Monthly Minimum Temperatures Research Articles

Reproductive phenology and its drivers in a tropical rainforest national park in China: Implications for Hainan gibbon (Nomascus hainanus) conservation

Reproductive plant phenology, such as the seasonal timing of flowering or fruiting, influences many aspects of tropical forest communities, but there are few studies examining potential drivers of phenological patterns, especially in south China. We explored phenological patterns in Hainan Tropical Rainforest National Park (HNNP) in China and their major climatic and biotic drivers, based on phenological and distribution data for >2000 species from the Flora of Hainan. Specifically, we address the following questions: (1) What are the phenological patterns for all species in HNNP and for Hainan gibbon food plants only? (2) What are the environmental drivers of reproductive phenology? (3) Whether the flowering and fruiting dates are phylogenetic conserved? (4) Does reproductive phenology vary with functional groups, like growth forms, pollination modes and fruit types? Our results show flowering peaks in late spring and early summer, while fruiting phenology was concentrated during late summer and fall. Closely related species tend to have similar flowering and fruiting dates. The number of flowering species was primary controlled by mean maximum monthly temperature while minimum monthly temperature was the most important variable in shaping fruiting patterns of all species. For fruiting of gibbon food plants, mean monthly precipitation was the most important variable. Our study found January to May was food shortage period, which has important implications for Hainan gibbon conservation. We should take targeted ecological restoration by planting gibbon food species with phenologies between January and May, and take targeted provisioning to attract gibbons into a new habitat by providing fruits between January and May.

Outcomes of a quantitative analysis of 48 soil chronosequence studies in humid mid and high latitudes: Importance of vegetation in driving podzolization

There are various hypotheses on the relative importance of the individual soil-forming factors of Jenny’s (1941) functional-factorial approach. The quantitative influence of each soil-forming factor on the expression and rates of soil-forming processes, and in particular the influence of the different factors in combination, have not yet been fully explored. The aim of this study was thus to quantify the influence of the soil-forming factors on the rates of podzolization, using data from 48 studies of soil chronosequences developed in sandy parent materials, located in the humid mid and high latitudes. These studies contained altogether 259 soil profiles of known age, for which we tested existing hypotheses on the influence of different soil-forming factors. The formation of an E horizon and its increase in thickness over time is one of the characteristic features of Podzol formation. As it is one of the few features that was described in all 48 studies, we used it as an indicator of progressive podzolization. Through statistical analysis, we investigated how E horizon thickness is affected by latitude, longitude, mean annual precipitation, mean annual temperature, range between minimum and maximum monthly temperature, annual number of days with frost, vegetation class, sand content, clay content, and soil age.Because E horizon thickness exhibited a zero-inflated (semi-)continuous distribution, we opted for a zero-altered gamma (ZAG) model, consisting of a Bernoulli and a Gamma part. The Bernoulli part shows how the probability of the presence of an E horizon changes with soil age and environmental conditions. The Gamma part of the ZAG model allows for capturing the effects of the covariates on E horizon thickness.Our results indicate that vegetation is the most important factor for both (1) the age at which podzolization starts (used indicator: first occurrence of an E horizon), and (2) the rate of podzolization thereafter (used measure: increase of E horizon thickness with soil age). Climatic factors such as mean annual precipitation and range of temperature play subordinate roles. They can be important in determining the time to initiation of podzolization, but are less important for the rates of podzolization. Within this set of sandy soils, we did not identify a significant influence of sand content, neither on the start nor the rates of podzolization.

