A Method for Drought Disaster Response and Risk Assessment in Humid and Semi-humid Regions

Abstract

This invention discloses a method for drought disaster response and risk assessment in humid and semi-humid regions, proposing an assessment index for the impact of memory effects of severe agricultural droughts in these regions, and adopting corresponding disaster response strategies based on the index. The memory effect of severe agricultural droughts can affect soil moisture recovery, vegetation physiological functions, and farmland ecosystem stability for a long period after the drought ends, thereby altering the vulnerability and risk pattern of subsequent agricultural production. Establishing scientific assessment indicators for the memory effects of severe agricultural droughts not only helps to comprehensively characterize the cumulative and lagged effects of drought disasters, but also provides new theoretical support for revealing crop-environment interaction mechanisms and improving drought monitoring and early warning. More importantly, quantitative memory indicators will provide decision-making basis for agricultural water resource allocation, disaster risk management, and food security, promoting a shift from traditional post-disaster response to proactive adaptation, which has significant scientific significance and practical necessity.

Description

TECHNICAL FIELD

[0001] The application relates to a drought disaster monitoring, early warning and forecasting method, in particular to a drought disaster response and risk assessment method in humid and semi-humid regions. BACKGROUND

[0002] Under the dual effects of global climate change and human activities, agricultural drought events in humid and semi-humid regions show a trend of increasing frequency, prolonged duration and expanding influence. Traditional drought assessment in humid and semi-humid regions focuses on intensity and duration, but ignores the residual effect of drought process in the soil-vegetation system, i.e. "drought memory", leading to problems in the subsequent application of traditional assessment indicators. First, in terms of farmland water regulation, traditional assessment indicators cannot comprehensively consider the continuous impact of drought memory on soil available water supply when formulating irrigation systems and agricultural water plans, resulting in poor water regulation effect. Second, in terms of crop production and variety selection, traditional assessment indicators cannot reflect the coupling effect of the soil-crop system, and therefore cannot provide references for related optimization of farming systems, layout of drought-tolerant varieties and adjustment of agronomic measures. Third, in terms of drought monitoring and risk management, ignoring the memory of severe droughts may underestimate the risk of subsequent droughts, leading to delayed early warning and policy response. Finally, in terms of ecological environment, the memory of severe droughts has a profound impact on the resilience of vegetation and the stability of farmland ecosystems, which may exacerbate land degradation and decline in ecosystem services. SUMMARY

[0003] The application provides a drought disaster response and risk assessment method in humid and semi-humid regions, which addresses the lack of intensity measurement of drought memory, improves farmland water regulation effect and improves the accuracy of drought monitoring.

[0004] The technical scheme adopted by the application is a drought disaster response method in humid and semi-humid regions, which comprises the following steps:

[0005] According to the historical time series of daily soil moisture conditions in the research area, a sequence of daily soil water deficit indices is calculated;

[0006] According to the sequence of daily soil water deficit indices, a severe agricultural drought event is identified, as well as the pre-drought period and stable memory period in the complete process corresponding to the severe agricultural drought event; the pre-drought period is the stage before the start of the drought, and the stable memory period is the stage after the end of the drought when the soil water deficit index value reaches a relative stability;

[0007] According to the sequence of daily soil water deficit indices, the delta quantile value SWDI of daily soil water deficit indices in the pre-drought period is calculated T0, δand the δ quantile value of the daily soil water deficit index in the stable memory period T3, δ ; δ is a preset value, 0 < δ ≤ 25%; the SWDI T3, δ and the ratio of the SWDI T0, δ are calculated to obtain an extreme agricultural drought memory influence value; according to the extreme agricultural drought memory influence value and a drought memory influence grade division, the drought memory influence intensity is determined;

[0008] Disaster response measures are implemented according to the drought memory influence intensity.

[0009] The daily soil moisture history time series of the research area includes daily soil water content history time series, daily soil field capacity history time series and daily soil available water content history time series of each research site in the research area with the same soil texture.

