Continuous Hydrological Model Research Articles

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman–Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman–Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Assessing Recession Constant Sensitivity and Its Interaction with Data Adjustment Parameters in Continuous Hydrological Modeling in Data-Scarce Basins: A Case Study Using the Xinanjiang Model

Data scarcity plays the crucial role in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (cg): (i) assessing the relationship between the recession constant (cg) and the data adjustment parameter (Cep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas.

Linking catchment and hydrodynamic models for environmental flow analysis in hypersaline lakes

Abstract This research work proposes a novel method of environmental flow analysis in hypersaline lakes in which the outputs of the hydraulic rating method considering the base flow as a known approach for environmental flow assessment in rivers are analyzed in terms of providing suitable aquatic habitats in the lake by linking a continuous hydrological model and a hydrodynamic model of salinity simulation. Soil and Water Assessment Tool (SWAT) as a hydrological model was applied to simulate the natural inflow of the lake's ecosystem in the simulated period. Moreover, a hydrodynamic model was applied to simulate the salinity distribution of the lake in the same simulated period. Based on the results of the models and monthly analysis of the environmental flows, both models are robust for simulations. The Nash–Sutcliffe Efficiency (NSE) of both hydrological and hydrodynamic models average more than 0.5, which means that they are reliable for simulating the natural inflow and salinity distribution respectively. Furthermore, outputs indicated that using the hydraulic rating method considering the base flow for assessing the environmental flow of rivers is not able to provide environmental requirements in the lake's ecosystem of the case study.

Continuous Hydrologic Modeling of a Parking Lot and Related Best Management Practices with PCSWMM

AbstractPermeable pavements are a green infrastructure stormwater management practice that can serve as a functional component of the site design. However, previous field studies suggest high uncertainty in the parameters used for performing hydrologic calculations for permeable pavements. The Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) within the PCSWMM software package was used to simulate the hydrologic dynamics of a parking lot that is 25% covered with permeable interlocking concrete pavers in Auburn, AL. The model was calibrated to field observations of water level at two points where the pavement system outflows to a bioretention basin and rainfall data from a nearby weather station. The use of the Curve Number (CN) method within SWMM resulted in good prediction of pavement outflow by the calibrated model, with R2 and Nash‐Sutcliffe model efficiency both greater than 0.8, except where issues with precipitation data coverage occurred. This demonstrates that permeable pavements can be modeled as a land cover type rather than as detention storage. The calibrated value of the runoff CN for permeable pavement was 60, much lower than what is recommended in many design guidelines for the underlying soil type at the research site, which is hydrologic soil group B. Based on evaluation of alternative model scenarios, the permeable pavement reduced runoff by 11‐38% across contrasting rain events.

The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology

This R package technically gathers the methods developed and lessons learnt over recent years for estimating discharge of ungauged outlets, in a runoff–runoff type approach (discharge is both the input and output). It uses the observed discharge of neighbouring gauged basins and a geomorphology-based deconvolution–convolution modelling approach. The continuous hydrological modelling is based on the description of the hydro-geomorphometry of the river drainage network that can be easily described for any outlet considered. An inversion of this model allows us to deconvolute the observed discharge series and to estimate the signal of the water flowing into rivers from the hillslope, namely the net rainfall. The transfer of this net rainfall to a targeted ungauged basin enables the simulation of the discharge series there. The methodology has been tested in several hydro-climatic contexts, and with this package we aim to encourage further evaluation and improvement as well as subsequent operational applications.

The Benefit of Continuous Hydrological Modelling for Drought Hazard Assessment in Small and Coastal Ungauged Basins: A Case Study in Southern Italy

Rainfall-runoff modelling in small and ungauged basins represents one of the most common practices in hydrology. However, it remains a challenging task for researchers and practitioners, in particular in a climate change context and in areas subject to drought risk. When discharge observations are not available, empirical or event-based approaches are commonly used. However, these schemes can be affected by several relevant assumptions. In the last years, continuous models have been developed in order to address the major drawbacks of event-based approaches. With this goal in mind, in this work we applied a synthetic rainfall generation model (STORAGE; stochastic rainfall generator), constituting the implementation of a modified version of Neymann-Scott rectangular pulse (NSRP) model, and a continuous rainfall-runoff framework (COSMO4SUB; continuous simulation modelling for small and ungauged basins) specifically designed for ungauged basins within a climate change context. The modeling approach allows one to investigate the drought hazard using specific indicators for rainfall and runoff in a small watershed located in southern Italy. Results show that the investigated area seems to tend to a mild/moderate drought in a future time period of approximately 30 years, with a decrease in seasonal water volumes availability in the range of 15–30%. Finally, our results confirm that the continuous modelling is suitable for rapid and effective design simulations supporting drought hazard assessment.

