concentration index (PCI) revealed the highest percentage (97.1%) of irregular distributions in annual rainfall pattern at Kolla AEZ, followed by Woina Dega (82.9%). Standardized rainfall anomalies (SRAs) calculated in the study also showed higher percentage (28.6%) of drought in Kolla AEZ, which experienced drought once in every 3 or 4 years. The study revealed negative annual rainfall anomalies for 18 years in Kolla and 16 years in both Dega and Woina Dega AEZs. Rising temperatures and irregular rainfall patterns may induce some adverse impacts on the livelihoods of rural populations in general, their subsistence agricultural production processes and food availability in particular. Policymakers and stakeholders should give priority in designing and introducing pro-poor plus geographically differentiated adaptive strategies.

Impact of Climate Change on Water Resources and Crop Production in Western Nepal: Implications and Adaptation Strategies

Irrigation-led farming system intensification and efficient use of ground and surface water resources are currently being championed as a crucial ingredient for achieving food security and reducing poverty in Nepal. The potential scope and sustainability of irrigation interventions under current and future climates however remains poorly understood. Potential adaptation options in Western Nepal were analyzed using bias-corrected Regional Climate Model (RCM) data and the Soil and Water Assessment Tool (SWAT) model. The RCM climate change scenario suggested that average annual rainfall will increase by about 4% with occurrence of increased number and intensity of rainfall events in the winter. RCM outputs also suggested that average annual maximum temperature could decrease by 1.4 °C, and average annual minimum temperature may increase by 0.3 °C from 2021 to 2050. Similarly, average monthly streamflow volume could increase by about 65% from March–April, although it could decrease by about 10% in June. Our results highlight the tight hydrological coupling of surface and groundwater. Farmers making use of surface water for irrigation in upstream subbasins may inadvertently cause a decrease in average water availability in downstream subbasins at approximately 14%, which may result in increased need to abstract groundwater to compensate for deficits. Well-designed irrigated crop rotations that fully utilize both surface and groundwater conversely may increase groundwater levels by an average of 45 mm from 2022 to 2050, suggesting that in particular subbasins the cultivation of two crops a year may not cause long-term groundwater depletion. Modeled crop yield for the winter and spring seasons were however lower under future climate change scenarios, even with sufficient irrigation application. Lower yields were associated with shortened growing periods and high temperature stress. Irrigation intensification appears to be feasible if both surface and groundwater resources are appropriately targeted and rationally used. Conjunctive irrigation planning is required for equitable and year-round irrigation supply as neither the streamflow nor groundwater can provide full and year-round irrigation for intensified cropping systems without causing the degradation of natural resources.

Climatic Trends of Variable Temperate Environment: A Complete Time Series Analysis during 1980–2020

The western Himalayan region is susceptible to minor climate changes because of its fragile ecology, which might threaten the valley’s prestigious ecosystems and socio-economic components. The Himalayas’s local climate and weather are vulnerable to and interlinked with world-scale climatic changes since the region’s hydrology is predominantly dominated by snow and glaciers. The Himalayas, notably the Jammu and Kashmir region in the western Himalayas, has clearly shown distinct and robust evidence of climate change. This study used observed data to examine the climatic variability and trends of change in precipitation and temperature for the Kashmir valley between 1980 and 2020. Gulmarg, Pahalgam, Kokernag, Qazigund, Kupwara, and Srinagar (Shalimar) meteorological stations in the Kashmir valley were studied in detail for long- and short-term as well as localized fluctuations in temperature and precipitation. The annual temperature and precipitation fluctuations were calculated using Sen’s slope approach, and the sloping trend was determined using linear regression. The research showed statistically insignificant growing trends in maximum and minimum temperatures throughout the Kashmir valley. The average annual temperature in the Kashmir valley increased by 1.55 °C during the last 41 years (from 1980 to 2020), with a higher rise in maximum and minimum temperature by 2.00 and 1.10 °C, respectively. However, precipitation showed a non-significant decreasing trend concerning time series analysis over 1980 to 2020 in Kashmir valley. Results of annual average maximum temperature at all the stations revealed that Pahalgam (2.2 °C), Kokernag (1.8 °C), and Kupwara (1.8 °C) displayed a steep upsurge and statistically significant trends; however, annual average minimum temperature followed an increasing trend from 1980 to 2020 at all the stations except Shalimar. However, non-significant declining trends in precipitation were recorded at all the locations in Kashmir valley. This changing pattern of temperature and precipitation could have significant environmental consequences, affecting the western Himalayan region’s food security and ecological sustainability.