[0010] The daily soil water deficit index is calculated by the following formula:

[0011] ,

[0012] wherein, SWDI is a soil water deficit index value, is an actual soil water content, is a soil field capacity, and the soil available water content corresponding to the soil texture is represented.

[0013] According to the daily soil water deficit index sequence, an extreme agricultural drought event is identified, including: according to the agricultural drought grade division of the soil water deficit index, if the soil water deficit index value SWDI is less than or equal to -10, the extreme agricultural drought event is identified.

[0014] The complete process of the extreme agricultural drought event is identified, including: in the extreme agricultural drought event, the soil water deficit index value less than 0 is the drought starting time, and the period before the drought starting time is identified as the drought pre-period T0; after the drought starting time, the soil water deficit index value greater than 0 is the drought ending time, and the period from the drought starting time to the drought ending time is the drought period T1; the fluctuation rate of the soil water deficit index value after the drought ending time is calculated, and the time corresponding to the minimum value of the soil water deficit index value fluctuation rate is identified as the starting time of the stable memory period, and the period after the time is identified as the memory stable period T3; the fluctuation rate of the soil water deficit index value is calculated by using the rolling standard deviation method, and the time window length is several months.

[0015] According to the extreme agricultural drought memory influence value and the drought memory influence grade division, the drought memory influence intensity is determined, including:

[0016] If M falls in (1, 1.25], it is low memory;

[0017] If M falls in (1.25, 1.5], it is medium memory;

[0018] If M falls in (1.5, 1.75], it is strong memory;

[0019] If M falls in (1.75, + ), it is extremely strong memory.

[0020] According to the drought memory influence intensity, disaster response measures are implemented, including:

[0021] (1) The drought disaster response measures corresponding to low memory include: irrigation, fertilization and agronomy according to the routine plan, and routine record of root zone soil water content;

[0022] (2) The drought disaster response measures corresponding to medium memory include: monitoring the soil water content of 0-30 cm and 30-60 cm of soil profile, focusing on irrigation according to crop demand, and giving priority to main crops or crops in critical growth period when irrigation resources are limited; non-critical period or secondary crops are allowed to reduce 10-15% of standard water requirement;

[0023] (3) The drought disaster response measures corresponding to strong memory include: ensuring that the main crops reach 100-110% of the standard water requirement in the critical growth period; non-critical period or secondary crops are allowed to reduce 20-40% of the standard water requirement; hierarchical water supply is adopted when irrigation resources are limited; and the amount of groundwater extraction is allowed to be increased;

[0024] (4) The drought disaster response measures corresponding to extremely strong memory include:

[0025] 0-3 months short term, only guarantee the irrigation of key production area to reach 110-130% of the standard water requirement, start the emergency discharge of regional reservoirs or storage projects; carry out temporary fallow or cover on part of the land to prevent further degradation;

[0026] 3 months to several years medium and long term, apply organic matter and modifier to improve water holding capacity; adjust the crop structure in the next round of planting, and select drought-resistant varieties.

[0027] The application also proposes a drought disaster risk assessment method in humid and semi-humid areas, comprising:

[0028] According to the daily soil moisture history time series of the research area, the daily soil water deficit index sequence is calculated;

[0029] ​According to the daily soil moisture deficit index sequence, a super agricultural drought event is identified, and the super agricultural drought event corresponds to a drought early stage and a stable memory period in a complete process; the drought early stage is a stage before the drought starting moment, and the stable memory period is a stage after the drought ending moment, and the soil moisture deficit index value reaches a relatively stable stage;

[0030] According to the daily soil moisture deficit index sequence, the δ quantile value SWDI of the daily soil moisture deficit index in the drought early stage period is calculated T0, δ , and the δ quantile value SWDI of the daily soil moisture deficit index in the stable memory period is calculated T3, δ ; δ is a preset value, 0 < δ ≤ 25%; the ratio of SWDI T3, δ and SWDI T0, δ is calculated to obtain a super agricultural drought memory influence value.

[0031] The super agricultural drought memory influence value is taken as one of influence factors, and is included in drought risk comprehensive evaluation of a research area, and a region with drought risk is identified according to an evaluation result.

