Journal of Geographical Sciences >
Hedging effect alleviates the impact of land use on mainstream hydrological regimes: Evidence from Jinsha River, China
Gao Wei (1986-), PhD and Associate Professor, specialized in the simulation of watershed environmental processes. E-mail: gaoweird@163.com |
Received date: 2022-09-07
Accepted date: 2023-05-29
Online published: 2023-10-08
Supported by
National Key Research and Development Program of China(2021YFC3201004)
Global extreme hydrological events pose considerable challenges to the sustainable development of human society and river ecology. Land use/cover change (LUCC) is a visible manifestation of human activity and has caused substantial alterations in extreme hydrological regimes across rivers worldwide. The Jinsha River lies upstream of the Yangtze River and its hydrological variability has had profound socioeconomic and environmental effects. In this study, we developed Hydrological Simulation Program-FORTRAN (HSPF) and land-use simulation models of the entire watershed to simulate the effects of LUCC on hydrological extremes and quantify the inter-relationships among them. The main land-use changes between 1995 and 2015 were those associated with cropland, forest land, and grassland. Between 2015 and 2030, it is estimated that the coverage of forest land, grassland, construction land, and unused land will increase by 0.64%, 0.18%, 69.38%, and 45.08%, respectively, whereas that of cropland, water bodies, and snow- and ice-covered areas will decline by 8.02%, 2.63%, and 0.89%, respectively. LUCC has had irregular effects on different hydrological regimes and has most severely altered stream flows. The responses of hydrological extremes to historical land-use change were characterized by spatial variation. Extreme low flows increased by 0.54%-0.59% whereas extreme high flows increased by 0%-0.08% at the lowest outlet. Responses to future land-use change will be amplified by a 0.72%-0.90% reduction in extreme low flows and a 0.08%-0.12% increase in extreme high flows. The hedging effect caused by irregular changes in tributary stream flow was found to alleviate the observed flow in mainstream rivers caused by land-use change. The extreme hydrological regimes were affected mainly by the net swap area transferred from ice and snow area to forest (NSAIF) and thereafter to cultivated land (NSAIC). Extreme low flows were found to be positively correlated with NSAIF and NSAIC, whereas extreme high flows were positively correlated with NSAIC and negatively correlated with NSAIF.
Key words: FLUS; hedging effect; hydrological extreme; HSPF; IHA; LUCC
GAO Wei , LIU Yong , DU Zhanpeng , ZHANG Yuan , CHENG Guowei , HOU Xikang . Hedging effect alleviates the impact of land use on mainstream hydrological regimes: Evidence from Jinsha River, China[J]. Journal of Geographical Sciences, 2023 , 33(10) : 2011 -2030 . DOI: 10.1007/s11442-023-2163-1
Figure 1 Location of the Jinsha River Watershed and the distribution of gauging stations |
Figure 2 Overall process flow of the HSPF model |
Figure S1 Spatial distribution of subbasins of the Jinsha River Watershed |
