Analysis of historical climate datasets for hydrological modelling across South Asia

Transcription

1 LAND AND WATER Analysis of historical climate datasets for hydrological modelling across South Asia Yun Chen 1, Junfeng Shui 2, Kaifang Shi 3, Hongxing Zheng 1 1 CSIRO Land and Water, Canberra, Australia ( yun.chen@csiro.au); 2 Institute of Soil and Water Conservation, Northwest A&F University, Shaanxi, China; 3 Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China August 2016

2 Citation Chen Y, J Shui, K Shi, H Zheng (2016) Analysis of historical climate datasets for hydrological modelling across south Asia. CSIRO Sustainable Development Investment Portfolio project. Technical report. CSIRO Land and Water, Australia. This report designed and implemented by CSIRO contributes to the South Asia Sustainable Development Investment Portfolio and is supported by the Australian Government. Copyright With the exception of the Australian government crest, Australian Aid and CSIRO logos, and where otherwise noted, all material in this publication is provided under a Creative Commons Attribution 4.0 International License Under this licence you are free to share (copy and redistribute in any medium or format) and adapt (remix, transform) for any purpose, even commercially, under the following terms: The authors request attribution as Australian Government Department of Foreign Affairs and Trade (DFAT) (Sustainable Development Investment Portfolio). Important disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. Acknowledgments The authors gratefully thank Francis Chiew, Mobin Ahmad and Susan Cuddy for their review of this report. [EP165920]

3 Contents 1 Introduction 1 2 Gridded datasets Aphrodite, Princeton and IMD data WATCH data ANUSPLIN generated data (ANU surfaces) Data comparisons Methods Results Regional scale Catchment scale Sub catchment scale Data applications Hydrological modelling Characterisation of climate extremes... 16

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5 1 Introduction This report summarises the results of comparative analysis on gridded historical rainfall and temperature datasets for hydrological modelling in south Asia. These datasets have been used for consistent modelling across south Asia to characterise the hydroclimate of the region and to model future climate and hydrology impacted by climate change and other drivers. The results have been used to supplement local datasets in the modelling of the Indus, Koshi and Brahmani Baitarni River basins (Figure 1, conducted as part of CSIRO s contribution to the Sustainable Development Investment Portfolio (SDIP) of the Australian Government. The purpose of undertaking this work was not to recommend the use of one dataset over another as that is dependent on need and circumstance. Rather it has been to evaluate their usability across a range of scales region, basin, and sub catchment. Our intent was to understand the datasets their assumptions, limitations and constraints through comparison. Some results were compared to observed data, but these results are not reported here. This report follows on from the Chen et al (2015) technical report which describes the processing of these datasets. Figure 1 Study region showing boundaries of basins modelled as part of the SDIP Analysis of historical climate datasets for hydrological modelling across south Asia 1

6 2 Gridded datasets The spatial datasets have two sources. The first is the existing global/regional daily datasets for south Asia: IMD (India Meteorological Department) data Aphrodite (Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the Water Resources) data Princeton data WATCH (Water and Global Change) data. Except for the IMD data, these datasets are freely available for download from the web. IMD data must be purchased from the Indian Department of Meteorology. The second is project generated (from ground observations) regional datasets: the ANU spline based monthly precipitation and temperature surfaces for Nepal, Pakistan and Indus River Basin. These datasets are not available due to licensing restrictions on their acquisition. 2.1 Aphrodite, Princeton and IMD data Detailed descriptions of the Aphrodite, Princeton and IMD datasets are in Chen et al. (2015). The key features of the four existing datasets are summarised in Table 1. Table 1 Summary of existing historical climate datasets used in the SDIP project IMD APHRODITE PRINCETON WATCH Name Indian daily gridded rainfall data set (IMD4) Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Princeton Global Meteorological Forcing Dataset Water and Global Change (WATCH) dataset Spatial coverage India Continental Asia Global Global Period (Precipitation) (temperature) (Precipitation) (Temperature) Temporal resolution Daily Daily Daily Daily Spatial resolution Precipitation: 0.25x0.25 degree (~25km x 25km) Temperature: 1x1 degree (~100km x 100km) 0.25x0.25 degree (~25km x 25km) 0.5x0.5 degree (~50km x 50km) 0.5x0.5 degree (~50km x 50km) Type Interpolation from onground meteorological stations (about 7,000 stations across India) Interpolation from onground meteorological stations Reanalyses data combining meteorological forcings, satellite data and on ground observations Reanalysis data with elevation correction and monthlycorrection based on gridded observations (downscaled and bias corrected) 2 Analysis of historical climate datasets for hydrological modelling across south Asia

