scholarly journals High-spatial-resolution monthly temperature and precipitation dataset for China for 1901–2017

2019 ◽  
Author(s):  
Shouzhang Peng ◽  
Yongxia Ding ◽  
Zhi Li
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Time Series Data ◽  
High Spatial Resolution ◽  
Series Data ◽  
Climatic Research Unit ◽  
Climate Data ◽  
Large Area ◽  
Mountainous Regions ◽  
Weather Stations

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 regions, especially mountainous regions. This study describes a high-spatial-resolution (0.5’, ∼1 km) dataset of monthly temperatures (minimum, maximum, and mean TMPs) and precipitation (PRE) for the main land area of China for the period 1901–2017. The dataset was spatially downscaled from raw 30’ climatic research unit (CRU) time series data and validated using data from 745 weather stations across China. Compared to raw CRU data of low spatial resolution, the mean absolute error decreased by 0.56 °C for the TMPs and 10.1 % for PRE, the root-mean-square error decreased by 0.65 °C for the TMPs and 11.6 % for PRE, and the Nash–Sutcliffe efficiency coefficients increased from 0.83 to 0.95 for the TMPs and from 0.63 to 0.76 for PRE. Indirect validations from site-scale observations indicated that the dataset captured the climatology well, as well as the annual and seasonal monotonic trends in each climatic variable considered. We concluded that the new high-spatial-resolution dataset is sufficiently reliable for use in investigation of climate change across China. This dataset will be useful in investigations related to climate change across China. The dataset presented in this article is 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 temperatures (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file.

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

2019 ◽  
Vol 11 (4) ◽  
pp. 1931-1946 ◽  
Author(s):  
Shouzhang Peng ◽  
Yongxia Ding ◽  
Wenzhao Liu ◽  
Zhi Li
Keyword(s):  
Spatial Resolution ◽  
Nearest Neighbor ◽  
Climatic Research Unit ◽  
Climate Data ◽  
Bilinear Interpolation ◽  
Large Area ◽  
Mountainous Regions ◽  
Air Temperatures ◽  
Weather Stations

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.

scholarly journals 1-km monthly temperature and precipitation dataset for China from 1901–2017

2019 ◽  
Author(s):  
Shouzhang Peng ◽  
Yongxia Ding ◽  
Zhi Li
Keyword(s):  
Spatial Resolution ◽  
Data Availability ◽  
Climatic Research Unit ◽  
Climate Data ◽  
Bilinear Interpolation ◽  
Interpolation Methods ◽  
Mountainous Regions ◽  
Air Temperatures ◽  
Weather Stations

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 regions, especially in mountainous regions. This study describes a 0.5' (~ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean TMPs) and precipitation (PRE) for China from 1901–2017. The dataset was spatially downscaled from 30' climatic research unit (CRU) time series dataset with the climatology dataset of WorldClim by using Delta spatial downscaling and evaluated using observations during 1951–2016 from 496 weather stations across China. Moreover, the bicubic, bilinear, and nearest-neighbor interpolation methods were compared in the downscaling processes. Among the three interpolation methods, bilinear interpolation exhibited the best performance to generate the downscaled dataset. Compared with the evaluations of the original CRU dataset, the mean absolute error of the new dataset (i.e., 0.5' downscaled dataset with the bilinear interpolation) relatively decreased by 35.4 %–48.7 % for TMPs and 25.7 % for PRE, the root-mean-square error relatively decreased by 32.4 %–44.9 % for TMPs and 25.8 % for PRE, the Nash–Sutcliffe efficiency coefficients relatively increased by 9.6 %–13.8 % for TMPs and 31.6 % for PRE, and the correlation coefficients relatively increased by 0.2 %–0.4 % for TMPs and 5.0 % for PRE. Further, the new dataset could provide detailed climatology data and annual trend of each climatic variable across China, and the results could be well evaluated using observations at the station. Although the evaluation of new dataset was not carried out before 1950 owing to a lack of data availability, the downscaling procedure used data from CRU and WordClim and did not incorporate observations. Thus the quality of the new dataset before 1950 mainly depended on that of the CRU and WordClim datasets. The evaluations showed that the overall quality of the CRU and WordClim datasets was satisfactory, and the downscaling procedure further improved the quality and spatial resolution of the CRU dataset. The new dataset will be useful in investigations related to climate change across China. The dataset presented in this article has been published in Network Common Data Form (NetCDF) at http://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and http://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file.

scholarly journals SnowClim v1.0: High-resolution snow model and data for the western United States

2021 ◽  
Author(s):  
Abby C. Lute ◽  
John Abatzoglou ◽  
Timothy Link
Keyword(s):  
Climate Change ◽  
United States ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Western United States ◽  
Climate Data ◽  
Western Us ◽  
Snow Water ◽  
Snow Model

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. Existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

