Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Preprocessing
2.3. Methodology
2.3.1. Soil Moisture Simulation
2.3.2. Agricultural Drought Assessment
2.3.3. Partial Correlation
2.3.4. Quantifying the Contributions to SMAPI
3. Results
3.1. VIC Model Simulation
3.2. Monitoring and Evaluation of Agricultural Drought
3.2.1. Time Series Analysis of Drought
- 1.
- Analysis of annual drought characteristics
- 2.
- Analysis of seasonal drought characteristics
3.2.2. Analysis of Spatial Characteristics of Drought
- 3.
- Distribution characteristics of drought frequency
- 4.
- Distribution characteristics of drought intensity
- 5.
- Drought distribution in typical years.
3.3. Analysis of Driving Factors for SMAPI
3.3.1. Partial Correlation between SMAPI and Major Meteorological Factors
3.3.2. Contribution of Climate Change and Other Factors to SMAPI
4. Discussion
4.1. Drought Characteristics in the Study Area
4.2. The Impact and Role of CC and OF on SMAPI
5. Conclusions
- The VIC model adequately simulated the rainfall-runoff process of the Daling River Basin, with Nash efficiency coefficients of 0.58 (calibration) and 0.67 (validation).
- Drought was most frequent in winter, followed by spring, summer, and autumn. Over 39 years, the basin experienced slight-or-worse drought in 24 years, moderate-or-worse in 15 years, and severe drought in 4 years. Drought frequency from 2001–2019 was 10 times higher compared to 1981–2000.
- SMAPI was positively correlated with precipitation (accounting for 69.52%) and solar radiation (60.05%), while negatively correlated with temperature (63.97%) from 1981 to 2000. From 2001 to 2019, these correlations were 80.6%, 75.06%, and 82.68%, respectively.
- Climate change was the dominant factor increasing SMAPI from 1981–2000, while other factors were the main factors decreasing SMAPI from 2001–2019, indicative of intensifying agricultural drought.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- He, L.; Qin, Q.; Ren, H.; Du, J.; Meng, J.; Du, C. Retrieving farmland surface soil moisture using multi temporal Sentinel-1 SAR data. J. Agric. Eng. 2016, 32, 142–148. [Google Scholar]
- Legates, R.D. Soil moisture: A central and unifying theme in physical geography. Prog. Phys. Geogr. 2011, 35, 65–86. [Google Scholar] [CrossRef]
- Cai, S.; Song, X.; Hu, R.; Leng, P.; Li, X.; Guo, D.; Hao, Y.; Wang, Y. Spatiotemporal characteristics of agricultural droughts based on soil moisture data in Inner Mongolia from 1981 to 2019. J. Hydrol. 2021, 603, 127104. [Google Scholar] [CrossRef]
- Possega, M.; Ojeda, V.G.M.; Fortis, G.R.S. Multi-Scale Analysis of Agricultural Drought Propagation on the Iberian Peninsula Using Non-Parametric Indices. Water 2023, 15, 2032. [Google Scholar] [CrossRef]
- Baik, J.; Zohaib, M.; Kim, U.; Aadil, M.; Choi, M. Agricultural drought assessment based on multiple soil moisture products. J. Arid. Environ. 2019, 167, 43–55. [Google Scholar] [CrossRef]
- Ajaz, A.; Taghvaeian, S.; Khand, K.; Gowda, P.H.; Moorhead, J.E. Development and Evaluation of an Agricultural Drought Index by Harnessing Soil Moisture and Weather Data. Water 2019, 11, 1375. [Google Scholar] [CrossRef]
- Chen, S.; Liu, Y.; Wen, Z. Review on the study of retrieving soil moisture by satellite remote sensing. Prog. Earth Sci. 2012, 27, 1192–1203. [Google Scholar]
- Li, B.; Zhou, G. Advance in the study on drought index. Acta Ecol. Sin. 2014, 34, 1043–1052. [Google Scholar]
- Wu, Z.; Lu, G.; Guo, H.; Kuang, Y. Drought monitoring technology based on simulated soil moisture. J. Hohai Univ. Nat. Sci. Ed. 2012, 40, 28–32. [Google Scholar]
- Lu, G.; Kuang, Y.; Wu, Z.; He, H. Analysis of spatio-temporal characteristics of simulated soil moisture in different climatic regions of China. China’s Rural. Water Conserv. Hydropower 2013, 5, 15–19. [Google Scholar]