7 IMD APHRODITE PRINCETON WATCH Variables Precipitation (mm) Temperature ( o C) Precipitation (mm) Temperature ( o C) Precipitation (kg m 2 s 1 ) Air temperature at 2m above ground (K) Maximum air temperature (K) Minimum air temperature (K) Downward longwave at surface (Wm 2) Downward shortwave at surface (Wm 2) Surface pressure (Pa) Specific humidity (kg kg 1) Wind speed (m s 1) Elevation (m) Areal potential evapotranspiration (APET; mm) estimated by SDIP project using above climate data Air temperature at 2m above ground (K) Surface pressure at 10m above ground (Pa) Specific humidity at 2m above ground (kg kg 1) Wind speed at 10m above ground (m s 1) Downwards long wave (infrared) radiation flux (Wm 2) Downwards short wave (solar) radiation flux (Wm 2) Rainfall (kg m 2 s 1 ) Snowfall (kg m 2 s 1 ) 2.2 WATCH data The WATCH data (Weedon et al. 2011) product WFDEI (WATCH Forcing Data ERA Interim) was produced post WATCH using Watch Forcing Data (WFD) methodology applied to ERA Interim data (Dee et al. 2011). It is a meteorological forcing dataset extending from 1979 to It has eight meteorological variables at 3 hourly time steps, and as daily averages, for the global land surface at 0.5 o x 0.5 o (about 50km) resolution including Antarctica. The snowfall in WATCH data is a unique variable which is particularly important to hydrological modelling in south Asia. WATCH data are based on climate reanalysis data, plus a spatial interpolation (from one degree to half degree), elevation correction and monthly correction based on gridded observations. As such, the WATCH Forcing data are designed for use in modelling (e.g. hydrological modelling) and for where good meteorological observations are not readily available. The key features of the WATCH dataset are summarised in Table 1 along with the Aphrodite, Princeton and IMD datasets. As an example of data comparison, summary plots showing spatial distribution of the mean annual, Jun Jul Aug and Dec Jan Feb rainfall from the four datasets are presented in Figure 2, Figure 3, and Figure 4 respectively. Analysis of historical climate datasets for hydrological modelling across south Asia 3

8 Figure 2 Spatial distribution of mean annual precipitation/snowfall from (a) Aphrodite ( ), (b) Princeton ( ), (c) IMD4 ( ), and (d) Watch ( ) 4 Analysis of historical climate datasets for hydrological modelling across south Asia

9 Figure 3 Spatial distribution of mean Jun Jul Aug precipitation/snowfall from (a) Aphrodite ( ), (b) Princeton ( ), (c) IMD4 ( ), and (d) Watch ( ) Analysis of historical climate datasets for hydrological modelling across south Asia 5

10 Figure 4 Spatial distribution of mean Dec Jan Feb precipitation from (a) Aphrodite ( ), (b) Princeton ( ), (c) IMD4 ( ), and (d) Watch ( ) 2.3 ANUSPLIN generated data (ANU surfaces) The ANUSPLIN software (Hutchinson and Xu, 2013) has been used to generate climate surfaces using gauged historical daily data. ANUSPLIN provides a facility for transparent analysis and interpolation of noisy multi variate data using thin plate smoothing splines. It contains programs for fitting surfaces to noisy data as functions of one or more independent variables, and for interrogating the fitted surfaces by providing comprehensive statistical analyses, data diagnostics and spatially distributed standard errors. The key features of the ANUSPLIN generated datasets are summarised in Table 2. 6 Analysis of historical climate datasets for hydrological modelling across south Asia

11 Table 2 Summary of generated historical climate datasets used in the SDIP project Monthly dataset Daily dataset Spatial coverage Indus River Basin, Pakistan and Nepal Indus River Basin and Pakistan Period Average gauge density (Precipitation and temperature for Indus and Pakistan) (Precipitation for Nepal) (Temperature for Nepal) 1 station per 20,000km2 (Indus and Pakistan) 1 station per 2,500 km2 (Nepal) (Temperature for Indus and Pakistan) 1 station per 20,000km2 (Indus and Pakistan) DEM SRTM 90m DEM SRTM 90m DEM Spatial resolution 0.01x0.01 degree (~1km x 1km) 0.025x0.025 degree (~2.5km x 2.5km) Type Variables Interpolation from on ground meteorological stations using ANUSPLIN v4.5 Mean precipitation (mm) Minimum temperature ( o C) Maximum temperature ( o C) Interpolation from on ground meteorological stations using ANUSPLIN v4.5 Minimum temperature ( o C) Maximum temperature ( o C) The example plots showing spatial distribution of the mean monthly precipitation, maximum and minimum monthly temperature for Pakistan and the Indus Basin are presented in Figure 5 below. Figure 5 Spatial distribution of monthly mean precipitation (mm; top panel), maximum temperature (middle panel) and minimum temperature in Pakistan and Indus River Basin ( ) Analysis of historical climate datasets for hydrological modelling across south Asia 7