Calibration of a continuous hydrologic simulation model in the urban Gowrie Creek catchment in Toowoomba, Australia

Study regionToowoomba, Queensland, Australia Study focusIn this study we derive loss model parameters suitable for use in the dynamic loss Australian Representative Basin Model (ARBM) through the calibration of a continuous simulation hydrologic model. We compare the derived parameters to those published in the literature, and our results highlight the need to develop a database of calibrated loss parameters for urban catchments. New hydrological insightsThe development of design storms for flood modelling commonly uses the initial loss/continuous loss model to estimate the conversion of rainfall to runoff. This loss model, when applied to pervious areas, uses parameters that have been calibrated for gauged rural catchments. These same parameters are often applied to the pervious component of ungauged urban catchments with minimal understanding of the resulting impact on runoff. This research uses a continuous simulation modelling approach to calibrate parameters suitable for use in the ARBM loss model built into the hydrological modelling software XPRAFTS. Through a two-stage calibration approach, the model offered a satisfactory fit (Nash Sutcliffe Efficiency > 0.5) for 9 of the 11 selected storm events, with seven events exceeding a Nash Sutcliffe Efficiency of 0.75. Events used in the calibration/validation included peak flows as low as 9 m3/s and as high as 600 m3/s. Developing these loss model parameters offers new insights into the suitability of a dynamic loss model approach in an urban catchment in regional Australia and provides an alternative to the parameters already available in the literature which were found to overestimate the peak flow in frequent events.

Performing Hydrological Monitoring at a National Scale by Exploiting Rain-Gauge and Radar Networks: The Italian Case

Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes.

Estimation of hydrogeological parameters using physically based hydrological modelling and lithology data in ungauged creek catchments of southern Taiwan

ABSTRACT Streamflow recession analysis is widely used for estimating catchment-scale hydrogeological parameters in gauged catchments because of its low cost, ease of use, and efficiency in terms of results. In this work, however, the same methodology was applied to ungauged creek catchments, where low recession flows were simulated using physically based, semi-distributed, continuous hydrological modelling. Moreover, a combination approach was used to reasonably estimate the effective aquifer depth (D) with borehole lithology and digital elevation model data. The estimated hydraulic conductivity (k) values ranged between 10–5 and 10–3 m s–1, and they were comparable with the results of a field test. By contrast, the specific yield (Sy ) values obtained through streamflow recession analysis (10–7 to 10–5) were lower than the results of the field tests at different aquifer depths. However, the Sy values obtained for alluvial catchments (on the order of 10–5) were comparable. Thus, the results of the comparison support the application of the streamflow recession analysis scheme for estimating hydrogeological parameters in ungauged creek catchments.

Coupling artificial neural networks with the artificial bee colony algorithm for global calibration of hydrological models

Hydrological models are widely used tools in water resources management. Their successful application requires an efficient calibration of the model parameters. Nowadays, there are very powerful global search methods applied to this end, but they have the disadvantage of presenting a high computational cost, because the numerical model to be calibrated needs to be evaluated a large number of times with different parameter sets. In this context, surrogate models can reduce significantly the run time of hydrological models, easing the total computational burden of global search methods. In the present work, we propose and explore the combination of a swarm intelligence-based optimization method, the artificial bee colony algorithm, with a surrogate model based on artificial neural networks in order to calibrate hydrological models. The proposed approach (ABC-ANN) is applied to the calibration and validation of a nine-parameter continuous hydrological model in two basins located in the northwest of Spain. Several aspects of the algorithm are evaluated, including its capability to calibrate the model parameters and its efficiency in terms of CPU time compared to a standard implementation of the ABC algorithm. Results show that the ABC-ANN algorithm is able to identify the location of suitable parameter sets with an accuracy rate within 89 and 99 %, and a reduction in CPU time of more than three orders of magnitude when compared to a sequential implementation. In addition, the frequency distribution of the parameter sets identified gives valuable information about the sensitivity of model output to the input parameters.

