# Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Overview of the Studying Area

#### 2.1. The Water Resources System in Lancang County

^{2}. The average maximum flow for many years is approximately 110 m

^{3}/s. Lancang County is rich in groundwater resources, and the exploitable groundwater resources are higher than the national average leve. The karst water content is particularly rich, with the old Zhutang factory—Mianxupu—Greenland River—Ganhe—Tianshengqiao underground river being the most significant. The existing forms of groundwater mainly include pore water, bedrock fissure water, and karst fissure water. Porewater mainly occurs in Quaternary clay, silty clay, and sandy soil. It mainly receives infiltration recharge from rainwater, and discharges runoff to low-lying valleys and underlying bedrock. The dynamic change of groundwater is large, the water richness is weak, and the water permeability is poor [11]. Bedrock fissure water mainly occurs in volcanic rocks, metamorphic rocks, sand, and shale, with weak to moderate water richness and weak to moderate water permeability. Relatively speaking, the groundwater content in volcanic rocks and sandstones is relatively rich. Karst fissure water mainly occurs in limestone, dolomitic limestone, dolomite, and calcareous dolomite. On the basis of the development of structural fissures, dissolved fissures, dissolved pores, karst caves, and underground rivers are relatively developed, and the karst water that occurs is rich in karst water. The water is strong, and it is a medium to strong aquifer.

#### 2.2. The Agricultural Resources System in Lancang County

## 3. Methods

#### 3.1. Type-2 Triangular Fuzzy Sets

_{1}, h

_{2}, h

_{3}, … are the values of the fuzzy parameters c

_{ij}, respectively, and there exists at least one pair of i ≠ j such that h

_{i}≠ h

_{j}. The fuzzification process of the fuzzy parameter c

_{ij}is as follows:

- (1)
- Calculate the relative distance matrix D = |d
_{ij}|_{t}_{×t}, where d_{ij}= |h_{i}− h_{j}|; - (2)
- Calculate the average of relative distances $\overline{{d}_{i}}={\displaystyle {\sum}_{i=1}^{t}{d}_{ij}}/(t-1)$;
- (3)
- Introduce the pairwise comparison number p
_{ij}, ${p}_{ij}=\overline{{d}_{j}}$/$\overline{{d}_{i}}$, the pairwise matrix P = |p_{ij}|_{t}_{×t}; - (4)
- Calculate the real weight w
_{j}of h_{j}, ${w}_{j}=1/{{\displaystyle \sum}}_{i=1}^{t}{p}_{ij}$; - (5)
- Calculate the core ${a}_{2}$ of the fuzzy number, ${a}_{2}={{\displaystyle \sum}}_{i=1}^{t}{w}_{i}{h}_{i}$;
- (6)
- Define the mean deviation σ, choose and calculate $s={{\displaystyle \sum}}_{i=1}^{t}{w}_{i}\left|{h}_{i}-{a}_{2}\right|$ to replace σ;
- (7)
- Define η as the distance from the left end to the right end of the fuzzy number, select and calculate $\overrightarrow{\eta}=\frac{{a}_{2}-{h}^{l}}{{h}^{r}-{a}_{2}},\text{}{h}^{l}={{\displaystyle \sum}}_{i\in A}{w}_{i}{h}_{i}$/${{\displaystyle \sum}}_{i\in A}{w}_{i}$, ${h}^{r}={{\displaystyle \sum}}_{i\in B}{w}_{i}{h}_{i}$/${{\displaystyle \sum}}_{i\in B}{w}_{i}$, A = {i|h
_{i}< a_{2}, i ∈ I}, B = {i|h_{i}> a_{2}, i ∈ I}; - (8)
- Calculate a
_{1}= a_{2}− 3(1 + η) ησ/(1 + η^{2}), a_{3}= a_{2}+ 3(1 + η) σ/(1 + η^{2}). From this, the traditional fuzzy number F = (a_{1}, a_{2}, a_{3}) of c_{ij}can be obtained.

_{1}, α

_{2}, and α

_{3}are the primary membership degrees of $\tilde{\xi}$, and m, 1.0, and n are the corresponding secondary membership degrees, respectively.

