Hydrological Model Parameters Research Articles

Integrated hydrological modelling of two contrasting watersheds with a terminal reservoir in the Upper Tapi River basin, India

Abstract The present study developed an integrated hydrologic model for sustainable utilisation and water management in two complex watersheds with varying physioclimatic features and reservoirs. The soil and water assessment tool (SWAT) is used for predicting integrated inflows into the Hatnur reservoir from the Burhanpur and Purna watersheds of the Upper Tapi River basin, while outflows are simulated using a rule curve. The influence of watershed complexities on hydrological model parameters and the watershed processes are investigated using extensive multisite and multivariable calibration (1998–2007) and validation (2008–2013) approaches, including sensitivity and uncertainty analyses. The sensitive parameters are related to curve number (CN), groundwater, slope, and main channel characteristics. The annual streamflow (m3/km2/mm of rainfall) in the Burhanpur watershed is 568.7, which is 4.2 times higher than the Purna watershed's streamflow of 136.2. The hypsometric analysis, areal rainfall, and flow duration curves revealed a substantially different streamflow pattern and a larger coefficient of variation in the spatial distribution of water balance components over sub-watersheds in the Burhanpur watershed compared to the Purna watershed due to diverse topographic features. The developed model would be useful for planning controlled releases from the terminal reservoir to mitigate hazards in the downstream reaches of the Tapi River basin.

A Framework for Algorithmic Improvement to Mitigate the Effects of Equifinality in the Calibration of High-dimensional Parameters for Hydrological Models

A Framework for Algorithmic Improvement to Mitigate the Effects of Equifinality in the Calibration of High-dimensional Parameters for Hydrological Models

Parallelization of AMALGAM algorithm for a multi-objective optimization of a hydrological model

A calibration procedure is essential step to achieve a realistic model simulation particularly in hydrological model which simulates water cycle in the basin. This process is always faced with challenges due to selection of objective function and highly time-consuming. This study aimed to take advantage of parallel processing to accelerate the computations involved with simulation process of hydrologic model linked with the multi-objective optimization algorithm of AMALGAM for multi-site calibration of SWAT hydrologic model parameters. In order to illustrate how meaningful SWAT model calibration trade-off between the four objective functions involved in AMALGAM optimization program, the Pareto solution sets were provided. Furthermore, it is implemented a group of model runs with a number of cores involved (from one to eight) to demonstrate and evaluate the running of parallelized AMALGAM with taking advantages of “spmd” method to decrease the running time of the SWAT model. The results revealed the robustness of the method in reducing computational time of the parameter calibration significantly. This strategy with 4-objective functions focuses on high streamflow (Nash–Sutcliffe coefficient), low streamflow (Box–Cox transformed root–mean–square error), water balance (runoff coefficient error), and flashiness (slope of the flow duration curve error) provided an efficient tool to decide about the best simulation based on the investigated objective functions. This study also provides a strong basis for multi-objective optimization of hydrological and water quality models and its general analytical framework could be applied to other parts of the world.

Exploring an intelligent adaptation method of hydrological model parameters for flood simulations based on the light gradient-boosting machine

Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods.

A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting

Conventional hydrological modeling is usually based on the assumption that parameters are statically time-invariant. However, recent studies suggest that the influences of climate change and human interventions have made this hypothesis questionable. Meanwhile, machine learning techniques are increasingly used to extract patterns and insights from the ever-increasing hydrometeorological data. Here, we proposed a hybrid framework (HSPDM) to improve the precision of real-time flood forecasting in response to nonstationary conditions. It utilizes multiple machine learning techniques to dynamically retrieve calibrated hydrological model parameters from historical similar floods, thus continuously obtaining hourly time-variant parameters in real-time flood forecasting operations. Using the Quzhou Basin in China as a case study, the effectiveness and advancement of HSPDM framework was examined. Three schemes, including traditional time-invariant parameters (scheme 1), hourly time-variant parameters (scheme 2), and probabilistic forecasting scheme (scheme 3), were built for comparison purpose. The results were summarized as follows: (1) The proposed framework can successfully identify continuous flood subsequence with a high retrieval accuracy (1.74) and acceptable time consumption (175.05 s) by adopting k-means, K-Nearest Neighbor (KNN), and embedding-based subsequence matching (EBSM) method. (2) Compared to scheme 1, scheme 2 provided more reliable forecasting results with higher accuracies, in terms of the general goodness-of-fit (higher NSE value) and reproducing flood peak and flood process. (3) The streamflow hydrographs forecasted by scheme 2 fell exactly in the predictive uncertainty bounds of scheme 3 and even showed superiority compared to a preferred deterministic forecast (Q50) of scheme 3. The major scientific contribution of this study lies in advancing the technique of real-time flood forecasting based on the hourly time-variant model parameters, thereby strengthening our understanding of model behaviors under changing conditions. The proposed framework can also act as a new alternative for flood control and ultimately contribute to the mitigation of flooding disasters.