Continuous modelling of the Bouregreg watershed (Morocco) using the HEC-HMS model

A deep understanding of the rainfall-runoff mechanism is essential to estimate the runoff generated in a given basin. In this regard, this paper aims to develop a continuous hydrological model of the Bouregreg watershed. The objective of this modelling is to evaluate the inflow to the Sidi Mohamed Ben Abdellah (SMBA) dam, located at the outlet of this basin. To this end, using the HEC-HMS model, the Soil Moisture Accounting (SMA) Loss Method was used to model infiltration losses. The SCS Unit hydrograph (SCS UH) and the Recession method were chosen as transform model and baseflow model, respectively. As a result, the comparison shows an acceptable agreement between observed and simulated flow in terms of streamflow distribution and peak values (NSE=0.57, R2=0.58). During validation, the model retained its ability to sufficiently reproduce the rainfall-runoff mechanism of the studied basin with a slight overestimation of peaks (NSE=0.61, R2=0.60). This study allows to assess and predict the inter-annual and intra-annual variation of the SMBA dam reservoir’ inflows, and therefore to forecast the climate change impact on this basin.

On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

Abstract. Calibration of a conceptual distributed model is challenging due to a number of reasons, which include fundamental (model adequacy and identifiability) and algorithmic (e.g., local search vs. global search) issues. The aim of the presented study is to investigate the potential of the variational approach for calibrating a simple continuous hydrological model (GRD; Génie Rural distributed involved in several flash flood modeling applications. This model is defined on a rectangular 1 km2 resolution grid, with three parameters being associated with each cell. The Gardon d'Anduze watershed (543 km2) is chosen as the study benchmark. For this watershed, the discharge observations at five gauging stations, gridded rainfall and potential-evapotranspiration estimates are continuously available for the 2007–2018 period at an hourly time step. In the variational approach one looks for the optimal solution by minimizing the standard quadratic cost function, which penalizes the misfit between the observed and predicted values, under some additional a priori constraints. The cost function gradient is efficiently computed using the adjoint model. In numerical experiments, the benefits of using the distributed against the uniform calibration are measured in terms of the model predictive performance, in temporal, spatial and spatiotemporal validation, both globally and for particular flood events. Overall, distributed calibration shows encouraging results, providing better model predictions and relevant spatial distribution of some parameters. The numerical stability analysis has been performed to understand the impact of different factors on the calibration quality. This analysis indicates the possible directions for future developments, which may include considering a non-Gaussian likelihood and upgrading the model structure.

Quantification of Expected Changes in Peak Flow Quantiles in Climate Change by Combining Continuous Hydrological Modelling with the Modified Curve Number Method

Climate projections point to modifications in the magnitude, frequency and timing of floods in the future. However, robust methodologies to quantify how climate change will modify the catchment response in flood events are required. Continuous hydrological modelling usually smooth magnitudes of extreme events. This paper proposes a methodology to improve the assessment of flood changes in the future driven by climate change. Climate change projections of the EURO-CORDEX programme obtained under the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) supplied are used. Four catchments located on the Douro River Basin have been considered as case studies. Precipitation and temperature projections have been bias corrected to reduce errors with observations in the control period (1971–2004). The HBV continuous hydrological simulation model has been used to simulate the soil moisture content on the day of occurrence of the maximum annual rainfalls in the four catchments. The modified curve number method has been utilized to obtain the changes expected in the future in flood magnitudes, considering the initial soil moisture contents estimated with the HBV model and the expected changes in annual maximum rainfalls. The methodology has been applied to the control period (1971–2004) to check the validity of the process. Then, the methodology has been applied to the future period (2011–2095), to quantify the changes expected in the future in flood magnitudes under climate change conditions. The combined use of the HBV continuous hydrological simulation with the modified curve number method improves the results provided by the HBV model. The proposed methodology allows a better characterization of the response of catchments in flood events. It also considers the expected variation in the antecedent moisture content in catchments in the future, driven by increasing temperatures and decreasing mean annual precipitations in the future. The results show that flood quantiles will increase in three of the four catchments considered.

