# Spatio-Temporal Analysis of Drought Indicated by SPEI over Northeastern China

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## Abstract

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## 1. Introduction

## 2. Study Area and Data

^{2}and a population of about 110 million. The northeast is China’s most important source of soybeans, corn, and japonica rice, accounting for 41% of the country’s soybean production, 34% of its corn output, and 30–50% percent of its japonica rice crop. The landscape is mostly characterized by plains and mountains with elevations from 3 to 1017 m. The high latitude makes northeast the coolest region in China with long durations of severely cold winters. The annual precipitation decreases from southeast (900 mm) to northwest (400 mm) as the distance from the sea increases, and the annual temperature varies from −4.2 to 10.9 °C, decreasing from south to north. The precipitation is generally concentrated in the summer and autumn, and it coincides with the crop growing season, in which the main crops are sowed at the beginning of May and harvested at the end of September.

## 3. Methodology

#### 3.1. Standardized Precipitation Evapotranspiration Index (SPEI)

_{i}values are aggregated at different time scales. The difference in a given month j and year i depends on the chosen timescale k. For example, the accumulated difference for one month in a particular year i with a 12-month timescale can be calculated as follows [29]:

_{i},

_{l}is the P − PET difference in the l month of year i.

_{i}is an ordered random sample x

_{1}≤ x

_{2}≤ x

_{n}, and n is the sample size.

_{0}= 2.515517, C

_{1}= 0.802853, C

_{2}= 0.010328, d

_{1}= 1.432788, d

_{2}= 0.189269, and d

_{3}= 0.001308.

#### 3.2. Trend Free Prewhitening Mann–Kendall Nonparametric Test

_{1}of the detrended series ${X}_{t}^{\prime}$ is computed using Equation (11) and then the Auto Regression Model (1) is removed from the ${X}_{t}^{\prime}$ by Equation (12).

_{1−α/2}at a given α confidence level, the original hypothesis is unacceptable. That is to say, at the 1 − α confidence level, the time series has a distinct upward or downward trend. The Z values of ±1.96, and ±1.64 are equal to the confidence levels of 0.05, and 0.1, respectively. Based on these confidence levels, the detected trend could be classified into six zones according to Z value [42]: (1) Z < −1.96, indicating significant decreasing trend; (2) Z ∈ [−1.96 to −1.64), indicating weak decreasing trend; (3) Z ∈ [−1.654 to 0), indicating no significant decreasing trend; (4) Z ∈ (0 to 1.64], indicating no significant increasing trend; (5) Z ∈ (1.64 to 1.96], indicating weak increasing trend; and (6) Z > 1.96, indicating the significant increasing trend.

#### 3.3. Distinct Empirical Orthogonal Function

## 4. Results and Discussion

#### 4.1. Variation Characteristics of Precipitation and Temperature

#### 4.2. Results and Uncertainty of SPEI

_{May–Sep}has decreasing trend. While for SPEI6 and SPEI12, the changing trends are not very obvious.

_{j}, j = 1, 2, …, 999, 1000, and the lower bound (LB = SPEI

_{51}) and upper bound (UB = SPEI

_{950}) of the 90% confidence interval (CI) estimation are calculated, accordingly.

_{May–Sep}as an example, Figure 6 shows the results of uncertainty of SPEI6

_{May–Sep}due to sampling in three typical meteorological stations. It can be seen that the 90% CI is very narrow compared to the expected value, which means sampling uncertainty is small in our paper. Therefore, the SPEI results are directly used for further analyses.

#### 4.3. Spatial Patterns of Temporal Trends in Drought

#### 4.4. Spatial Variability of Drought Using DEOF

_{May–Sep}and 13.4% for SPEI1, respectively. The spatial distribution of DEOF2 indicates the high positive spatial behavior of drought in the Songnen Plain and the Lesser Hinggan Mountains (SL region). The SL region belongs to the cold temperate zone where the climate characteristics are significantly affected by the Lesser Hinggan Mountains, while Liaohe River Plain and area surrounding its east are near the ocean which mainly receive the influence of seawater vapor. In addition, this region is also affected by the air currents of the Sea of Japan and the Sea of Okhotsk. Although two DEOFs are unable to extract the full temporal variation of drought across the region, their loadings seem to divide the whole region into two sub-regions that is LS and SL regions, characterized by different drought variabilities.