To what extent does river routing matter in hydrological modeling?

Abstract. Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration.

A dataset of forest leaf area indexes in Xishuangbanna (2010–2020)

Leaf area Index (LAI) is typically defined as the ratio of the total area of vegetation, including leaves, stems, flowers, fruits and other components to the corresponding land area. LAI is an indispensable component parameter in ecosystem carbon cycle, hydrological and climate models. It has been widely used in forest monitoring, vegetation evaluation and crop yield estimation. As an effective tool for assessing forest health, LAI can better reflect forest growth. It can not only affect forest carbon absorption, but also directly affect regional and even global climate by changing the ratio of sensible heat and latent heat flux. As a member of the China Ecosystem Research Network (CERN) and the National Field Scientific Observation and Research Station Network (CRERN), Xishuangbanna Tropical Ecosystem Research Station (Banna Station) of the Chinese Academy of Sciences (CAS) conducts monitoring strictly in accordance with the Terrestrial Ecosystem Biological Observation Indicators and Norms. The quality of the observation data is strictly controlled pursuant to the Quality Assurance and Quality Control of Terrestrial Ecosystem Biological Observation Data. In this dataset, we collected the data of leaf area index (LAI) of tree, shrub and grass layers of forest plant communities in five long-term monitoring plots of Banna Station from 20010 to 2020 to provide scientific foundation for the study of tropical forest ecosystem and carbon cycle.

A Critical Evaluation of Streamflow Estimation of an Ungauged Watershed using a Gauged Watershed of Same River Basin – Case of Nilwala River Basin

Transferring hydrological model parameters of a nearby gauged watershed to an ungauged watershed is being widely practiced to predict streamflow in the ungauged watershed. However, proper conclusions on transferring data from the main to sub-watershed and vice versa need to be established, then only watershed managers can plan where to locate or ensure optimum use of resources when establishing gauging stations. Therefore, the present study aimed to evaluate the performance of spatial transferability by using the Tank Model, which was initially calibrated to daily streamflow from water year 2008/09 to 2017/18 observed at two gauged watersheds in Nilwala River Basin, and then transferred from main to sub-watershed and vice versa. The efficiency of predictions under two parameter transferability scenarios was highly acceptable where the Mean Ratio of Absolute Error (MRAE) values were 0.35-0.39 and, the accuracy of annual water balance was 91.8%-93.3%. Most importantly, the best results were in the intermediate flow regime with an accuracy of 61%-70%. The monthly-scaled streamflow indicated an accuracy of 83%-92%. The present study demonstrated the liability of the Tank Model for successful estimation of daily streamflow with an average accuracy of 63% under spatial parameter transferability thereby with only a single wellmanaged streamflow gauging station, Tank Model can be applied for sustainable water resources management in similar ungauged watersheds of the basin.

Evaluation of best management practices to reduce sediment yield in the upper Gilo watershed, Baro akobo basin, Ethiopia using SWAT

The increasing sediment yield in the watershed is caused by natural and human activities, which significantly shifts the hydro-meteorological in the watershed. The Modified Universal Soil Loss Equation (MUSLE) equation in the Soil and Water Assessment Tool (SWAT) was used to estimate sediment yields for each hydrological response unit (HRU) based on peak runoff, daily runoff volume, area of hydrological response unit, and other estimated and default hydrological model parameters. The amount of sediment yield from each HRU is then summed to give the total soil erosion for the watershed.The spatio-temporal variations of sediment yield in the Upper Gilo watershed was simulated to identify the hotspot area and select the effective management practices (BMPs) for reducing significant problems. Model calibration and validation were carried out using sediment yield data from 1990 to 2004 and 2005 to 2014. The results indicated that the watershed total sediment yield is 1021.8 tonnes/yr. Furthermore, 17 sub-basins (37.8% of total watershed area) are severely threatened by high soil erosion. According to the simulation results, the filter strips, terraces, and contours reduced the watershed sediment yield by up to 53.2%, 45.4%, and 48%. Overall, the selected BMPs are highly effective in reducing sediment yield in watershed-prone areas.

