Performance Of Hydrological Models Research Articles

Modelling of hydrological processes in snowmelt-governed permafrost-free catchments of the Western Siberian Lowlands

To date, no examples of small- to meso-scale hydrological simulations exist in the southern part of the Western Siberian lowlands, despite intensive agriculture and high vulnerability to climate change. We propose a first simulation approach in which we assess the importance of surface and groundwater processes on hydrological model performance. Therefore, we simulated three catchments, using four different model setups incorporating different landscape characteristics and processes. An objective calibration and comparison framework was applied to assess the different setups which reached very diverse performance: the setups where physically-based surface retention is considered, showed slightly more realistic surface runoff driven peak flows and the setups with a more complex groundwater concept improved the depiction of surface runoff, the recession phase and the contributing baseflow significantly. The best performing, most complex setup was used to assess the prevailing hydrological processes of the lowland with its cold, continental climate in more detail.

Review and Development of ASABE Engineering Practice 621: “Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality Models”

Abstract. In 2010, the Natural Resources and Environmental Systems Hydrology Committee (NRES-21) of ASABE initiated a long-term process to develop guidelines to improve modeling practice through better understanding of the calibration, validation, and evaluation process across applications and more effective interpretation and communication of model performance. This effort generated a compilation of 23 articles with model-specific descriptions and guidance (2012), a position paper outlining guidance for evaluating, interpreting, and communicating performance of hydrologic and water quality models considering intended use (2014), and a compilation of ten articles addressing key topics related to model calibration and validation (2015). In 2016, the first draft of ASABE Engineering Practice 621 (EP621), “Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality (H/WQ) Models,” was developed, subsequently revised, and ultimately approved by the ASABE Standards Committee in 2017. EP621 provides guidelines, not prescriptive requirements, and as such recommends “good” modeling practices to enhance calibration, validation, evaluation, and communication of H/WQ models through establishment of consistent terminology; model selection; compilation and processing of input data and calibration, validation, and evaluation data; determination of model performance measures; model parameterization and calibration; re-examination of input and calibration data and/or consideration of model refinement; re-evaluation of model performance; and documentation of modeling process and results. EP621 can be obtained from the ASABE Technical Library at https://elibrary.asabe.org/abstract.asp?aid=47804. The objectives of this technical note are to review the process and rationale used to develop EP621 and to briefly summarize its major components. Keywords: Hydrologic processes, Hydrology, Model calibration, Modeling, Model validation, Water quality.

Influence of calibration data on hydrological model prediction

Hydrological model performance depends on the information content and hydrological variability in the calibration data. The hypothesis for this study was that a priori predictability of the model performance can be made based on knowledge about the data used for calibration. If the validation time period has similar hydrological variability to the calibration period where the model is expected to perform better and it is termed as the 'interpolation case', otherwise it is 'extrapolation', where a better performance cannot be expected. In this study a geometrical property of data was used to discriminate between the cases of interpolation or extrapolation. The methodology was demonstrated using WaSiM-ETH model in Rems catchment. The results show that the relative residual is very high at extrapolation case as compared to the interpolation case. The result of this study can be used to make a priori estimate of the error in prediction without running the model.

Transferability of Conceptual Hydrological Models Across Temporal Resolutions: Approach and Application

The temporal resolution of observed data is a critical element in determining the parameters, prediction performance, and applicability of hydrological models. In this study, runoff simulations were performed at different temporal resolutions using the Xinanjiang model to evaluate the influence of temporal resolution on the model parameters and performance. Based on the sensitivity analysis of the model parameters, the posterior distribution of the sensitive parameters was derived using the Bayesian method and Differential Evolution Adaptive Metropolis (DREAM) algorithm at different temporal resolutions. The transformation functions of the model parameters were put forward to transform the parameters according to the regulatory between the parameters and time-steps on the basis of the parameters posterior distribution. The model performance and uncertainty for runoff simulation were compared and discussed at each temporal resolution. The results show that (1) the parameters related with the process of the water balance and runoff routing are identified as sensitive to the temporal resolutions, and there exist linear or power function relationships between parameter values at different temporal resolutions; and (2) the quantitative relationship equations have been verified to have good capacity for model simulation when the model parameters are transformed from other temporal resolution.