12 3 Data comparisons 3.1 Methods The comparisons were undertaken in the overlapping area among the datasets. Five indicators were used to evaluate the similarity and difference between two datasets: (1) Difference: (2) Absolute difference: (3) Root mean square difference: (4) Correlation coefficients: where y is IMD dataset, x is Aphrodite dataset, i is a specific grid, and n is sample size. (5) Ratio of two datasets. 3.2 Results Regional scale The results of comparison conducted in the overlapping of the Aphrodite and IMD temperature datasets in India (Figure 6) are presented in Figure 7 and Figure 8. 8 Analysis of historical climate datasets for hydrological modelling across south Asia

13 Figure 6 Monthly Aphrodite (top panel) and IMD (bottom panel) temperature data Figure 7 Temporal (top panel) and spatial distribution (bottom panel) of the D, AD, RMSD and CC for monthly temperature between the Aphrodite and IMD datasets averaged across the comparison area (top panel) Analysis of historical climate datasets for hydrological modelling across south Asia 9

14 Figure 8 Correlation coefficients (CC) of mean monthly temperature between the Aphrodite and IMD datasets Catchment scale The results of comparisons conducted by calculating the ratios of Aphrodite, Watch and AUN surfaces against locally collected 1km resolution (PMD) data for the upper catchment area of the Indus Basin are presented below. Mean monthly precipitation and temperature summarised from the three datasets during the overlapped period of 1979 to 2007 are plotted in Figure 9 and Figure 10. Comparison results are mapped in Figure 11 and Figure Analysis of historical climate datasets for hydrological modelling across south Asia

15 Figure 9 Spatial distribution of mean monthly precipitation (mm; ) Analysis of historical climate datasets for hydrological modelling across south Asia 11

16 Figure 10 Spatial distribution of mean monthly temperature ( o C; ) 12 Analysis of historical climate datasets for hydrological modelling across south Asia

17 Figure 11 Ratio of mean monthly precipitation (mm; ): Aphrodite/PMD (top panel), Watch/PMD (middle panel), and ANU/PMD (bottom panel) Analysis of historical climate datasets for hydrological modelling across south Asia 13

18 Figure 12 Ratio of mean monthly temperature ( o C; ): Aphrodite/PMD (top panel), Watch/PMD (middle panel), and ANU/PMD (bottom panel) 14 Analysis of historical climate datasets for hydrological modelling across south Asia

19 3.2.3 Sub catchment scale The results of comparisons by extracting mean annual precipitation and temperature data in overlapped areas (the Hunza and Mangla sub catchments of the Indus River) from various datasets are presented in Figure 13 and Figure 14. Figure 13 Spatial distribution of mean annual precipitation (mm) in Hunza (left) and Mangla (right) sub catchments over the period 1979 to 2007 from five data sources Figure 14 Spatial distribution of mean annual temperature (oc) in Hunza (left) and Mangla (right) sub catchments over the period 1979 to 2007 from four data sources Based on the interpretation of the above datasets, some simply guidance on the application of these datasets can be provided. In terms of rainfall datasets, IMD is the wettest, Aphrodite is the driest. WATCH differentiates mountains, but appear blend (and probably incorrect) in south. For India, IMD is the best. Elsewhere either Princeton or Aphrodite, but we know they underestimate. Obviously, interpolation, with ANUSPLINE or similar is best, but only where there is data to interpolate from, and also need extra resources. Analysis of historical climate datasets for hydrological modelling across south Asia 15

