NL2032761A

NL2032761A - Method for constructing scene point for surface water pesticide exposure model of dryland crops in watershed

Info

Publication number: NL2032761A
Application number: NL2032761A
Authority: NL
Inventors: Zhu Xin; Long Tao; Liu Renbin; Jiang Jinlin; Shi Jiaqi; Liao Jianhua; Cheng Yan
Original assignee: Nanjing Institute Of Environmental Sciences Mini Of Ecology And Environment
Priority date: 2022-02-21
Filing date: 2022-08-16
Publication date: 2023-08-25
Also published as: CN114529199A; NL2032761B1

Abstract

The present disclosure discloses a method for constructing a scene point for a surface water pesticide exposure model of dryland crops in a watershed, which includes obtaining land use type and distribution of a target watershed, obtaining meteorological data and soil texture data where the watershed is located to determine specific locations and crop types of the scene point, and determining a final scene point from preliminary screened alternative scene points based on factors such as expert decision— making and judgment. By Ineans of the construction. method, of a scene point for an exposure model, a large number of collected meteorological data and soil quality data are screened to obtain a small number of alternative scene points, then a representative scene point is finally determined by recalculating and analyzing the small number of alternative scene points.

Description

METHOD FOR CONSTRUCTING SCENE POINT FOR SURFACE WATER PESTICIDE

EXPOSURE MODEL OF DRYLAND CROPS IN WATERSHED

TECHNICAL FIELD

The present disclosure relates to the technical field of sur- face water pesticide exposure of dryland crops in a watershed, in particular to a method for constructing a scene point for a sur- face water pesticide exposure model of dryland crops in watershed.

BACKGROUND ART

Due to abundant water resources, large-scale crops are usual- ly planted in a watershed, and the crops are sprayed with pesti- cides many times during the growth cycle. After being used in the field, a large part of pesticides will enter the soil, run off in- to the surface water with the surface runoff or leaching into the groundwater with the soil pore water and flow into rivers, which causes great pollution to the river and the downstream ecology.

An exposure scene is a scene about how the exposure occurs, which includes various environmental conditions, risk source fea- tures and various activities that lead to the exposure when the exposure occurs. The exposure scene is a combination of various conditions related to agricultural production and pesticide use in a certain region, and is indispensable when using a model for ex- posure assessment. Without scene information, the model will not be able to run. The construction of the exposure scene is a highly comprehensive systematic work that requires comprehensive consid- eration of various factors. Therefore, the construction of the ex- posure scene is one of the key points of pesticide ecological risk assessment. A standard exposure scene represents typical “the re- alistic worst case” of a region, that is, the actual conditions that are most conducive to the pesticide pollution. The construc- tion of the standard exposure scene follows certain principles, if pesticides have low risks under the standard exposure scene, then pesticides should be safe to apply under other actual conditions.

The dryland crop-surface water exposure scene simulates the entry of pesticides into surface water after dryland crop application.

Scene information includes land use data, topographic data, mete- orological data, scil data, crop data and surface water related parameters of dryland crops.

SUMMARY

In view of the above problems, the present disclosure aims to provide a method for constructing a scene point for a surface wa- ter pesticide exposure model of dryland crops in a watershed, in which, alternative scene points compliant with principles are screened by using meteorological data and soil data, and targeted pesticide exposure scene points along the watershed is determined, so that the simulation of pesticide exposure in a small region of the exposure scene points and the selection of a key pesticide va- riety list are carried out in the future.

In order to achieve the above-mentioned purpose, the tech- nical solutions adopted in the present disclosure are as follows: a method for constructing a scene point for a surface water pesti- cide exposure model of dryland crops in a watershed includes fol- lowing steps: 1) obtaining a land use distribution map in the watershed; 2) principles of constructing the scene point, wherein the principles include a site selection principle, a crop selection principle and a water body selection principle; 3) constructing a scene a, preliminarily screening the scene point: calculating per- centile and protection degree for each division region of the wa- tershed according to soil characteristics and precipitation condi- tions, and determining one or more alternative scene points pre- liminarily in the watershed according to collected precipitation and soil organic matter content of each division region; and b, determining the scene point: determining a specific loca- tion and a crop type of the scene point according to a crop plant- ing area, the protection degree and soil texture; and in the al- ternative scene points screened preliminarily, determining, based on expert decision-making and judgment, a final scene point ac- cording to planting situation, the soil texture and soil species distribution factors of a specific dryland crop.