Strategies analysis for improving SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions

Sediment yields in a watershed are usually affected by various natural disturbances and anthropogenic activities, and can be simulated by using hydrological models. However, due to the availability and type of data (i.e., continuous or discrete) of measured sediment data, model calibration and validation performances could be significantly affected and the calibrated parameters may not be representative for the area. In this study, the sediment rating curves (SRCs) were developed to increase measurements of sediments for model calibration, and five strategies of using measured and estimated sediment data on improving a hydrological model (Soil and Water Assessment Tool, SWAT) calibration and validation for multiple sediment stations were analyzed. The five strategies were: using measured sediment data (S1), using measured data and different portions of estimated sediment during typhoon events (S2 to S4), and using estimated sediment data during entire simulation period (S5). The results showed that although the S1 mostly performed better than other sediment strategies, S4 and S5 were also suitable for improving sediment simulation at the downstream stations (STN1, 2, and 3) and upstream station (STN4), respectively. Moreover, the impact of incorporating different portions of typhoon-induced sediment data (S2, S3, and S4) on the model performance showed differently at stations. In sum, the proposed analytical procedure is expected to be useful for calibrating sediment parameters of hydrological models with limited or highly unevenly distributed measured sediment data.

Machine learning-enabled calibration of river routing model parameters

Abstract Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.

Massively Parallel Monte Carlo Sampling for Xinanjiang Hydrological Model Parameter Optimization Using CPU-GPU Computer Cluster

The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. The MCS method is an internally concurrent algorithm that can be parallelized. It has the potential to execute on massively parallel hardware systems such as multi-node computer clusters equipped with multiple CPUs and GPUs, which are known as heterogeneous hardware systems. To take advantage of this, we parallelize the algorithm and implement it on a multi-node computer cluster that hosts multiple INTEL multi-core CPUs and NVIDIA many-core GPUs by using C++ programming language combined with the MPI, OpenMP, and CUDA parallel programming libraries. The parallel parameter optimization method is coupled with the Xinanjiang hydrological model to test the acceleration efficiency when tackling real-world applications that have a very high computational burden. Numerical experiments indicate, on the one hand, that the computational efficiency of the massively parallel parameter optimization method is significantly improved compared to single-core CPU code, and the multi-GPU code achieves the fastest speed. On the other hand, the scalability property of the proposed method is also satisfactory. In addition, the correctness of the proposed method is also tested using sensitivity and uncertainty analysis of the model parameters. Study results indicate good acceleration efficiency and reliable correctness of the proposed parallel optimization methods, which demonstrates excellent prospects in practical applications.

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population.

A surrogate modeling method for distributed land surface hydrological models based on deep learning

Surrogate modelling methods have been used to estimate spatially distributed parameters of land surface hydrological models due to their computational efficiency. However, traditional surrogate techniques have trouble in modelling high-dimensional input–output maps. In addition, their applications to a distributed model over large spatial and temporal domains are very unlikely to produce consistent spatial pattern and dynamic relationship of model input and output, leading to unbalanced model performance across different spatiotemporal domains. To address these issues, we employ a deep autoregressive neural network to construct an accurate and reliable surrogate system of distributed dynamic model outputs. The network uses a deep convolutional encoder-decoder architecture to take full advantage of the deep convolutional layers in high-dimensional image processing and spatial feature extraction. Moreover, the autoregressive strategy makes the network good at handling dynamic relationship between the time-varying model input and output. We apply this deep learning (DL) network to building a surrogate model of the Variable Infiltration Capacity (VIC) model simulated soil moisture over the Huaihe river basin of China. The results show that the adopted deep autoregressive neural network can provide an accurate surrogate model of the high-dimensional dynamic relationship between 15 input fields and 1 output field, each covering 408 grids, over multiple years. This surrogate model significantly outperforms the popular long short-term memory (LSTM) model, achieving an average mean squared error (MSE) of 0.26 and an average R2 of 0.76 using the test datasets. The surrogate's MSE and R2 performance represents an average improvement of 43% and 33%, respectively, compared to the LSTM model. In addition, it shows better spatial performance than the LSTM model, demonstrating its superior performance in approximating spatial patterns of VIC simulated soil moisture. The accuracy of this surrogate method and its ability to handle high-dimensional data is promising for advancing parameter uncertainty quantification of distributed land surface hydrological models, which usually have high dimensionality due to the large number of variables involved, including distributed parameters, input forcing variables, and output variables.

Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia

Uncertainty quantification of hydrological model parameters plays an important role in the streamflow prediction of river basins. It is still a challenge to evaluate the effect of parameter uncertainty on hydrological models in association with moderate computation and high accuracy. In this study, a PCE-ANOVA-RF (abbreviated as PAR) method is developed to analyze the individual and interactive effects of multiple uncertain parameters in the Soil and Water Assessment Tool (SWAT) model as well as generate probabilistic forecasts of daily streamflow. The proposed PAR method can not only unveil the effect of parameter uncertainty and save computation time without reducing the simulation accuracy, but also expand PCE’s ability to predict future streamflow processes. The Amu Darya River Basin in Central Asia is introduced to verify the feasibility and applicability of PAR. The results disclose that (i) compared with the Sequence Uncertainty Fitting ver-2 (SUFI-2) algorithm, the simulation accuracy of PAR is acceptable (e.g., NSE = 0.85 in calibration period and 0.81 in validation period); (ii) in terms of variance analysis and streamflow simulation, PAR is more efficient and can largely reduce the number of model runs (about 88%); (iii) the parameters related to snow melting process have significant effects on both average flow and peak flow; besides, the interaction among these parameters are more obvious than that of other sensitive parameters.