Improving hydrological simulations by incorporating GRACE data for model calibration

Hydrological model parameters are typically calibrated by observed streamflow data. This calibration strategy is questioned when the simulated hydrological variables of interest are not limited to streamflow. Well-performed streamflow simulations do not guarantee the reliable reproduction of other hydrological variables. One of the reasons is that hydrological model parameters are not reasonably identified. The Gravity Recovery and Climate Experiment (GRACE)-derived total water storage change (TWSC) data provide an opportunity to constrain hydrological model parameterizations in combination with streamflow observations. In this study, a multi-objective calibration scheme based on GRACE-derived TWSC and streamflow observations was compared with the traditional single-objective calibration scheme based on only streamflow simulations. Two hydrological models were employed on 22 catchments in China with different climatic conditions. The model evaluations were performed using observed streamflows, GRACE-derived TWSC, and actual evapotranspiration (ET) estimates from flux towers and from the water balance approach. Results showed that the multi-objective calibration scheme provided more reliable TWSC and ET simulations without significant deterioration in the accuracy of streamflow simulations than the single-objective calibration. The improvement in TWSC and ET simulations was more significant in relatively dry catchments than in relatively wet catchments. In addition, hydrological models calibrated using GRACE-derived TWSC data alone cannot obtain accurate runoff simulations in ungauged catchments. This study highlights the importance of including additional constraints in addition to streamflow observations to improve performances of hydrological models.

Evaluation of Conceptual Hydrological Models in Data Scarce Region of the Upper Blue Nile Basin: Case of the Upper Guder Catchment

The prediction of dominant hydrological processes is imperative with the available information in data scarce regions by means of the lumped hydrological models for the purpose of water resource management. This study is aims at an intercomparison of the performances of the conceptual hydrological models in predicting streamflow. The Veralgemeend Conceptueel Hydrologisch (VHM) and NedborAfstromnings Model (NAM) lumped rainfall–runoff models were manually calibrated and validated for periods of 1 January 1990–31 December 2000 and 1 January 2001–31 December 2005, respectively. Some of the parameters of the models (i.e., recession constants of subflow components) were estimated from the preprocessing of the streamflow data using the Water Engineering Time Series PROcessing tool (WETSPRO). These parameters were used for the initial model setup and subjected to slight adjustments during calibration. The performances of the models were evaluated by graphical and statistical means. The results depicted that the models reproduced the streamflow in a good way and that the overall shape of the hydrograph was properly captured. A Nash Sutcliffe efficiency (NSE) of 0.71 and 0.67 were obtained during calibration, whereas, for the validation period, NSE of 0.6 and 0.58 were obtained for VHM and NAM, respectively. The water balance discrepancy (WBD) of −0.1% and −13.7% were achieved for calibration, while −17% and −9% were acquired during validation for VHM and NAM, respectively. Though the models underestimated the high flows, the low flows were relatively well simulated. From the overall evaluation of the models, it is noted that the NAM model performed better than the VHM model in predicting the flow. In conclusion, the models can be used for water resource management and planning with precautions for extreme flow.

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73–0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error – observations standard deviation ratio (RSR) values <0.7 (0.39–0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32–0.52, PBIAS: ±32.73%, and RSR: 0.63–0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59–0.61, PBIAS: ±13.70%, and RSR: 0.63–0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

Comparative Assessment of SWAT Model Performance in two Distinct Catchments under Various DEM Scenarios of Varying Resolution, Sources and Resampling Methods

The aim of this study is to evaluate the performance of hydrological model - Soil & Water Assessment Tool (SWAT) in two distinct catchments, under various Digital Elevation Model (DEM) scenarios of varying resolution (from 30 m to 300 m), sources (SRTM, ASTER and CartoDEM) and resampling methods (nearest neighbour, bilinear interpolation, majority and cubic convolution) available with ArcGIS software package. A comparison was made in between model response of highly elevated Himalayan Upper Teesta catchment and peninsular monsoon dominated Upper Narmada catchment in India. Model performance was assessed & subsequently compared based on statistical measures such as coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE), for monthly runoff and sediment yield. The sensitivity of monthly model outputs of runoff, sediment yield, Total Nitrogen (TN) & Total Phosphorous (TP) towards DEM scenarios was studied based on Relative Difference (RD). The key findings of this study are: 1) topographic characteristics of Upper Teesta catchment were found to be more sensitive towards various DEM scenarios compared with Upper Narmada catchment; 2) model performance in simulating monthly runoff was found to be unaffected for both catchments due to changes in DEM resolution & resampling method; 3) in simulating monthly sediment yield, model performance was affected due to all DEM scenarios for Upper Narmada catchment, while scenarios of changing DEM resolution and resampling method have affected model performance for Upper Teesta catchment.

