Analysis of Runoff Changes and Their Driving Forces in the Minjiang River Basin (Chengdu Section) in the Last 30 Years
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Data Sources
2.3. Study Framework
2.4. Study Methods
2.4.1. SWAT Model Construction and SUFI-2 Algorithm
2.4.2. Mann–Kendall Test and Theil–Sen Slope
2.4.3. Relative Importance Analysis
2.4.4. The Partial Least Squares Structural Equation Modeling (PLS-SEM) Construction
3. Results
3.1. The Construction of the SWAT Model
3.2. Runoff Simulation Analysis
3.2.1. Inter-Annual Variability of Runoff
3.2.2. Characteristics of the Spatial Distribution of Runoff
3.3. Relative Importance of Drivers
3.4. The PLS-SEM Model Assessment
3.4.1. Assessment of Measurement Models
3.4.2. Assessment of Structural Models
4. Discussion
4.1. Spatial and Temporal Variability of Runoff
4.2. Analysis of the Drivers of Runoff Change
4.2.1. Significant Positive Impact of Natural Factors
4.2.2. Significant Negative Impacts of Human Activities
4.2.3. Significant Negative Effects of Landscape Factors
4.2.4. Non-Significant Effects of Climatic Factors
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Types | Specific Figures | Data Sources | Data Description |
---|---|---|---|
Topographic data | Elevation maps, Slope maps, Topographic humidity index maps | Geospatial Data Cloud (http://www.gscloud.cn/, accessed on 7 July 2023) | 30 m, slope and Topographic Wetness Index were extracted from elevation |
Land use data | 1990, 1995, 2005, 2015, and 2019 | Pixel Information Expert Engine, accessed on 10 July 2023 | 30 m |
Soil data | Soil organic carbon content, organic matter content, clay content, sand content, and silt content data | Harmonized World Soil Database, accessed on 15 July 2023 | 1000 m |
Climate data | / | National Meteorological Data Centre (http://data.cma.cn/, accessed on 17 July 2023) | Daily |
Hydrological data | / | Sichuan Hydrological Statistical Yearbook (accessed on 18 July 2023) | Monthly runoff |
Socio-economic data | Gross Domestic Product (GDP), population density | Centre for Resource and Environmental Sciences and Data, Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 20 July 2023) | 1000 m, where the 1990 GDP is interpolated through the statistical yearbook |
Latent Variables | Apparent Variables | Explanation |
---|---|---|
Natural factors | Relative elevation | T = Tmax − Tmin (Difference between the maximum and minimum elevation values within the hydrological response cell), Unit: m |
Relative slope | S = Smax − Smin(Difference between the maximum and minimum slope values within the hydrological response cell), Unit:° | |
Topographic Wetness index | TWI = ln(α/tanβ) Where: α represents the unit grid catchment area; β represents the slope | |
Soil pH | Soil potential of hydrogen | |
Clay contents | Percentage of clay in soil, Unit: % | |
Sand contents | Percentage of sand in soil, Unit: % | |
Silt contents | Percentage of silt in soil, Unit: % | |
Soil organic matter | Carbon-containing organic compounds in soil, Unit: % | |
Soil organic carbon | Humus formed through microbial action, Unit: % | |
Climatic factors | Average annual precipitation | Multi-year average precipitation, 1 km resolution, Unit: mm |
Average annual temperature | Multi-year average temperature, 1 km resolution, Unit: °C | |
Average annual evaporation | Multi-year average evaporation, 1 km resolution, Unit: mm | |
Socio-economic factors | Gross Domestic Product (GDP) | Based on spatialisation of national sub-county GDP statistics, Unit: millions of yuan. |
Population density | Based on spatialisation of national sub-county demographics, Unit: persons. | |
Landscape factors | Landscape change | Raster values with no landscape change were recorded as 0 and those with landscape change were recorded as 1. The number of rasters with landscape change was counted as a proportion of each hydrological response cell. |
Landscape connectivity | Landscape connectivity indicators were constructed based on the relative importance of each of these indicators: Connectivity = α1AREA_MN + α2COHESION + α3PLADJ + α4AI + α5LPI Where: Connectivity represents landscape connectivity, and α1 − α5 represent the relative importance share of each metric, respectively. | |
Landscape fragmentation | A landscape fragmentation index was constructed based on the relative importance of each of the above indicators: Fragmentation = β1DIVISION + β2SHDI + β3SPLIT Where: Fragmentation represents the degree of landscape fragmentation, and β1-β3 represent the relative importance of each indicator. |
Parameters | t-Stat | p-Value | Parameter Optimum | Sensitivity | Connotation |
---|---|---|---|---|---|
v__SFTMP.bsn | 0.04 | 0.97 | 4.16 | NO | Snowfall temperature/°C |
v__SMTMP.bsn | −0.44 | 0.66 | −7.72 | NO | Base temperature for snowmelt/°C |