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.

Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them.

三江源国家公园植被净初级生产力变化趋势及影响因素

PDF HTML阅读 XML下载 导出引用 引用提醒 三江源国家公园植被净初级生产力变化趋势及影响因素 DOI: 10.5846/stxb202106131577 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 第二次青藏高原综合科学考察研究项目（2019QZKK0302）；中国科学院青海省人民政府三江源国家公园联合研究专项专项（LHZX-2020-07） Changes and influencing factors of vegetation net primary productivity in the Sanjiangyuan National Park Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:净初级生产力（NPP）是评估全球气候变化和人类活动下生态系统状况、过程和机制的重要指标之一。研究以中国首批国家公园之一的三江源国家公园为对象，利用GLOPEM-CEVSA耦合模型，以1981-2018年空间插值的气象数据和基于遥感反演的FPAR数据为输入，分别估算仅气候驱动的潜在NPP （NPPCL）和气候遥感共同驱动的现实NPP （NPPRS），以二者之差厘定人类活动影响的NPP （NPPHA），进而探究全球气候变化下人类活动的影响。结果表明：（1）三江源地区NPPRS多年均值为309.70 g C m-2 a-1，占NPPCL的61.65%。其中，黄河源、长江源和澜沧江源园区NPPRS分别为249.88 g C m-2 a-1、140.18 g C m-2 a-1和330.55 g C m-2 a-1。（2）全区NPPRS以2.00 g C m-2 a-1速率显著增加，高于NPPCL（1.74 g C m-2 a-1），其中黄河源、长江源和澜沧江源园区NPPRS增长速率分别占各自NPPCL增长速率的89.13%、90.23%和77.43%，澜沧江源园区整体受人类活动影响最大。（3）气候影响方面，年降水、年平均日最高气温和年平均日最低气温共同可解释全区NPPCL和NPPRS年际变化的51%和73%，可分别解释黄河源、长江源和澜沧江源园区的48%和58%、52%和69%、42%和50%，其中气温对NPP年际变化趋势影响更大。（4）人类活动在大部分区域呈负影响，存在西北向东南负面影响增强的空间分布特征，但2000年前后人类活动对生产力变化趋势呈负影响的面积从79.12%降低到56.34%，NPP变化量从-71.41 Tg C降低到-38.72 Tg C，作为主导因子的变化范围从18.73%增加至38.76%，三江源地区生态保护与恢复等措施促进了植被生产力增加，但需进一步实施生态保护与恢复措施，这是一项长期而艰巨的任务。 Abstract:The Net Primary Productivity (NPP) is an important indicator quantifying ecosystem status, process and undying mechanisms due to global climate change and human activities. The potential NPP (NPPCL) and actual NPP (NPPRS) were estimated through GLOPEM-CEVSA model driven by the only interpolated climate data, and both climate and remote sensing data, respectively, for the near 40 years from 1981 to 2018. The human activities dominated NPP (NPPHA) was defined as the difference between NPPRS and NPPCL. The method was applied in the Sanjiangyuan National Park, one of the first national parks in China, to explore the impacts of human activity under global climate change. The results showed that:(1) the average NPPRS was 309.70 g C m-2 a-1, accounting for 61.65% of the NPPCL for the whole region. For the three sub-parks, the NPPRS were 249.88 g C m-2 a-1, 140.18 g C m-2 a-1 and 330.55 g C m-2 a-1 in the Yellow River headwater, Yangtze River headwater and Lancang River headwater, respectively. (2) The NPPRS was increasing significantly by a speed of 2.00 g C m-2 a-1, which was faster than the NPPCL (1.74 g C m-2 a-1). The speed ratio of NPPRS to NPPCL were 89.13%, 90.23% and 77.43% for the three sub-parks by the above oder, respectively, which meant the Lancang Park was most impacted by human activities. (3) In the terms of climate impact, temperature had a stronger impact on the trend of NPP. Specifically, annual total precipitation, average annual maximum temperature and minimum temperature explained 51% and 73% of the interannual variation of NPPCL and NPPRS in the whole region, which can explain 48% and 58%, 52% and 69%, 42% and 50% of the interannual variability in the NPP timeseries in the Yellow River, Yangtze River and Lancang River sub-park, respectively. (4) Human activities had a negative influence over the most areas and became stronger to the southeast from the northwest. And the area showing a negative influence from human activities on the productivity changes decreased from 79.12% before 2000 to 56.34% after 2000. The total NPP over the whore negative area decreased from -71.41 Tg C to -38.72 Tg C, and the human activity as the dominant factor increased from 18.73% to 38.76% for the same two periods. It illustrated that the ecological protection and restoration of the Three-River Headwaters Region have contributed to an increasing vegetation productivity, but the protection and restoration measures should be further implemented. 参考文献 相似文献 引证文献