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R2) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed streamflow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out-performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.

Assessment of variable source area hydrological models in humid tropical watersheds

ABSTRACTThe objective of this study was to compare the performances of hydrological models that incorporate the Variable Source Area (VSA) mechanism of runoff generation with that of the Soil and Water Assessment Tool (SWAT), which uses the infiltration-excess mechanism. One of the VSA-based model used, SWAT–VSA, has been proposed as a re-conceptualization of the SWAT and uses a topography-based wetness index to identify source areas. In this study, the topography-based wetness index was replaced with a Modified Normalized Difference Water Index (MNDWI) derived from satellite imagery resulting in the SWAT–MNDWI model. Model performances were evaluated through their application in two humid tropical watersheds (Hemavathi – 2974 km2; Harangi – 538.8 km2) located in the Upper Cauvery River Basin, India. Using relevant data inputs, the three models were applied separately to both watersheds. Models were calibrated for the historical period 2000–2003 and validated for the period 2004–2006 using observed daily observed streamflow records at the watershed outlets. Overall, the SWAT–MNDWI model was the best one in simulating daily streamflow with Nash–Sutcliffe efficiency of 0.85, coefficient of determination of 0.88, percentage bias of 13.2% and root mean square error of 37.48 m3/s for the Hemavathi watershed and corresponding values of 0.88, 0.88, 1.09% and 16.67 m3/s for the Harangi watershed. The spatial patterns of surface runoff generation were similar for the SWAT–VSA and SWAT–MNDWI models, but completely different for the SWAT model. Overall results have demonstrated that models incorporating VSA hydrology, and in particular the proposed SWAT–MNDWI model, provide accurate and convenient tools for distributed hydrologic modelling in humid tropical watersheds.

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

Abstract. Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.

The effects of current landscape configuration on streamflow within a Yellow River HUC-10 watershed of the Atlanta Metropolitan Region

This study evaluated effects of current landscape configuration (arrangement) on streamflow within a 162.0mi2 (419.6km2) watershed of the Yellow River (Hydrologic Unit Code 10, HUC-10) of the Atlanta, Georgia metropolitan region located within the Southeastern United States (U.S.). Monthly and daily Thornthwaite Water Balance models using landscape configuration (contagion-adjusted curve numbers, CON-adjusted CNs) derived from the most current (2011) land cover/land use (LC/LU) dataset out-performed original CN watershed simulations over the 5-year (60-month and 1826-day) period from January 2009 to December 2013. This was based upon evaluation from several statistically-based, hydrologic model performance predictors. At the reclassified level (urban; forest; and agricultural), configurational metrics related to CON known as the aggregation index (AI) and clumpiness index (CI) indicated greater landscape heterogeneity that may lead to complex flow patterns difficult to simulate within a HUC-10 streamflow model. On the other hand, these related metrics also indicated higher levels of clumping within these reclassified LC/LUs may allow for improved accuracy in simulating streamflow-generating processes across landscapes within a HUC-10 streamflow model. Results also inferred that localized landscape configuration within the largest sub-watershed (HUC-12) of a HUC-10 watershed could have a disproportionate impact on runoff and should be prioritized within regional U.S. watershed management plans.

Process‐based interpretation of conceptual hydrological model performance using a multinational catchment set

AbstractMost of previous assessments of hydrologic model performance are fragmented, based on small number of catchments, different methods or time periods and do not link the results to landscape or climate characteristics. This study uses large‐sample hydrology to identify major catchment controls on daily runoff simulations. It is based on a conceptual lumped hydrological model (GR6J), a collection of 29 catchment characteristics, a multinational set of 1103 catchments located in Austria, France, and Germany and four runoff model efficiency criteria. Two analyses are conducted to assess how features and criteria are linked: (i) a one‐dimensional analysis based on the Kruskal‐Wallis test and (ii) a multidimensional analysis based on regression trees and investigating the interplay between features. The catchment features most affecting model performance are the flashiness of precipitation and streamflow (computed as the ratio of absolute day‐to‐day fluctuations by the total amount in a year), the seasonality of evaporation, the catchment area, and the catchment aridity. Nonflashy, nonseasonal, large, and nonarid catchments show the best performance for all the tested criteria. We argue that this higher performance is due to fewer nonlinear responses (higher correlation between precipitation and streamflow) and lower input and output variability for such catchments. Finally, we show that, compared to national sets, multinational sets increase results transferability because they explore a wider range of hydroclimatic conditions.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

An improved multi-cores parallel artificial Bee colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee the population’s diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash–Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters’ optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.

