scholarly journals CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins

2016 ◽  
Vol 8 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Tomasz Berezowski ◽  
Mateusz Szcześniak ◽  
Ignacy Kardel ◽  
Robert Michałowski ◽  
Tomasz Okruszko ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Daily Precipitation ◽  
Hydrological Modelling ◽  
Temperature Data ◽  
Maximum Temperature ◽  
System Modelling ◽  
Climatic Data ◽  
Data Set ◽  
Spatial Coverage ◽  
Precipitation And Temperature

Abstract. The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Forcing Data–Gridded Daily Precipitation & Temperature Dataset–5 km (CPLFD-GDPT5) consists of 1951–2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from the Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), and European Climate Assessment and Dataset (ECAD) and National Oceanic and Atmosphere Administration–National Climatic Data Center (NOAA-NCDC) (Slovak, Ukrainian, and Belarusian stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of the Vistula and Oder basins and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in the 1950s up to about 180 for temperature and 700 for precipitation in the 1990s. The precipitation data set was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross validation revealed low root-mean-squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively, and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures, and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971–2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Oder basins. Link to the data set: doi:10.4121/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07.

scholarly journals CPLFD-GDPT5: high-resolution gridded daily precipitation and temperature dataset for two largest Polish river basins

2015 ◽  
Vol 8 (2) ◽  
pp. 1021-1060 ◽  
Author(s):  
T. Berezowski ◽  
M. Szcześniak ◽  
I. Kardel ◽  
R. Michałowski ◽  
T. Okruszko ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Daily Precipitation ◽  
Hydrological Modelling ◽  
Indicator Kriging ◽  
Maximum Temperature ◽  
System Modelling ◽  
Climatic Data ◽  
Air Temperatures ◽  
Spatial Coverage ◽  
Precipitation And Temperature

Abstract. The CHASE-PL Forcing Data-Gridded Daily Precipitation and Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951–2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), ECAD and NOAA-NCDC (Slovak, Ukrainian and Belarus stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of Vistula and Odra basin and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in 1950 up to about 180 for temperature and 700 for precipitation in 1990. The precipitation dataset was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were: kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross-validation revealed low root mean squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971–2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Odra basins. Link to the dataset: http://data.3tu.nl/repository/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07

scholarly journals G2DC-PL+: a gridded 2 km daily climate dataset for the union of the Polish territory and the Vistula and Odra basins

2021 ◽  
Vol 13 (3) ◽  
pp. 1273-1288
Author(s):  
Mikołaj Piniewski ◽  
Mateusz Szcześniak ◽  
Ignacy Kardel ◽  
Somsubhra Chattopadhyay ◽  
Tomasz Berezowski
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Relative Humidity ◽  
Hydrological Modelling ◽  
Maximum Temperature ◽  
Climate Projections ◽  
System Modelling ◽  
Original Dataset ◽  
Spatial Coverage ◽  
Precipitation And Temperature

Abstract. G2DC-PL+, a gridded 2 km daily climate dataset for the union of the Polish territory and the Vistula and Odra basins, is an update and extension of the CHASE-PL Forcing Data – Gridded Daily Precipitation and Temperature Dataset – 5 km (CPLFD-GDPT5). The latter was the first publicly available, high-resolution climate forcing dataset in Poland, used for a range of purposes including hydrological modelling and bias correction of climate projections. While the spatial coverage of the new dataset remained the same, it has undergone several major changes: (1) the time coverage was increased from 1951–2013 to 1951–2019; (2) its spatial resolution increased from 5 to 2 km; (3) the number of stations used for interpolation of temperature and precipitation approximately doubled; and (4) in addition to precipitation and temperature, the dataset consists of relative humidity and wind speed data. The main purpose for developing this product was the need for long-term areal climate data for earth-system modelling, and particularly hydrological modelling. Geostatistical methods (kriging) were used for interpolation of the studied climate variables. The kriging cross-validation revealed improved performance for precipitation compared to the original dataset expressed by the median of the root mean squared errors standardized by standard deviation of observations (0.59 vs. 0.79). Kriging errors were negatively correlated with station density only for the period 1951–1970. Values of the root mean squared error normalized to the standard deviation (RMSEsd) were equal to 0.52 and 0.4 for minimum and maximum temperature, respectively, suggesting a small to moderate improvement over the original dataset. Relative humidity and wind speed exhibited lower performance, with median RMSEsd equal to 0.82 and 0.87, respectively. The dataset is openly available from the 4TU Centre for Research Data at https://doi.org/10.4121/uuid:a3bed3b8-e22a-4b68-8d75-7b87109c9feb (Piniewski et al., 2020).