Impact of urban evolution on local temperature trends of Rawalpindi and Islamabad

Rawalpindi and Islamabad commonly known as twin cities of Pakistan have 3.2 million population. Twin cities have rapidly urbanized in the last three decades. The objective of the present study is to compute the urban growth and its effect on evolution of local temperature trends of twin cities. To compute the land-cover change such as built-up area, vegetation cover, water and barren land, Landsat images of 1980, 1992, 2000 and 2013 are classified by using the supervised image classification with maximum likelihood rule and probability surface method. To evaluate the change in temperature trends, homogenized time series data of daily averaged monthly minimum (Tmin) and maximum (Tmax) temperatures for the period of 1983 to 2013 is analyzed by using the linear regression. The results show that built-up area of twin cities increased from 66 km2 in 1983 to 148 km2 in 2013 with an increase of 120 per cent within 31 years. Due to resulted urbanization, Tmin and Tmax of twin cities have been increasing. Tmin is increased more in Rawalpindi than Islamabad and Tmax is increased more in Islamabad than Rawalpindi. The highest increase in Tmin and Tmax at both stations is observed during spring season.

Projected temperature increases may require shifts in the growing season of cool-season crops and the growing locations of warm-season crops

Predicting the effects of climate change on where and when crops can be grown under future conditions is critical for maintaining crop production, particularly in Mediterranean ecosystems. The diverse range of Mediterranean climatic conditions in California supports high crop diversity and production, yet California also faces future increased temperatures and more frequent extreme weather events indicative of a changing climate. Evaluating the effect of temperature increase is a crucial first step in estimating future impacts of warming. We compare the temperature constraints under climate projections for five annual crops. We determine maximum and minimum monthly temperatures of historical and future projections for the mid-21st century based on four climate projections (two climate models × two climate change scenarios). We estimate where temperatures were suitable for each crop historically and in the future at two spatial scales (4 km grid-cell; statewide) and two temporal scales (monthly; for each crop's growing season). We found differences between warm- and cool-season crops: temperature affects when cool-season crops (broccoli; lettuce) could be grown more than where, but temperature affects where warm-season crops (cantaloupe; tomato; carrots) could be grown more than when. More than 99% of land where lettuce and broccoli have been grown historically will have temperatures suitable for each crop by mid-century; the increased winter temperatures will enable spring and fall growing seasons to merge in more than 75% of land where each crop has been grown. Only 34–87% of land historically used for growing tomatoes will have temperatures appropriate for tomatoes due to the increase in summer temperatures. We do not predict cantaloupes and carrots to cross their upper temperature threshold. Integration of our results with other factors that affect crops – including management, water availability and helpful and harmful insects – provides guidance for adapting Mediterranean agriculture to climate change.

Air temperature and precipitation regime in Ukraine in 2021-2050 by CORDEX model ensemble

Global temperatures over the period of 2081–2100 are expected to rise by 0.3–4.8 °C compared to the period of 1986–2005. According to the previous studies, the average annual air temperature in all regions of Ukraine will keep increasing in the near future and the maximum increase in precipitation is expected mainly in the western and northern regions during winter and spring, whereas the decrease in precipitation will be registered in the central, eastern and southern regions during summer and autumn. This article aims to identify the features of changes in air temperature and precipitation for different regions of Ukraine in 2021–2050 based on the modelling results of the ensemble of CORDEX models as per the RCP4.5 scenario. 16 simulation runs for 7 regional climate models were selected for the analysis and the results were presented for five regional centers of Ukraine: Kyiv, Lviv, Kropyvnytskyi, Kharkiv and Odesa.
 It is shown that future monthly precipitation in all regions tends to increase by an average of 20–40 mm during autumn, winter and spring, whereas the decrease is expected to occur in summer. According to some models, the monthly precipitation will be close to zero in the Southern Ukraine in July and August, which is typical for the Mediterranean climate. Compared to the period of 1961–1990, the average monthly temperature will undergo small changes (up to 1 °C) in spring and autumn, while the temperature in summer and winter will increase by 2.5–3.5 °C. In Odesa, in contrast to the present-day situation, a positive average monthly air temperature will be expected to be recorded throughout the whole year, and only 25% of the runs show negative average monthly minimum temperatures. In the Northern Ukraine, the average monthly minimum and maximum temperatures in winter will increase by 2.0–2.5 °C, and in summer only the maximum air temperature will increase significantly.
 Thus, we can assume a change in the regime of moisture supply in Ukraine over the next thirty years. One can also assume a high probability of snow cover absence throughout the whole winter in the Southern Ukraine as a result of positive temperatures.

Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) finding and filling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal differencing and one first-order differencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-fit SARIMA models are developed for each of the fourteen variables. The study revealed that $$\text{SARIMA}(2,1,1)(1,1,1)_{12}$$ as the ideal forecasting model for eight out of the fourteen time-series datasets.

Assessment of Temperature Variability Effect on Rice Production in Nasarawa State, Nigeria

The output of cereal farmlands is imperative for sustainable global food security. Quantity of production from cereal croplands are partly a function of climatic elements and are connected to the pulses of climatic variation. Hence, this paper assessed temperature variability effect on rice production in Nasarawa State, Nigeria. Daily maximum and minimum temperature data were obtained from the Nigerian Meteorological Agency and converted into monthly averages while annual rice production data was obtained from the office of Nasarawa State’s Agricultural Development Programme. Acquired data were analysed using Linear Multiple Regression Model, coefficient of variation and spatial data analysis techniques. Although rice production in the State is being affected by the fluctuations in both minimum and maximum monthly temperature, the later poses grave concern for sustainability of rice production with a negative effect size of -3.145 and a coefficient value of -191,324.30 metric tons. This negative impact of maximum temperature fluctuations on rice production indicates that rice production in Nasarawa State is vulnerable to climate variability with increasing maximum temperature. LGAs in the south senatorial district has more favourable locations for rice production in comparison to those in the North and West districts given that less temperature fluctuation was observed in the former. Government and non-governmental institutions as well as individuals planning to establish rice farm project(s) in the study area should consider doing so in the South Senatorial District in order to avoid the adverse effect of temperature variability.

Historical Radial Growth of Chinese Torreya Trees and Adaptation to Climate Change

Chinese Torreya is a vital crop tree with an average life span of a thousand years in subtropical China. Plantations of this tree are broadly under construction, to benefit the local economy. Information on the growth and adaptation to climate change for this species is limited, but tree rings might show responses to historical climate dynamics. In this study, six stem sections from Chinese Torreya trees between 60 and 90 years old were acquired and analyzed with local climate data. The results indicated that the accumulated radial growth increased linearly with time, even at the age of 90 years, and the average radial increment of each tree ranged from 1.9 to 5.1 mm/year. The variances of basal area increment (BAI) increased with time, and correlated with the variances of precipitation in the growing seasons. Taylor’s power law was present in the radial growth, with the scaling exponents concentrated within 1.9–2.1. A “Triangle”-shaped relationship was found between the precipitation in the growing seasons and annual radial increments. Similar patterns also appeared for the standard precipitation index, maximum monthly air temperature and minimum monthly air temperature. The annual increases were highly correlated with the local climate. Slow growth, resilience to drought and multiple stems in one tree might help the tree species adapt to different climate conditions, with the implications for plantation management discussed in this paper.

Evaluation of Monthly Maximum, Minimum and Average Temperature Changes Observed for Many Years in Nevsehir Province of Turkey

Evaluation of Monthly Maximum, Minimum and Average Temperature Changes Observed for Many Years in Nevsehir Province of Turkey

Assessing Impact of Climate Change on Hydrology of Melka Kuntrie Subbasin, Ethiopia with Ar4 and Ar5 Projections