The importance of aspect for modelling the hydrological response in a glacier catchment in Central Asia

AbstractUnderstanding how explicit consideration of topographic information influences hydrological model performance and upscaling in glacier dominated catchments remains underexplored. In this study, the Urumqi glacier no. 1 catchment in northwest China, with 52% of the area covered by glaciers, was selected as study site. A conceptual glacier‐hydrological model was developed and tested to systematically, simultaneously, and robustly reproduce the hydrograph, separate the discharge into contributions from glacier and nonglacier parts of the catchment, and establish estimates of the annual glacier mass balance, the annual equilibrium line altitude, and the daily catchment snow water equivalent. This was done by extending and adapting a recently proposed landscape‐based semidistributed conceptual hydrological model (FLEX‐Topo) to represent glacier and snowmelt processes. The adapted model, FLEXG, allows to explicitly account for the influence of topography, that is, elevation and aspect, on the distribution of temperature and precipitation and thus on melt dynamics. It is shown that the model can not only reproduce long‐term runoff observations but also variations in glacier and snow cover. Furthermore, FLEXG was successfully transferred and up‐scaled to a larger catchment exclusively by adjusting the areal proportions of elevation and aspect without the need for further calibration. This underlines the value of topographic information to meaningfully represent the dominant hydrological processes in the region and is further exacerbated by comparing the model to a model formulation that does not account for differences in aspect (FLEXG,nA) and which, in spite of satisfactorily reproducing the observed hydrograph, does not capture the influence of spatial variability of snow and ice, which as a consequence reduces model transferability. This highlights the importance of accounting for topography and landscape heterogeneity in conceptual hydrological models in mountainous and snow‐, and glacier‐dominated regions.

Impacts of incorporating dominant crop rotation patterns as primary land use change on hydrologic model performance

Abstract Crop rotation is a commonly used management practice in the central U.S. whereby different agricultural crops are grown on a farm field in successive years to help maintain the productive capacity of the soil. Understanding hydrologic responses to crop rotation is critical for developing appropriate crop rotation strategies for sustainable water resources management. These responses are not well understood due to limited availability of multi-year, crop-specific land use-land cover data. The study focuses on the Smoky Hill River watershed in west-central Kansas, a typical agro-ecosystem watershed in the Central Great Plains of the U.S. A multi-year land use dataset developed for the state of Kansas was used to identify 3-year crop rotation patterns in the watershed for three different periods (2006–08, 2008–10, 2010–12). Out of 276 unique rotations, 21 rotations were found to be dominant, with rotation patterns of grain sorghum-fallow-winter wheat (G-F-W), winter wheat-winter wheat-fallow (W-W-F), and continuous winter wheat (W-W-W) occupying more than 81% of the cropland in each rotation period. From 2006 to 2012, the coverage of grain sorghum increased by 26% and corn by 305%, while winter wheat decreased by 20%. Three 11-year simulation scenarios based on three 3-year crop rotation patterns were implemented in the SWAT model and separately calibrated for streamflow at two gauge sites. The SWAT model produced good calibration statistics for the periods corresponding to the applied crop rotations, while the statistics tended to decrease for other periods, with Nash-Sutcliffe Efficiency coefficient becoming unsatisfactory for periods of six to eight years outside of the calibration period. Applying a different number of dominant crop rotations to the SWAT model, it was found that the number can be reduced down to the five most dominant without significant loss in model performance. While the optimum number of crop rotations must be evaluated for each agricultural watershed, this finding provides valuable information for watershed model development and calibration, which can help to avoid issues of over-parameterization and equifinality.