Further, in the step of formulating the principle of con- structing the scene point, the site selection principle includes: annual precipitation: selecting a region with 70th to 90th percentile annual precipitation range; soil organic matter: se- lecting a region with 10th to 30th percentile organic matter range; soil texture: selecting a region with loam or moderate clay; the crop selection principle includes: selecting the dryland crops with a larger planting area in the watershed, or selecting local characteristic and typical crops; and the water body selection principle: water body types include ponds and rivers.

Further, the sub-step of preliminarily screening the scene point for constructing the scene includes: calculating percentile of the precipitation and the organic matter of various soil spe- cies by using a Percentile function, calculating the 70th to 90th percentile annual precipitation range and the 10th to 30th percen- tile organic matter range, and screening out points compliant with the protection degree around 95th as the alternative scene points.

Further, in the sub-step of preliminarily screening the scene point, a formula for calculating the protection degree is:

P=1-X{(1-Y) wherein, X represents percentile of the organic matter; Y represents percentile of the precipitation.

Further, in the sub-step of determining the scene point for constructing the scene, principles of the expert decision-making and judgment include: whether the soil texture is the loam or the moderate clay; whether the crop planting area is one of main local dryland crop planting regions, and is representative of crop planting situation in an entire region; and whether the construct- ed scene point meets the protection degree around 95th.

The beneficial effects of the present disclosure are: by means of the construction method of a scene point for an exposure model, a large number of collected meteorological data and soil quality data are screened to obtain a small number of alternative scene points, then, a representative scene point is finally deter-

mined by recalculating and analyzing the small number of alterna- tive scene points. On this basis, in the collection of pesticide varieties in the later period, the detailed collection and analy- sis of soil quality data is greatly reduced, so that in a rela- tively short period of time, the representative pesticide exposure varieties in the watershed are obtained, and the ecological envi- ronment of the watershed can be duickly restored by replacing or stopping their use in a targeted manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of constructing a scene point for a surface water pesticide exposure model of dryland crops in a wa- tershed of the present disclosure; and

FIG. 2 is a land use distribution map in Shaying watershed of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to enable those skilled in the art to better under- stand the technical solutions of the present disclosure, the tech- nical solutions of the present disclosure are further described below with reference to the accompanying drawings and embodiments.

With reference to a method for constructing a scene point for a surface water pesticide exposure model of dryland crops in a wa- tershed shown in FIG. 1, taking Shaying River as an example, a method for constructing a scene point for an exposure model in- cludes the following steps: 1) obtaining a land use distribution map in the watershed: the land use distribution map in Shaying River watershed is ob- tained from Resource and Environmental Science Data Center of In- stitute of Geographic Sciences and Natural Resources Research,

Chinese Academy of Sciences (see FIG. 2), land use data in the wa- tershed is analyzed, and the land use type in the study region is mainly dryland. 2) principles of constructing the scene point: when con- structing exposure scene, the general principle is the principle of “the realistic worst case”, that is, when determining the fea- tures of each factor included in the scene, it is necessary to se-

lect some “worst” feature that exists in reality as much as possi- ble, that is, the feature most conducive to pesticide pollution.

However, it should be noted that the “worst” herein does not refer to extremely bad conditions, but relatively bad conditions that 5 exist in reality. Only in this way can the obtained exposure value not be too conservative, and the high-level assessment can reflect its significance.