scholarly journals G2DC-PL+ A gridded 2 km daily climate dataset for the union of the Polish territory and the Vistula and Odra basins

2020 ◽  
Author(s):  
Mikołaj Piniewski ◽  
Mateusz Szcześniak ◽  
Ignacy Kardel ◽  
Somsubhra Chattopadhyay ◽  
Tomasz Berezowski
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Hydrological Modelling ◽  
Maximum Temperature ◽  
Climate Projections ◽  
System Modelling ◽  
Climate Data ◽  
Geostatistical Methods ◽  
Original Dataset ◽  
Spatial Coverage

Abstract. G2DC-PL+ – a gridded 2 km daily climate dataset for the union of the Polish territory and the Vistula and Odra basins is an update and extension of the CHASE-PL Forcing Data – Gridded Daily Precipitation & Temperature Dataset – 5 km (CPLFD-GDPT5). The latter was the first publicly available, high-resolution climate forcing dataset in Poland, used for a range of purposes including hydrological modelling and bias correction of climate projections. While the spatial coverage of the new dataset remained the same, it has undergone several major changes: (1) the time coverage was increased from 1951–2013 to 1951–2019; (2) its spatial resolution increased from 5 to 2 km; (3) the number of stations used for interpolation of temperature and precipitation approximately doubled; (4) in addition to precipitation and temperature, the dataset consists of relative humidity and wind speed data. The main purpose for developing this product was the need for long-term areal climate data for earth-system modelling, and particularly hydrological modelling. Geostatistical methods (kriging) were used for interpolation of the studied climate variables. The kriging cross-validation revealed improved performance for precipitation compared to the original dataset expressed by the median of the root mean squared errors standardised by standard deviation of observations (0.59 vs. 0.79). Kriging errors were negatively correlated with station density only for the period 1951–1970. RMSEsd values were equal to 0.52 and 0.4 for minimum and maximum temperature, respectively, suggesting a small to moderate improvement over the original dataset. Relative humidity and wind speed exhibited lower performance, with median RMSEsd equal to 0.82 and 0.87, respectively. The dataset is openly available from the 4TU Centre for Research Data at https://doi.org/10.4121/uuid:a3bed3b8-e22a-4b68-8d75-7b87109c9feb (Piniewski et al., 2020).

scholarly journals Simulating the Impact of Climate Change on Maize Productivity in Trans-gangetic Plains using Info Crop Model

2020 ◽  
Author(s):  
Baljeet Kaur ◽  
Som Pal Singh ◽  
P.K. Kingra
Keyword(s):  
Climate Change ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Data Set ◽  
Gangetic Plains ◽  
Impact Of Climate Change ◽  
Model Average ◽  
In Trans ◽  
The Impact