Assessing future challenges in water resources management is crucial to the Melka Kuntrie (MK) subbasin suffering water shortage. Impact assessments are evaluated by the HBV hydrological model with six scenarios, including two GCMs of AR4-A2 and two GCMs of AR5-RCP4.5 and RCP8.5, for the time periods 2021–2050 and 2071–2100. Evapotranspiration is expected to increase under all scenarios—due to rising temperature—and induce more water stress on rainfed agriculture of the area. However, the increase in the monthly minimum temperature is beneficial to crops against chilling damages. Five out of six projections show significant increases of rainfall and streamflow in both annual and major rainy seasons, except ECHAM-A2. Annual rainfall (streamflow) is expected to increase by 38% (23%) and 57% (49%) during 2021–2050 and 2071–2100, respectively, under RCP8.5 scenarios. Greater flashflood risk is a concern because of the projected increase in streamflow. The projection of decreased streamflow with ECHAM-A2 will exacerbate the existing water shortage, especially in the minor rainy season. Water harvesting during the major rainy season would be vital to enhance water management capacities and reduce flashflood risks. Lacking sufficient hydraulic and irrigation infrastructures, the MK subbasin will be more vulnerable to the impacts of climate change.

Modelling Climate Change Impact on Irrigation Water Requirement and Yield of Winter Wheat (Triticum aestivum L.), Barley (Hordeum vulgare L.), and Fodder Maize (Zea mays L.) in the Semi-Arid Qazvin Plateau, Iran

It is very important to determine the irrigation water requirement (IR) of crops for optimal irrigation scheduling under the changing climate. This study aimed to investigate the impact of climate change on the future IR and yield of three strategic crops (winter wheat, barley, fodder maize) in the semi-arid Qazvin Plateau, Iran, for the periods 2016–2040, 2041–2065, and 2066–2090. The Canadian Earth System Model (CanESM2), applying IPCC scenarios rcp2.6, rcp4.5, and rcp8.5, was used to project the monthly maximum and minimum temperatures and monthly precipitation of the region. The results indicated that the maximum and minimum temperatures will increase by 1.7 °C and 1.2 °C, respectively, under scenario rcp8.5 in the period 2066–2090. The precipitation will decrease (1%–13%) under all scenarios in all months of the future periods, except in August, September, and October. The IR of winter wheat and barley will increase by 38%–79% under scenarios rcp2.6 and rcp8.5 in the future periods. The increase in the IR of fodder maize will be very slight (0.7%–4.1%). The yield of winter wheat and barley will decrease by ~50%–100% under scenarios rcp2.6 and rcp8.5 in the future periods. The reduction in the yield of maize will be ~4%. Serious attention has to be paid to the water resources management of the region. The use of drought-tolerant cultivars in the region can be a good strategy to deal with the predicted future climatic conditions.

Sezonowe zróżnicowanie temperatury wody na przykładzie wybranych rzek nizinnych Mazowsza = Seasonal differentiation of water temperature on the example of lowland Mazovian rivers

Thermal regime has a critical impact on the lotic environment, as maximum temperature determines the boundaries of the occurrence of aquatic species, seasonal and diurnal water temperature variations affect their bioenergetics, while the timing of specific water temperature values during the year is important in the context of spawning and migrations. However, despite the great importance of water temperature studies in the context of environmental management and fisheries, as well as the development of accurate measurement techniques, such investigations have received relatively limited attention in Poland. The current study attempted to examine the seasonal differentiation of water temperature in lowland rivers. For this purpose, water temperature was recorded from the 1st of May 2015 to the 30th of April 2019 with a temporal resolution of 30-minutes. Digital temperature reorders used to make the measurements were distributed across six sites in Jeziorka, Świder and Utrata catchments located on the Mazovian Lowland and the Southern Podlachia Lowland near Warsaw. The hydrometeorological background of the water temperature monitoring was determined on the basis of data from the Warszawa-Okęcie station and water gauging stations. On the basis of the measurement data, mean, maximum, and minimum monthly water temperatures were calculated and presented on the background of the appropriate air temperature data, while statistical distribution of the 30-minute water temperature, aggregated in a monthly timescale, was presented on the box and whiskers plots. The Ward method was used to group months similar in terms of their thermal conditions, while the Pearson correlation coefficient was applied to evaluate the strength of the relationship between water and air temperature. The results indicate that the seasonal course of water temperature follows the course of air temperature, with the highest mean monthly water temperatures recorded in July, while the lowest in January. Statistical distribution analysis of water temperature in individual months and its grouping by the Ward method allowed to identify two periods characterized by relatively stable thermal conditions and two periods of dynamic changes of water temperature. In contrast to the maximum values of water temperature, which were observed in the summer as a result of intensive solar radiation and low streamflow rates, the greatest variability of water temperature, as indicated by reference to mean daily range and standard deviation, was found in the spring months, i.e. in April and May, while the lowest in winter, from December to February. The relationship between daily mean water temperature and air temperature, established with the use of the Pearson correlation coefficient on a monthly basis, was clearly stronger during the spring increase and the autumn fall of the water temperature, which can be linked with greater vulnerability to atmospheric heat fluxes. A definitely weaker relationship was found in the winter and summer months, when greater importance can be attached to other drivers of stream temperature, like the presence of ice cover, cloudiness, riparian shading, and groundwater inflows.