- Wu, Z.; Xu, Z.; Xiao, H.; Wu, H. Analysis of the spatio-temporal characteristics of drought events in the upper reaches of the Yangtze River based on simulated soil moisture. Resour. Environ. Yangtze River Basin 2018, 27, 176–184. [Google Scholar]
- Zhang, J.; Zhang, Q.; Zhao, H.; Zhang, P. Principle and application of quantitative remote sensing inversion of crop water potential. J. Ecol. 2008, 27, 916–923. [Google Scholar]
- Liang, X.; Lettenmaier, D.P.; Wood, E.F.; Burges, S.J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos. 1994, 99, 14415–14428. [Google Scholar] [CrossRef]
- Liang, X.; Wood, E.F.; Lettenmaier, D.P. Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Glob. Planet. Chang. 1996, 13, 195–206. [Google Scholar] [CrossRef]
- Wang, H.; Rogers, J.C.; Munroe, D.K. Commonly Used Drought Indices as Indicators of Soil Moisture in China. J. Hydrometeorol. 2015, 16, 1397–1408. [Google Scholar] [CrossRef]
- Zhu, Y.; Liu, Y.; Ma, X.; Ren, L.; Singh, V.P. Drought Analysis in the Yellow River Basin Based on a Short-Scalar Palmer Drought Severity Index. Water 2018, 10, 1526. [Google Scholar] [CrossRef]
- Fan, Y.; Dool, H.V.D. Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J. Geophys. Res. Atmos. 2004, 109, D10102. [Google Scholar] [CrossRef]
- Leng, G.; Tang, Q.; Rayburg, S. Climate change impacts on meteorological, agricultural and hydrological droughts in China. Glob. Planet. Chang. 2015, 126, 23–34. [Google Scholar] [CrossRef]
- Wu, Z.Y.; Lu, G.H.; Wen, L.; Lin, C.A. Reconstructing and analyzing China’s fifty-nine year (1951–2009) drought history using hydrological model simulation. Hydrol. Earth Syst. Sci. 2011, 8, 2881–2894. [Google Scholar] [CrossRef]
- Taká, J. Assessment of Drought in Agricultural Regions of Slovakia Using Soil Water Dynamics Simulation. Agriculture 2013, 59, 74–87. [Google Scholar] [CrossRef]
- Wang, C.; Guo, J.; Chen, H.; Liu, X. Drought dynamic monitoring indicators based on soil moisture simulation and their applicability. J. Ecol. 2011, 30, 7. [Google Scholar]
- Du, C.; Chen, J.; Nie, T.; Dai, C. Spatial–temporal changes in meteorological and agricultural droughts in Northeast China: Change patterns, response relationships and causes. Nat. Hazards 2021, 110, 155–173. [Google Scholar] [CrossRef]
- Qin, D. National Assessment Report on Extreme Weather Climate Events and Disaster Risk Management and Adaptation in China; Science Press: Beijing, China, 2015; pp. 70–71. [Google Scholar]
- Yin, X.; Yang, L.; Wang, X. Hydrological characteristics analysis of Daling River basin. Agric. Technol. 2007, 27, 168–171. [Google Scholar]
- Liu, X. Study on flood and drought law and runoff simulation in Linghe River basin. Dissertation, Shenyang Agricultural University, Shenyang, China, 2015. [Google Scholar]
- Liang, X.; Xie, Z. A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models. Adv. Water Resour. 2001, 24, 1173–1193. [Google Scholar] [CrossRef]
- Liang, X.; Xie, Z. Important factors in land–atmosphere interactions: Surface runoff generations and interactions between surface and groundwater. Glob. Planet. Chang. 2003, 38, 101–114. [Google Scholar] [CrossRef]
- Zhao, Q.; Ye, B.; Ding, Y.; Zhang, S.; Yi, S.; Wang, J.; Shangguan, D.; Zhao, C.; Han, H. Coupling a glacier melt model to the Variable Infiltration Capacity (VIC) model for hydrological modeling in north-western China. Environ. Geol. 2013, 68, 87–101. [Google Scholar] [CrossRef]
- Zhao, R. Hydrologic Simulation of Basin: Xin’anjiang Model and Northern Shaanxi Model; Water Resources and Electric Power Press: Nanjing, China, 1984. [Google Scholar]
- Nijssen, B.; Schnur, R.; Lettenmaier, D.P. Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–1993. J. Clim. 1999, 14, 1790–1808. [Google Scholar] [CrossRef]
