Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions
- Previous studies were predominantly focused on hydrologic processes [24,25,27,28,29]. Although studies involving water quality simulations evaluated the potential effect of improved LAI on sediment yield , the extent of such effects on nutrient (e.g., nitrate) loads—the most critical issue in agricultural watersheds from water quality management standpoint—is still unknown.
- The basic model: LAI was simulated by the model based on input land use and associated biophysical parameters—a common approach in watershed modeling. This was our baseline to measure the degree of improvement in model results in the subsequent configuration.
- The LAI assimilation model: The same setup as in the basic model, except MODIS LAI was directly inserted by replacing the simulated LAI values in each of the spatial units of the model (e.g., hydrologic response units or subbasins).
2.1. The Basic Model
2.2. The LAI-Assimilation Model
2.3. Model Calibration
2.4. Model Verification
- Assessments of hydrologic simulation:
- We assessed the accuracy of daily streamflow simulation for a five-year period (2013–2017) and at a separate gage station not included in the calibration process (for model verification) (Figure 1b).
- We then compared simulated LAI with MODIS data to identify how the calibrated models differ in their respective spatiotemporal representation of vegetation dynamics. For evaluating the spatial accuracy in LAI simulation, we considered specific day(s) in the 2016 crop growing season (June–August). We focused on crop growing season because this is the most active period in terms of vegetation growth and associated water-energy exchanges. We selected 2016 for temporal evaluation because this was a year with average precipitation, hence representative of the watershed’s general hydrologic response.
- To further assess improvements in hydrologic simulation, we compared the spatial consistency of soil moisture between model simulations and Soil Moisture Active Passive (SMAP) mission level 4 estimates (Table 1). SMAP mission provides 9-km gridded estimates of rootzone soil moisture, which is a model-assimilated product of remotely sensed surface moisture observations . Briefly, we produced subbasin-level aggregated SMAP data using the same web-based tool and areal averaging procedure described in Section 2.2, took the daily average of rootzone volumetric moisture content over the 2016 crop growing season (June–August), and subsequently generated a watershed soil moisture distribution map to enable spatial comparison with model outputs. We considered the same period for assessment of LAI and soil moisture as it would produce a consistent comparison across two mutually dependent processes. Furthermore, selecting summer months ensured that SMAP data used in our model assessments were not affected by snow cover and frozen soil—the common sources of inaccuracies in remotely sensed soil moisture .
- Assessment of water quality simulation:We assessed the accuracy of daily nitrate load simulation for the same five-year period and at the same location used for streamflow verification (Figure 1b). To the best of our knowledge, this is the first study to verify the effect of LAI data assimilation on water quality simulations and that using long-term daily observations as the reference.
3.1. Effect of LAI Data Assimilation on Hydrologic Simulation Accuracy
3.2. Effect of LAI Data Assimilation on Water Quality Simulation Accuracy
4.1. What Is the Link between Improved LAI Data and Improved Model Predictions?
4.2. Which LAI Datasets Are Appropriate for Watershed-Scale Modeling?
4.3. What Modeling Factors Are Important to Make LAI Data Assimilation Efficient?
- The basic model gave right answers for wrong reasons, with reasonably good daily streamflow simulation despite a large bias in LAI. The accuracy of daily streamflow simulation improved throughout the nine-year period. However, this improvement was significant during medium-to-low flow conditions.
- The LAI assimilation model adopted a physically realistic water balance by increasing rootzone soil moisture storage, therefore improving the model’s spatial consistency with reference estimates (from the SMAP satellite mission).
- Assimilation of LAI data into our watershed model substantially improved nitrate load simulations, reproducing long-term in-situ observations at a daily timescale. Our study is the first to show such an effect.