STUDY OF CLIMATE CHANGE AND ITS IMPACTS ON WATER RESOURCE: A CASE STUDY FROM SIKLES VILLAGE OF ACAP REGION

Climate-induced water related problems are major challenge for mountain communities and little is known on changes and impacts to local people. This paper focuses on climate change issues and its water related consequences to people of Sikles village. Climatic data were collected and analyzed for trend and variability analysis. Water availability was analyzed based on discharge measurement through a volumetric approach and demand was estimated from total population and per capita daily water requirement as prescribed by WHO. Local perception was assessed from household questionnaire survey (n=60). The result showed that average annual maximum and minimum temperature is increasing at the rate of 0.049℃ and 0.006℃ annually. Similarly, annual precipitation is also slightly increasing but in unpredictable pattern whereas number of rainy days is decreasing at 1.123 days per year. Both hot and cold climate extremes and intense rainfall are seen frequently in recent years. Landslide is major water related problem in the area and has more risk due to intense rainfall. Locals perceived that water availability in the sources is decreasing, also current demand and supply analysis in dry period showed there is scarcity of water if collection time concentered only on morning and evening. Water source protection and plantation is most adopted practices to protect water and minimize landslide hazards. Local water use regulation for efficient use of water, development of water storage and distribution system and promotion of green interventions could be important future strategy to face the predicted water related problems in the village.

Assessment of Hydroclimatological Changes in Eastern Himalayan River Catchment of Northeast India

This article focuses on the assessment of climate change impacts on the hydroclimatology of the Subansiri River basin, which is the largest tributary of the Brahmaputra River in the northeastern part of India. Three representative concentration pathway (RCP) emission scenarios, namely, RCP2.6, RCP6.0, and RCP8.5, of three general circulation models (GCM) archived by the Geophysical Fluid Dynamics Laboratory (GFDL), were utilized for the projection of climatic variables (such as precipitation and temperature). Long-term (2011–2100) projections of precipitation and temperature for different emission scenarios were made using the statistical downscaling technique. The soil and water assessment tool (SWAT) hydrological model was used for hydrological modeling of the river basin. The observed streamflow series for the period of 2002–2013 has been utilized for calibration and validation of the hydrological model. Parameterization, uncertainty analysis, and parameter sensitivity analysis of the model were performed using a sequential uncertainty fitting (SUFI2) program. The coefficient of determination (R2) for the calibration and validation of the hydrological model on the monthly streamflow time series was found to be 0.86 and 0.80, respectively. Future projections of the precipitation and temperature suggest an increase in the annual average maximum temperature (Tmax), annual average minimum temperature (Tmin), and annual precipitation of the river basin. These projected climatic variables were used as the primary input in the hydrological model for the projection of the streamflow for the period of 2016–2100. The flow duration curve analysis of streamflow projections reveals an increase in the discharge for a particular percent of the dependable flow in the case of all the RCP scenarios. Water yield analysis also suggests an increase in the annual average water yield in all cases of emission scenarios.