- Hamlet, A.F.; Mote, P.W.; Clark, M.P.; Lettenmaier, D.P. Twentieth-Century Trends in Runoff, Evapotranspiration, and Soil Moisture in the Western United States. J. Clim. 2007, 20, 1468–1486. [Google Scholar] [CrossRef]
- Zhang, Y.; Wu, Z.; He, H. Agricultural drought assessment based on hydrological crop coupling model and CWAPI index. J. Water Resour. 2022, 53, 1168–1179. [Google Scholar]
- Zhang, B.; Wu, P.; Zhao, X.; Wang, Y.; Gao, X.; Cao, X. A drought hazard assessment index based on the VIC–PDSI model and its application on the Loess Plateau, China. Theor. Appl. Climatol. 2013, 114, 125–138. [Google Scholar] [CrossRef]
- Zhang, Y.; You, Q.; Chen, C.; Li, X. Flash droughts in a typical humid and subtropical basin: A case study in the Gan River Basin, China. J. Hydrol. 2017, 551, 162–176. [Google Scholar] [CrossRef]
- Wu, Z.; Lu, G.; Wen, L.; Lin, C.A.; Zhang, J.; Yang, Y. Thirty-Five Year (1971–2005) Simulation of Daily Soil Moisture Using the Variable Infiltration Capacity Model over China. Atmosphere-Ocean 2007, 45, 37–45. [Google Scholar] [CrossRef]
- Zhang, Y.; Deng, L.; Yan, W.; Shangguan, Z.P. Interaction of soil water storage dynamics and long-term natural vegetation succession on the Loess Plateau, China. Catena 2016, 137, 52–60. [Google Scholar] [CrossRef]
- Ge, W.; Deng, L.; Wang, F.; Han, J. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci. Total Environ. 2021, 773, 145648. [Google Scholar] [CrossRef] [PubMed]
- Peng, Q.; Wang, R.; Jiang, Y.; Li, C. Contributions of climate change and human activities to vegetation dynamics in Qilian Mountain National Park, northwest China. Glob. Ecol. Conserv. 2021, 32, e01947. [Google Scholar] [CrossRef]
- Burrell, L.A.; Evans, P.J.; Liu, Y. Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND). Remote Sens. Environ. 2017, 197, 4. [Google Scholar] [CrossRef]
- Luo, L.; Ma, W.; Zhuang, Y.; Zhang, Y.; Yi, S.; Xu, J.; Long, Y.; Ma, D.; Zhang, Z. The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor. Ecol. Indic. 2018, 93, 24–35. [Google Scholar] [CrossRef]
- Xie, Z.; Yuan, F. A parameter estimation scheme of the land surface model VIC using the MOPEX databases. IAHS Publ. 2006, 307, 169. [Google Scholar]
- Huang, M.; Xu, L. On the assessment of the impact of reducing parameters and identification of parameter uncertainties for a hydrologic model with applications to ungauged basins. J. Hydrol. 2006, 320, 37–61. [Google Scholar] [CrossRef]
- Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I â A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
- Sun, S.; Wang, Y. Analysis of Summer Drought in Liaoning Province in 2006. Northeast Water Resour. Hydropower 2007, 276, 31–33. [Google Scholar]
- Shi, Z. Study on Comprehensive Drought Index of Daling River Basin Based on Hydrological model. Water Resour. Plan. Des. 2016, 4, 48–51. [Google Scholar]
- Sun, C.; Wang, L.; Wu, J. Analysis of the current situation of water resources in the Daling River basin and countermeasures for sustainable utilization. South North Water Divers. Water Conserv. Sci. Technol. 2007, 2, 46–49. [Google Scholar]
- Liu, Q.; Gao, L.; Ma, M.; Wang, L.; Lin, H. Study on the downscaling of temperature and precipitation in Daling River Basin, Liaoning. Water Resour. Hydropower Technol. 2021, 52, 16–31, (In Chinese and English). [Google Scholar]
- Zhang, Y.; Fang, Y. Gong Qiang Spatial and temporal characteristics of drought during the growing season in Liaoning Province based on SPEI index. J. Ecol. 2017, 36, 8. [Google Scholar]
- Zhang, D.; Zhou, H. Study on the change trend and causes of water resources in the upper reaches of Daling River Basin. Hydrology 2011, 31, 81–87. [Google Scholar] [CrossRef]
- Wu, L.; Zhang, A. Impact of climate change and human activities on runoff in the upper reaches of Daling River. Prog. Water Resour. Hydropower Sci. Technol. 2016, 36, 10–15. [Google Scholar]
