The Sources and Quantification of Soil Water in the Root Zone of Typical Crops in the Shajiang Black Soil Region of Huaibei Plain

[Objective] The sources and quantitative characteristics of soil moisture in the root zone of crops in the Huaibei Plain from 1990 to 2022 were explored to provide scientific guidance for crop irrigation management and water resource utilization. [Methods] The contribution of precipitation and groundwater to soil water in the root zone was calculated by Pearson correlation analysis and multiple regression, and soil water quantitative simulation was performed using multiple linear regression and long short-term memory network (LSTM) model. [Results] 1) For summer corn, in the 0—10 cm soil layer, except for the 55% contribution of precipitation during the seedling-jointing period, the contribution of groundwater in other stages could reach 61%—68%; in the soil layer below 10 cm, the soil water in the root zone mainly came from soil water in the adjacent upper layer, accounting for more than 74%. In the later stage, the root system gradually growed to 100 cm, especially in the 40—80 cm soil layer, the contribution of groundwater to soil water increased significantly, accounting for 9%—22%. 2) For winter wheat, in the 0—10 cm soil layer, except for the sowing-seedling period and the greening-jointing period, the contribution of groundwater in other stages could reach 54%—73%; in the soil layer below 10 cm, the soil water in the root zone mainly came from soil water in the adjacent upper layer, accounting for more than 63%. However, in the 40—80 cm soil layer, the contribution of groundwater and precipitation increased. 3) The LSTM model was more stable and accurate than multiple linear regression in quantitatively simulating soil water. The model evaluation index could reach R2>0.6 and MSE<10, which could better reflect the dynamic changes of soil moisture. The application of the above simulation results at other stations in the Huaibei Plain further verified the feasibility of the model. [Conclusion] The main source of soil water in the 0—10 cm layer for summer corn is groundwater, and the main source of soil water in the 0—10 cm layer for winter wheat is precipitation; the main source of soil water below 10 cm is soil water in the adjacent upper layer (including precipitation, irrigation water, etc.). Performing quantitative simulation based on the sources of soil water in the root zone at different growth stages, and dynamically adjusting the irrigation plan through the changing trend of soil moisture in the root zone of the crop is helpful to optimize irrigation management and improve water resource utilization efficiency.

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-04-01
DOI
https://doi.org/10.13870/j.cnki.stbcxb.2025.02.014
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Sources and Quantification of Soil Water in the Root Zone of Typical Crops in the Shajiang Black Soil Region of Huaibei Plain

SHILAP Revista de lepidopterología
Soil Moisture and Remote Sensing
article

The Sources and Quantification of Soil Water in the Root Zone of Typical Crops in the Shajiang Black Soil Region of Huaibei Plain

article en

Abstract

[Objective] The sources and quantitative characteristics of soil moisture in the root zone of crops in the Huaibei Plain from 1990 to 2022 were explored to provide scientific guidance for crop irrigation management and water resource utilization. [Methods] The contribution of precipitation and groundwater to soil water in the root zone was calculated by Pearson correlation analysis and multiple regression, and soil water quantitative simulation was performed using multiple linear regression and long short-term memory network (LSTM) model. [Results] 1) For summer corn, in the 0—10 cm soil layer, except for the 55% contribution of precipitation during the seedling-jointing period, the contribution of groundwater in other stages could reach 61%—68%; in the soil layer below 10 cm, the soil water in the root zone mainly came from soil water in the adjacent upper layer, accounting for more than 74%. In the later stage, the root system gradually growed to 100 cm, especially in the 40—80 cm soil layer, the contribution of groundwater to soil water increased significantly, accounting for 9%—22%. 2) For winter wheat, in the 0—10 cm soil layer, except for the sowing-seedling period and the greening-jointing period, the contribution of groundwater in other stages could reach 54%—73%; in the soil layer below 10 cm, the soil water in the root zone mainly came from soil water in the adjacent upper layer, accounting for more than 63%. However, in the 40—80 cm soil layer, the contribution of groundwater and precipitation increased. 3) The LSTM model was more stable and accurate than multiple linear regression in quantitatively simulating soil water. The model evaluation index could reach R2>0.6 and MSE<10, which could better reflect the dynamic changes of soil moisture. The application of the above simulation results at other stations in the Huaibei Plain further verified the feasibility of the model. [Conclusion] The main source of soil water in the 0—10 cm layer for summer corn is groundwater, and the main source of soil water in the 0—10 cm layer for winter wheat is precipitation; the main source of soil water below 10 cm is soil water in the adjacent upper layer (including precipitation, irrigation water, etc.). Performing quantitative simulation based on the sources of soil water in the root zone at different growth stages, and dynamically adjusting the irrigation plan through the changing trend of soil moisture in the root zone of the crop is helpful to optimize irrigation management and improve water resource utilization efficiency.

SHILAP Revista de lepidopterología
Hohai University (CN)
Clean water and sanitation
Openalex Percentile: Top 60%
Soil Moisture and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.