1 km monthly temperature and precipitation dataset for China from 1901 to 2017

Abstract. High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some (e.g., mountainous) regions. This study describes a 0.5′ (∼ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs) and precipitation (PRE) for China in the period of 1901–2017. The dataset was spatially downscaled from the 30′ Climatic Research Unit (CRU) time series dataset with the climatology dataset of WorldClim using delta spatial downscaling and evaluated using observations collected in 1951–2016 by 496 weather stations across China. Prior to downscaling, we evaluated the performances of the WorldClim data with different spatial resolutions and the 30′ original CRU dataset using the observations, revealing that their qualities were overall satisfactory. Specifically, WorldClim data exhibited better performance at higher spatial resolution, while the 30′ original CRU dataset had low biases and high performances. Bicubic, bilinear, and nearest-neighbor interpolation methods employed in downscaling processes were compared, and bilinear interpolation was found to exhibit the best performance to generate the downscaled dataset. Compared with the evaluations of the 30′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by 9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation coefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new dataset in the period of 1901–2017 depended on the quality of the original CRU and WorldClim datasets. Therefore, the new dataset was reliable, as the downscaling procedure further improved the quality and spatial resolution of the CRU dataset and was concluded to be useful for investigations related to climate change across China. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) at https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b) and includes 156 NetCDF files compressed in zip format and one user guidance text file.

Application of the Climograph for the Greenhouse Plan of Subtropical and Tropical Regions

The technological levels of greenhouse influence significantly the yields of the crop production and increase the investment cost. The balance between the development of the technological level and the achieving of the increased crop production in the controlled environment is always the primary concern. In this study, the climograph was obtained by plotting the mean minimum monthly temperature versus mean maximum monthly temperature. This climagraph was combined at the optimum temperature ranges of three fruit vegetables and a simple microclimate model was introduced to evaluate the controlling performance of internal air temperature in greenhouses. In subtropical areas, seven locations were selected in order to demonstrate the application of the climograph for the cultivation of species. The rotation of temperate and thermophile plant in Yulin, Taiwan was proposed. Melon, as the thermophile crop, was the only crop that could be cultivated in greenhouses in the two locations of low land areas. The results in three locations of high land areas indicated that all temperate vegetables were suited well to be cultivated in these areas. Thus, the significant highlight of this method was that it could also be successfully used for other species at different locations for the greenhouse production to reduce the energy consumption.