#### 3.2. Chance-Constrained Programming (CCP)

_{ij}is deterministic and b

_{i}is random (for all p

_{i}values), (ii) a

_{ij}and b

_{i}are separate random coefficients, with p

_{i}$\ge $ max

_{r}

_{=1,2,…,R}(1 − ${q}_{r}$), where q

_{r}is the probability consorted with the realization r, or (iii) a

_{ij}and b

_{i}all have Gaussian distributions, with p

_{i}$\ge $ 0.5. When a

_{ij}is deterministic and b

_{i}is random for model (8), given the distribution function of b

_{i}(t) as F[b

_{i}(t)], then ${b}_{i}{\left(t\right)}_{i}^{\left(p\right)}={F}^{-1}\left({p}_{i}\right)$. According to the description of distribution function, we have:

_{ij}is deterministic and b

_{i}is random, the constraint becomes linearization:

_{i}, and the probability of defaulting constraint i. The problem with Equation (12) can only reflect the instance when A is deterministic. If both A and B are uncertain, the bunch of feasible constraints may become more complex. One potential method to deal with uncertainties in A, B and C is combining the type-2 fuzzy programming with the CCP framework, where type-2 fuzzy sets are used for reflecting the uncertainty in A and C [15].

#### 3.3. Type-2 Fuzzy Chance-Constrained Programming (T2FCCP) Construction and Its Solution

- (1)
- Surface water supply constraints$$\sum}_{j=1}^{J}CI{A}_{ij}\xb7\frac{C{I}_{ijt}}{C{E}_{i}\xb7F{E}_{i}}\le C{W}_{it}+S{R}_{i\left(t-1\right)$$$$Cr\left\{P\tilde{S}W\xb7{\displaystyle \sum}_{t=1}^{T}C{W}_{it}\le T\tilde{S}{W}_{i}\right\}\ge 1-{\lambda}_{c}$$
- (2)
- Groundwater supply constraints$$Cr\left\{{\displaystyle \sum}_{j=1}^{J}P\tilde{G}W\xb7WI{A}_{i}\xb7{\displaystyle \sum}_{t=1}^{T}\left(W{I}_{it}/FE\right)\le T\tilde{G}{W}_{i}\right\}\ge 1-{\lambda}_{w}$$
- (3)
- Water demand constraints$$\sum}_{j=1}^{J}(C{I}_{it}\xb7CI{A}_{ij}+W{I}_{it}\xb7WI{A}_{ij}+T{I}_{it}\xb7TI{A}_{ij})+{\delta}_{it}\xb7WT{Q}_{it}\xb7C{E}_{i}\xb7F{E}_{i}\xb7CI{A}_{ij}+E{\tilde{P}}_{it}^{I}\xb7\left(CI{A}_{ij}+WI{A}_{ij}+TI{A}_{ij}\right)\ge W{\tilde{R}}_{it}^{I$$
- (4)
- Water balance constraints$$S{R}_{i\left(t-1\right)}=S{R}_{i\left(t-2\right)}+C{W}_{i\left(t-1\right)}-{\displaystyle \sum}_{j=1}^{J}CI{A}_{ij}\xb7\left[C{I}_{ij\left(t-1\right)}/\left(C{E}_{i}\xb7F{E}_{i}\right)\right]S{R}_{i0}=0$$
- (5)
- Water distribution constraints$$\sum}_{j=1}^{J}\left[CI{A}_{ij}\xb7{\displaystyle \sum}_{t=1}^{T}C{I}_{ijt}+WI{A}_{ij}\xb7{\displaystyle \sum}_{t=1}^{T}W{I}_{ijt}+TI{A}_{ij}\xb7{\displaystyle \sum}_{t=1}^{T}T{I}_{ijt}+CI{A}_{ij}\xb7{\displaystyle \sum}_{t=1}^{T}({\delta}_{it}\xb7WT{Q}_{it}\xb7C{E}_{i}\xb7F{E}_{i})\right]\le \frac{1}{\mu}{\displaystyle \sum}_{j=1}^{J}({d}_{j}\times {P}_{j})$$
- (6)
- Cropping area constraints$$CI{A}_{min,ij}\le CI{A}_{ij}\le CI{A}_{max,ij}$$$$WI{A}_{min,ij}\le WI{A}_{ij}\le WI{A}_{max,ij}$$$$TI{A}_{min,ij}\le TI{A}_{ij}\le TI{A}_{max,ij}$$
- (7)
- Food security constraints$${{\displaystyle \sum}}_{j=1}^{n}[YA{G}_{ij}\xb7\left(CI{A}_{ij}+WI{A}_{ij}+TI{A}_{ij}\right)]\ge P{O}_{i}\xb7Pf$$
- (8)
