A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Xingu River Basin (XRB)
2.2. The LASH Model Structure
2.3. Database for Running LASH in the XRB
2.4. Calibration and Validation of the LASH Model
2.5. Agribusiness Land-Use Scenarios for the XRB
- -
- Scenario 1 (S1): 50% increase in grasslands over native forest, focusing on the regions where this practice has occurred more expressively.
- -
- Scenario 2 (S2): 100% increase in grasslands over native forest, where this practice has been occurring more expressively.
- -
- Scenario 3 (S3): 50% increase in soybean plantations over native forest, where this practice has been occurring more expressively.
- -
- Scenario 4 (S4): 100% advance in soybean plantations over native forest, where this practice has been occurring more expressively.
3. Results and Discussion
3.1. Calibration and Validation of the LASH Model
3.2. Sensitivity Analysis of the Vegetation Parameters in the LASH Model for the XRB
3.3. Hydrological Simulation of the Agribusiness Land-Use Scenarios in the XRB
3.4. Limitations of Hydrological Models in Simulating the Land-Use Impacts at the Basin-Scale
4. Conclusions
- The LASH model showed a good performance in the XRB (NS > 0.85 and NSlog > 0.86 in both calibration and validation), including the Proxy Basin test (NS = 0.77; NSlog = 0.88), which allows for understanding the hydrological processes’ simulation in this Amazon basin.
- Regarding the hydrological analysis of the agribusiness land-use scenarios, land use based on deforestation in the XRB would increase the direct surface runoff (from 4.4%–S1 to 29.8%–S4). A reduction in the baseflow was mainly observed in the grassland scenarios, being −1.6% and −4.9% for S1 and S2, respectively, which was clearer for the sub-basins in the headwater region of the basin, where the scenarios were more effective.
- The peak flows were more pronounced for the S2 and S4 scenarios, which considers 100% of the deforestation for grasslands and soybean, respectively, and where agribusiness activities have been more frequent.
- The baseflow could be significantly reduced in all the projected scenarios, especially for S2 in the headwater sub-basins (−16.3%), which can compromise the water yield in the basin.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Land Use Class | Albedo | h (m) | LAI (m2.m−2) | SR (s.m−1) | Pr (mm) |
---|---|---|---|---|---|
Native forest (Amazon) | 0.13–0.18 [24] | 10 [14] | 6.25 [24] | 140 [25] | 2000 [9] |
Undergrowth | 0.2–0.25 [25] | 0.5 [26] | 0.5 [11] | 65 [26] | 500 [23] |
Grassland | 0.2–0.26 [25] | 0.5 [26] | 1.86–3.99 [27] | 60–80 [28] | 500 [23] |
Soybean | 0.15–0.26 [29] | 0.0–1.1 [26] | 0.4–7.0 [30] | 60–90 [27] | 500 [23] |
Bare soil | 0.1–0.35 [25] | 0 | 0 | 545.3 [13] | 500 [23] |
Urbanization | 0.1–0.35 [25] | 0 | 0 | 545.3 [13] | 500 [23] |
Waterbody | 0.12 [15] | 0 | 0 | 0 | 0 |
Classes | S0 (km2) | S1 (km2) | S2 (km2) | S3 (km2) | S4 (km2) |
---|---|---|---|---|---|
Native forest (Amazon) | 387,440.6 | 341,989.3 | 277,497.4 | 344,938.9 | 294,312.6 |
Undergrowth | 11,231.1 | 11,231.1 | 11,231.1 | 11,231.1 | 11,231.1 |
Grassland | 2670.4 | 2670.4 | 2670.4 | 45,172.1 | 95,798.3 |
Soybean | 42,856.6 | 88,307.9 | 152,799.8 | 42,856.6 | 42,856.6 |
Bare soil | 357.1 | 357.1 | 357.1 | 357.1 | 357.1 |
Urbanization | 31.8 | 31.8 | 31.8 | 31.8 | 31.8 |
Waterbody | 3434.5 | 3434.5 | 3434.5 | 3434.5 | 3434.5 |
mm Year−1 | S0 | S1 | S2 | S3 | S4 |
---|---|---|---|---|---|
Rainfall | 1826.31 | 1826.31 | 1826.31 | 1826.31 | 1826.31 |
ETr | 978.00 | 989.00 (+1.1%) | 965.07 (−1.3%) | 975.55 (−0.3%) | 939.20 (−4.0%) |
It | 196.49 | 187.36 (−4.6%) | 173.45 (−11.7%) | 186.75 (−5.0%) | 174.61 (−11.1%) |
At | 159.70 | 147.58 (−7.6%) | 125.49 (−21.4%) | 147.52 (−7.6%) | 132.96 (−16.7%) |
Dsup | 23.38 | 24.40 (+4.4%) | 30.02 (+28.4%) | 25.09 (+7.3%) | 30.34 (+29.8%) |
Dbase | 16.37 | 16.11 (−1.6%) | 15.57 (−4.9%) | 16.23 (−0.8%) | 16.05 (−1.9%) |
mm Year−1 | Sub-Basin 44 | Sub-Basin 80 | ||||
---|---|---|---|---|---|---|
S0 | S1 | S2 | S0 | S3 | S4 | |
Rainfall | 2158.1 | 2158.1 | 2158.1 | 1678.3 | 1678.3 | 1678.3 |
ETr | 1000.0 | 1063.5 (+6.3%) | 1027.7 (+2.8%) | 1212.2 | 1187.7 (−2.0%) | 957.9 (−21.0%) |
It | 240.0 | 195.6 (−18.5%) | 136.1 (−43.3%) | 174.9 | 142.2 (−18.7%) | 97.2 (−44.4%) |
At | 195.6 | 132.0 (−32.5%) | 49.1 (−74.9%) | 91.2 | 84.6 (−7.2%) | 40.2 (−55.9%) |
Dsup | 405.1 | 469.5 (+15.9%) | 708.9 (+75.0%) | 99.6 | 141.6 (+42.3%) | 410.5 (+312.3%) |
Dbase | 233.2 | 219.6 (−5.8%) | 195.2 (−16.3%) | 128.2 | 134.0 (+4.5%) | 144.2 (+12.5%) |
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Cunha, Z.A.; Mello, C.R.; Beskow, S.; Vargas, M.M.; Guzman, J.A.; Moura, M.M. A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin. Land 2023, 12, 1422. https://doi.org/10.3390/land12071422
Cunha ZA, Mello CR, Beskow S, Vargas MM, Guzman JA, Moura MM. A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin. Land. 2023; 12(7):1422. https://doi.org/10.3390/land12071422
Chicago/Turabian StyleCunha, Zandra A., Carlos R. Mello, Samuel Beskow, Marcelle M. Vargas, Jorge A. Guzman, and Maíra M. Moura. 2023. "A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin" Land 12, no. 7: 1422. https://doi.org/10.3390/land12071422
APA StyleCunha, Z. A., Mello, C. R., Beskow, S., Vargas, M. M., Guzman, J. A., & Moura, M. M. (2023). A Modeling Approach for Analyzing the Hydrological Impacts of the Agribusiness Land-Use Scenarios in an Amazon Basin. Land, 12(7), 1422. https://doi.org/10.3390/land12071422