[0032] The super agricultural drought memory influence value is taken as one of influence factors, and is included in drought risk comprehensive evaluation of a research area, including: the super agricultural drought memory influence value is standardized to obtain a memory sub-index; the memory sub-index is included in calculation of a risk total score, and a calculation formula of the risk total score is:

[0033] R_total=w1*S_intensity+w2*S_duration+w3*S_memory+w4* S_vulnerability,

[0034] Wherein, R_total is the risk total score, S_intensity is a drought intensity sub-index, S_duration is a drought duration sub-index, S_memory is the memory sub-index, S_vulnerability is a region vulnerability sub-index, and w1-w4 are weight coefficients corresponding to each index.

[0035] The application further provides a computer program product, including a computer program and / or instructions, which are executed by a processor to realize the drought risk evaluation method of the humid and semi-humid area.

[0036] Beneficial effects: the existing evaluation method of the influence degree of drought memory is very limited and almost blank, and the in-depth research shows that in the humid and semi-humid area, the general field drought can completely recover to the state before the drought after the drought ends with the soil moisture recovery such as rainfall, and for the super large agricultural drought, the intensity and duration are often far more than the general drought, and the drought recovery to the normal state after the drought ends is often not the soil moisture state before the drought (the situation in the semi-arid and arid areas has not been studied). The memory influence degree of different super large agricultural droughts in the same place is obviously different, but the existing technology lacks the measurement method and index of the memory influence degree. Moreover, the memory influence degree of the same super large agricultural drought in the same region is also significantly different due to different soil textures. The application innovatively proposes the evaluation index of the memory influence of the super large agricultural drought in the humid and semi-humid area, and according to the index, the corresponding countermeasures are taken or the more accurate future drought risk evaluation result is obtained. By segmenting the whole process of the super large agricultural drought event (T0 drought pre-period, T1 drought period, T2 drought recovery period and T3 stable period), and calculating the ratio of the SWDI quantile values in the key period (T0 and T3), a quantitative grading system of light memory, medium memory, strong memory and extremely strong memory is established. The application first proposes the memory evaluation index system of the super large agricultural drought in the humid and semi-humid area, realizes the quantification and grading of the memory effect of the super large agricultural drought event, fills the blank of the related research, has clear physical meaning, strong operability, high application value and the like. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flow chart of the drought disaster response method in the humid and semi-humid area described in the application;

[0038] Figure 2 is a schematic view of the time periods before, during, after and after the recovery of the super large agricultural drought event;

[0039] Figure 3 is the time course change data of the SWDI values of the loamy sand soil quality of each soil moisture station in the A basin. DETAILED DESCRIPTION

[0040] The technical solutions of the application will be further described below with reference to the drawings and examples.

[0041] Example 1:

[0042] The embodiment of the present application takes a basin soil moisture station as an example for illustration. The evaluation index of the super large agricultural drought memory influence in the humid and semi-humid region according to the present application is shown in a flow chart as Figure 1 The specific implementation steps are as follows (each step is only for convenient illustration, not representing a fixed sequence) :

[0043] Step 1: Obtain the daily soil moisture history time series of the area to be studied.

[0044] The daily soil moisture history time series of the area to be studied includes the daily soil water content history time series, the daily soil field water capacity history time series and the daily soil available water content history time series of each research station with the same soil texture in the research area. In this example, the long series of daily soil moisture data of 15 stations in the A basin with soil texture belonging to loamy sand from 2011 to 2024 is obtained.

[0045] Step 2: Calculate the daily soil moisture deficit index sequence according to the daily soil moisture history time series of the area to be studied.

[0046] Based on the above soil moisture data, the daily soil moisture deficit index (SWDI) is calculated, and the formula for calculating SWDI is:

[0047]

[0048] Among them, is the actual soil water content, is the soil field water capacity, represents the soil available water content corresponding to the soil texture.

[0049] Step 3: According to the daily soil moisture deficit index sequence, the super large agricultural drought event is identified, and the pre-drought and stable memory period in the complete process corresponding to the super large agricultural drought event are obtained.