- Water transfer constraints$${\epsilon}_{it},{\delta}_{it}=\{\begin{array}{c}0,C{I}_{ijt}\xb7CI{A}_{ij}+W{I}_{ijt}\xb7WI{A}_{ij}+T{I}_{ijt}\xb7TI{A}_{ij}+E{\tilde{P}}_{it}^{I}\ge W{\tilde{D}}_{min,it}^{I}\\ 1,C{I}_{ijt}\xb7CI{A}_{ij}+W{I}_{ijt}\xb7WI{A}_{ij}+T{I}_{ijt}\xb7TI{A}_{ij}+E{\tilde{P}}_{it}^{I}W{\tilde{D}}_{min,it}^{I}\end{array}$$
- (9)
- Structure constraints$$C{I}_{ijt}\ge 0;W{I}_{ijt}\ge 0;T{I}_{ijt}\ge 0;S{R}_{it}\ge 0;C{W}_{it}\ge 0;CI{A}_{ij}\ge 0;WI{A}_{ij}\ge 0;TI{A}_{ij}\ge 0$$
^{6}); $s{\tilde{c}}_{i}^{I}$, $G{\tilde{C}}_{i}^{I}$, $B{\tilde{C}}_{i}^{I}$, and $T{\tilde{C}}_{i}^{I}$ are the canal irrigation cost (yuan/m^{3}), well irrigation cost (yuan/m^{3}), reclaimed water treatment, distribution, operation and maintenance costs (yuan/m^{3}), and water resource dispatch cost (yuan/m^{3}), respectively; $CI{A}_{ij}$, $WI{A}_{ij},$ and $TI{A}_{ij}$ are the i tributary j crop canal irrigation area (hm^{2}), i tributary j crop well irrigation area (hm^{2}), and i tributary j crop reclaimed water irrigation area (hm^{2}), respectively; $P{\tilde{C}}_{ij}^{I}$ is the planting cost (yuan/hm^{2}); $C{I}_{ijt}$ and $W{I}_{ijt}$ are the net canal irrigation water volume (m^{3}/hm^{2}) and net well irrigation water volume (m^{3}/hm^{2}), respectively; $C{E}_{i}$ and $F{E}_{i}$ are the canal water utilization coefficient and effective use coefficient of field water, respectively; ${\epsilon}_{it}$ and ${\delta}_{it}$ are 0–1 variables; $WQ{T}_{it}$ and $WT{Q}_{it}$ are the reclaimed water quota and external water transfer quota (m^{3}/hm^{2}), respectively; $C{W}_{it}$ is the canal water supply (m^{3}); $S{R}_{i\left(t-1\right)}$ is the residual surface water (m^{3}); $P\tilde{S}W$ is the canal irrigation water intake coefficient; $T\tilde{S}{W}_{i}$ is the surface water availability (m^{3}); ${\lambda}_{c}$ is the confidence level of canal irrigation; $P\tilde{G}W$ is the well irrigation water intake coefficient; $T\tilde{G}{W}_{i}$ is the groundwater availability (m^{3}); $WI{A}_{i}$ is the well irrigation area of i tributary (hm^{2}); ${\lambda}_{w}$ is the confidence level of well irrigation; $E{\tilde{P}}_{it}^{I}$ is the effective precipitation; $W{\tilde{R}}_{it}^{I}$ is the water requirements to ensure the basic normal growth of crops (m^{3}); $\mu $ is the effective utilization rate of cultivated land irrigation water; ${d}_{j}$ is the irrigation water quota for the j th major crop (m^{3}/hm^{2}); ${P}_{j}$ is the proportion of the sown area of the j th major crop (%); $CI{A}_{max,ij}$ and $CI{A}_{min,ij}$ are the maximum canal irrigation area (hm^{2}) and minimum canal irrigation area (hm^{2}), respectively; $WI{A}_{max,ij}$ and $WI{A}_{min,ij}$ are the maximum well irrigation area (hm^{2}) and minimum well irrigation area (hm^{2}), respectively; $TI{A}_{max,ij}$ and $TI{A}_{min,ij}$ are the maximum reclaimed water irrigation area (hm^{2}) and minimum reclaimed water irrigation area (hm^{2}), respectively; $YA{G}_{ij}$ is the yield (kg/hm^{2}); $P{O}_{i}$ is the population for i tributary; $Pf$ is the food demand per capita (kg/capita); $W{\tilde{D}}_{min,it}^{I}$ is the minimum water requirement (m^{3}/hm^{2}).