v__SMFMX.bsn | 0.17 | 0.87 | −1.62 | NO | Snowmelt factor on 21 June (mm/(°C·day)) |
v__TIMP.bsn | 0.25 | 0.80 | 0.23 | NO | Snow temperature lag factor |
v__SURLAG.bsn | −1.30 | 0.20 | 20.16 | YES | Surface runoff hysteresis factor |
v__TLAPS.sub | 0.58 | 0.57 | 10.74 | NO | direct temperature decrease (°C/km) |
v__SLSUBBSN.hru | −2.76 | 0.01 | 43.67 | YES | Average slope length (m) |
v__HRU_SLP.hru | −0.24 | 0.81 | 1.49 | NO | Average specific gravity (m/m) |
v__CANMX.hru | −0.65 | 0.52 | 73.21 | NO | Maximum canopy retention (mm) |
v__ESCO.hru | −0.40 | 0.69 | 0.43 | NO | Soil evaporation compensation factor |
v__EPCO.hru | 1.16 | 0.24 | 1.14 | YES | Plant uptake compensation factor |
v__OV_N.hru | 1.41 | 0.16 | 0.12 | YES | Values of Manning’s coefficient n for diffuse flow on slopes |
r__CN2.mgt | 5.95 | 0.00 | 0.45 | YES | Initial SCS runoff curve number for moisture condition II |
r__BIOMIX.mgt | 1.20 | 0.23 | −0.80 | YES | Biomixing efficiency |
v__CH_COV1.rte | −0.11 | 0.91 | 0.32 | NO | Channel erosion factor |
v__CH_COV2.rte | 0.43 | 0.67 | 0.99 | NO | Stream cover factor |
v__GW_DELAY.gw | −1.42 | 0.16 | −428.75 | YES | Time delay in groundwater (day) |
v__ALPHA_BF.gw | 0.64 | 0.52 | 0.66 | NO | Baseflow α factor (day) |
v__GWQMN.gw | 7.94 | 0.00 | 2260.49 | YES | Water level threshold of the shallow aquifer required for return flow to occur (mm) |
v__GW_REVAP.gw | 2.12 | 0.03 | 0.13 | YES | Groundwater revap coefficients |
v__REVAPMN.gw | 0.80 | 0.42 | 522.34 | NO | Water level threshold of the shallow aquifer required for revap or infiltration into the deep aquifer to occur (mm) |
v__RCHRG_DP.gw | 6058 | 0.00 | 0.23 | YES | Permeability coefficient of deep aquifers |
a__SOL_AWC.sol | −1.33 | 0.18 | 0.11 | YES | Effective water content of the soil layer (mm/mm) |
Slope | Z_Value | Trends in Runoff | Area/km2 | Proportion of Study Area/% |
---|---|---|---|---|
Slope > 0 | |Z| > 1.96 | Significant increase | 52.25 | 0.96 |
Slope > 0 | |Z| < 1.96 | No significant increase | 3440.44 | 63.01 |
Slope < 0 | |Z| < 1.96 | No significant decrease | 1917.2 | 35.11 |
Slope < 0 | |Z| > 1.96 | Significant decrease | 50.02 | 0.92 |
Latent Variable | AVE | CR | DG.rho | Loading | t-Test | Observed Variable | Test Results |
---|---|---|---|---|---|---|---|
Natural factors | 0.619 | 0.882 | 0.887 | 0.859 | 2.604 | Relative elevation | accept |
0.927 | 2.565 | Relative slope | accept | ||||
0.863 | 2.546 | Topographic humidity index | accept | ||||
0.148 | 1.329 | Soil PH | reject | ||||
0.045 | 0.434 | Clay contents | reject | ||||
0.695 | 2.498 | Sand contents | accept | ||||
0.703 | 2.478 | Silt contents | accept | ||||
0.369 | 2.060 | Organic matter contents | reject | ||||
0.029 | 0.435 | Organic carbon contents | reject | ||||
Climatic factors | 0.618 | 0.841 | 0.813 | 0.855 | 2.214 | Average annual precipitation | accept |
0.644 | 2.357 | Average annual temperature | accept | ||||
0.960 | 2.316 | Average annual evaporation | accept | ||||
Socio-economic factors | 0.696 | 0.976 | 0.984 | 0.982 | 91.722 | GDP | accept |
0.985 | 135.008 | Population density | accept | ||||
Landscape factors | 0.968 | 0.671 | 0.827 | 0.912 | 20.478 | Landscape change | accept |
0.748 | 6.471 | Landscape connectivity | accept | ||||
0.277 | 2.392 | Landscape fragmentation | reject | ||||
Runoff | 1.000 | 1.000 | 1.000 | 1.000 | Runoff | accept |
Direct Paths | Path Coefficient | T-Value | p-Value | Test Results |
---|---|---|---|---|
Socio-economic—>Runoff | −0.210 | 4.251 | 0.000 | significant |
Socio-economic—>Landscape | 0.446 | 8.223 | 0.000 | significant |
Socio-economic—>Climate | 0.219 | 2.018 | 0.044 | significant |
Landscape—>Runoff | −0.131 | 2.164 | 0.030 | significant |
Climate—>Runoff | −0.102 | 1.266 | 0.205 | insignificant |
Climate—>Landscape | 0.004 | 0.042 | 0.966 | insignificant |
Nature—>Socio-economic | −0.268 | 1.734 | 0.083 | insignificant |
Nature—>Runoff | 0.367 | 2.901 | 0.037 | significant |
Nature—>Landscape | 0.075 | 0.879 | 0.879 | insignificant |
Nature—>Climate | −0.620 | 2.200 | 0.028 | significant |
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Liu, J.; Yan, K.; Liu, Q.; Lin, L.; Peng, P. Analysis of Runoff Changes and Their Driving Forces in the Minjiang River Basin (Chengdu Section) in the Last 30 Years. Hydrology 2024, 11, 123. https://doi.org/10.3390/hydrology11080123
Liu J, Yan K, Liu Q, Lin L, Peng P. Analysis of Runoff Changes and Their Driving Forces in the Minjiang River Basin (Chengdu Section) in the Last 30 Years. Hydrology. 2024; 11(8):123. https://doi.org/10.3390/hydrology11080123
Chicago/Turabian StyleLiu, Jingjing, Kun Yan, Qin Liu, Liyang Lin, and Peihao Peng. 2024. "Analysis of Runoff Changes and Their Driving Forces in the Minjiang River Basin (Chengdu Section) in the Last 30 Years" Hydrology 11, no. 8: 123. https://doi.org/10.3390/hydrology11080123