[0050] Draw the time change graph of daily SWDI, according to the SWDI agricultural drought grade, if SWDI is less than or equal to -10, it is a super large agricultural drought, and the super large agricultural drought event ED is identified.

[0051] As Figure 2 ​As shown, the complete process of a severe agricultural drought event includes the pre-drought, during-drought, recovery, and post-recovery periods. The drought begins when the SWDI is less than 0, marking the start of the drought; the period before this point is identified as the pre-drought phase (T0). When the SWDI is greater than 0, the drought ends; the period from the start to the end of the drought is identified as the drought period (T1). After the drought ends, the SWDI value fluctuates from a relatively high level to a relatively stable level; the period from the end of the drought to the point where the SWDI reaches this relatively stable level is identified as the recovery period (T2). The period after the recovery period, characterized by relatively small fluctuations in the SWDI value and relative stability, is identified as the memory plateau period (T3). Generally, the longer the data series, the better, as the data series should be able to identify the relatively stable phases of T0 and T3. The stable phase of SWDI value fluctuations can be calculated and determined using the rolling standard deviation, with the rolling time limited to several months.

[0052] Step 4: Calculate the quartile values ​​(SWDI) of the daily soil moisture deficit index during the early drought period based on the daily soil moisture deficit index series. T0,25% And the interquartile values ​​of the daily soil moisture deficit index (SWDI) during the stable memory period. T3,25% .

[0053] like Figure 3 As shown in the figure, the different colored lines represent the time-historical variation data of SWDI values ​​at 15 soil moisture monitoring stations (all soil textures are loam and sandy soil) within the A watershed of the humid region, from which the pre-drought period and the stable memory period are identified. The quartile values ​​of the daily soil moisture deficit index (SWDI) at time T0 during the pre-drought period are calculated. T0,25% The interquartile value of the daily soil moisture deficit index (SWDI) during the stable memory period T3 is -3.6. T3,25% It is -6.2.

[0054] The quartile values ​​here can also be any positive value less than 25%, and the final calculated drought memory impact intensity index will meet the requirements. It should be noted that the quartile values ​​for different periods T0 and T3 must be in the same percentile.

[0055] Step 5: Calculate SWDI T3,25% and SWDI T0,25% The ratio SWDI T3,25% / SWDI T0,25% The impact value of the severe agricultural drought memory was obtained; based on the impact value of the severe agricultural drought memory and the classification of drought memory impact levels, the intensity of drought memory impact was determined.

[0056] Calculate SWDI T3,25% / SWDI T0,25%The ratio is denoted as M, and M is the value of the extraordinary agricultural drought memory effect. In this example, M is -6.2 / -3.6 = 1.73. According to the extraordinary agricultural drought memory effect value and the drought memory effect grade division, as shown in Table 1 below, this extraordinary agricultural drought is a strong memory effect.

[0057] Table 1: Grade division standard of M value of extraordinary agricultural drought memory effect

[0058]

[0059] Step 6: Implement disaster response measures according to the intensity of drought memory effect.

[0060] In actual agricultural and water resources management, the memory effect of extraordinary agricultural drought has many aspects. First, in the regulation of farmland water, drought memory may cause soil moisture to be lower than normal for a long time after drought recovery, so that crops still face implicit water stress in the subsequent growing season. Therefore, when formulating irrigation systems and agricultural water plans, the continuous impact of drought memory on soil available water supply needs to be considered to improve the effect of farmland water regulation. Further, according to the drought memory effect evaluation index, the vegetation recovery and the stability of the farmland ecosystem can be improved, and land degradation and ecosystem service decline can be reduced. Second, in crop production and variety selection, drought memory will affect the coupling effect of soil-crop system, and the sensitivity of different crops and varieties to soil residual deficit is obviously different, which provides a reference for the optimization of farming system, the layout of drought-resistant varieties, and the adjustment of agronomic measures.

[0061] According to step 5, the drought memory effect intensity of this drought is strong memory. Strong memory indicates that water needs to be quickly made up, system vulnerability needs to be reduced, and long-term recovery and reconstruction needs to be started. Measures need to be taken as follows:

[0062] Emergency short-term (0-3 months):

[0063] Irrigation: Irrigation in key production areas reaches 110-130% of crop water requirement (priority is given to ensuring the minimum safe yield).