_{May–Sep}, since crop growing season is our primary concern. Figure 9a reveals that the LS region (DPC1) experienced severe drought in years 1982 and 1999 to 2001, which is consistent with Yue et al. [24] describing that drought hot spots mainly occur in the western part of the study area in 2000–2004. These detected droughts correspond well with the China’s Drought Statistics Yearbook, which recorded that northeastern China suffered from severe drought in 1982. Especially in the Liaohe River Plain and the Second Songhua River basin, most of the moisture was merely 5–10% for 50-cm deep surface soil. It also recorded that 3-year consecutive drought has occurred in Liaoning province during 1999–2001, with 90% rivers were witnessed cutoff. For the SL region (DPC2), the only year extreme drought was observed in 1995 with DPC score less than −2.5. In 1995, nine counties and cities in Heilongjiang province were recorded suffering from extreme drought, and more than half of the agricultural areas were in a drought state. The rest 1964, 2001, 2004, 2007, and 2010 years detected as severe dry were all recorded severe drought in the LS region.

## 5. Summary and Conclusions

- (1)
- The annual precipitation changing trends increase from south to north, with decreasing trends in nearly the entire Liaoning province, and increasing trends in Heilongjiang and Jilin provinces. However, the majority of the trends are insignificant. The warming trends are more obvious and straightforward, as increasing trends are detected over entire northeastern China.
- (2)
- TFPW-MK test detects the changing trends of SPEI at various timescales. Overall, significant increasing drought trends are observed in the coastal region of Liaodong Gulf, southeastern Liaodong Peninsula, and the downstream region of Hunhe River Basin, while significant decreasing trends are noticed in the northwest corner of Heilongjiang province, the southeast of Lesser Hinggan Mountains and the northeast of Changbai Mountains. Further, drought increasing trends are more dominant at crop growing seasonal scale, thus the drought situation may be even worse during crop growing season than the rest of the year.
- (3)
- DEOF is used to identify two main sub-regions of drought variability—the Liaohe River Plain and the Second Songhua River basin (LS region), and the Songnen Plain and the Lesser Hinggan Mountains (SL region). Based on the crop growing seasonal DPC scores, the LS region experienced severe droughts in the years 1982 and 1999 to 2001. In the SL region, severe to extreme droughts were observed in 1964, 1995, 2001, 2004, 2007, and 2010.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Trends of precipitation and temperature over northeastern China. (The Z values of ±1.64, ±1.96 and ±2.58 are equal to the confidence levels of 0.1, 0.05 and 0.01, respectively.).

**Figure 3.**Areal average precipitation anomaly curves. (

**a**) Annual. (

**b**) Crop growing season. (The dotted lines represent the decadal averages).

**Figure 4.**Areal average temperature anomaly curves. (

**a**) Annual. (

**b**) Crop growing season. (The dotted lines represent the decadal averages).

**Figure 7.**Spatial structure of long-term SPEI trends at various timescales. (The Z values of ±1.64, ±1.96 and ±2.58 are equal to the confidence levels of 0.1, 0.05 and 0.01, respectively.).

Categories | SPEI Values |
---|---|

Extremely wet | [2.0, +∞) |

Severely wet | [1.5, 2.0) |

Moderately wet | [1.0, 1.5) |

Slightly wet | [0.5, 1.0) |

Near normal | (−0.5, 0.5) |

Slightly dry | (−1.0, −0.5] |

Moderately dry | (−1.5, −1.0] |

Severely dry | (−2.0, −1.5] |

Extremely dry | (−∞, −2.0] |

Categories | Explained Variance | |
---|---|---|

DEOF1 (%) | DEOF2 (%) | |

SPEI1 | 45.2 | 13.4 |

SPEI3 | 44.6 | 13.6 |

SPEI6 | 43.3 | 13.7 |

SPEI12 | 40.2 | 13.9 |

SPEI6_{May–Sep} | 42.5 | 14.1 |

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**MDPI and ACS Style**

Ye, L.; Shi, K.; Zhang, H.; Xin, Z.; Hu, J.; Zhang, C.
Spatio-Temporal Analysis of Drought Indicated by SPEI over Northeastern China. *Water* **2019**, *11*, 908.
https://doi.org/10.3390/w11050908

**AMA Style**

Ye L, Shi K, Zhang H, Xin Z, Hu J, Zhang C.
Spatio-Temporal Analysis of Drought Indicated by SPEI over Northeastern China. *Water*. 2019; 11(5):908.
https://doi.org/10.3390/w11050908

**Chicago/Turabian Style**

Ye, Lei, Ke Shi, Hairong Zhang, Zhuohang Xin, Jing Hu, and Chi Zhang.
2019. "Spatio-Temporal Analysis of Drought Indicated by SPEI over Northeastern China" *Water* 11, no. 5: 908.
https://doi.org/10.3390/w11050908