#### 3.4. Harmony Evaluation Method

- (1)
- Single-index quantification. According to the relationship between the changes from small to large indicators and the changes in the harmony degree of their representations, they can be divided into positive indicators and reverse indicators. The positive index harmony degree increases with the increase of the index value; the reverse index harmony degree decreases with the increase of the index value. Suppose a, b, c, d, and e are the worst value, poor value, passing value, better value and optimal value of a positive or negative index, respectively. Using feature points (a, 0), (b, 0.3), (c, 0.6), (d, 0.8), and (e, 1), then the calculation formula of harmony degree of the positive index and the reverse index are, respectively:$${D}_{SIi}=\{\begin{array}{c}0,{x}_{i}{a}_{i}\\ 0.3\frac{{x}_{i}-{a}_{i}}{{b}_{i}-{a}_{i}},{a}_{i}\le {x}_{i}{b}_{i}\\ 0.3+0.3\frac{{x}_{i}-{b}_{i}}{{c}_{i}-{b}_{i}},{b}_{i}\le {x}_{i}{c}_{i}\\ 0.6+0.2\frac{{x}_{i}-{c}_{i}}{{d}_{i}-{c}_{i}},{c}_{i}\le {x}_{i}{d}_{i}\\ 0.8+0.2\frac{{x}_{i}-{d}_{i}}{{e}_{i}-{d}_{i}},{c}_{i}\le {x}_{i}{d}_{i}\\ 1,{e}_{i}\le {x}_{i}\end{array}$$$${D}_{SIi}=\{\begin{array}{c}1,{x}_{i}{e}_{i}\\ 0.8+0.2\frac{{d}_{i}-{x}_{i}}{{d}_{i}-{e}_{i}},{e}_{i}\le {x}_{i}{d}_{i}\\ 0.6+0.2\frac{{c}_{i}-{x}_{i}}{{c}_{i}-{d}_{i}},{d}_{i}\le {x}_{i}{c}_{i}\\ 0.3+0.3\frac{{b}_{i}-{x}_{i}}{{b}_{i}-{c}_{i}},{c}_{i}\le {x}_{i}{b}_{i}\\ 0.3\frac{{a}_{i}-{x}_{i}}{{a}_{i}-{b}_{i}},{b}_{i}\le {x}_{i}{a}_{i}\\ 0,{a}_{i}\le {x}_{i}\end{array}$$
- (2)
- Multi-indicator synthesis. Based on the calculation of the harmony degree of a single index, the harmony degree of each subsystem and the water resources–economic society–ecological environment system is calculated separately according to the method of weighted summation. Assume that the value of a quantitative index at time t is ${Y}^{i}\left(t\right)$, its harmony degree is ${D}_{SIi}\left({Y}^{i}\left(t\right)\right)$, then the water resources subsystem harmony degree (WHD), the economic and social subsystem harmony degree (SEHD), and the environmental subsystem harmony degree (EHD) are, respectively:$${D}_{WH}\left(t\right)={\displaystyle \sum}_{i=1}^{n1}{\omega}_{i}\times {D}_{SIi}\left({Y}_{1}^{i}\left(t\right)\right)$$$${D}_{SEH}\left(t\right)={\displaystyle \sum}_{i=1}^{n2}{\omega}_{i}\times {D}_{SIi}\left({Y}_{2}^{i}\left(t\right)\right)$$$${D}_{EH}\left(t\right)={\displaystyle \sum}_{i=1}^{n3}{\omega}_{i}\times {D}_{SIi}\left({Y}_{3}^{i}\left(t\right)\right)$$
- (3)
- Multi-criteria integration. Based on the WHD, SEHD, and EHD calculated above, the water resources–economical society–environmental system harmony degree (WSEHD) is calculated by the weighted sum method, and the formula is$${D}_{WSEH}\left(t\right)={k}_{1}{D}_{WH}\left(t\right)+{k}_{2}{D}_{SEH}\left(t\right)+{k}_{3}{D}_{EH}\left(t\right)$$

## 4. Application

#### 4.1. Data Preparation

#### 4.2. Result Analysis and Discussions

#### 4.2.1. Water Resources Optimal Allocation in Lancang County

^{7}m

^{3}, followed by the groundwater volume, which varies within 1~1.5 × 10

^{7}m

^{3}. From SSP1 to SSP3, both surface water and groundwater volume are increasing. The water volume of reclaimed water and circulating water is relatively small, below 1 × 10

^{7}m

^{3}, and the water volume under SSP2 in 2022 and 2023 is the largest under the three SSPs. However, in other years, the amount of water under SSP2 is the least, below 5 × 10

^{6}m

^{3}[31,32].