Background: Climate change is a nonpareil threat to the food security of hundred millions of people who depends on agriculture for their livelihood. A change in climate affects agricultural production as climate and agriculture are intensely interrelated global processes. Global warming is one of such changes which is projected to have significant impacts on environment affecting agriculture. Agriculture is the mainstay economy in trans-gangetic plains of India and maize is the third most important crop after wheat and rice. Heat stress in maize cause several changes viz. morphological, anatomical and physiological and biochemical changes. Methods: In this study during 2014-2018, impact of climate change on maize yield in future scenarios was simulated using the InfoCrop model. Average maize yield from 2001-15 was collected for Punjab, Haryana and Delhi to calibrate and validate the model. Future climatic data set from 2020 to 2050 was used in the study to analyse the trends in climatic parameters.Result: Analysis of future data revealed increasing trends in maximum temperature and minimum temperature. Rainfall would likely follow the erratic behaviour in Punjab, Haryana and Delhi. Increase in temperature was predicted to have negative impact on maize yield under future climatic scenario.

scholarly journals Impact of Climate Change on the Streamflow Modulated by Changes in Precipitation and Temperature in the North Latitude Watershed of Nepal

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 117
Author(s):  
Manisha Maharjan ◽  
Anil Aryal ◽  
Rocky Talchabhadel ◽  
Bhesh Raj Thapa
Keyword(s):  
Climate Change ◽  
Temperature Distribution ◽  
Assessment Tool ◽  
Rate Of Change ◽  
Maximum Temperature ◽  
Water Yield ◽  
Impact Of Climate Change ◽  
Northern Latitudes ◽  
The Impact ◽  
Precipitation And Temperature

It is unambiguous that climate change alters the intensity and frequency of precipitation and temperature distribution at the global and local levels. The rate of change in temperature in the northern latitudes is higher than the worldwide average. The annual distribution of precipitation over the Himalayas in the northern latitudes shows substantial spatial and temporal heterogeneity. Precipitation and temperature are the major driving factors that impact the streamflow and water availability in the basin, illustrating the importance of research on the impact of climate change on streamflow by varying the precipitation and temperature in the Thuli Bheri River Basin (TBRB). Multiple climate models were used to project and evaluate the precipitation and temperature distribution changes in temporal and spatial domains. To analyze the potential impact of climate change on the streamflow in the basin, the Soil and Water Assessment Tool (SWAT) hydrological model was used. The climate projection was carried out in three future time windows. The result shows that the precipitation fluctuates between approximately +12% and +50%, the maximum temperature varies between −7% and +7%, and the minimum temperature rises from +0.7% to +5% in intermediate- and high-emission scenarios. In contrast, the streamflow in the basin varies from −40% to +85%. Thus, there is a significant trend in the temperature increase and precipitation reduction in the basin. Further, the relationship between precipitation and temperature with streamflow shows a substantial dependency between them. The variability in precipitation and streamflow is successfully represented by the water yield in the basin, which plays an important role in the sustainability of the water-related projects in the basin and downstream to it. This also helps quantify the amount of water available for hydropower generation, agricultural production, and the water ecosystem in the TBRB.

scholarly journals Constructing Retrospective Gridded Daily Precipitation and Temperature Datasets for the Conterminous United States

2008 ◽  
Vol 47 (2) ◽  
pp. 475-497 ◽  
Author(s):  
Mauro Di Luzio ◽  
Gregory L. Johnson ◽  
Christopher Daly ◽  
Jon K. Eischeid ◽  
Jeffrey G. Arnold
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
Temporal Distribution ◽  
Climatic Data ◽  
Digital Archives ◽  
Reference Sites ◽  
National Climatic Data Center ◽  
Noticeable Improvement ◽  
Temporal Coverage ◽  
Precipitation And Temperature

Abstract This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS.