Table S1 Reclassification of land use types in the Jinsha River Watershed |
Old code | Old land use type | New code | New land use type |
---|---|---|---|
10 | Cropland, rained | 1 | Cultivate land |
11 | Herbaceous cover | 3 | Grassland |
12 | Tree or shrub cover | 2 | Forest land |
20 | Cropland, irrigated or post-flooding | 1 | Cultivate land |
30 | Mosaic cropland >50% / natural vegetation(tree, shrub, herbaceous cover) <50% | 1 | Cultivate land |
40 | Mosaic natural vegetation(tree, shrub, herbaceous cover) >50% / cropland <50% | 2 | Forest land |
50 | Tree cover, broadleaved, evergreen, closed to open >15% | 2 | Forest land |
60 | Tree cover, broadleaved, deciduous, closed to open >15% | 2 | Forest land |
61 | Tree cover, broadleaved, deciduous, closed >40% | 2 | Forest land |
70 | Tree cover, needleleaved, evergreen, closed to open >15% | 2 | Forest land |
100 | Mosaic tree and shrub >50% / herbaceous cover <50% | 2 | Forest land |
110 | Mosaic herbaceous cover >50% / tree and shrub <50% | 3 | Grassland |
120 | Shrubland | 3 | Grassland |
121 | Shrubland evergreen | 3 | Grassland |
122 | Shrubland deciduous | 3 | Grassland |
130 | Grassland | 3 | Grassland |
150 | Sparse vegetation (tree, shrub, herbaceous cover) <15% | 3 | Grassland |
170 | Tree cover, flooded, saline water | 4 | Water body |
180 | Shrub or herbaceous cover, flooded, fresh/saline/brakish water | 4 | Water body |
190 | Urban areas | 5 | Construction land |
200 | Bare areas | 6 | Unused land |
201 | Consolidated bare areas | 6 | Unused land |
202 | Unconsolidated bare areas | 6 | Unused land |
210 | Water bodies | 4 | Water body |
220 | Permanent snow and ice | 7 | Ice and Snow |
Table 1 Estimated PEST parameter values for the Jinsha River Watershed |
Parameter | Description | Units | Possible values | Calibrated values | ||
---|---|---|---|---|---|---|
Min | Max | Min | Max | |||
KMELT | Degree-day factor | in/day.F | 0.001 | 1.000 | 0.015 | 1.000 |
LZSN | Lower zone nominal storage | in | 2.000 | 15.000 | 2.000 | 14.458 |
UZSN | Upper zone nominal storage | in | 0.050 | 2.000 | 0.278 | 2.000 |
INFILT | Soil infiltration capacity index | in/hr | 0.001 | 0.500 | 0.001 | 0.500 |
IRC | Interflow recession parameter | 1/day | 0.300 | 0.850 | 0.300 | 0.850 |
BASETP | Fraction of remaining potential evapotranspiration that can be satisfied from baseflow if enough is available | none | 0.010 | 0.200 | 0.010 | 0.200 |
AGWETP | Fraction of remaining potential evapotranspiration that can be satisfied from active groundwater storage if enough is available | none | 0.001 | 0.200 | 0.001 | 0.200 |
AGWRC | Ground water recession coefficient | 1/day | 0.850 | 0.999 | 0.850 | 0.999 |
INTFW | Interflow inflow parameter | none | 1.000 | 10.000 | 1.000 | 10.000 |
DEEPFR | Fraction of groundwater inflow to deep recharge | none | 1.000E-7 | 0.500 | 2.916E-4 | 0.500 |
LZETP | Lower zone ET parameter | none | 0.100 | 0.900 | 0.100 | 0.500 |
Figure S2 Simulation results of daily recharge of calibration period and validation period |
Table S2 Extreme hydrological index and its ecosystem influences |