Modeling and Trend Analysis of Climatic Variables of Ranchi District, Jharkhand

In this paper, an attempt has been made to study the temporal variation in monthly, seasonal and annual rainfall, and average annual maximum and minimum temperature for the period 1901-2015 over Ranchi district of Jharkhand, India. Long-term changes in rainfall, temperature was determined by Man-Kendall rank statistics and Sen’s slope, and forecasting of time series was determined by ARIMA model. The results revealed that there was significant decrease of average rainfall in the month of February and August while increase in month May and Pre-monsoon season. Average rainfall in the month of February, May, August and Pre-monsoon season showing insignificant increasing as well as decreasing rainfall trend. The average annual maximum and minimum temperature showing decreasing and increasing trend over Ranchi district during the period 1901 to 2015. This paper also describes five-year prediction of rainfall and temperature climatic variables.

Climate Change Projections and their Consequences on Agro-Climate in Sugarcane Growing Areas of &lt;em&gt;Sevanagala&lt;/em&gt;, Sri Lanka

An analysis was conducted to estimate the agro-climatic changes, which would incur due to projected climate change scenarios on sugarcane cultivation in Sevanagala, Sri Lanka over the next 70 years. The general circulation model developed by the Beijing Meteorological Centre was used with Representative Concentration Pathways scenario 6.0 to simulate the climate variables of maximum and minimum temperatures, and rainfall for years 2030, 2050, and 2090. The goodness of fit test was conducted against simulated and observed data using 10-year dataset from 2010 to 2019. The future climate was compared using a baseline data set, which was generated by averaging 10 years of data from 2010 to 2019. The results indicated that by 2090, the average annual minimum and maximum temperatures would increase by 1.2°C and 1.4°C, respectively. However, annual maximum and minimum temperatures may remain within the favourable range for sugarcane cultivation. The annual rainfall is expected to increase by 6.0% in 2090 relative to baseline values. The results demonstrated the stability of the effective rainfall. The evapotranspiration would increase by 3.7% by the year 2090 relative to baseline values. The soil moisture deficits for sugarcane planted in the Yala and Maha planting seasons are also expected to increase by 6.7% and 12.6%, respectively in 2090. As a result of increased evapotranspiration, additional irrigation might be needed to maintain the productivity of sugarcane cultivation indicating the climate-related risks for future rain-fed sugarcane cultivations in Sevanagala, Sri Lanka.

Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques

Study regionThe present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, and the northern and southern parts of the basin receive the highest amounts. Study focusIn this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models. New hydrological insights for the regionThe results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.

Cropping patterns along an altitudinal gradient and their implications to wildlife conservation in Rombo, Tanzania

Ecosystem and livelihood sustainability of small-scale farmers in Eastern Africa are often challenged by climate change and unsustainable land use practices. Little is known on the small-scale temporal processes of cropping systems in relation to long-term climatic conditions. To understand the climatic influence on the cropping patterns, and its implications to wildlife/elephant conservation, our study used a combination of time-series datasets on crop and climate as well as geo-spatial layers spanning more than three decades. To validate the information analyzed from secondary data, we carried out participatory observation and interviews, whereby about 5% of village households were selected along an altitudinal gradient of Rombo area, Tanzania. We generated land use change maps using Geographical Information System (ArcGIS 10.4) and used Spearman’s correlation analysis to assess the influence of rainfall, temperature, and crop yield on the observed longitudinal changes in the crop types and other land use patterns both within and across altitudinal gradients. We found that perennial crops like coffee and banana concentrated in upper agro-ecological zones, with a clear division line above 1230masl while seasonal crops like maize and beans were mostly cultivated in lowlands (below 1230masl). The results further showed that coffee yields significantly declined with higher average annual minimum temperatures over the last 16 years. Within the same time period, there was a disproportionate shift from coffee to seasonal crop cultivation such as maize and beans. Elephant numbers declined by about 38%, while human settlement area increased by 28% over the last 30 years. We conclude that changes in land use and shifts in climate and cropping patterns have important implications for elephant conservation and sustainability of the ecosystem in Rombo area, Tanzania.