Catchment-scale assessment of future changes in flood regimes in Norway using stochastic weather generation based on GCM output

2020 ◽  
Author(s):  
Deborah Lawrence ◽  
Abdelkader Mezghani ◽  
Marie Pontopiddan ◽  
Rasmus Benestad ◽  
Kajsa Parding ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Weather Generator ◽  
Temperature Data ◽  
Temperature Series ◽  
Maximum Temperature ◽  
Catchment Scale ◽  
Noise Component ◽  
Stochastic Weather Generation ◽  
Precipitation And Temperature

<p>Assessment of climate change impacts on hydrological processes is often based on simulations driven by precipitation and temperature series derived from bias-adjusted output from Regional Climate Models (RCMs) using boundary conditions from Global Climate Models (GCMs).  This procedure gives, in principle, locally ‘correct’ results, but is also very demanding of time and resources. In some cases, the dynamical downscaling (i.e. RCM) followed by bias adjustment procedures fails to preserve the climate change signal found in the underlying GCM simulations, thus undermining the reliability of the resulting hydrological simulations. As an alternative, we have used the stochastic weather generator D2Gen (Mezghani and Hingray, 2009, J. Hydrol., 377(3–4): 245–60) to create multiple realisations of catchment-scale precipitation and temperature data series directly from two GCMs (MPI-ESM-LR and NorESM-M1) for the period 1951-2100. D2Gen builds on a suite of Generalised Linear Models (GLMs) to generate precipitation and temperature (i.e. predictands) as a function of explanatory climate variables (or predictors) derived from the GCM such as surface temperature, sea level pressure, westerly and zonal wind components, relative humidity and total precipitation. In this study, we have applied D2Gen on area-averaged precipitation and temperature data for 18 hydrological catchments distributed across Norway. Weather generation is then undertaken based on the expected mean modelled by the GLM plus a noise component to account for local features and random effects introduced by local physical processes that are otherwise not accounted for.  The weather generator was trained for each catchment based on observed precipitation and temperature series for the period 1985-2014, and stochastic weather generation was then performed to construct catchment-scale precipitation and temperature series for the period 1951-2100 that were further used in hydrological simulations based on the HBV hydrological model for the 18 catchments. </p><p>Validation of the D2Gen results was based on comparisons with observed annual, seasonal and maximum temperature and precipitation, as well as with observed average annual and maximum annual discharge using 30-year time slices.  Comparisons were also made with projected changes generated from hydrological simulations based on a) EURO-CORDEX RCM simulations (MPI-ESM-LR_SMHI-RCA4 and MPI_CCLM-CM5) for the MPI GCM; and b) high resolution (4 km) simulations with the WRF model driven by a bias-corrected NorESM GCM.  Results suggest that in most catchments the D2gen approach performs equally well or sometimes even better than the traditional ‘bias-corrected RCM approach’ in reproducing the 30-year average annual flood during the historical period. We also found that for the projection period, the simulations based directly on the GCM output (via d2gen) tend to give slightly larger projected increases in the average annual flood in rainfall-dominated catchments than does the use of bias-corrected RCM simulations. Overall, the results indicate that the D2Gen weather generator offers a feasible alternative approach for projecting catchment-scale impacts on changes in flood regimes under a changing climate.  It also offers the significant advantage that it can be used directly with the CMIP-6 ensemble of GCMs without the time delay associated with the production of the next round of EURO-CORDEX based simulations.</p>

Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India

2020 ◽  
Author(s):  
Sachidanand Kumar ◽  
Kironmala Chanda ◽  
Srinivas Pasupuleti
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Spatio Temporal ◽  
Precipitation And Temperature

<p><strong>Abstract</strong></p><p>This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in ‘wet days’ (daily precipitation greater than 95<sup>th</sup> percentile) and ‘dry days’ (daily precipitation lower than 5<sup>th</sup> percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in ‘warm days’ (daily maximum temperature greater than 95<sup>th</sup> percentile), ‘warm nights’ (daily minimum temperature greater than 95<sup>th</sup> percentile), ‘cold days’ (daily maximum temperature lower than 5<sup>th</sup> percentile) and ‘cold nights’ (daily minimum temperature lower than 5<sup>th</sup> percentile) are also investigated for the aforementioned period. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in ‘warm days’ (‘cold days’) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in ‘warm nights’ and a decreasing trend in ‘cold nights’ in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.</p><p><strong>Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India</strong></p><p><strong>References:</strong></p><p>Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.</p>

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