Seasonal and Diurnal Cycles of Surface Boundary Layer and Energy Balance in the Central Andes of Perú, Mantaro Valley

The present study presents a detailed analysis of the diurnal and monthly cycles the surface boundary layer and of surface energy balance in a sparse natural vegetation canopy on Huancayo observatory (12.04 ∘ S, 75.32 ∘ W, 3313 m ASL), which is located in the central Andes of Perú (Mantaro Valley) during an entire year (May 2018–April 2019). We used a set of meteorological sensors (temperature, relative humidity, wind) installed in a gradient tower 30 m high, a set of radiative sensors to measure all irradiance components, and a set of tensiometers and heat flux plate to measure soil moisture, soil temperatures and soil heat flux. To estimate turbulent energy fluxes (sensible and latent), two flux–gradient methods: the aerodynamic method and the Bowen-ratio energy-balance method were used. The ground heat flux at surface was estimated using a molecular heat transfer equation. The results show minimum mean monthly temperatures and more stable conditions were observed in June and July before sunrise, while maximum mean monthly temperatures in October and November and more unstable conditions in February and March. From May to August inverted water vapor profiles near the surface were observed (more intense in July) at night hours, which indicate a transfer of water vapor as dewfall on the surface. The patterns of wind direction indicate well-defined mountain–valley circulation from south-east to south-west especially in fall–winter months (April–August). The maximum mean monthly sensible heat fluxes were found in June and September while minimum in February and March. Maximum mean monthly latent heat fluxes were found in February and March while minimum in June and July. The surface albedo and the Bowen ratio indicate semi-arid conditions in wet summer months and extreme arid conditions in dry winter months. The comparisons between sensible heat flux ( Q H ) and latent heat flux ( Q E ), estimated by the two methods show a good agreement (R 2 above 0.8). The comparison between available energy and the sum of Q E and Q H fluxes shows a good level of agreement (R 2 = 0.86) with important imbalance contributions after sunrise and around noon, probably by advection processes generated by heterogeneities on the surface around the Huancayo observatory and intensified by the mountain–valley circulation.

Temperature regime of Fibrist Histosols of the Bakchar Mire (West Siberia)

Temperature regime of Fibrist Histosols of the Bakchar Mire (West Siberia)

A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years

We present a continuous land-based climate reconstruction dataset extending back 60 kyr from 0 BP (1950) at 0.5° resolution on a monthly timestep for 0°N to 90°N. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5° resolution (using simple bilinear interpolation) and bias corrected. Here we present surface air temperature, precipitation, incoming shortwave energy, minimum monthly temperature, snow depth, wind chill and number of rainy days per month. This is one of the first open access climate datasets of this kind and can be used to study the impact of millennial to orbital-scale climate change on terrestrial greenhouse gas cycling, northern extra-tropical vegetation, and megaflora and megafauna population dynamics.

Comparative Study of HBV Light and GR4J Conceptual Hydrological Models for Assessment of Impact of Climate Change on Water Availability of Awata River Watershed, Genale Dawa Basin; Southern Ethiopia

This research was conducted to study impacts of climate change on water resource of Awata River watershed. Statistical Downscaling Model version 5.1.1 has been used to downscale the daily maximum temperature, minimum temperature, and precipitation in 30-year intervals from the second generation of the Earth System Model (CanESM2) under two Representative Concentration Pathways (RCP) Scenarios (RCP4.5 and RCP8.5). Climate change scenarios for precipitation and temperature were developed for two future periods 2018-2047 (2020s) and 2048-2077 (2050s). According to the projected climate data, the monthly minimum and maximum temperature are likely to have an increasing trend +2.94°C and 2.25°C respectively. Regarding the rainfall change, under the RCP4.5 scenario the study shows annual average increment of by 26.8% and 35.1% at near (2020) and mid-term (2050) respectively. On monthly bases rainfall shows there is incensement in some months and decrease in some months. The HBV Light and GR4J hydrological models were successfully calibrated (2003-2012) and validated (2013-2017) using current climatic inputs and observed river flows. The overall performances of both models were good, at monthly time scale; on calibration (R 2 =0.87 and NSE = 0.78) and validation (R 2 =0.85 and NSE=0.80) with HBV Light whereas, calibration (R 2 =0.81 and NSE=0.72) and validation (R 2 =0.84 and NSE= 0.75) for GR4J model. Over the two projected periods (2018-2047 and 2048-2077) under both RCP4.5 and RCP8.5 scenario HBV-Light and GR4J models simulation results showed that the mean annual discharge will significantly increase related to the baseline periods (1988-2017). HBV light indicate the percentage increment of total average annual flow volume 7.3% (2018-2047) and 7.0% (2048-2077) for RCP 4.5 scenario, and For RCP 8.5 scenario, the increment ranges between 7.7% (2018-2047) to 7.9% (2048-2077). The GR4J model indicates the percentage increment of total average annual flow volume 6.4% (2018-2047) and 6.6% (2048-2077) for RCP 4.5 scenario, and For RCP 8.5 scenario, the increment ranges between 6.2% (2018-2047) to 7.6% (2048-2077). In conclusion increase in average total annual, seasonal and monthly flow volume is observed for periods which show a corresponding increases in mean annual, seasonal and monthly precipitation during scenario developments so that, future studies must recognize the over-all implication of climate change in the Awata watershed. Keywords: Awata, Climate change, canESM2, HBV, GR4J, SDSM, Ethiopia. DOI : 10.7176/JNSR/9-22-01 Publication date: November 30 th 2019