[0064] Water resources allocation: Start emergency water release of regional reservoirs / regulation projects.

[0065] Land management: Temporary fallow or cover for high-risk plots to prevent further degradation.

[0066] Medium and long term (3 months to several years):

[0067] Soil repair: Large-scale application of organic matter (5-10 t / ha) and modifiers to improve water holding capacity; promote deep plowing / ridge combination to enhance infiltration and water retention.

[0068] Replanting / rotation adjustment: promote crop structure adjustment in arid areas (e.g., corn → wheat / drought-tolerant crops).

[0069] The measures to be taken under different levels of memory are as follows.

[0070] 1) Low memory, indicating that normal production and normalized monitoring can be maintained. Specific measures:

[0071] Irrigation: 90-100% of the standard water requirement of crops is implemented (i.e., regular plan).

[0072] Fertilization / agronomy: return to regular management, without special water addition or transplanting.

[0073] Monitoring: record the soil moisture content of the root zone every week.

[0074] 2) Medium memory, indicating the need for fine monitoring and local regulation: maintain yield and avoid deterioration. Specific measures:

[0075] Irrigation: supplement as needed, with priority given to critical growth periods.

[0076] Crop management: maintain normal density during critical periods; secondary fields (non-critical) can be moderately reduced by 10-15% to protect the main production area.

[0077] Monitoring: observe soil profile (0-30, 30-60 cm) every week.

[0078] 3) Strong memory, indicating the need for targeted remediation and adjustment: reduce residual deficit as soon as possible, protect main yield and reduce next year's risk. Specific measures:

[0079] Irrigation strategy (priority): ensure that the main crop critical irrigation (heading stage / grain filling stage) reaches 100-110% of the standard water requirement; reduce irrigation by 20-40% during non-critical periods or for secondary crops; if water resources are limited in the area, use hierarchical water supply: 100% for main production area → 60% for secondary area → 30% for non-production area.

[0080] Groundwater / well water use: allow short-term increase in extraction, but set an upper limit: at most 30% more than normal pumping, and record the water level decline rate.

[0081] Soil improvement: promote mulching, water-retaining agent, or improved tillage (fallow, reduce plowing) — quantification: prioritize fully covered plots, apply 20-40 kg / ha of water-retaining agent (adjust according to supply and cost).

[0082] Crop / variety adjustment: consider drought-tolerant varieties for the next round of planting.

[0083] 4) Strong memory, indicating the need for rapid water deficit recovery, reduced system vulnerability, and initiation of long-term recovery and reconstruction. Measures to be taken as follows:

[0084] Emergency short-term (0-3 months):

[0085] Irrigation: Irrigation in key production areas to reach 110-130% of crop water requirement (priority to ensure minimum safe yield).

[0086] Water resources allocation: Initiate emergency water release of regional reservoirs / regulation projects.

[0087] Land management: Temporary fallow or cover for high-risk plots to prevent further degradation.

[0088] Medium and long term (3 months to several years):

[0089] Soil repair: Large-scale application of organic matter (5-10 t / ha) and amendments to improve water retention; promote deep tillage / ridge combination to enhance infiltration and water retention.

[0090] Replanting / rotation adjustment: Promote adjustment of crop structure in arid areas (e.g. corn→wheat / drought-tolerant crops).

[0091] Example 2:

[0092] The drought risk assessment method in humid and semi-humid areas described in the present invention comprises: calculating the memory influence value M of the severe agricultural drought according to the method of Example 1, and taking the drought memory influence intensity as one of the influence factors for drought risk assessment in certain areas of humid and semi-humid areas.

[0093] Specifically, the memory influence value M of the severe agricultural drought is standardized as a memory sub-index S_memory and included in the drought risk comprehensive assessment system. Based on this index, a risk grading and spatial identification method is constructed to accurately identify and dynamically monitor vulnerable areas and high-risk farmland.

