Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China
"> Figure 1
<p>Map illustrating the topography and station distribution in the TRB.</p> "> Figure 2
<p>Methodological roadmap for this study.</p> "> Figure 3
<p>Comparison of simulation results from the SWAT and SWATAI models on the diurnal scale.</p> "> Figure 4
<p>Hourly simulation results during the calibration period (2013).</p> "> Figure 5
<p>Hourly simulation results during the validation period (2014).</p> "> Figure 6
<p>Springtime NDVI distribution in the research area.</p> "> Figure 7
<p>Relationship between the herbaceous green space NDVI and snow and ice melt: (<b>a</b>) relationship between the amount of snow and ice melt calculated by the SWAT model and the herbaceous green space NDVI<b>;</b> (<b>b</b>) relationship between the amount of snow and ice melt calculated by the SWATAI model and the herbaceous green space NDVI.</p> "> Figure 8
<p>Relationship between the paddy field NDVI and snow and ice melt: (<b>a</b>) relationship between the amount of snow and ice melt calculated by the SWAT model and the paddy field NDVI; (<b>b</b>) relationship between the amount of snow and ice melt calculated by the SWATAI model and paddy field NDVI.</p> "> Figure 9
<p>Relationship between the dry land NDVI and snow and ice melt: (<b>a</b>) relationship between the amount of snow and ice melt calculated by the SWAT model and the dry land NDVI; (<b>b</b>) relationship between the amount of snow and ice melt calculated by the SWATAI model and the dry land NDVI.</p> "> Figure 10
<p>Comparison between the simulated flood peak values and the measured data of all 25 flood events during the model calibration and validation periods.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.3. Methods
2.3.1. Calculation of Accumulated Temperature
2.3.2. Calculation of Ice Melt
2.3.3. Calibration, Validation, and Sensitivity
3. Results
3.1. Daily Simulations
3.2. Sub-Daily Simulations
3.3. Effects of Parameters on the Simulated Results
3.4. Relationship between NDVI and Model Modification
4. Discussion
4.1. Model Modification
4.2. Model Performance Comparison
4.3. Analysis of Parameter Sensitivity and Uncertainty
4.4. Relationship Between NDVI and Snow and Ice Melt in Spring
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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LUCC | Area (km2) | Percentage (%) |
---|---|---|
Glacier and snowfield | 469.96 | 8.35 |
Bare soil | 815.54 | 14.49 |
Bare rock | 324.38 | 5.77 |
Meadow | 1249.17 | 22.20 |
Sparse grass | 1324.22 | 23.54 |
River | 69.67 | 1.24 |
Marsh | 5.65 | 0.10 |
Evergreen coniferous shrub | 3.98 | 0.07 |
Grassland | 1202.74 | 21.38 |
Broadleaved deciduous forest | 3.49 | 0.06 |
Evergreen needleleaved forest | 138.68 | 2.46 |
Dryland | 17.42 | 0.31 |
Settlement place | 1.51 | 0.03 |
Period | R2 | NSE | PBIAS (%) | |||
---|---|---|---|---|---|---|
SWAT | SWATAI | SWAT | SWATAI | SWAT | SWATAI | |
Calibration (2013) | 0.80 | 0.87 | 0.73 | 0.77 | 5.42 | 4.55 |
Validation (2014) | 0.78 | 0.84 | 0.71 | 0.75 | −6.89 | 4.85 |
Overall (2013–2014) | 0.77 | 0.82 | 0.69 | 0.74 | 8.65 | 5.42 |
Date | Deviation of Flood Peak Value (m3·s−1) | Deviation of Timing (h) | ||
---|---|---|---|---|
SWAT | SWATAI | SWAT | SWATAI | |
2013.5 | 14.52 | 3.88 | 7 | 0 |
2013.6 | 63.13 | 8.83 | 8 | −1 |
2013.7 | 14.33 | 11.59 | 3 | −1 |
2013.8 | 57.05 | 9.7 | −1 | 0 |
2013.9 | −2.88 | −2.16 | 2 | 1 |
2014.5 | 52.7 | 5.04 | −8 | 2 |
2014.6 | 11.23 | 4.37 | −6 | 0 |
2014.7 | 26.25 | −2.29 | 0 | 0 |
2014.8 | 96.05 | 26.94 | −2 | 1 |
2014.9 | −10.15 | −3.03 | 4 | −1 |
File Extension | Parameter | Description | Range of Values | Daily Simulation Calibrated Value | Sub-daily Simulation calibrated Value |
---|---|---|---|---|---|
.bsn(New) | ETSI | Elevation threshold between snow and ice | 2000–6000 | 3500 | 3500 |
.bsn(New) | IMTP | Ice-melt base temperature | −40 | 5.39 | 5.87 |
.bsn(New) | IMTP_A | Ice-melt base accumulated temperature | 0–40 | 24.68 | 26.77 |