Group | IHA parameters | Ecosystem influences |
---|---|---|
Extreme high flow | Annual 1-day minimum flow | (1) Balance of competitive, ruderal, and stress- tolerant organ-isms (2) Creation of sites for plant colonization (3) Structuring of aquatic ecosystems by abiotic vs. biotic factors (4) Structuring of river channel morphology and physical habitat conditions (5) Soil moisture stress in plants (6) Dehydration in animals (7) Anaerobic stress in plants (8) Volume of nutrient exchanges between rivers and floodplains (9) Duration of stressful conditions such as low oxygen and concentrated chemicals in aquatic environments (10) Distribution of plant communities in lakes, ponds, flood-plains (11) Duration of high flows for waste disposal, aeration of spawning beds in channel sediments |
Annual 3-day minimum flow | ||
Annual 7-day minimum flow | ||
Annual 30-day minimum flow | ||
Annual 90-day minimum flow | ||
Extreme low flow | Annual 1-day maximum flow | |
Annual 3-day maximum flow | ||
Annual 7-day maximum flow | ||
Annual 30-day maximum flow | ||
Annual 90-day maximum flow |
Figure S3 Actual map (a) and simulated map (b) of land use types in the Jinsha River Watershed in 2018 |
Figure 3 Distributions and changes in land-use type in the Jinsha River Watershed between 1995 and 2030 (a-c. distributions; d. percentage land-use change) |
Table S3 Land use transfer matrix of Jinsha River Watershed from 1995 to 2015 (km2) |
Hydrologic regionalizations | Land use types | Cultivated land | Forest land | Grass- land | Water body | Construction land | Unused land | Ice and snow |
---|---|---|---|---|---|---|---|---|
Upstream of Jinsha River | Cultivated land | 14874.76 | 41.47 | 283.45 | 3.65 | 68.15 | 0.23 | 0 |
Forest land | 71.28 | 58924.09 | 749.08 | 6.95 | 18.36 | 1.32 | 0 | |
Grassland | 931.21 | 1984.53 | 173115.70 | 51.49 | 51.56 | 431.61 | 0.57 | |
Water body | 4.08 | 0.93 | 23.79 | 1586.96 | 1.83 | 1.49 | 0 | |
Construction land | 0.73 | 0.08 | 1.11 | 0 | 87.840 | 0 | 0 | |
Unused land | 0.08 | 0.16 | 143.94 | 0.54 | 0 | 2465.79 | 0 | |
Ice and snow | 0.12 | 0 | 0.51 | 0 | 0 | 0 | 1553.58 | |
Yalong River | Cultivated land | 7711.32 | 30.72 | 144.21 | 1.71 | 46.31 | 0 | 0 |
Forest land | 41.95 | 48687.63 | 496.09 | 17.42 | 10.41 | 0 | 0.03 | |
Grassland | 1096.42 | 1018.88 | 67846.19 | 17.14 | 16.78 | 0.09 | 0.23 | |
Water body | 0.30 | 2.37 | 1.51 | 428.93 | 0.10 | 0 | 0 | |
Construction land | 0.54 | 0.13 | 0.05 | 0.17 | 40.69 | 0 | 0 | |
Unused land | 0 | 0 | 0.80 | 0 | 0 | 138.51 | 0 | |
Ice and snow | 0.04 | 0.07 | 0.20 | 0 | 0 | 0 | 261.72 | |
Downstream of Jinsha River | Cultivated land | 18372.26 | 58.12 | 21.89 | 3.91 | 274.37 | 0 | 0 |
Forest land | 52.74 | 54055.50 | 107.25 | 15.71 | 61.17 | 0 | 0 | |
Grassland | 61.83 | 751.08 | 10549.93 | 1.23 | 122.79 | 0 | 0 | |
Water body | 5.28 | 4.23 | 1.15 | 525.67 | 5.79 | 0 | 0 | |
Construction land | 0.42 | 0.20 | 0.48 | 0.18 | 178.31 | 0 | 0 | |
Unused land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Ice and snow | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Table S4 Land use transfer matrix of Jinsha River Watershed from 2015 to 2030 (km2) |