Assessing the potential of satellite-based precipitation estimates for flood frequency analysis in ungauged or poorly gauged tributaries of China’s Yangtze River basin

Flood frequency analysis (FFA) is critical for water resources engineering projects, particularly the design of hydraulic structures such as dams and reservoirs. However, it is often difficult to implement FFA in ungauged or poorly gauged basins because of the lack of consistent and long-term records of streamflow observations. The objective of this study was to evaluate the utility of satellite-based precipitation estimates for performing FFA in two presumably ungauged tributaries, the Jialing and Tuojiang Rivers, of the upper Yangtze River. Annual peak flow series were simulated using the Coupled Routing and Excess STorage (CREST) hydrologic model. Flood frequency was estimated by fitting the Pearson type III distribution of both observed and modeled streamflow with historic floods. Comparison of satellite-based precipitation products with a ground-based daily precipitation dataset for the period 2002–2014 reveals that 3B42V7 outperformed 3B42RT. The 3B42V7 product also shows consistent reliability in streamflow simulation and FFA (e.g., relative errors −20%−5% in the Jialing River). The results also indicate that complex terrain, drainage area, and reservoir construction are important factors that impact hydrologic model performance. The larger basin (156,736km2) is more likely to produce satisfactory results than the small basin (19,613km2) under similar circumstances (e.g., Jialing/Tuojiang calibrated by 3B42V7 for the calibration period: NSCE=0.71/0.56). Using the same calibrated parameter sets from the entire Jialing River basin, the 3B42V7/3B42RT-driven hydrologic model performs better for two tributaries of the Jialing River (e.g., for the calibration period, NSCE=0.71/0.60 in the Qujiang River basin and 0.54/0.38 in the Fujiang River basin) than for the upper mainstem of the Jialing River (NSCE=0.34/0.32), which has more cascaded reservoirs with all these tributaries treated as ungauged basins for model validation. Overall, this study underscores that the simulated flood quantiles from satellite precipitation-driven streamflow are generally consistent with observations, demonstrating the potential hydrologic utility of satellite precipitation data for FFA in the Jialing River basin and possibly other poorly gauged basins.

Intercomparison of global river discharge simulations focusing on dam operation—multiple models analysis in two case-study river basins, Missouri–Mississippi and Green–Colorado

We performed an intercomparison of river discharge regulated by dams under four meteorological forcings among five global hydrological models for a historical period by simulation. This is the first global multimodel intercomparison study on dam-regulated river flow. Although the simulations were conducted globally, the Missouri–Mississippi and Green–Colorado Rivers were chosen as case-study sites in this study. The hydrological models incorporate generic schemes of dam operation, not specific to a certain dam. We examined river discharge on a longitudinal section of river channels to investigate the effects of dams on simulated discharge, especially at the seasonal time scale. We found that the magnitude of dam regulation differed considerably among the hydrological models. The difference was attributable not only to dam operation schemes but also to the magnitude of simulated river discharge flowing into dams. That is, although a similar algorithm of dam operation schemes was incorporated in different hydrological models, the magnitude of dam regulation substantially differed among the models. Intermodel discrepancies tended to decrease toward the lower reaches of these river basins, which means model dependence is less significant toward lower reaches. These case-study results imply that, intermodel comparisons of river discharge should be made at different locations along the river's course to critically examine the performance of hydrological models because the performance can vary with the locations.

Evaluation of the TRMM multisatellite precipitation analysis and its applicability in supporting reservoir operation and water resources management in Hanjiang basin, China

In this study, we first evaluated a satellite-based precipitation product (3B42V7) using gauge observations and then investigated its utility in supporting reservoir operation and water resources management in Hanjiang basin from January 1998 to December 2013. Direct comparison of 3B42V7 with gauge observations shows that it can well capture the spatial and temporal characteristics of precipitation over the study basin. However, the 3B42V7 estimates generally show slight underestimation of precipitation, especially for extreme precipitation events, which need be considered in the future algorithm development. Next, we conducted the long-term (2008–2013) hydrologic evaluation of the 3B42V7 product using a calibrated monthly hydrologic model. The results show that the performance of the monthly hydrologic model driven by 3B42V7 is compatible to the results driven by gauge-based simulations according to high values of Nash-Sutcliffe coefficients (0.83 and 0.66 for observation-driven and 3B47V7 driven simulations, respectively) and small values of biases (−8.16% and −3.98%). We further evaluated the applicability of 3B42V7 in reservoir operation through a set of operation experiments, in which modeled inflow series were used to make decisions. The results indicate that reservoir operations based on modeled streamflow using the 3B42V7 estimates perform well in water allocation decision-making and strongly agree with actual inflow based operations. Despite that 3B42V7 tends to slightly underestimate precipitation, the resultant operations do not impact the functions and benefits of reservoir operation much. This suggests that the 3B42V7 precipitation estimates are valuable and useful for monthly streamflow simulation and long-term reservoir operation in Hanjiang basin. This study provides a new insight on the evaluation and utility of the remote sensing based precipitation estimates.