Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

Application of the SWAT Model for Evaluating Discharge and Sediment Yield in the Huay Luang Catchment, Northeast of Thailand

The climate change and insufficient data of the discharge and sediment yield in the catchment system are the main cause of the conflict amongst the consumers. The application of a semi-distributed hydrologic model and geographic information system can be a solution to this conflict. This study implemented the SWAT model to estimate the discharge and sediment yield in the Huay Luang Catchment, Northeast of Thailand. The accuracy of the model was affirmed and compared with the data from the Kh103 observed station during 2008–2016 via SWAT-CUP. The study outcome suggested that the SWAT model provided favourable results compared to the observed data where R2, NSE, and PBIAS of the discharge were 0.79, 0.77, and -18.1% respectively and those of the sediment yield were 0.68, 0.65, and -22.7% respectively. Additionally, the quantitative analysis on 22 sub-catchments as the spatial map derived from the Watershed Delineation indicated that both discharge and sediment yield during 2008–2011 were higher than the regular values by 35.9% and 109.6% consecutively, whereas during 2012–2015 were lower than the regulars by 22.4% and 45.4%. In the raining season, more than 50% of the sub-catchments demonstrated 9–20 cubic meter per second of the discharge and 1,000–5,000 tons of the sediment yield, while during the drought season, both volumes in most of the catchments indicated less than 6 cubic meter per second and 1,000 tons, respectively. These happened due to the changes of the rainfall each year. Hopefully, the result and spatial information from this study could be a great contribution to the water resource management and development in any catchment with insufficient data.

Study on Hydrologic Effects of Land Use Change Using a Distributed Hydrologic Model in the Dynamic Land Use Mode

It is reasonable to simulate the hydrologic cycle in regions with drastic land use change using a distributed hydrologic model in the dynamic land use mode (dynamic mode). A new dynamic mode is introduced into an object-oriented modularized model for basin-scale water cycle simulation (MODCYCLE), a distributed hydrologic model based on sub-watersheds, and the hydrological response unit (HRU). The new mode can linearly interpolate data for the years without land use data and consistently transfer HRU water storage between two adjacent years after a land use data update. The hydrologic cycle simulation of the Sanjiang Plain in China was carried out from 2000 to 2014 in the dynamic mode using land use maps of 2000, 2005, 2010, and 2014. Through calibration and validation, the performance of the model reached a satisfactory level. Replacing the land use data of the calibrated model using that of the year 2000, a comparison model in the static land use mode (static mode) was built (i.e., land use unchanged since 2000). The hydrologic effects of land use change were analyzed using the two models. If the land use pattern remained unchanged from 2000, despite the average annual runoff increasing by 4% and the average annual evapotranspiration decreasing by 4% in this region only, the groundwater storage of the plain areas in 2014 would increase by 4.6 bil. m3 compared to that in 2000, rather than the actual decrease of 4.7 bil. m3. The results show that the fluxes associated with groundwater are obviously more disturbed by land use change in the Sanjiang Plain. This study suggests that the dynamic mode should be used to simulate the hydrologic cycle in regions with drastic land use change, and the consistent transfer of HRU water storage may be considered in the dynamic mode.

A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.

Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.