In the scene point construction of a surface water pesticide exposure model of dryland crops in the Shaying River watershed, on the basis of following the general principle, combined with the characteristics of dryland crops and surface water, some specific principles need to be followed in the actual construction, which include: a site selection principle: ® annual precipitation: regions with high annual precipita- tion (within 70th to 90th percentile precipitation range, the larger the average annual precipitation, the easier it is to cause pesticides to enter surface water bodies with surface runoff, or leaching into groundwater) are selected; 2) soil organic matter: regions with low organic matter con- tent (within 10th to 30th percentile organic matter range, the or- ganic matter content is low, the pesticides are not easy to de- grade, and it is easy to cause the pesticides to enter the surface water) are selected; 3) soil texture: loam or moderate clay (for loam or moderate clay, pesticides are not easily leached into groundwater, but are more likely to enter surface water with surface runoff; for sand, loam or sandy soil, pesticides are easily leached into groundwa- ter) is selected. a crop selection principle: the dryland crops with a larger planting area in the Shaying

River watershed, such as common wheat, corn, peanuts, rapeseed, fruit trees, etc., are selected. At the same time, considering the representativeness of the scene, local characteristic and typical crops are also selected. a water body selection principle:

Commonly considered water types include ponds, rivers and so on. In the Shaying River watershed, the water body type of river is mainly considered. 3) constructing a scene a, preliminarily screening the scene point: soil data of counties and cities in the Shaying River watershed is collected from “China Soil Database” of the Nanjing Institute of Soil Sci- ence, Chinese Academy of Sciences, and basic soil characteristics of all counties and cities in the Shaying River watershed are col- lected, including: province, county and city, soil species name, soil subtype, land use, soil texture, soil species area and organ- ic matter content. Soil species data whose land use is dryland in the watershed is screened out, and the average organic matter in the topsoil layer of each soil species is calculated. A total of 81 soil species meet the requirements. See Table 1 for soil spe- cies data (partial data).

Table 1 Soil species data of counties and cities in Shaying

River watershed area average county and Soil species soil tex-

Province soil subtype (Hec- soil organic city name ture tares) matter(%)