Study Area | Geographic Location | Drainage Area | Climate Type | Hydrometeorological Characteristics | Topographical Conditions | Soil Type | Drought Situation |
---|---|---|---|---|---|---|---|
Control basin of Linghai hydrological station of Daling River | Northwest Liaoning Province, 119°00′–122°00′ E, 40°30′–42°30′ N | 23,057 km2 | Temperate continental monsoon climate, cold and dry in winter, hot and humid in summer, dry and windy in spring and autumn. | The average precipitation is 400~600 mm, the average temperature is 7~10 °C, the average wind speed is 2~3 m/s, the average evaporation is 900~1200 mm, and the average runoff is 1.633 billion m3. | Low mountain and hilly area, 17~1311 M above sea level, and the terrain decreases from west to east. | Mainly brown soil, combined with brown forest soil. | There were 10 major droughts from 1901 to 1949. In the 20 years from 1959 to 1978, there were 10 Spring Droughts and 8 autumn droughts. In 2009, it suffered the most serious historical drought in 60 years, and in 2015, Liaoning suffered the most serious drought in 64 years. A major drought occurs on average every 6–7 years. |
Data Type | Data Name | Data Source |
---|---|---|
Meteorological data | Daily precipitation, daily average temperature and daily average wind speed from 1981 to 2019 | China Meteorological Data Network (http://data.cma.cn/url: accessed on 20 October 2022) |
Hydrological data | Daily runoff from 1981 to 2017 | National water and rain information website (http://xxfb.mwr.cnurl: accessed on 20 October 2022) |
DEM | SRTM 90 m resolution digital elevation | Geospatial data cloud (http://www.gscloud.cn/searchurl: accessed on 20 May 2018) |
Vegetation data | China WESTDC series land cover data products | Science and technology center in cold and dry regions (http://www.landcover.org/data/landcover/data.shtmlurl: accessed on 20 May 2022) |
Soil data | 5 min FAO soil map of the world | Food and Agriculture Organization of the United Nations (http://www.fao.org/statistics/zh/url: accessed on 20 May 2022) |
Drought data | Drought-affected area of crops from 2004 to 2014 | Statistical system for flood control and Drought Relief (Dynamic Statistics of Agricultural Drought) |
Year | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
---|---|---|---|---|---|---|---|---|---|---|---|
Liaoning | 240.32 | 1.47 | 5350.29 | 6567.58 | 785.18 | 31,366 | 2065.82 | 951.76 | 122.19 | 1312.44 | 23.34 |
Drought Level | Frequency | SMAPI/% |
---|---|---|
Extreme drought | 0.005 | ≤−18 |
Severe drought | 0.020 | −18~−14 |
Moderate drought | 0.100 | −14~−9 |
Slight drought | 0.200 | −9~−3.85 |
Non-drought | 0.675 | ≥−3.85 |
K | Driving Factors | CCcon | OFcon | Contribution (%) | Scenario | ||
---|---|---|---|---|---|---|---|
CC | OF | ||||||
Increasing SMAPI | >0 | CC and OF | >0 | >0 | ICO | ||
CC | >0 | <0 | 100 | 0 | IC | ||
OF | <0 | >0 | 0 | 100 | IO | ||
Decreasing SMAPI | <0 | CC and OF | <0 | <0 | DCO | ||
CC | <0 | >0 | 100 | 0 | DC | ||
OF | >0 | <0 | 0 | 100 | DO |
Drought Grade | Extreme Drought | Severe Drought | Moderate Drought | Slight Drought | Non-Drought |
---|---|---|---|---|---|
Spring | 0.008 | 0.014 | 0.074 | 0.226 | 0.678 |
Summer | 0 | 0.011 | 0.078 | 0.202 | 0.709 |
Autumn | 0.006 | 0.011 | 0.099 | 0.177 | 0.707 |
Winter | 0.007 | 0.047 | 0.123 | 0.195 | 0.629 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ding, M.; Lv, J.; Qu, Y.; Jiang, T. Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation. Water 2023, 15, 3809. https://doi.org/10.3390/w15213809
Ding M, Lv J, Qu Y, Jiang T. Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation. Water. 2023; 15(21):3809. https://doi.org/10.3390/w15213809
Chicago/Turabian StyleDing, Mei, Juan Lv, Yanping Qu, and Tianliang Jiang. 2023. "Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation" Water 15, no. 21: 3809. https://doi.org/10.3390/w15213809