#### 4.2.2. Planting Area Results of Different Crops in Lancang County

#### 4.2.3. The System Costs and Benefits in Lancang County

^{8}to about ¥1.2 × 10

^{8}under SSP1; from ¥2.5 × 10

^{8}to about ¥1.4 × 10

^{8}under SSP2; and from ¥2.5 × 10

^{8}to about ¥1.5 × 10

^{8}under SSP3; the benefits all showed an upward trend; from ¥1 × 10

^{8}to about ¥1.7 × 10

^{8}under SSP1; from ¥7.5 × 10

^{7}to about ¥1.5 × 10

^{8}under SSP2; and from ¥5 × 10

^{7}to about ¥1.5 × 10

^{8}under SSP3. However, under SSP3, the cost of Lancang County’s water and soil resources system is always higher than the benefit. Under SSP1, the cost and benefit will be the same at the end of 2023, and under SSP2, the cost and benefit will be the same at the end of 2024, both at around¥1.5 × 10

^{8}. Then, the benefit exceeds the cost [34,35].

#### 4.2.4. Harmony Evaluation in Lancang County

#### 4.2.5. Evaluation of Agricultural Production Irrigation Carrying Capacity in Lancang County

- (1)
- Dry year scenario

^{3}, and the total water supply was 26.078 billion m

^{3}. The water supply and utilization data of various water resources are shown in Table 4.

^{3}farmland in Lancang County exceeded the groundwater resource by 246 million m

^{3}. Since 2021 is a dry year, the irrigated farmland area of Lancang County that can carry 16,200 mu is calculated based on the comprehensive irrigation quota under the precipitation frequency of 75% of the dry year. If the farmland is irrigated according to 60% of the actual total groundwater resources in that year, it can only carry 8170 mu of irrigated farmland. If it needs to reach the level of the irrigated area of the year, 6.512 billion m

^{3}of non-groundwater must be transferred for irrigation.

- (2)
- Wet year scenario

^{3}, and the total water supply will be 280.2 billion m

^{3}. The water supply and utilization data of various water resources are shown in Table 6.

^{3}farmland in Lancang County can use groundwater, far less than 60% of the total groundwater resources of the year. Since 2022 is a non-dry year, the area of irrigated farmland that can be carried in Lancang County is 22,300 mu according to the comprehensive irrigation quota at 50% of the precipitation frequency.

- (3)
- Planning Scenario

^{3}is the upper limit of agricultural irrigation that groundwater in Lancang County can carry. The irrigation quota of Lancang County in normal years is used to estimate the irrigation carrying capacity of farmland that can be carried by groundwater in Lancang County in the future, with an area of no more than 23,580 mu.

#### 4.2.6. Cost Comparison between CCP, FCCP, T2FCCP Model and Status Quo

^{8}~¥2.75 × 10

^{8}. As shown in Figure 6, under different system default probabilities, the cost gradually decreases from SSP1 to SSP3, and the cost of T2FCCP model is the lowest among four results.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 6.**System cost calculated by the CCP, FCCP, T2FCCP model and status quo in SSPs under different probabilities.