Conflicts of Interest
|No.||Parameter a||Definition b||Spatial Scale||Initial Range c|
|1||ALPHA_BF||Baseflow recession constant (days)||HRU||0.001–1|
|2||CH_K2||Channel hydraulic conductivity (mm/h)||Subbasin||5–100|
|3||CH_N1||Tributary channel Manning’s n||Subbasin||0.001–0.15|
|4||CH_N2||Main channel Manning’s n||Subbasin||0.001–0.15|
|5||OV_N||Overland Manning’s n||HRU||0.12 d|
|6||CN2||Curve number (moisture condition II)||HRU||−0.08–0.08|
|7||SURLAG||Surface runoff lag coefficient (days)||Basin||0.05–10|
|8||EPCO||Plant uptake compensation factor||HRU||0.01–1|
|9||ESCO||Soil evaporation compensation factor||HRU||0.01–1|
|10||GW_DELAY||Groundwater delay (days)||HRU||−10–10|
|11||GW_REVAP||Groundwater “revap” coefficient||HRU||0.01–0.2|
|12||GWQMN||Threshold depth for return flow (mm H2O)||HRU||0.01–5000|
|13||REVAPMN||Re-evaporation threshold (mm H2O)||HRU||0.01–500|
|14||RCHRG_DP||Deep aquifer percolation fraction||HRU||0–1|
|15||SOL_AWC||Available soil water capacity (mm/mm)||HRU||−0.15–0.15|
|16||SOL_K||Soil hydraulic conductivity (mm/h)||HRU||−0.15–0.15|
|17||TIMP||Snow pack temperature lag factor||Basin||0–1|
|18||SFTMP||Snowfall temperature (°C)||Basin||0–5|
|19||SMFMN||Min snowmelt factor (mm H2O/°C-day)||Basin||0–10|
|20||SMFMX||Max snowmelt factor (mm H2O/°C-day)||Basin||0–10|
|21||SMTMP||Snowmelt base temperature (°C)||Basin||−2–5|
|No.||Parameter a||Definition b||Spatial Scale||Initial Range c|
|1||SPCON||Linear sediment routing factor||Basin||0.0081 d|
|2||SPEXP||Exponent sediment routing factor||Basin||1 d|
|4||CH_COV1||Channel erodibility factor||Subbasin||0.22 d|
|3||CH_COV2||Channel cover factor||Subbasin||0.1 d|
|5||PHOSKD||Phosphorus soil partitioning coefficient||Basin||167 d|
|6||PPERCO||Phosphorus percolation coefficient||Basin||11.2 d|
|7||SOL_ORGP||Initial organic P conc. in soil layer (mg P/kg)||HRU||10 d|
|8||SOL_ORGN||Initial organic N conc. in soil layer (mg N/kg)||HRU||1–50|
|9||SOL_NO3||Initial NO3 concentration in soil layer (mg N/kg)||HRU||1–50|
|10||SOL_CBN||Organic carbon content in soil layer (% weight)||HRU||0.05–10|
|11||BIOMIX||Biological mixing efficiency||HRU||0.001–1|
|12||CDN||Denitrification exponential rate||Basin||0.001–3|
|13||CMN||Mineralization rate of active N and P||Basin||0.001–0.003|
|14||HLIFE_NGW||Half-life of nitrogen in ground water (days)||Basin||0–500|
|15||NPERCO||Nitrate percolation coefficient||Basin||0.001–1|
|16||SDNCO||Denitrification threshold water content||Basin||0.001–1.1|
|17||ANION_EXCL||Fraction of porosity to exclude anions||Basin||0.01–1|
|18||BC1||Biological oxidation rate (NH3) (day−1)||Basin||0.1–1|
|19||BC2||Biological oxidation rate (NO2-NO3) (day−1)||Basin||0.2–2|
|20||BC3||Biological oxidation rate (organic N-NH3) (day−1)||Basin||0.02–0.4|
|21||RS4||Organic N settling rate in the channel (day−1)||Subbasin||0.001–0.1|
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|Topography||30-m National Elevation Dataset |
|Land use||30-m 2011 Cropland Data Layer |
|Soil texture||1:250,000 State Soil Geographic (STATSGO) dataset |
|Slope||Five slope classes: 0–2%, 2–4%, 4–6%, 6–8%, >8% [3,39]|
|Subsurface drainage||Tile drains were assigned on low slope (<2%) with row crops and poorly drained soils (hydrologic soil group D) [3,37,38]|
|Agricultural management operations||(a) Timing of plantation and harvest; (b) timing, type, rate, and place of fertilizer applications; (c) corn–soybean annual rotation [3,37]|
|Precipitation||Total daily precipitation from 1-km gridded DAYMET product |
|Energy-related weather forcing||Minimum and maximum daily temperature from 1-km gridded DAYMET product ; solar radiation, wind speed and relative humidity from the historical weather generator within the SWAT source-code ; potential evapotranspiration (PET) estimated with built-in Penman-Monteith equation |
|Streamflow and nitrate data||Six U.S. Geological Survey (USGS) gage stations; five for model calibration and one for model verification (Figure 1b)|
|Rootzone (100 cm) soil moisture data||Soil Moisture Active Passive (SMAP) mission 3-hourly L4 global 9-km rootzone soil moisture; used for model verification |
|LAI data||MODIS MCD15A3H LAI/FPAR four-day L4 global 500 m v006 ; assimilated in the model replacing the model’s default LAI values|
|Location a||Basic Model||LAI-Assimilation Model|
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Rajib, A.; Kim, I.L.; Golden, H.E.; Lane, C.R.; Kumar, S.V.; Yu, Z.; Jeyalakshmi, S. Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions. Remote Sens. 2020, 12, 2148. https://doi.org/10.3390/rs12132148
Rajib A, Kim IL, Golden HE, Lane CR, Kumar SV, Yu Z, Jeyalakshmi S. Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions. Remote Sensing. 2020; 12(13):2148. https://doi.org/10.3390/rs12132148Chicago/Turabian Style
Rajib, Adnan, I Luk Kim, Heather E. Golden, Charles R. Lane, Sujay V. Kumar, Zhiqiang Yu, and Saranya Jeyalakshmi. 2020. "Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions" Remote Sensing 12, no. 13: 2148. https://doi.org/10.3390/rs12132148