Regional hotspots of temperature extremes under 1.5 °C and 2 °C of global mean warming

Local- and regional-scale heat extremes can increase at a significantly greater rate than global mean changes, presenting challenges for human health, infrastructure, industry and ecosystems. We examine changes in regional absolute temperature extremes for a suite of global regions under 1.5 °C and 2 °C of warming above pre-industrial levels, as described by the Paris Agreement. We focus on area-average values of observed monthly averages of daily maximum and minimum temperatures in 12 regions and calculate the most extreme monthly records observed. Next, using a large ensemble (HAPPI; Half a Degree Additional warming, Prognosis, and Projected Impacts) of decade-long simulations both of the present day and stabilised at these higher warming thresholds, we explore how changes in temperature extremes temperatures scale with global mean warming in these timeslice simulations. In the models, we focus on the 99th percentile values of monthly maximum temperatures and the 1st percentile of the monthly minimum temperatures. We define and identify hotspots of warming for various global mean warming levels, where projected changes in regional extremes are greater than global mean temperature changes. We identify overall hotspots of extremes, which are regions where the tail of the temperature distribution (above 99th percentile) warms at a faster rate than the rest of the temperature distribution in response to mean global warming increase. For monthly maximum temperatures, Central Europe, North Asia, West and East North America experience the greatest projected increases in extremes relative to means, and for monthly minimum temperatures, Central, West, East and North Asia, and East North America are identified as extremes hotspots. Although the scaling of increasing extremes with global mean temperatures is regionally variable, all regions benefit from the reduced severity of monthly maximum temperatures under lower global warming thresholds.

Assessment of Future Climate Change Projections Using Multiple Global Climate Models

Nowadays, the hydrological cycle which alters river discharge and water availability is affected by climate change. Therefore, the understanding of climate change is curial for the security of hydrologic conditions of river basins. The main purpose of this study is to assess the projections of future climate across the Upper Ayeyarwady river basin for its sustainable development and management of water sector for this area. Global Ten climate Models available from CMIP5 represented by the IPCC for its fifth Assessment Report were bias corrected using linear scaling method to generate the model error. Among the GCMs, a suitable climate model for each station is selected based on the results of performance indicators (R2 and RMSE). Future climate data are projected based on the selected suitable climate models by using future climate scenarios: RCP2.6, RCP4.5, and RCP8.5. According to this study, future projection indicates to increase in precipitation amounts in the rainy and winter season and diminishes in summer season under all future scenarios. Based on the seasonal temperature changes analysis for all stations, the future temperature are predicted to steadily increase with higher rates during summer than the other two seasons and it can also be concluded that the monthly minimum temperature rise is a bit larger than the maximum temperature rise in all seasons.