THE ROLE OF GROUNDWATER TO MITIGATE THE DROUGHT AND AS AN ADAPTATION TO CLIMATE CHANGE IN THE PHITSANULOK IRRIGATION PROJECT, IN THE NAN BASIN, THAILAND

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
Sucharit Koontanakulvong ◽  
Chokchai Suthidhummajit
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Water Shortage ◽  
Development Project ◽  
Series Data ◽  
Climate Data ◽  
Adaptation To Climate Change ◽  
Irrigation Project ◽  
Central Plain

The Phitsanulok Irrigation Project is located in the Nan Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. Land use and climate changes are the important factors to determine the runoff from the watershed.  The changes also affected to runoff volume/pattern to the dam operation and may cause flood and drought situations in the downstream area. Sirikit Dam is one of the biggest dams in Thailand which cover about 25 % of the runoff into the Central Plain where the Bangkok Capital is located. Though there is the Sirikit Dams storing water to be used during dry period but water allocation is limited and still caused water shortage during dry season. The study aims to determine the role of groundwater to mitigate the drought situation from the past and to study the groundwater use for adaptation to climate change in The Phitsanulok Irrigation Development Project. In this study, the relationship of recharge rate with climate data was developed in terms of precipitation, evapotranspiration, temperature and soil type under monthly time series data and the study found that there were in good relationship. Groundwater will take an important role to alleviate from the water shortage situations in climate change conditions when compared with the situations based on the existing water use pattern. The limit of ground water to alleviate water shortage will be 80 and 77 MCM/year in average, in near future and far future periods to keep water table drawn down in the safe manner even when the Sirikit’s reservoir operation rule is improved.

scholarly journals Evaluation of Approaches to Depicting First Nations, Inupiat and Inuvialuit Environmental Information in GIS Format: Options for the Handling of Spatial Information in the Arctic Borderlands Ecological Knowledge Co-Op Database

2021 ◽  
Author(s):  
Jadah Elizabeth Folliott
Keyword(s):  
Climate Change ◽  
First Nations ◽  
Spatial Information ◽  
Time Series Data ◽  
Atmospheric Transport ◽  
Data Gathering ◽  
The Arctic ◽  
Series Data ◽  
Ecological Knowledge ◽  
Sea Levels

As the pace of climate change continues to accelerate in the North, traditional environmental knowledge systems are increasingly recognized by researchers, land use planners, government agencies, policy-makers and indigenous peoples as important contributors to environmental impact and climate change assessment and monitoring. Increasing temperatures, melting glaciers, reductions in the extent and thickness of sea ice, thawing permafrost and rising sea levels all provide strong evidence of increasing temperatures in the Arctic. This warming climate has the potential to change migration patterns, the diversity, range, and distribution of animal and plant species, and increase contaminants in the food chain from atmospheric transport of organic pollutants and mercury, thus raising concerns regarding the safety of traditional foods. Since 1996, the Arctic Borderlands Ecological Knowledge Co-op (ABEKC) has systematically recorded First Nations, Inupiat and Inuvialuit observations of landscape changes in the lower Mackenzie, Northern Yukon and eastern Alaska. Time-series data (regarding berry, caribou, fish, weather, ice and snow, plants, and other animal observations) have been obtained through annual interviews with the most active fishers, harvesters and hunters in the communities of Aklavik, Arctic Village, Fort McPherson, Kaktovik, Old Crow, and more recently, in Inuvik, Tsiigehtchic, and Tuktoyaktuk. An evaluation of the spatial utility of the ABEKC database and the many steps that are involved in the collection, storage, and organization of the Co-op’s data was documented. The ABEKC database provided an excellent opportunity to explore the problem of depicting complex qualitative information on northern landscape change in an intelligible GIS format. Initial attempts to develop the database in spatial format were critically evaluated and recommendations were provided in order to explore whether the data gathering and subsequent mapping process can be improved, whether more useful information can be obtained from the data, and to ensure the proper handling of the data in future years.

scholarly journals Effect of climate change in paddy production and adaptation strategies in Tharu communities of Dang district, Nepal

2018 ◽  
Vol 1 (1) ◽  
pp. 62-75
Author(s):  
Pradip Raj Poudel ◽  
Narayan Raj Joshi ◽  
Shanta Pokhrel
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Time Series Data ◽  
Sampling Technique ◽  
Adaptation Strategies ◽  
Primary Data ◽  
Series Data ◽  
Rice Varieties ◽  
Cross Sectional ◽  
Maximum Rainfall