Hydrologic regionalizations | Land use types | Cultivated land | Forest land | Grass- land | Water body | Construction land | Unused land | Ice and snow |
---|---|---|---|---|---|---|---|---|
Upstream of Jinsha River | Cultivated land | 9247.03 | 1760.01 | 4713.46 | 43.99 | 75.12 | 8.64 | 1.58 |
Forest land | 1449.82 | 53577.32 | 5809.81 | 39.61 | 27.88 | 18.96 | 4.3 | |
Grassland | 3822.84 | 4912.47 | 162490.85 | 243.31 | 84.78 | 2049.31 | 180.43 | |
Water body | 19.75 | 34.03 | 292.61 | 1259.48 | 1.96 | 40.47 | 0.31 | |
Construction land | 39.83 | 17.22 | 13.2 | 0.91 | 156.58 | 0 | 0 | |
Unused land | 3.27 | 4.38 | 740 | 27.15 | 0 | 2040.19 | 10.98 | |
Ice and snow | 4.03 | 2.78 | 187.51 | 0.61 | 0 | 12.12 | 1314.8 | |
Yalong River | Cultivated land | 4999.45 | 1172.13 | 2552.37 | 22.90 | 61.85 | 0.35 | 0.39 |
Forest land | 778.72 | 45320.65 | 3429.34 | 35.14 | 14.29 | 0.65 | 1.95 | |
Grassland | 1926.20 | 3143.01 | 62612.54 | 77.19 | 12.24 | 47.98 | 24.6 | |
Water body | 5.39 | 43.48 | 120.99 | 288.92 | 2.34 | 0 | 0 | |
Construction land | 18.4 | 11.78 | 3.14 | 1.13 | 79.66 | 0 | 0 | |
Unused land | 0.24 | 0.49 | 49.13 | 0 | 0 | 76.24 | 6.74 | |
Ice and snow | 0.52 | 1.04 | 32.02 | 0 | 0 | 7.08 | 211.67 | |
Downstream of Jinsha River | Cultivated land | 12841.18 | 3808.50 | 885.04 | 81.06 | 343.80 | 0 | 0 |
Forest land | 2874.8 | 49170.15 | 1880.75 | 37.66 | 95.81 | 0 | 0 | |
Grassland | 1081.84 | 2544.82 | 6703.85 | 19.98 | 148.75 | 0 | 0 | |
Water body | 32.31 | 51.92 | 48.15 | 400.59 | 11.88 | 0 | 0 | |
Construction land | 46.45 | 32.78 | 6.34 | 5.53 | 539.84 | 0 | 0 | |
Unused land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Ice and snow | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Figure 4 Net swap area of land use change in the Jinsha River Watershed (CuL: cultivated land; FL: forest land; GL: grassland; WB: water body; CoL: construction land; UL: unused land; IS: ice and snow area) |
Table S5 Variation of hydrological index under different LUCC scenarios in the Jinsha River Watershed |
Extreme hydrological index | Upstream of Jinsha River | Yalong River | Downstream of Jinsha River | ||||||
---|---|---|---|---|---|---|---|---|---|
1995 | 2015 | 2030 | 1995 | 2015 | 2030 | 1995 | 2015 | 2030 | |
Annual runoff yield (108×m3) | 450.87 | 451.43 | 451.54 | 517.32 | 517.20 | 517.32 | 189.15 | 190.16 | 189.98 |
Annual 1-day minimum (m3 s-1) | 131.80 | 133.00 | 130.00 | 454.20 | 456.90 | 453.40 | 674.70 | 678.60 | 672.50 |
Annual 3-day minimum (m3 s-1) | 132.60 | 133.80 | 130.80 | 455.00 | 457.80 | 454.30 | 676.20 | 680.20 | 674.00 |
Annual 7-day minimum (m3 s-1) | 135.30 | 136.50 | 133.50 | 458.50 | 461.20 | 457.80 | 680.80 | 684.80 | 678.70 |
Annual 30-day minimum (m3 s-1) | 156.10 | 157.40 | 154.50 | 483.70 | 486.50 | 483.10 | 718.90 | 723.10 | 717.00 |
Annual 90-day minimum (m3 s-1) | 215.70 | 217.10 | 214.20 | 537.10 | 539.80 | 536.50 | 817.20 | 821.60 | 815.70 |
Annual 1-day maximum (m3 s-1) | 4699.00 | 4706.00 | 4712.00 | 5948.00 | 5943.00 | 5949.00 | 11870.00 | 11880.00 | 11890.00 |