[0094] R_total=w1*S_intensity+w2*S_duration+w3*S_memory+w4* S_vulnerability,

[0095] Where R_total is the total risk score, S_intensity is the drought intensity sub-index, S_duration is the drought duration sub-index, S_memory is the memory sub-index, S_vulnerability is the regional vulnerability sub-index, and w1-w4 are the weight coefficients corresponding to each index.

[0096] According to the total risk score R_total, the high-value risk area is identified by spatial clustering method and marked as vulnerable area and high-risk farmland.

[0097] In drought monitoring and risk management, ignoring the memory of extreme drought may underestimate the risk of subsequent drought, leading to delayed early warning and policy response. Incorporating the memory of extreme drought into the drought risk assessment framework can more accurately identify vulnerable areas and high-risk farmland, providing scientific basis for agricultural insurance, food reserves and regional disaster reduction policies. By incorporating the memory effect of extreme agricultural drought into the risk assessment framework, the above embodiments can overcome the shortcomings of traditional assessment methods that only focus on drought intensity and duration, and achieve quantitative characterization of the cumulative effect and lagging impact of drought. This method can significantly improve the identification accuracy of vulnerable areas and high-risk farmland, providing scientific support for drought monitoring, risk management and policy response.

[0098] Embodiment 3:

[0099] In one embodiment, a computer program product is provided, comprising computer programs / instructions that, when executed by a processor, implement the drought risk assessment method for humid and sub-humid regions described above.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to generate a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the functions specified in the flowchart Figure 1 one flowchart or multiple flowcharts and / or one block or multiple blocks in the flowchart Figure 1 steps of the functions specified in the flowchart or flowcharts and / or blocks.

Claims

1. A method for responding to a disaster in a humid or semi-humid region, characterized by, The application relates to a method for identifying a super agricultural drought event and implementing disaster response measures based on a daily soil moisture deficit index. The method comprises the following steps: calculating a daily soil moisture deficit index sequence according to a daily soil moisture history time sequence of a research area; According to the daily soil moisture deficit index sequence, δ quantile value of daily soil moisture deficit index in the pre-drought period SWDI T0, δ , and δ quantile value of daily soil moisture deficit index in the stable memory period SWDI T3, δ are calculated; δ is a preset value, 0 < δ ≤ 25%; the ratio of SWDI T3, δ and SWDI T0, δ is calculated to obtain an extra-large agricultural drought memory influence value; according to the extra-large agricultural drought memory influence value and drought memory influence grade division, the drought memory influence intensity is determined; identifying a super agricultural drought event according to the daily soil moisture deficit index sequence, and identifying a drought pre-period and a stable memory period in a complete process corresponding to the super agricultural drought event; the drought pre-period is a stage before a drought starting moment, and the stable memory period is a stage after a drought ending moment and when a soil moisture deficit index value reaches a relatively stable stage; implementing disaster response measures according to a drought memory influence strength; ， wherein SWDI is the soil water deficit index value, is the actual soil water content, is the field capacity of the soil, represents the effective soil water content corresponding to the soil texture; the calculation formula of the daily soil moisture deficit index is as follows: the super agricultural drought event is identified according to an agricultural drought grade division of the soil moisture deficit index, and the super agricultural drought event is identified if a soil moisture deficit index value SWDI is less than or equal to -10; 2. The method for responding to a disaster in a humid or semi-humid region according to claim 1, characterized in that: the drought pre-period and the stable memory period in the complete process corresponding to the super agricultural drought event are identified, the drought starting moment is identified if the soil moisture deficit index value is less than 0 in the super agricultural drought event, the drought pre-period T0 is identified before the drought starting moment; the drought ending moment is identified if the soil moisture deficit index value is greater than 0 after the drought starting moment, the drought period T1 is a stage from the drought starting moment to the drought ending moment, the fluctuation rate of the soil moisture deficit index value after the drought ending moment is calculated, the starting moment of the stable memory period is identified when the fluctuation rate of the soil moisture deficit index value drops to a minimum value, and the moment after the starting moment is recorded as the memory stable period T3; the fluctuation rate of the soil moisture deficit index value is calculated by using a rolling standard deviation method, and the time window length is several months.