Henan Xuchang Black soi! Chao soil 42333.3 SiC 0.99

Lingbao stand

Henan xuchang cinnamon soil 219533.3 SCL 0.86 loess

Less ginger calcareous

Henan xuchang grey black sand ginger 22533.3 LC 1.42 ginger soil black soil

Young cinna-

Henan xuchang cinnamon soil 42266.7 SiCL 0.82 mon soil

Henan xuchang silt Chao soil 5247333 LC 1.07

Dehydrated two- Dehydrated

Henan Yanling 132133.3 CL 0.8 component soil soil

Henan Zhengzhou white surface calcareous 163600 SCL 1.16 soil cinnamon soil

Thin hemp Neutral

Henan Zhengzhou bone stone coarse bone 1159000 SL 3.11 soil soil

Irrigation

Henan Zhengzhou thin tidal clay 33666.7 SiC 1.17

Chao soil

Gangging Dehydrated

Henan Zhengzhou 59666.7 LS 0.41 sandy soil soil

Red stiff petal

Henan Zhengzhou red clay 155733.3 LC 0.72 soil

Thick silt tide Irrigation

Henan Zhengzhou 22733.3 SiC 1.09 clay Chao soil

Calcium

Henan Zhengzhou Ballast soil coarse bone 28533.3 SL 1.25 soil

Live red stiff

Henan Zhengzhou red clay 57200 LC 1.47 petal soit

Ginger lying

Henan Zhengzhou cinnamon soil 27666.7 CL 0.89 loess

Lingbao stand

Henan Zhengzhou cinnamon soil 219533.3 SCL 0.86 loess calcareous

Henan Zhengzhou loessial soil 138000 SiCL 0.95 cinnamon soil

Mongolia gold

Henan Zhengzhou Chao soil 21066.7 L 0.87 soil

Neutral gravel ballast

Henan Zhengzhou coarse bone 29733.3 SL 2.13 soil soil

Wet light Salinized

Henan Zhengzhou 34466.7 SCL 0.77 saline soil Chao soil

Warm and

Salinized

Henan Zhengzhou medium saline 4133.3 CL 0.70

Chao soil soil

Henan Zhengzhou lying loess cinnamon soll 71800 CL 1.12

Little Mongo-

Henan Zhengzhou Chao soil 10600 SCL 0.6 lia gold soil

Henan Zhengzhou Waist sand Chao soil 8666.7 L 0.83

- wattw- compound soit

Henan Zhengzhou Waist sand silt Chao soil 13800 LC 1.07

Ginger lying

Henan Zhengzhou cinnamon soil 27666.7 CL 0.89 loess

Lingbao stand

Henan Zhengzhou cinnamon soil 219533.3 SCL 0.86 loess calcareous

Henan Zhengzhou loessial soil 138000 SiCL 0.95 cinnamon soil “Note: meaning of the soil tezture includes, C: clay; S: sand;

L: loam; and Si: silty soil.

Precipitation data: the data comes from the “China Ground Da- ta 1981-2010 Climate Standard Value Dataset” or local meteorologi- cal administration, and the data includes district station number, province, station name, longitude, latitude and average annual precipitation over 30 years. The average annual precipitation of 18 counties and cities in the Shaying watershed is collected, see

Table 2 (partial data).

Table 2 Average annual precipitation of counties and cities in the Shaying watershed “disteice

Average annual station Province station name longitude latitude precipitation (mm) number 57075 Henan Ruzhou 112.52 3410 63457 57078 Henan Ruyang 112.28 34.09 674.68 57083 Henan Zhengzhou 113.39 34.43 639.03 57089 Henan Xuchang 113.52 34.02 728.37 57095 Henan Yanling 114.09 34.07 738.74 57099 Henan Taikang 114.51 34.04 794.00 57162 Henan Songxian 112.05 34.09 691.14 57171 Henan Pingdingshan 113.07 33.46 925.63 57186 Henan Luohe 114.03 33.36 811.61 57192 Henan Huaiyang 114.51 33.44 788.93 57194 Henan Shangcai 114.16 33.17 864.15 57196 Henan Xiangxcheng 114.52 33.28 796.38 58100 Henan Dancheng 115.1 33.39 778.37

58101 Henan Luyi 115.29 33.52 755.99 58108 Anhui Jieshou 115.2 33.14 898.54 58109 Anhui Taihe 115.37 33.11 904.18 58203 Anhui Fuyang 115,44 32.52 966.47

Percentile of the precipitation and the organic matter of various soil species are calculated by using a Percentile function to obtain the 10th to 30th percentile organic matter range and the 70th to 90th percentile annual precipitation range in the water- shed.

Protection degree represents the conservation degree of the scene point and the protection of crops. If protection degree is too high, simulation results are too conservative and meaningless, and if protection degree is too low, most crops cannot be protect- ed. Therefore, the protection degree around 95th is selected. The protection degree of soil species is calculated according to per- centile of soil organic matter and corresponding percentile of the precipitation, and a calculation formula is:

P=1-X(1-Y) wherein, X represents the percentile of the organic matter; Y represents the percentile of the precipitation.

Table 3 Protection degree of some scene points average average protec- area soil soil annual

Prov- county Soil species tion soil subtype {Hec- tex- organic precipita- ince and city name de- tares) ture matter ta- gree(%) (%) tion{mm)

Ping- grey-green

Henan Grey Chao soil 20400 SL 0.45 925.63 0.995 dingshan sandy soil

Shuangmiao Sticky yellow

Anhui Fuyang 44333.3 CL 0.69 966.47 0.993

Chao Liver Soil cinnamon soil

White ginger sand ginger

Anhui Fuyang 233666.7 CL 0.76 966.47 0.988 soil black soil

Zheng- Gangqing Dehydrated

Henan 59666.67 LS 0.41 639.03 0.988 zhou sandy soi soil

Wolong bot-

Henan Luohe tom sand-ash Grey Chao soit 6333.3 SCL 0.55 811.61 0.983 two-compound soil

Dai'an Chao Sticky yellow

Anhui Fuyang 102000 LC 0.82 966.47 0.983

Liver Soil cinnamon soil

Little Mongolia

Henan Zhoukou Chao soi! 10600 SCL 0.60 823.80 0.982

Gold Soi!