30 Years Climate Change Impact on Weather Elements and Green Coverage: GIS and Remote Sensing Geo-Environmental Case Study in Jordan

This study deals with climate change and its geo-environmental impact in Jordan for thirty years period (1982-2012). It comprised the building of a geographic information system (GIS) database for the most important basic climatic elements of temperature, rainfall, and relative humidity. Data is obtained from the Meteorological Department of Jordan for the study period. Digital descriptive and statistical analysis methods for the GIS database are implemented using ArcGIS 10.4 software and Normalized Difference Vegetation Index extracted from Moderate Resolution Imaging Spectroradiometer satellite images to forecast that impact on weather elements and green vegetation cover, respectively. Three different criteria are used for analysis and verification to achieve the objectives of the study. The criteria are the average annual minimum and maximum temperature, which is considered the most important criterion for this study, average annual rainfall, and average annual relative humidity. Results showed a speedy increase in the annual rate of the local temperature, particularly since 1990. Despite the local temperatures' average volatility, the increment reached about 1.5-2.0 oC degrees Celsius. An increase in the relative humidity is observed, but with no evident change in the average annual rainfall, both in Jordan's northern and eastern parts. At the same time, there were increases for Jordan's central region during the period 1985-2012. The green vegetation cover area showed a decrease during the studied period 2002-2008 as that is probably due to the evident increase in annual average temperature and evaporation. Results reveal the increase of the greater temperature change region has increased in the northern part of Jordan by eight times for the 1982-2002 period as mentioned in this study. The area of vegetated area decreased to 3725.8 km² in 2008 compared to 9305.5 km² in 2002. The study demonstrated efficiency in applying descriptive and statistical GIS analysis methods on the climatic database, which better understanding of the climate change phenomenon.

Response relationship between the abrupt temperature change‐climate warming hiatus and changes in influencing factors in China

AbstractThe mechanism of the abrupt temperature change‐climate warming hiatus is not clear, and understanding the response relationships between it and the influencing factors is significant to the study the mechanism. Based on the annual average temperature (Tave), annual average minimum temperature (Tmin), and annual average maximum temperature (Tmax) of 622 meteorological stations in China from 1951 to 2018, the response relationships between the three temperatures and the influencing factors were analyzed. The results showed that In the periods before, during, and after an abrupt temperature change, For the abrupt changes in the three temperatures, the regions significantly influenced by the solar radiation (SR), AMO, multivariate El Niño southern oscillation (ENSO) index (MEI), and Pacific Decadal Oscillation (PDO) accounted for more than 80% of China, and for the abrupt changes in Tmin, the regions with significant influences covered the whole of China. The regions significantly influenced by the AO accounted for more than 60% of China, and the regions significantly influenced by the remaining factors accounted for more than 30% of China. The AO mainly influenced the abrupt temperature change in the northern regions, the AGG and CO2 mainly influenced all the regions except for Northeast China, and the influencing scopes of the atmospheric pressure (AP) were relatively small and not concentrated. In summary, when some conditions continued for a period and a tendency rate or value was reached, abrupt temperature change occurred, and the conditions were as follows: the AGG continued to increase, the PDO was in a positive phase, the AMO and SR continued to rise, the MEI changed abruptly, and the AP in each zone continued to decline/increase and experienced the subsequent trend change. In the periods before, during, and after the climate warming hiatus, few influencing factors had a significant correlation with the temperatures. The MEI and PDO influenced more than 80% of the regions that experienced the climate warming hiatus, and other factors influenced less than 60% of these regions. In the 1990s, especially after 1998, the increase in the AGG slowed, the PDO declined or was in a negative phase, the MEI and SR decreased, and the AP in each zone continued to decline/increase and experienced the subsequent trend change; when those conditions continued for a period and a tendency rate or value was reached, the climate warming hiatus occurred. The abrupt temperature change‐climate warming hiatus was the result of the joint action of multiple influencing factors.

