Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa

This study aims to assess simulated surface runoff before and after dam construction in the Mono catchment (West Africa) using two lumped models: GR4J (Rural Engineering with 4 Daily Parameters) and IHACRES (Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Stream data) over two different periods (1964–1986 and 1988–2010). Daily rainfall, mean temperature, evapotranspiration and discharge in situ data were collected for the period 1964–2010. After the model's initialization, calibration and validation; performances analysis have been carried out using multi-objectives functions developed in R software (version 3.5.3). The results indicate that statistical metrics such as the coefficient of determination (R2), the Kling–Gupta Efficiency (KGE), the Nash–Sutcliffe coefficient (NSE) and the Percent of Bias (PBIAS) provide satisfactory insights over the first period of simulation (1964–1986) and low performances over the second period of simulation (1988–2010). In particular, IHACRES model underestimates extreme high runoff of Mono catchment between 1964 and 1986. Conversely, GR4J model overestimates extreme high runoff and has been found to be better for runoff prediction of the river only between 1964 and 1986. Moreover, the study deduced that the robustness of runoff simulation between 1964 and 1986 is better than between 1988 and 2010. Therefore, the weakness of simulated runoff between 1988 and 2010 was certainly due to dam management in the catchment. The study suggests that land cover changes impacts, soil proprieties and climate may also affect surface runoff in the catchment.

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling

Despite showing great success of applications in many commercial fields, machine learning and data science models generally show limited success in many scientific fields, including hydrology (Karpatne et al., 2017). The approach is often criticized for its lack of interpretability and physical consistency. This has led to the emergence of new modelling paradigms, such as theory-guided data science (TGDS) and physics-informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on genetic programming (GP), namely the Machine Learning Rainfall–Runoff Model Induction (ML-RR-MI) toolkit. ML-RR-MI is capable of developing fully fledged lumped conceptual rainfall–runoff models for a watershed of interest using the building blocks of two flexible rainfall–runoff modelling frameworks. In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall–runoff models. The meaningfulness and reliability of hydrological inferences gained from lumped models may tend to deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties is significant. This was the motivation behind developing our machine learning approach for distributed rainfall–runoff modelling titled Machine Induction Knowledge Augmented – System Hydrologique Asiatique (MIKA-SHA). MIKA-SHA captures spatial variabilities and automatically induces rainfall–runoff models for the catchment of interest without any explicit user selections. Currently, MIKA-SHA learns models utilizing the model building components of two flexible modelling frameworks. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA's model induction capabilities have been tested on the Rappahannock River basin near Fredericksburg, Virginia, USA. MIKA-SHA builds and tests many model configurations using the model building components of the two flexible modelling frameworks and quantitatively identifies the optimal model for the watershed of concern. In this study, MIKA-SHA is utilized to identify two optimal models (one from each flexible modelling framework) to capture the runoff dynamics of the Rappahannock River basin. Both optimal models achieve high-efficiency values in hydrograph predictions (both at catchment and subcatchment outlets) and good visual matches with the observed runoff response of the catchment. Furthermore, the resulting model architectures are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists. MIKA-SHA-induced semi-distributed model performances were compared against existing lumped model performances for the same basin. MIKA-SHA-induced optimal models outperform the lumped models used in this study in terms of efficiency values while benefitting hydrologists with more meaningful hydrological inferences about the runoff dynamics of the Rappahannock River basin.

HydroLang: An Open-Source Web-Based Programming Framework for Hydrological Sciences

This paper presents HydroLang, an open-source and integrated community-driven computational web framework to support research and education in hydrology and water resources. HydroLang uses client-side web technologies and standards to perform different routines which aim towards the acquisition, management, transformation, analysis and visualization of hydrological datasets. HydroLang is comprised of four main high-cohesion low-coupling modules for: (1) retrieving, manipulating, and transforming raw hydrological data, (2) statistical operations, hydrological analysis, and creating models, (3) generating graphical and tabular data representations, and (4) mapping and geospatial data visualization. Two extensive case studies (i.e., evaluation of lumped models and development of a rainfall disaggregation model) have been presented to demonstrate the framework’s capabilities, portability, and interoperability. HydroLang’s unique modular architecture and open-source nature allow it to be easily tailored into any use case and web framework and promote iterative enhancements with community involvement to establish the comprehensive next-generation hydrological software toolkit.