A New Model-Based Approach for the Evaluation of the Net Contribution of the European Union Rural Development Program to the Reduction of Water Abstractions in Agriculture

Agriculture is an essential driving force in water resources management and has a central role in the European Union’s Rural Development Programme (RDP). In this study, the solution developed addresses countries characterised by relatively small farms, vast spatial and temporal variability and severe data scarcity. The proposed model-based approach is directly relevant to the evaluation of agricultural policies affecting water abstraction based on multisource data. The evaluation process utilises an entirely spatially distributed, continuous hydrological model. The model provides a gridded output of the main hydrological balance components, as well as vegetation water deficit and irrigation water requirements, on a daily temporal step on a country scale. It provides information at the farm level and facilitates the estimation of water abstractions in agriculture, taking into consideration all the pertinent information included in the Integrated Administration and Control System database that is maintained by RDPs in Europe. Remote sensing data also are used to validate crop patterns. The obtained results were analysed to estimate the net effect of the RDP to the reduction of water abstractions in agriculture. This work produces valuable information concerning the evaluation of agricultural policies and the assessment of land use, and climate change adaptation and mitigation strategies.

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach

Abstract. Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.

Integrating stormwater management and stream restoration strategies for greater water quality benefits.

Urbanization alters the delivery of water and sediment to receiving streams, often leading to channel erosion and enlargement, which increases loading of sediment and nutrients, degrades habitat, and harms sensitive biota. Stormwater control measures (SCMs) are constructed in an attempt to mitigate some of these effects. In addition, stream restoration practices such as bank stabilization are increasingly promoted as a means of improving water quality by reducing downstream sediment and pollutant loading. Each unique combination of SCMs and stream restoration practices results in a novel hydrologic regime and set of geomorphic characteristics that interact to determine stream condition, but in practice, implementation is rarely coordinated due to funding and other constraints. In this study, we examine links between watershed-scale implementation of SCMs and stream restoration in Big Dry Creek, a suburban watershed in the Front Range of northern Colorado. We combine continuous hydrologic model simulations of watershed-scale response to SCM design scenarios with channel evolution modeling to examine interactions between stormwater management and stream restoration strategies for reducing loading of sediment and adsorbed phosphorus from channel erosion. Modeling results indicate that integrated design of SCMs and stream restoration interventions can result in synergistic reductions in pollutant loading. Not only do piecemeal and disunited approaches to stormwater management and stream restoration miss these synergistic benefits, they make restoration projects more prone to failure, wasting valuable resources for pollutant reduction. We conclude with a set of recommendations for integrated planning of SCMs and stream restoration to simultaneously achieve water quality and channel protection goals.

RETRACTED ARTICLE: Uncertainty Analysis of a Continuous Hydrological Model Using DREAM-ZS Algorithm

RETRACTED ARTICLE: Uncertainty Analysis of a Continuous Hydrological Model Using DREAM-ZS Algorithm

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

A continuous modelling approach for design flood estimation on sub-daily time scale

ABSTRACT Design flood estimation is an essential part of flood risk assessment. Commonly applied are flood frequency analyses and design storm approaches, while the derived flood frequency using continuous simulation has been getting more attention recently. In this study, a continuous hydrological modelling approach on an hourly time scale, driven by a multi-site weather generator in combination with a -nearest neighbour resampling procedure, based on the method of fragments, is applied. The derived 100-year flood estimates in 16 catchments in Vorarlberg (Austria) are compared to (a) the flood frequency analysis based on observed discharges, and (b) a design storm approach. Besides the peak flows, the corresponding runoff volumes are analysed. The spatial dependence structure of the synthetically generated flood peaks is validated against observations. It can be demonstrated that the continuous modelling approach can achieve plausible results and shows a large variability in runoff volume across the flood events.

Evaluation of an instantaneous dryness index-based calibration-free continuous hydrological model in India

AbstractTraditional continuous hydrological models have a large number of free parameters whose values need to be determined through calibration, and thus their applicability is limited to gauged basins. For prediction in ungauged catchments, hydrologists generally follow regionalization methods to develop region-specific calibration-free continuous models. An alternative attempt was made recently to develop a calibration-free model by proposing an empirically derived universal ‘decay function’ that enables definition of instantaneous dryness index as a function of antecedent rainfall and solar energy. The model was earlier tested in the USA, and its performance was found to be comparable to that shown by regionalization-based models. Here, we test the instantaneous dryness index-based calibration-free model considering data from 108 Indian catchments. The medians of coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE) values for the study catchments, respectively, are 0.50, 0.38 and 0.40. Furthermore, the model's performance significantly improved upon Box–Cox transformation (RBC2, NSEBC and KGEBC, respectively, are 0.70, 0.52 and 0.57), suggesting that the model predicts discharge quite well except during flood periods. Overall, our results suggest the model can be used as an alternative platform for predicting discharge in ungauged catchments in the USA and peninsular India, if not in every part of the world.