Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan
Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolu...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/3584030 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560352230899712 |
|---|---|
| author | Zain Nawaz Xin Li Yingying Chen Xufeng Wang Kun Zhang Naima Nawaz Yanlong Guo Akynbekkyzy Meerzhan |
| author_facet | Zain Nawaz Xin Li Yingying Chen Xufeng Wang Kun Zhang Naima Nawaz Yanlong Guo Akynbekkyzy Meerzhan |
| author_sort | Zain Nawaz |
| collection | DOAJ |
| description | Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions. |
| format | Article |
| id | doaj-art-dbea75321d0f46a5baba3c54d22b074d |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-dbea75321d0f46a5baba3c54d22b074d2025-02-03T01:27:55ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/35840303584030Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of PakistanZain Nawaz0Xin Li1Yingying Chen2Xufeng Wang3Kun Zhang4Naima Nawaz5Yanlong Guo6Akynbekkyzy Meerzhan7Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaInstitute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaInstitute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Rural Sociology, University of Agriculture, Faisalabad 38040, PakistanInstitute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaReliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.http://dx.doi.org/10.1155/2020/3584030 |
| spellingShingle | Zain Nawaz Xin Li Yingying Chen Xufeng Wang Kun Zhang Naima Nawaz Yanlong Guo Akynbekkyzy Meerzhan Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan Advances in Meteorology |
| title | Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan |
| title_full | Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan |
| title_fullStr | Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan |
| title_full_unstemmed | Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan |
| title_short | Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan |
| title_sort | spatiotemporal assessment of temperature data products for the detection of warming trends and abrupt transitions over the largest irrigated area of pakistan |
| url | http://dx.doi.org/10.1155/2020/3584030 |
| work_keys_str_mv | AT zainnawaz spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT xinli spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT yingyingchen spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT xufengwang spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT kunzhang spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT naimanawaz spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT yanlongguo spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan AT akynbekkyzymeerzhan spatiotemporalassessmentoftemperaturedataproductsforthedetectionofwarmingtrendsandabrupttransitionsoverthelargestirrigatedareaofpakistan |