Crop production in the Hexi Corridor challenged by future climate change

Fu, Jing; Niu, Jun; Kang, Shaozhong; Adeloye, Adebayo J.; Du, Taisheng

doi:10.1016/j.jhydrol.2019.124197

Cited by 30 publications

(16 citation statements)

References 32 publications

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“…Meteorological drought can reflect the characteristics of drought to some extent, while the agricultural drought often has a lag of 3-6 months after meteorological drought [49,50]. Thus, a 3month time scale was chosen as the agriculture was closely related with SPEI-3, according to Zhang et al [51] and Fu et al [52].…”

Section: Climate Satellite Data and Meteorological Indicesmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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Section: Climate Satellite Data and Meteorological Indicesmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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“…In addition to the reduction of rainfall distribution biases at the GCM scale, the main advantage of this method is its ability to bring up more realistic variability at finer resolution than simpler spatial interpolation techniques [62]. On top of these advantages, the selection of this method was motivated by its previous use under similar climate and physical conditions [66][67][68].…”

Section: Projected Climate Variablesmentioning

confidence: 99%

Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches

Brouziyne

Abouabdillah

Chehbouni

et al. 2020

Water

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Understanding the spatiotemporal distribution of future droughts is essential for effective water resource management, especially in the Mediterranean region where water resources are expected to be scarcer in the future. In this study, we combined meteorological and hydrological drought indices with the Soil and Water Assessment Tool (SWAT) model to predict future dry years during two periods (2035–2050and 2085–2100) in a typical Mediterranean watershed in Northern Morocco, namely, Bouregreg watershed. The developed methodology was then used to evaluate drought impact on annual water yields and to identify the most vulnerable sub-basins within the study watershed. Two emission scenarios (RCP4.5 and RCP8.5) of a downscaled global circulation model were used to force the calibrated SWAT model. Results indicated that Bouregreg watershed will experience several dry years with higher frequency especially at the end of current century. Significant decreases of annual water yields were simulated during dry years, ranging from −45.6% to −76.7% under RCP4.5, and from −66.7% to −95.6% under RCP8.5, compared to baseline. Overall, hydrologic systems in sub-basins under the ocean or high-altitude influence appear to be more resilient to drought. The combination of drought indices and the semi-distributed model offer a comprehensive tool to understand potential future droughts in Bouregreg watershed.

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“…Fu et al . (2019) investigated the correlations between climatic crop yield and multi‐scale drought index in Hexi Corridor using Pearson correlation analysis, and analysed the future drought conditions under climate change scenarios. Kheir et al .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China

Huang

et al. 2021

Intl Journal of Climatology

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Frequent drought events under climate change are endangering food security and sustainable agricultural development. Quantitative assessment of crop yield anomalies under drought conditions is essential for effective water resources management and adaptative drought risk mitigation strategies. In this study, a copula‐based bivariate probabilistic framework model is developed to assess the impacts of drought events on crop yield, where the correlation of crop yield anomalies and standardized precipitation evapotranspiration index (SPEI) at multi‐month lags are quantified. This model has advantages in quantifying the impacts of drought scales on crop yield through the joint probability of the corresponding series. Then, the model is applied to Xinjiang Province in Northwestern China, an arid region with extensive agricultural activities. SPEI and yield anomalies of wheat, maize and cotton during 1984–2015 are firstly identified, and then copula‐based framework is constructed to quantify probabilistic crop yield losses caused by drought events. Finally, crop water amounts and irrigation requirements under different drought severity conditions are compared and analysed. Our findings are: (a) wheat and maize yield anomalies are vulnerable to long drought time‐scales, with exceedance probabilities of required yield anomalies returning to normal are 19.3% for wheat and 21.1% for maize, while cotton is susceptible to short drought time‐scales, with exceedance probability of 42.3% for drought recovery; (b) response of wheat crop yield anomalies to the SPEI at 1‐, 3‐, 6‐ and 12‐month scales is the most sensitive, followed by maize, and cotton is the least; (c) under extreme drought conditions, the probability of cotton yield reduction is lower than that of wheat and maize. Results may enhance our understanding of the impacts of drought scales on crops during the growing season, thus providing general guidance for rational irrigation management of crops and incentives for irrigation to mitigate drought risk, ultimately promoting sustainable agricultural development.

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Crop production in the Hexi Corridor challenged by future climate change

Cited by 30 publications

References 32 publications

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches

Probabilistic assessment of crop yield loss to drought time‐scales in Xinjiang, China

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