A comparison between lumped and distributed hydrological models for daily rainfall-runoff simulation

In Uruguay, the Santa Lucía Chico watershed has been studied in several hydrologic/hydraulic works due to its economic and social importance. However, few studies have been focused on water balance computation in this watershed. In this work, two daily rainfall-runoff models, a distributed (SWAT) and a lumped one (GR4J), were implemented at two subbasins of the Santa Lucía Chico watershed, with the aim of providing a thorough comparison for simulating daily hydrographs and identify possible scenarios in which each approach is more suitable than the other. Results showed that a distributed and complex model like SWAT performs better in watersheds characterized by anthropic interventions such as dams, which can be explicitly represented. On the other hand, for watersheds with no significant reservoirs, the use of a complex model may not be justified due to the higher effort required in modeling design, implementation, and computational cost, which is not reflected in a significant improvement of model performance.

Hydrologic Assessment of the Uncertainty of Six Remote Sensing Precipitation Estimates Driven by a Distributed Hydrologic Model in the Blue Nile Basin

Because of the sparseness of the ground monitoring network, precipitation estimations based on satellite products (PESPs) are currently requisite tools for hydrological simulation research and applications. The evaluation of six global high-resolution PESPs (TRMM 3B42V7, GPGP-1DD, TRMM 3B42RT, CMORPH-V1.0, PERSIANN, and PERSIANN-CDR) is the ultimate purpose of this research. Additionally, the distributed Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) is used to investigate their potential effects in streamflow predictions over the Blue Nile basin (BNB) during the period 2001 to 2007. The correctness of the studied PESPs is assessed by applying categorical criteria to appraise their performances in estimating and reproducing precipitation amounts, while statistical indicators are utilized to determine their rain detection capabilities. Our findings reveal that TRMM 3B42V7 outperforms the remaining product in both the estimation of precipitation and the hydrological simulation, as reflected in highest NSE and R2 values ranges from 0.85 to 0.94. Generally, the TRMM 3B42V7 precipitation product exhibits tremendous potential as a substitute for precipitation estimates in the BNB, which will provide powerful forcing input data for distributed hydrological models. Overall, this study will hopefully provide a better comprehension of the usefulness and uncertainties of various PESPs in streamflow simulations, particularly in this region.

Spatio-Temporal Discretization Uncertainty of Distributed Hydrological Models

Quantifying the uncertainty linked to the degree to which the spatio-temporal variability of the catchment descriptors (CDs), and consequently calibration parameters (CPs), represented in the distributed hydrology models and its impacts on the simulation of flooding events is the main objective of this paper. Here, we introduce a methodology based on ensemble approach principles to characterize the uncertainties of spatio-temporal variations. We use two distributed hydrological models (WaSiM and Hydrotel) and six catchments with different sizes and characteristics, located in southern Quebec, to address this objective. We calibrate the models across four spatial (100, 250, 500, 1000 $m^2$) and two temporal (3 hours and 24 hours) resolutions. Afterwards, all combinations of CDs-CPs pairs are fed to the hydrological models to create an ensemble of simulations for characterizing the uncertainty related to the spatial resolution of the modeling, for each catchment. The catchments are further grouped into large ($>1000 km^2$), medium (between 500 and 1000 $km^2$) and small ($<500km^2$) to examine multiple hypotheses. The ensemble approach shows a significant degree of uncertainty (over $100\%$ error for estimation of extreme streamflow) linked to the spatial discretization of the modeling. Regarding the role of catchment descriptors, results show that first, there is no meaningful link between the uncertainty of the spatial discretization and catchment size, as spatio-temporal discretization uncertainty can be seen across different catchment sizes. Second, the temporal scale plays only a minor role in determining the uncertainty related to spatial discretization. Third, the more physically representative a model is, the more sensitive it is to changes in spatial resolution. Finally, the uncertainty related to model parameters is dominant larger than that of catchment descriptors for most of the catchments. Yet, there are exceptions for which a change in spatio-temporal resolution can alter the distribution of state and flux variables, change the hydrologic response of the catchments, and cause large uncertainties.

A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models

Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream–downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives representative sub-grid river length and slope parameters, which are required for resolution-independent model results. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (∼ 1 km), 5 arcmin (∼ 10 km) and 15 arcmin (∼ 30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often-applied upscaling methods. Furthermore, we show that MERIT Hydro IHU minimizes the errors made in the timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is open source and fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.

A physically-based distributed karst hydrological model (QMG model-V1.0) for flood simulations

Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash–Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys.

Spatial Patterns in Actual Evapotranspiration Climatologies for Europe

Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.

On the evaluation of both spatial and temporal performance of distributed hydrological models

Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, however, have focused on either the temporal or spatial model performance evaluation. In addition, temporal (or spatial) model performance evaluation is often done in a spatially (or temporally) lumped approach. Here, we evaluated (1) the temporal model performance evaluation in a spatially distributed approach (spatiotemporal) and (2) the spatial model performance in a temporally distributed approach (temporospatial) model performance evaluation. This study demonstrated that both spatiotemporal and temporospatial model performance evaluations are necessary since they provide different aspects of the model performance. For example, spatiotemporal model performance evaluation helps in detecting the areas with an issue in the simulated temporal patterns. However, temporospatial model performance evaluation helps in detecting the time with an issue in the simulated spatial patterns. The results also show that an increase in the spatiotemporal model performance will not necessarily lead to an increase in the temporospatial model performance and vice versa, depending on the evaluation statistics. Overall, this study has highlighted the necessity of a joint spatiotemporal and temporospatial model performance evaluation to understand/improve spatial and temporal model behavior/performance.