Annual 3-day maximum (m3 s-1) | 4629.00 | 4635.00 | 4640.00 | 5755.00 | 5749.00 | 5756.00 | 11760.00 | 11760.00 | 11770.00 |
Annual 7-day maximum (m3 s-1) | 4499.00 | 4504.00 | 4509.00 | 5412.00 | 5405.00 | 5414.00 | 11380.00 | 11380.00 | 11390.00 |
Annual 30-day maximum (m3 s-1) | 4049.00 | 4053.00 | 4058.00 | 4493.00 | 4486.00 | 4494.00 | 9915.00 | 9916.00 | 9927.00 |
Annual 90-day maximum (m3 s-1) | 3439.00 | 3442.00 | 3446.00 | 3561.00 | 3557.00 | 3563.00 | 8398.00 | 8400.00 | 8410.00 |
Table 2 Relative changes in the extreme hydrological indices under different LUCC scenarios |
Hydrological index | Upstream of Jinsha River | Yalong River | Downstream of Jinsha River | |||
---|---|---|---|---|---|---|
1995-2015 (%) | 2015-2030 (%) | 1995-2015 (%) | 2015-2030 (%) | 1995-2015 (%) | 2015-2030 (%) | |
Annual runoff yield | 0.12 | 0.03 | -0.02 | 0.02 | 0.53 | -0.09 |
Annual 1-day minimum flow | 0.91 | -2.26 | 0.59 | -0.77 | 0.58 | -0.90 |
Annual 3-day minimum flow | 0.90 | -2.24 | 0.62 | -0.76 | 0.59 | -0.91 |
Annual 7-day minimum flow | 0.89 | -2.20 | 0.59 | -0.74 | 0.59 | -0.89 |
Annual 30-day minimum flow | 0.83 | -1.84 | 0.58 | -0.70 | 0.58 | -0.84 |
Annual 90-day minimum flow | 0.65 | -1.34 | 0.50 | -0.61 | 0.54 | -0.72 |
Annual 1-day maximum flow | 0.15 | 0.13 | -0.08 | 0.10 | 0.08 | 0.08 |
Annual 3-day maximum flow | 0.13 | 0.11 | -0.10 | 0.12 | 0 | 0.09 |
Annual 7-day maximum flow | 0.11 | 0.11 | -0.13 | 0.17 | 0 | 0.09 |
Annual 30-day maximum flow | 0.10 | 0.12 | -0.16 | 0.18 | 0.01 | 0.11 |
Annual 90-day maximum flow | 0.09 | 0.12 | -0.11 | 0.17 | 0.02 | 0.12 |
Figure 5 Relative changes in the extreme hydrological indices under the 1995-2015 LUCC scenarios for stream outlets with catchment areas >1000 km2 (%) |
Figure 6 Percentage stream outlet for each extreme hydrological index identified with an increase, a decrease, or no trend |
Table 3 Linear regression results of land-use changes and hydrological extremes |
Extreme hydrological index | Net swap types of land use | Constant | R2 | |||||
---|---|---|---|---|---|---|---|---|
IS to FL | IS to CuL | GL to CoL | FL to CoL | WB to CoL | FL to WB | |||
Annual 1-day minimum flow | 66.033 | 9.239 | - | - | - | - | 0.143 | 0.569 |
Annual 3-day minimum flow | 68.414 | 9.415 | - | - | - | - | 0.145 | 0.576 |
Annual 7-day minimum flow | 66.839 | 9.403 | - | - | - | - | 0.144 | 0.567 |
Annual 30-day minimum flow | 69.688 | 9.842 | - | - | - | - | 0.151 | 0.572 |
Annual 90-day minimum flow | 69.114 | 10.309 | - | - | - | - | 0.149 | 0.579 |
Annual 1-day maximum flow | - | 34.217 | 0.004 | - | - | - | -0.175 | 0.434 |
Annual 3-day maximum flow | - | - | 0.003 | - | - | - | 0.022 | 0.187 |
Annual 7-day maximum flow | -152.279 | 19.243 | 0.001 | - | - | - | -0.104 | 0.462 |
Annual 30-day maximum flow | -126.674 | 12.871 | - | 0.002 | - | - | -0.121 | 0.403 |
Annual 90-day maximum flow | -68.904 | 8.797 | - | - | 0.128 | 0.002 | -0.068 | 0.354 |
Note: CuL: cultivated land; FL: forest land; GL: grassland; WB: water body; CoL: construction land; UL: unused land; IS: ice and snow area |
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