Document | Author | Location | Method | How to Use Optimization Model | Index for Water Allocation | Temporal Scale | Spatial Scale | Scenarios |
---|---|---|---|---|---|---|---|---|

An inexact modeling approach for supporting water resources allocation under natural and social complexities in a border city of China and Myanmar | Chen et al., 2021 | Southwest China | ITSFCCP | Reflect the trade-offs between the system benefits and risks | Fuzzy sets, discrete intervals, probability distribution and credibility levels | 2005–2016 | Prefecture-level city | Flow levels |

A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle | Yan et al., 2018 | Northwest China | TSCCP | Considering field water cycle process | Different crops and months | 2000–2015 | Irrigation District level | Different flow levels and constraint-violation risk levels |

Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi-Objective Nonlinear Programming Model | Yan et al., 2018 | Northwest China | SMONLP | Trade-offs between NEB and IWUE | Different crops and months | From April to September in 2016 | Irrigation District level | weighting factor of objective functions and violating probability |

This study | Zhang et al., 2022 | Southwest China | T2FCCP | Conjunction with the Harmony Evaluation Method | Harmony level | 2021–2025 | County area | Three SSPs |

WSEHD | 0 | (0, 0.2) | [0.2, 0.4) | [0.4, 0.6) | [0.6, 0.8) | [0.8, 1) | 1 |
---|---|---|---|---|---|---|---|

Harmony level | totally discordant | less harmonious | basic dissonance | close to harmony | basic harmony | more harmonious | complete harmony |

2021 | 2022 | 2023 | 2024 | 2025 | ||
---|---|---|---|---|---|---|

Number | Tributaries | WSEHD Harmony Level | WSEHD Harmony Level | WSEHD Harmony Level | WSEHD Harmony Level | WSEHD Harmony Level |

1 | Shangyun River | 0.42 close to harmony | 0.41 close to harmony | 0.48 close to harmony | 0.62 basic harmony | 0.64 basic harmony |

2 | Hei River | 0.43 close to harmony | 0.42 close to harmony | 0.54 close to harmony | 0.63 basic harmony | 0.71 basic harmony |

3 | Mangpa River | 0.46 close to harmony | 0.51 close to harmony | 0.47 close to harmony | 0.65 basic harmony | 0.74 basic harmony |

4 | Nanlang River | 0.36 basic dissonance | 0.38 basic dissonance | 0.42 close to harmony | 0.44 close to harmony | 0.72 basic harmony |

Total Water Resources | Total Amount of Water Resources Is Higher than the Average over the Years | Precipitation Is Higher than the Multi-Year Average | Groundwater Resources | Farmland Irrigation Water Consumption | Non-Farmland Irrigation Water Consumption | Groundwater Supply | Surface Water Supply | Reclaimed Water Volume | Total Water Supply |
---|---|---|---|---|---|---|---|---|---|

125.96 | −28.62% | −35.72% | 108.06 | 110.52 | 115.34 | 118.08 | 78.08 | 12.8 | 260.78 |

^{6}m

^{3}.

**Table 5.**Agricultural water consumption in Lancang County under the assumption of non-agricultural priority use of surface water in 2021.

Total Surface Water Supply and Reclaimed Water | Non-Agricultural Water Use Exceeds Surface Water | Remaining after Deduction of Groundwater | 60% of the Groundwater Resources of the Year |
---|---|---|---|

80.21 | 70.62 | 135.28 | 64.85 |

^{6}m

^{3}.

Total Water Resources | Total Amount of Water Resources Is Higher than the Average over the Years | Precipitation Is Higher than the Multi-Year Average | Groundwater Resources | Farmland Irrigation Water Consumption | Non-Farmland Irrigation Water Consumption | Groundwater Supply | Surface Water Supply | Reclaimed Water Volume | Total Water Supply |
---|---|---|---|---|---|---|---|---|---|

135.68 | 25.48% | 28.20% | 112.65 | 56.48 | 58.25 | 65.4 | 30.5 | 18.2 | 280.2 |

^{6}m

^{3}.

**Table 7.**Agricultural water consumption in Lancang County under the assumption of non-agricultural priority use of surface water in 2022.

Total Surface Water Supply and Reclaimed Water | Non-Agricultural Water Use Exceeds Surface Water | Remaining after Deduction of Groundwater | 60% of the Groundwater Resources of the Year |
---|---|---|---|

85.28 | 66.4 | 140.6 | 67.6 |

^{6}m

^{3}.

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## Share and Cite

**MDPI and ACS Style**

Zhang, Y.; Yang, P. Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China. *Water* **2022**, *14*, 3641.
https://doi.org/10.3390/w14223641

**AMA Style**

Zhang Y, Yang P. Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China. *Water*. 2022; 14(22):3641.
https://doi.org/10.3390/w14223641

**Chicago/Turabian Style**

Zhang, Yunquan, and Peiling Yang. 2022. "Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China" *Water* 14, no. 22: 3641.
https://doi.org/10.3390/w14223641