Investigation of Appropriate Model Structures for Modelling Small Urban Catchments

Although the hydrologic modelling of small urban catchments has been practised for several decades, guidance on the development of models is still needed. This research evaluates and compares several modelling structures of small residential areas with and without low impact development implementation using distributed and lumped models. Hypothetical small areas were modelled to examine several grid based models with different grid sizes. The results were used to test the ability of uncalibrated models to predict runoff using three model configurations: 1) single catchment, 2) grid, and 3) homogenous areas, where every building, backyard, and street was modelled separately as a single catchment. The results of the models were compared and evaluated based on the total runoff volume, peak flow rate, and infiltration volume. The results of a real case study show that the grid model is an appropriate model structure for modelling small urban catchments.

Investigation of Appropriate Model Structures for Modelling Small Urban Catchments

Although the hydrologic modelling of small urban catchments has been practised for several decades, guidance on the development of models is still needed. This research evaluates and compares several modelling structures of small residential areas with and without low impact development implementation using distributed and lumped models. Hypothetical small areas were modelled to examine several grid based models with different grid sizes. The results were used to test the ability of uncalibrated models to predict runoff using three model configurations: 1) single catchment, 2) grid, and 3) homogenous areas, where every building, backyard, and street was modelled separately as a single catchment. The results of the models were compared and evaluated based on the total runoff volume, peak flow rate, and infiltration volume. The results of a real case study show that the grid model is an appropriate model structure for modelling small urban catchments.

Establishment of the Baseline for the IWRM in the Ecuadorian Andean Basins: Land Use Change, Water Recharge, Meteorological Forecast and Hydrological Modeling

This study was conducted in the Zamora Huayco (ZH) river basin, located in the inter-Andean region of southern Ecuador. The objective was to describe, through land use/land cover change (LUCC), the natural physical processes under current conditions and to project them to 2029. Moreover, temperature and precipitation forecasts were estimated to detail possible effects of climate change. Using remote sensing techniques, satellite images were processed to prepare a projection to 2029. Water recharge was estimated considering the effects of slope, groundcover, and soil texture. Flash floods were estimated using lumped models, concatenating the information to HEC RAS. Water availability was estimated with a semi-distributed hydrological model (SWAT). Precipitation and temperature data were forecasted using autoregressive and exponential smoothing models. Under the forecast, forest and shrub covers show a growth of 6.6%, water recharge projects an increase of 7.16%. Flood flows suffer a reduction of up to 16.54%, and the flow regime with a 90% of probability of exceedance is 1.85% (7.72 l/s) higher for 2029 than for the 2019 scenario, so an improvement in flow regulation is evident. Forecasts show an increase in average temperature of 0.11 °C and 15.63% in extreme rainfall by 2029. Therefore, intervention strategies in Andean basins should be supported by prospective studies that use these key variables of the system for an integrated management of water resources.

A Coupled Flow-Thermoregulation Lumped Model to Investigate Cardiac Function

Lumped (zero-dimensional) technique is a robust and widely used approach to mathematically model and explore bulk behaviour of different physical phenomena in a lower expense. In modelling of cardio/cerebrovascular fluid dynamics, this technique facilitates the assessment of relevant metrics such as flow, pressure, and temperature at different locations over a large network/domain. Furthermore, they can be employed as boundary conditions in multiscale modelling of physiological flows. In this methodology paper, a lumped model for the cardiovascular flow simulation along with a two-node thermoregulation model are employed. The lumped models are built upon previous studies and are amended appropriately to focus on cardiac function. The output of the coupled model can either be used for assessing the cardiac function in different physiological conditions or it can provide the input data for other investigations. Noteworthy to mention that, the present model has been specifically developed for investigation on the effects of atrial fibrillation on cardiac performance.