Medium alka- Alkaline Chao

Anhui Tathe 5066.7 SiC 0.66 904.18 0.981 fine silt soil

Ping- brown lying

Henan cinnamonsoil 127600 SCL 0.72 925.63 0.973 dingshan loess

Suwang sand

Anhui Fuyang core two com- Chao soil 40466.7 CL 1.09 966.47 0.961 bined soil

Bottom sand dehumidifica- Dehydrated

Henan Luohe 2800 CL 0.68 811.61 0.961 tion two- soil component soil

Ping- Yellow cin-

Henan stiff loess 4857333 CL 0.80 925.63 0.960 dingshan namon soil

Anhui Fuyang black mud soil Chao soil 285800 C 1.15 966.47 0.956 zheng- Little Mongolia

Henan Chao soil 10600 SCL 0.60 639.03 0.953 zhou Gold Soil black ginger sand ginger

Anhui Fuyang 445866.7 LC 1.18 966.47 0.951 soil black soil 16 calculated points that meet the protection degree around 95th are taken as alternative scene points, and the basic charac- teristics of the alternative scene points are shown in Table 4.

Table 4 Basic characteristics of alternative scene points average average protec- area soil soil annual

Prov- county Soil species tion soil subtype (Hec- tex- organic precipi- ince andcity name de- tares) ture matter tation gree(%) {%) {mm)

Shuangmiao Sticky yellow

Anhui Fuyang 44333.3 CL 0.69 966.47 0.993

Chao Liver Soil cinnamon soil

White ginger sand ginger

Anhui Fuyang 233666.6 CL 0.76 966.47 0.988 soil black soil

Zheng- Gangging Dehydrated

Henan 59666.6 LS 0.41 639.03 0.988 zhou sandy soil soil

Henan Luohe Wolong bot- Grey Chao soil 6333.3 SCL 0.55 811.61 0.983 tom sand-ash two- compound soil

Dai'an Chao Sticky yellow

Anhui Fuyang 102000 LC 0.82 966.47 0.983

Liver Soil cinnamon soil

Zhouko Little Mongo-

Henan Chao soil 10600 SCL 0.60 823.80 0.982 u lia Gold Soil

Medium Alkaline Chao

Anhui Taihe 5066.6 SiC 0.66 904.18 0.981 alkaline silt soil

Ping- brown lying

Henan dingsha cinnamon soil 127600 SCL 0.72 925.63 0.973 loess n

Suwang sand

Anhui Fuyang core two Chao soil 40466.6 CL 1.09 966.47 0.961 combined soil

Bottom sand dehumidifica-

Dehydrated

Henan Luohe tion two- | 2800 CL 0.68 811.61 0.961 soi component soil

Ping- yellow cinna-

Henan dingsha stiff loess 4857333 CL 0.80 925.63 0.960 mon soil n

Anhui Fuyang black mud soil Chao soil 285800 C 1.15 966.47 0.956

Zheng- Little Mongo-

Henan Chao soil 10600 SCL 0.60 639.03 0.953 zhou lia gold soil black ginger sand ginger

Anhui Fuyang 445866.6 LC 1.18 966.47 0.951 soil black soil

Ping- calcareous

Henan dingsha loessial soil 138000 SiCL 0.95 925.63 0.917 cinnamon soil n

Songxia brown lying

Henan cinnamonsoil 127600 SCL 0.72 691.14 0.916 n loess b, determining the scene point:

The scene is a composite of various information related to crops. Therefore, the determination of the scene point needs to be based on a large amount of basic data, combined with judgment and decision-making of experts in the industry. When determining the scene, first whether the soil texture is loam or moderate clay is considered, because in the loam or moderate clay, pesticides are not easy to degrade, and pesticides easily enter surface water with surface runoff; secondly, whether the crop planting area of the scene is one of the main local dryland crop planting regions, and is representative of crop planting situation in an entire re- gion are considered; finally, the constructed scene point meets the protection degree around 95th according to the basic princi- ples of scene construction. According to the principles of con- structing the scene, with the above screening methods, Ping- dingshan in Henan is finally determined as a dryland crop-surface water pesticide exposure scene point in the Shaying River water- shed by means of field research. The basic features of the scene point are shown in Table 5.