Base flow separation method in conditions of the karst catchment

AbstractGeological conditions are an important parameter on hydrological modeling that strongly affects runoff generation processes. They are not usually taken into account in the parameters of hydrological models, especially karst catchments, where water transfers through fissures, cavities, caves, and phreatic channels are regularly discarded in studies because of their nonlinear behavior and the variability of their typology. The study compared using of base flow separation methods in the conditions of karst catchments, where the reference base flow values measured are not available. The base flow separation was realized by various methods and the models available, including BFI+ and WHAT

A framework to assess future water-resource under climate change in northern Morocco using hydro-climatic modelling and water-withdrawal scenarios

Study regionThe Bas-Loukkos catchment, a Mediterranean catchment in northern Morocco exposed to growing water withdrawal caused mainly by agricultural development. Study focusFor adaptation to climate change, water managers have to consider the high and various uncertainties. To assess impacts of climate change on projected water resources, this study aimed to develop a smart analysis framework to provide scientific information by exploring the complexity of many projections combined with hydrological models. Uncertainties were quantified using 13 pair-wise combinations of 5 regional climate models forced by 4 global climate models under two emissions scenarios (RCP4.5 and RCP8.5), data with and without bias correction (using empirical quantile mapping), and two sets of GR2M hydrological model parameters corresponding to different precipitation conditions. The Budyko hypothesis was used to analyse combined effects of climate change on water resources according to water-withdrawal scenarios. Climate and hydrological projections have been analyzed over three periods: short-term [2020–2040], medium-term [2041–2060] and long-term [2081–2100]. New hydrological insights for the regionResults from all simulations indicate that, in the long term (2081–2100), precipitation and discharge will decrease by ca. 21–38% and ca. 50–71%, respectively, compared to the reference period (1981–2005). Consequently, this decline in water resources will require water management strategies to adapt to the future climatic conditions and water demand.

Regional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemes

Study Region: The present study was conducted in 24 watersheds located in the region of Galicia, in the northwest of Spain, covering an extension of approximately 13,000 km2.Study focus: This study is focused on the application and evaluation of different schemes for streamflow Prediction in Ungauged Basins (PUB). The MHIA model (Spanish acronym for Modelo HIdrológico Agregado), is first used to reproduce the observed time series of discharge in several gauged basins. Then, six different regionalisation schemes are applied to transfer the hydrological model parameters to ungauged catchments. For that purpose, we explore and compare two physical similarity, two spatial proximity and two regression-based regionalisation schemes. Output averaging (also known as ensemble modelling) as well as parameter averaging implementations of the physical similarity and spatial proximity methods are analysed.New hydrological insights: The most efficient methods are those based on output averaging, with acceptable success rates (SR) in 88% of the cases. On the other hand, the parameter averaging-based methods have the lowest SR. The methods based on spatial proximity output averaging provide the best performance when the receptor basin has a sufficient number of nearby donor basins. On the other hand, the methods based on physical similarity output averaging show a better performance in areas where there is a low density of donor catchments. The regression-based methods showed the lowest performance in all cases. The existence of correlations between the performance of the regionalisation schemes and the area of the receptor catchments was observed, with higher performances in large basins than in small basins.

Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China

Study Region: Nine mountain catchments (5 in northern and 4 in southern China).Study Focus: Flash floods in small mountain catchments are some of the most frequent and most destructive natural disasters of China. Mixed runoff hydrological model is a popular method for flood warning and forecasting. However, the key factors affecting runoff components in hydrological model parameter calibration need to be further studied. In this study, four different overland flow routing techniques are implemented into a semi-distributed mixed runoff hydrological model system (MRHMS), which are the triangle unit hydrograph (TUH), geomorphological instantaneous unit hydrograph (GIUH), geomorphoclimatic instantaneous unit hydrograph (GcIUH) and kinematic wave (KW). MRHMS-KW is the previously developed spatiotemporally-mixed runoff (SKBY) model.New hydrological insights for the region: The study finds that sensitivity parameters of the MRHMS in northern and southern China are related to their main runoff components. Different parameter orders in automatic calibration of MRHMS result in different model performances, and the suggested parameter orders are consistent with the sensitivity of parameters in northern and southern China. Soil moisture and primary runoff components in northern and southern China differ significantly. Only MRHMS-GcIUH can achieve similar performance in northern and southern catchments. MRHMS-GcIUH therefore enhances our understanding of runoff formation mechanisms in the heterogeneous hydroclimatic landscape of China and is a promising tool for a nationwide flood prediction and flood warning system.

Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.