.bsn(New) | IMFMX | Maximum melt rate for ice during the year | 0–20 | 12.35 | 13.04 |
.bsn(New) | IMFMN | Minimum melt rate for ice during the year | 0–20 | 15.87 | 16.55 |
.bsn(New) | ITIMP | Ice temperature lag factor | 0–1 | 0.61 | 0.65 |
.bsn(New) | SFTMP_A | Snowfall accumulated temperature | 0–40 | 28 | 29 |
.bsn | SFTMP | Snowfall temperature | −20 to 20 | 3.27 | 3.46 |
.bsn | SMTMP | Snow-melt base temperature | −20 to 20 | 3.06 | 2.85 |
.bsn | SMFMX | Maximum melt rate for snow during the year | 0–20 | 7.62 | 7.57 |
.bsn | SMFMN | Minimum melt rate for snow during the year | 0–20 | 9.4 | 8.19 |
.bsn | TIMP | Snowpack temperature lag factor | 0–1 | 0.55 | 0.54 |
.bsn | SNOCOVMX | Minimum snow water content corresponding to 100% snow cover | 0–500 | 38.33 | 37.84 |
.bsn | SFTMP | Snowfall temperature | −40 | 3.36 | 3.47 |
.bsn | SURLAG | Surface runoff lag time | 0.05–24 | 11.78 | 11.43 |
.gw | ALPHA_BF | Base flow alpha factor (days) | 0–1 | 0.15 | 0.18 |
.gw | GW_DELAY | Groundwater delay (days) | 0–500 | 222.68 | 224.12 |
.gw | GWQMN | Threshold water depth in the shallow aquifer required for return flow to occur (mm) | 0–5000 | 1175.84 | 1205.64 |
.gw | SHALLST | Initial water depth in the shallow aquifer (mm) | 0–50,000 | 4903.68 | 4958.74 |
.gw | GW_REVAP | Groundwater “revamp” coefficient | 0.02–0.2 | 0.06 | 0.05 |
.mgt | CN2 | SCS runoff curve number | 35–98 | 70.79 | 72.35 |
.ohru | OV_N | Manning’s “n” value for overland flow | 0.01–30 | 10.77 | 11.12 |
.ohru | ESCO | Soil evaporation compensation factor | 0–1 | 0.37 | 0.35 |
.ohru | EPCO | Plant uptake compensation factor | 0–1 | 0.39 | 0.32 |
.rte | CH_N2 | Manning’s “n” value for the main channel | −0.01 to 0.3 | 0.01 | 0.01 |
.rte | CH_K2 | Effective hydraulic conductivity in main channel alluvium | −0.01 to 500 | 47.38 | 48.12 |
.sol | SOL_K | Saturated hydraulic conductivity | 0–2000 | 861.31 | 874.58 |
.sol | SOL_AWC | Available water capacity of the soil layer | 0–1 | 0.32 | 0.36 |
.sub | PLAPS | Precipitation lapse rate | −20 to 20 | −5.5 | −5.36 |
.sub | TLAPS | Temperature lapse rate | −10 to 10 | −7.59 | −7.64 |
.sub | CH_N1 | Manning’s “n” value for the tributary channels | 0.01–30 | 5.42 | 5.13 |
.sub | CH_K1 | Effective hydraulic conductivity in tributary channel alluvium | 0–300 | 295.67 | 271.36 |
.sub | SNO_SUB | Initial snow water content | 0–150 | 95.39 | 97.33 |
Parameter | T-states | p-Value |
---|---|---|
CH_K2 | 21.67 | 0 |
PLAPS | 18.53 | 0 |
IMTP_A | 12.06 | 0 |
SMTMP | 10.98 | 0.01 |
TLAPS | 9.76 | 0.01 |
IMTP | 9.05 | 0.01 |
ETSI | 8.25 | 0.01 |
LAT_TTIME | 6.89 | 0.02 |
SMFMX | 6.05 | 0.02 |
SOL_K | 5.36 | 0.03 |
IMFMX | 5.09 | 0.03 |
SOL_AWC | 4.68 | 0.05 |
IMFMN | 4.47 | 0.05 |
SURLAG | 3.28 | 0.06 |
TIMP | 2.64 | 0.17 |
ITIMP | 2.36 | 0.18 |
GWQMN | 1.49 | 0.26 |
SNO_SUB | 1.05 | 0.34 |
REVAPMN | 1.01 | 0.57 |
EPCO | 0.72 | 0.64 |
SMFMN | 0.43 | 0.79 |
CH_N2 | 0.38 | 0.82 |
RCHRG_DP(Deep aquifer percolation fraction) | 0.21 | 0.89 |
OV_N | −0.02 | 0.91 |
CH_N1 | −0.57 | 0.71 |
SNOCOVMX | −1.05 | 0.53 |
SHALLST | −1.24 | 0.51 |
ALPHA_BF | −2.49 | 0.43 |
CN2 | −3.07 | 0.39 |
CH_K1 | −3.14 | 0.34 |
SFTMP | −3.88 | 0.26 |
GW_DELAY | −4.01 | 0.14 |
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Duan, Y.; Liu, T.; Meng, F.; Yuan, Y.; Luo, M.; Huang, Y.; Xing, W.; Nzabarinda, V.; De Maeyer, P. Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China. Remote Sens. 2020, 12, 179. https://doi.org/10.3390/rs12010179
Duan Y, Liu T, Meng F, Yuan Y, Luo M, Huang Y, Xing W, Nzabarinda V, De Maeyer P. Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China. Remote Sensing. 2020; 12(1):179. https://doi.org/10.3390/rs12010179
Chicago/Turabian StyleDuan, Yongchao, Tie Liu, Fanhao Meng, Ye Yuan, Min Luo, Yue Huang, Wei Xing, Vincent Nzabarinda, and Philippe De Maeyer. 2020. "Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China" Remote Sensing 12, no. 1: 179. https://doi.org/10.3390/rs12010179