3. The method of claim 1, wherein the method is characterized by: the daily soil moisture history time sequence of the research area comprises daily soil water content history time sequences, daily soil field water capacity history time sequences and daily soil effective water content history time sequences of each research site in the research area with the same soil texture. the drought memory influence strength is determined according to a super agricultural drought memory influence value and a drought memory influence grade division, and the drought memory influence strength comprises: if M falls in (1, 1.25], it is low memory; if M falls in (1.25, 1.5], it is medium memory; If M falls within (1.75, + (This indicates an extremely strong memory.) 4. The method of claim 3, wherein the method is characterized by: if M falls in (1.5, 1.75], it is strong memory; the disaster response measures are implemented according to the drought memory influence strength, and the disaster response measures comprise: (1) the drought disaster response measures corresponding to the low memory comprise: irrigation, fertilization and agronomy are carried out according to a conventional plan, and the root zone soil water content is recorded regularly; (2) the drought disaster response measures corresponding to the medium memory comprise: the soil water content of a soil profile 0-30cm and 30-60cm is monitored, irrigation is focused on according to crop demand, and irrigation resources are used for main crops or crops in a key growth period when irrigation resources are limited; the standard water requirement of non-key period or secondary crops is reduced by 10-15%; (3) the drought disaster response measures corresponding to the strong memory comprise: the standard water requirement of main crops in the key growth period is ensured to reach 100-110%; the standard water requirement of non-key period or secondary crops is reduced by 20-40%; hierarchical water supply is adopted when irrigation resources are limited; and the underground water extraction amount is allowed to be increased. (4) Extremely strong memory corresponding drought disaster response measures include: 0-3 months short-term, only to ensure key production area irrigation to achieve 110-130% of the standard water demand, start the emergency release of regional reservoirs or storage works; for some plots to implement temporary fallow or cover, to prevent further degradation; 3 months to several years medium-term, application of organic matter and amendments to improve water holding capacity; the next round of planting adjustment crop structure, selection of drought-tolerant varieties.

5. A method for assessing the risk of drought in humid and sub-humid areas, characterized in that, Including: According to the daily soil moisture history time series of the study area, the daily soil water deficit index sequence is calculated; According to the daily soil water deficit index sequence, the super large agricultural drought event is identified, and the corresponding complete process of the drought early stage and the stable memory period are obtained; the drought early stage is the stage before the drought starts, and the stable memory period is the stage after the drought ends, and the soil water deficit index value reaches a relatively stable stage; According to the daily soil moisture deficit index sequence, the δ quantile value of the daily soil moisture deficit index in the pre-drought period is calculated as SWDI T0, δ , and the δ quantile value of the daily soil moisture deficit index in the stable memory period is calculated as SWDI T3, δ ; δ is a preset value, 0 < δ ≤ 25%; the ratio of SWDI T3, δ and SWDI T0, δ is calculated to obtain the super agricultural drought memory influence value; The super large agricultural drought memory influence value is taken as one of the influence factors, and the drought risk comprehensive assessment of the study area is carried out, and the regions with drought risk are identified according to the evaluation results; The super large agricultural drought memory influence value is taken as one of the influence factors, and the drought risk comprehensive assessment of the study area is carried out, including: the super large agricultural drought memory influence value is standardized to obtain the memory sub-index; the memory sub-index is included in the calculation of the total score of the risk, and the calculation formula of the total score of the risk is: R_total=w1*S_intensity+w2*S_duration+w3*S_memory+w4* S_vulnerability, Wherein, R_total is the total score of the risk, S_intensity is the drought intensity sub-index, S_duration is the drought duration sub-index, S_memory is the memory sub-index, S_vulnerability is the regional vulnerability sub-index, and w1-w4 are the weight coefficients corresponding to each index.

6. A computer program product comprising computer programs and / or instructions, characterized in that, The computer program and / or instructions are executed by the processor to realize the drought risk assessment method in humid and semi-humid areas in claim 5.

Images