Table 5 Basic features of scene point average annual county Soil spe- soil sub- soil tex- average soil organ- protection

Province precipitation ( and city cies type ture ic matter (%) degree (%) mm) yellow

Ping-

Henan stiff loess cinnamon CL 0.80 925.63 96.0 dingshan soil

In the embodiment, based on the general principle of “the re- alistic worst case”, scene point construction of dryland crops in the Shaying River watershed is carried out, which mainly includes preliminarily screening the scene point and determining the scene point. Finally, after a comprehensive judgment, Pingdingshan in

Henan is determined as a surface water pesticide exposure scene point for dryland crops in the Shaying River watershed, whose pro- tection degree is 96th.

The above shows and describes the basic principles, main fea- tures and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not lim- ited by the above-mentioned embodiments, and the foregoing embodi- ments and descriptions in the specification are only intended to illustrate the principles of the present disclosure. Without de- parting from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes all fall within the scope of the claimed disclosure.

The claimed scope of the present disclosure is defined by the ap- pended claims and their equivalents.

Claims

CONCLUSIONS

A method for constructing a scene point for a surface water pesticide exposure model of dry crops in a river basin, comprising the steps of: 1) obtaining a land use distribution map in the river basin; 2) principles for constructing the scene point, where the principles include a site selection principle, a crop selection principle, and a water body selection principle; 3) constructing a scene a, preliminary screening of the scene point: calculating the percentile and degree of protection for each sub-area of the watershed based on soil characteristics and precipitation conditions, and preliminary determination of one or more alternative scene- points in the watershed based on accumulated precipitation and soil organic matter content of each sub-area; and b, determining the scene point: determining a specific location and a crop type of the scene point according to a crop planting area, the degree of protection and the soil texture; and in the pre-screened alternate scene points, determining, based on expert decision-making and judgment, a final scene point based on the plant situation, soil texture, and soil species distribution factors of a specific dryland crop.

A method of constructing a scene point for a pesticide exposure model in surface water of dry crops in a river basin according to claim 1, wherein: in the step of the principles of constructing the scene point, the site selection principle includes: annual precipitation: selecting from an area with 70th to 90th percentile annual precipitation range; soil organic matter: selecting an area with a range of 10th to 30th percentile organic matter; soil texture: select an area with loam or moderate clay; the crop selection principle includes: selecting the dry

washing with a larger plant area in the catchment area, or selecting local characteristic and typical crops; and the water body selection principle: water body types include ponds and rivers.

The method of constructing a scene point for a surface water pesticide exposure model of dry crops in a river basin as claimed in claim 2, wherein the sub-step of preliminary screening the scene point for constructing the scene comprises: calculating of the percentile of precipitation and organic matter of different soil types by using a percentile function, calculating the 70th to 90th percentile annual precipitation range and the 10th to 30th percentile organic matter range, and screening points that meet the degree of protection around the 95th as alternate scene points.

A method of constructing a scene point for a pesticide exposure model in surface water of dry crops in a river basin as claimed in claim 3, wherein, in the sub-step of preliminary screening of the scene point, a formula for calculating the degree of protection is: P =1-X(1-Y) where X represents the percentile of the organic matter; Y stands for percentile of rainfall.

A method of constructing a scene point for a surface water pesticide exposure model of dry crops in a river basin as claimed in claim 4, wherein, in the sub-step of determining the scene point for constructing the scene, principles of expert decision-making and assessment include: whether the soil texture is the loam or the moderate clay; whether the crop planting area is one of the main local dry crop planting regions, and is representative of the situation of crop planting in an entire region; and whether the constructed scene point meets the degree of protection around 95e.