20 4 Data applications 4.1 Hydrological modelling The above summary analysis has provided a foundation to underpin the applications of climate datasets to hydrological modelling and various components across the SDIP project (Table 3). Table 3 Applications of climate datasets in the SDIP project REGION/BASIN APPLICATION DATASET South Asia Hydrological modelling Princeton datasets Indus Hydrological modelling ANU surfaces Koshi Areas with an elevation > 3000m WATCH snowfall datasets Brahmani Baitarani Hydrological modelling IMD precipitation and Princeton temperature datasets Overall Maps and presentations All datasets 4.2 Characterisation of climate extremes A set of 30 indices characterising climate variables and extreme conditions, including amount, frequency, and intensity of precipitation and temperature, were derived and mapped over the region (Table 4 and Table 5). The annual and long term mean indices based on gridded daily values were estimated for 1975 to 2004, presenting a clearer picture of the patters of trends in climate extremes across the region than has been seen locally, or with raw station data (Figure 15 and Figure 16). The results can improve the crossboundary understanding of climate variability and its impact on the nexus between water, food and energy. It will provide useful insights and indication for resource planners, system managers, and policy makers concerning climate variability and change for supporting informed basin planning. Table 4 Indices for precipitation extremes Extreme Precipitation Indices (11) Wet days Heavy precipitation days Very heavy precipitation days Maximum 1 day precipitation Maximum 5 day precipitation Very wet day precipitation Extremely wet day precipitation Ann total wet day precipitation Simple daily intensity Consecutive dry days Consecutive wet days Definition Annual count of days with daily precipitation 1 mm Annual count of days with daily precipitation 10 mm Annual count of days with daily precipitation 30 mm Annual maximum 1 day precipitation total Annual maximum consecutive 5 day precipitation total Annual total precipitation when daily precipitation > 95th percentile Annual total precipitation when daily precipitation > 99th percentile Annual total precipitation on wet days (daily precipitation 1 mm) Annual total precipitation divided by the number of wet days (daily precipitation 1 mm) Maximum number of consecutive days with daily precipitation < 1 mm Maximum number of consecutive days with daily precipitation 1 mm 16 Analysis of historical climate datasets for hydrological modelling across south Asia

21 Table 5 Indices for temperature extremes Extreme Temperature Indices (19) Definition Very hot days Annual count of days with maximum temperature > 40 C Hot days Annual count of days with maximum temperature > 35 C Very hot nights Annual count of nights with minimum temperature > 25 C Hot nights Annual count of nights with minimum temperature > 20 C Cold days Annual count of days with maximum temperature < 15 C Very cold days Annual count of days with maximum temperature < 10 C Cold nights Annual count of nights with minimum temperature < 5 C Frost nights Annual count of nights with minimum temperature < 0 C Warm days Warm nights Cool days Cool nights Highest maximum temperature Highest minimum temperature Lowest maximum temperature Lowest minimum temperature Warm spell duration Cold spell duration Growing season length Percentage of days with maximum temperature > 90th percentile Percentage of nights with minimum temperature > 90th percentile Percentage of days with maximum T < 10th percentile Percentage of nights with minimum T < 10th percentile Annual maximum value of daily maximum temperature Annual maximum value of daily minimum temperature Annual minimum value of daily maximum temperature Annual minimum value of daily minimum temperature Annual count of days with at least 4 (or 6) consecutive days when daily maximum temperature > 90th percentile Annual count of nights with at least 6 consecutive nights when daily minimum temperature < 10th percentile Annual (1st Jan to 31st Dec) count between first span of 6 or more days with daily mean temperature > 15 C and first span of 6 or more days with daily mean temperature < 15 C Analysis of historical climate datasets for hydrological modelling across south Asia 17

22 Figure 15 Maps of precipitation extremes 18 Analysis of historical climate datasets for hydrological modelling across south Asia

23 Figure 16 Maps of temperature extremes Analysis of historical climate datasets for hydrological modelling across south Asia 19

24 References Chen Y, Singh R, Liu R (2015) Gridded climate datasets for hydrological modelling across South Asia. CSIRO report. Dee et al (2011) The ERA Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the royal Meteorological Society 137: Hutchinson MF, Xu T (2013) ANUSPLIN Version 4.4 User Guide. Fenner School of Environment and Society, Australian National University, Canberra. Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Österle H, Adam JC, Bellouin N, Boucher O, Best M (2011) Creation of the WATCH Forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometerology 12: Analysis of historical climate datasets for hydrological modelling across south Asia

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26 CONTACT US t e enquiries@csiro.au w YOUR CSIRO Australia is founding its future on science and innovation. Its national science agency, CSIRO, is a powerhouse of ideas, technologies and skills for building prosperity, growth, health and sustainability. It serves governments, industries, business and communities across the nation. FOR FURTHER INFORMATION Land and Water Yun Chen Senior Research Scientist t e yun.chen@csiro.au w Analysis of historical climate datasets for hydrological modelling across south Asia 1