A study on effects of climate change on rice (Oryza sativa) production in Tharu communities of Dang district of Nepal was conducted in 2018A.D to investigate the perception and major adaptation strategies followed by Tharu farmers. The study areas were selected purposively. Cross-sectional data was collected using a household survey of 120 households by applying simple random sampling technique with lottery method for sample selection. Primary data were collected using semi-structured and pretested interview schedule, focus group discussion and key informants interview whereas monthly and annual time series data on temperature and precipitation over 21years (1996-2016) were collected from Department of Hydrology and Meteorology, Kathmandu as secondary data. Descriptive statistics and trend analysis were used to analyze the data. The ratio of male and female was found to be equal with higher literacy rate at study area than district. Most of the farmers depended on agriculture only for their livelihood where there was large variation in land distribution. Farmers had better access to FM/radio for agricultural extension information sources. The study resulted that Tharu farmers of Dang perceived all parameters of climate. Temperature and rainfall were the most changing component of climate perceived by farmers. The trend analysis of temperature data of Dang over 21 years showed that maximum, minimum and average temperature were increasing at the rate of 0.031°C, 0.021°C and 0.072°C per year respectively which supports the farmers perception whereas trend of rainfall was decreased with 7.56mm per year. The yearly maximum rainfall amount was increased by 1.15mm. The production of local indigenous rice varieties were decreasing while hybrid and improved rice varieties were increasing. The district rice production trend was increasing which support the farmer’s perception. The study revealed that there were climate change effects on paddy production and using various adaptation strategies to cope in Dang district.

scholarly journals Trend analysis of climate change compound indices in Iran

2020 ◽  
Author(s):  
Ahmad Roshani ◽  
Fatemeh Parak ◽  
Hossein Esmaili
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Spatial Coherence ◽  
Series Data ◽  
Climate Index ◽  
Positive Trend ◽  
Climate Extreme ◽  
Small Areas ◽  
Thermal Climate ◽  
Tourism Climate

Abstract The time-placement scheme of climate extreme changes is important. In this regard, a set of a compound indices derived using daily resolution climatic time series data is examined to assess climate change in Iran. The compound indices were examined for 47 synoptic meteorological stations during 1981–2015. The results show that most stations experienced a negative trend for the cool/dry (CD) and cool/wet (CW) index and a positive trend in CW was observed in some dispersed small areas. Both warm/dry (WD) and warm/wet (WW) indices have similar behavior, but the magnitude and spatial consistency of WW days were much less than WD days. The results show that more than 80% of stations experienced a decrease in the annual occurrence of the cold modes and an increase in the annual occurrence of the warm modes. On the other hand, universal thermal climate index (UTCI) change demonstrated a significant increase in the annual occurrence of strong heat stress (32–38 °C) and significant decrease in the annual occurrence of no thermal stress class (9–26 °C). Moreover, trends in tourism climate index (TCI), including TCI≥ 60 and TCI≥ 80, showed similar changes but with weak spatial coherence.

Study on Global Burned Forest Areas Based on Landsat Data

2020 ◽  
Vol 86 (8) ◽  
pp. 503-508
Author(s):  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Guojin He ◽  
Mingyue Wei ◽  
Chao Tang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Cover ◽  
Time Series Data ◽  
Global Climate ◽  
Distribution Patterns ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Series Data ◽  
Burned Area ◽  
Burned Forest

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

scholarly journals Climatic Trends in Different Bioclimatic Zones in the Chitwan Annapurna Landscape, Nepal

Climate ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 136
Author(s):  
Dol Raj Luitel ◽  
Pramod K. Jha ◽  
Mohan Siwakoti ◽  
Madan Lall Shrestha ◽  
Rangaswamy Munniappan
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Lapse Rate ◽  
Series Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Average Annual Temperature ◽  
Bioclimatic Zone ◽  
The Impact ◽  
Precipitation Trends

The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones with major endemism of flora, unique agro-biodiversity, environmental, cultural and socio-economic importance. Not much is known about temperature and precipitation trends along the different bioclimatic zones nor how changes in these parameters might impact the whole natural process, including biodiversity and ecosystems, in the CHAL. Analysis of daily temperature and precipitation time series data (1970–2019) was carried out in seven bioclimatic zones extending from lowland Terai to the higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trends, which were quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. In the seven bioclimatic zones, precipitation showed a mixed pattern of decreasing and increasing trends (four bioclimatic zones showed a decreasing and three bioclimatic zones an increasing trend). Precipitation did not show any particular trend at decade intervals but the pattern of rainfall decreases after 2000AD. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is increasing significantly more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone (UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone (USBZ), and temperate bioclimatic zone (TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051 °C/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999–2009 and average decade level increases of temperature at different bioclimatic zones ranges from 0.2 to 0.27 °C /decade. The average temperature and precipitation was found clearly different from one bioclimatic zone to other. This is the first time that bioclimatic zone level precipitation and temperature trends have been analyzed for the CHAL. The rate of additional temperature rise at higher altitudes compared to lower elevations meets the requirements to mitigate climate change in different bioclimatic zones in a different ways. This information would be fundamental to safeguarding vulnerable communities, ecosystem and relevant climate-sensitive sectors from the impact of climate change through formulation of sector-wise climate change adaptation strategies and improving the livelihood of rural communities.

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