Testing the Efficiency of Parameter Disaggregation for Distributed Rainfall-Runoff Modelling

A variety of hydrological models is currently available. Many of those employ physically based formulations to account for the complexity and spatial heterogeneity of natural processes. In turn, they require a substantial amount of spatial data, which may not always be available at sufficient quality. Recently, a top-down approach for distributed rainfall-runoff modelling has been developed, which aims at combining accuracy and simplicity. Essentially, a distributed model with uniform model parameters (base model) is derived from a calibrated lumped conceptual model. Subsequently, selected parameters are disaggregated based on links with the available spatially variable catchment properties. The disaggregation concept is now adjusted to better account for non-linearities and extended to incorporate more model parameters (and, thus, larger catchment heterogeneity). The modelling approach is tested for a catchment including several flow gauging stations. The disaggregated model is shown to outperform the base model with respect to internal catchment dynamics, while performing similarly at the catchment outlet. Moreover, it manages to bridge on average 44% of the Nash–Sutcliffe efficiency difference between the base model and the lumped models calibrated for the internal gauging stations. Nevertheless, the aforementioned improvement is not necessarily sufficient for reliable model results.

Evaluation of 42 lumped rainfall-runoff models for the Wairau River catchment, New Zealand, using the MARRMoT toolbox

<p>For decades, lumped rainfall-runoff models have been used for hydrological analysis and forecasting such as operational flood forecasting. However, the accuracy of model forecasts depends on the ability of the model to approximate the dominant hydrological processes of the catchment under consideration. These processes are site specific and, therefore, the choice of a particular model is challenging. </p><p>A large number of hydrological models has been developed and applied in various regions of the world. Model choice has often been hampered in the past by technical problems such as different programming languages, different software platforms, and different input formats and requirements.</p><p>The Modular Assessment of Rainfall Runoff Model Toolbox (MARRMoT) unites 46 lumped models from around the world within the same Matlab® framework with standardized inputs. The model equations have been simplified and adapted for this purpose. As a result, it is possible to test a large number of different models with comparatively little effort. The models implemented in MARRMoT vary in their structural complexity and have between 1-24 parameters and between 1-8 storages.</p><p>Here MARRMoT was used in order to find a model or model ensemble suitable for the simulation of precipitation-runoff relationships in the Wairau River catchment, New Zealand. The catchment area is assumed to have predominantly homogeneous runoff-generating properties. Model input data (precipitation and potential evapotranspiration) was derived from the Virtual Climate Station Network by National Institute of Water and Atmospheric Research, NZ.</p><p>In a first scenario, 42 selected models from MARRMoT were calibrated for the Wairau River catchment using 45 years of Wairau River flow data, an in-built nonlinear unconstrained optimization algorithm and the model fitness criteria Kling-Gupta-Efficiency (KGE). In two further scenarios, calibrations using the KGE with inversely transformed flows (KGEi) as well as a mixed form of the two criteria (KGEm) were realized. </p><p>Model performance was further evaluated based on different performance criteria such as NSE, RMSE and R². It was demonstrated that the model ranking depends on the choice of the performance.</p><p>Evaluating the model performance for the different calibration scenarios showed that a few models with very different structures performed well to reproduce the flow data. No decisive structural feature could be identified which all models have in common and which led to a good representation of the rainfall-runoff processes in the Wairau River catchment. However, the differentiated consideration of flow routing and a high degree of flexibility seem to benefit model performance. Deficits in the modeling can be seen in the discharge peaks, which are not correctly simulated by many models. The simulation of fast direct runoff with lumped models seems to be less accurate for the relatively large catchment area of ​​the Wairau River (3430 km²).</p><p>Eventually, three models (GR4J, FLEX-I and HBV-96) demonstrated a high performance in all three calibration scenarios and were identified as suitable for further use in the Wairau River catchment.</p>