Impact of increased temperature on spring wheat yield in northern China

AbstractGlobal warming has been reported to cause reductions in crop yields. However, it was suggested that warming temperature might benefit crop productivity in some cool areas at high latitude. In this study, we conducted a 17‐year field experiment (2002–2018) on spring wheat in Inner Mongolia. Temperature changes during each growth stage of spring wheat were investigated. Responses of spring wheat yield to temperature changes during the specific growing stages were evaluated. Average annual maximum temperature (Tmax) and minimum temperature (Tmin) significantly increased over the past 17 years. However, Tmax did not show obvious increase trend during spring wheat growing seasons (p = 0.0672). Furthermore, Tmax also had no distinct change before or after anthesis. Tmin significantly increased during the whole growing season, as well as in pre‐ and post‐anthesis stages. Correlation analysis indicated that Tmax in the entire growing season and post‐anthesis did not affect spring wheat yield, but high Tmax during pre‐anthesis can improve grain yield. The Tmin during the life cycle and pre‐anthesis both had positive relationship with grain yield. Moreover, elevated temperature from seedling to stem elongation can benefit tiller formation and thus increasing spike number, which contributed to the significant yield increase (p = 0.0093). Overall, climate warming affect spring wheat yield in cool area, and increasing temperature that was below the optimum temperature can benefit wheat productivity.

Spatio\u2010temporal characteristics of meteorological drought under changing climate in semi\u2010arid region of northern Ethiopia

BackgroundBelow-normal availability of water for a considerable period of time induces occurrence of drought. This paper investigates the Spatio-temporal characteristics of meteorological drought under changing climate. The climate change was analyzed using delta based statistical downscaling approach of RCP 4.5 and RCP 8.5 in R software packages. The meteorological drought was assessed using the Reconnaissance Drought Index (RDI).ResultsThe result of climate change projections showed that the average annual minimum temperature will be increased by about 0.8–2.9 °C. The mean annual maximum temperature will be also increased by 0.9–3.75 °C. The rainfall projection generally showed an increasing trend, it exhibited an average annual increase of 3.5–13.4 % over the study area. The projected drought events reached its maximum severity indicated extreme drought in the years 2043, 2044, 2073, and 2074. The RDI value shows drought will occurred after 1–6 and 2–7 years under RCP 4.5 and RCP 8.5 emission scenarios respectively over the study area. Almost more than 72 % of the current and future spatial coverage of drought in the study area will be affected by extreme drought, 22.3 % severely and 5.57 % also moderate drought.ConclusionsTherefore, the study helps to provide useful information for policy decision makers to implement different adaptation and mitigation measures of drought in the region.

Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

An assessment of LARS-WG’s model to forecast meteorological parameters for climatic zones of cotton-harvested areas over Iran

Background and Objectives: Nowadays, climatologists and researchers are interested in the long-term forecast of climatic variables to be informed about the extent of their variations and to take measures to mitigate the adverse effects of climate change. Accordingly, general circulation models (GCMs) of atmosphere have been developed to predict climatic parameters. LARS-WG is a model to downscale the output of GCMs. It was used in the present research to generate data of daily precipitation, radiation, and minimum and maximum temperature across the cotton cultivation climatic zones (Meteorological stations) in Iran. The objective of this study is to estimate climatic factors (rainfall, radiation and minimum and maximum temperature) in the future period in major climatic cotton cultivation areas in Iran, which may be used for optimized water resource preservation and management in these regions. Also, the 5th IPCC report was used in this study, in contrast with the majority of investigations that use the 4th report. Materials and Methods: In this research, the important areas under cotton cultivation in Iran are the target area. The data of 23 synoptic stations were used in these areas. The required meteorological data recorded in the stations were daily rainfall, minimum temperature, maximum temperature and sunshine. The present study assessed the performance of five different GCM models in simulating the data of precipitation, radiation, minimum temperature, and maximum temperature in nine synoptic stations (SABZEVAR, GHOOCHAN, GHOM, HASHMABAD, LAR, BILESOWAR, EDAREGORGAN, HASANABADEDARAB, and MASHHAD) from 2011 to 2016, firstly. Finally, two GCMs (MIROC5 and GFDL-CM3) were selected for the research purpose based on the results of t-test, F-test, and the Kolmogorov-Smirnov non-parametric test. Then, the parameters were predicted by the selected GCM models for 20 years (2041-2060) under the emission scenarios of RCP4.5 and RCP8.5. Results: According to employing the scenarios of RCP4.5 and RCP8.5, the predicted solar radiation does not show a significant change in the future period versus the base period of 1981-2010 in all studied regions based on both GCM models. The highest change (0.249 MJ / m2 / day) is based on the MIROC5 model and the RCP8.5 scenario in climate zone 6102 (HASHMABAD station). The predictions revealed that the parameters of maximum and minimum temperature would be ascending for all the climatic regions over the future period. The highest variations in the average long-term annual minimum and maximum temperatures versus the base period would be 2.67 and 2.75°C happening in Climatic Region 6002 Includes stations (HASANABADEDARAB, KHAF, HAJI ABAD and GONBAD) over the 2041-2060 period under Scenario RCP8.5. For both scenarios RCP4.5 and RCP8.5, the highest temperature increase was observed in the climatic zones where located in south of Alborz and the east of Zagros mountain chains as well as the Central Iranian Dry Plateau and the lowest increasing was in the climatic regions located around the Caspian Sea, the Persian Gulf and the south of Iran. For the total annual precipitation over the future period (2041-2060), the highest amplification was predicted by MIROC5 to be 98.5 mm under Scenario RCP4.5 in Climatic Region 6202 (EDAREGORGAN station), and the greatest loss of precipitation was predicted by the same model to be -29.8 mm in Scenario RCP8.5 in Climatic Region 6102. Conclusion: Given the prediction of rising average annual minimum and maximum temperature and the decline of precipitation over the future period (2041-2060) in the climatic regions leading to the arid plain of central and southern Iran, it can be concluded that the variations of meteorological parameters induced by climate change will be significant in the cotton-growing climatic regions of Iran over the future decades.

Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change

Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980–2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m2. The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

Impact of Climate Change on Agriculture in Kavre District, Nepal

This study aims to assist the impact of climate change on agriculture in Banepa municipality, as a case study to the local climatic trends and its impacts, vulnerability, and adaptation. The study was conducted among the household of ward no. one to eleven except two and six in the municipality. A questionnaire survey, field observation, in-depth interview, and focus group discussion, methods were adopted for the information collection, cross-validation with verification, and using the secondary source of the information. Monthly precipitation and monthly minimum and maximum air temperatures data of Dhulikhel station were used to study their annual and seasonal trends.Agriculture is one of the vulnerable sectors of the impact of climate change. Trend analysis of temperature and precipitation over 27 years indicates that this region is facing various weather variability. The annual mean of the minimum temperature was showing the highest (13.530C) in 2014 and the lowest (7.860C) in 2001. The trend analysis shows that the annual average minimum temperature has also been increasing per year. Drought, delay in monsoon, hailstorm,and heavy rainfall are major challenges in agriculture.The study concluded that climate change affected the negative impact on agriculture in the Banepa Municipality of Kavre. The existing local and institutional strategies are not sufficient and sustainable to cope with climatic vagaries. There is a proactive need for climate change adaptation measures to address the negative impact of climate change in agriculture to sustain the food and nutrition security in the community through institutional support and long-term agricultural policy and strategic plan.