Semi-distributed Rainfall-runoff Model Research Articles

Developing Water Quality Formulations for a Semi-Distributed Rainfall–Runoff Model

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model’s ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input.

Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments

Abstract. A parsimonious semi-distributed rainfall–runoff model has been developed for flow prediction. In distribution, attention is paid to both the timing of the runoff and the heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into five gauged sub-catchments at which internal performance is evaluated. To test the effect of timing, first the excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from the storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire upper Ping River basin (UPRB), controlled by station P.1 located at the centre of Chiang Mai province. The outcome of FLEX-SD was compared to the (1) observations at the internal stations, (2) calibrated FLEXL model, and (3) the semi-distributed URBS model – another established semi-distributed rainfall–runoff model. FLEX-SD showed better or similar performance during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalised difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture-holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values and the annual average NDII values to construct two alternative models, namely FLEX-SD-NDIIMaxMin and FLEX-SD-NDIIAvg. The additional constraint on the moisture-holding capacity (Sumax) by NDII, particularly in FLEX-SD-NDIIAvg, improved both the model performance and the realism of its distribution across the UPRB, which corresponds linearly to the percentage of evergreen forests (R2=0.69). To check how well the models represents simulated root zone soil moisture (Sui), the performance of the FLEX-SD-NDII models was compared to the time series of the soil water index (SWI). The correlation between the Sui and the daily SWI appeared to be very good and was even better than the correlation with the NDII, which does not provide good estimates during wet periods. The SWI, which is model-based, was not used for calibration but appeared to be an appropriate index for validation.

Changes in the flow regime of High Arctic catchments with different stages of glaciation, SW Spitsbergen

This study investigates the response of four High Arctic catchments with differing proportions of glacierization to changing climatic conditions. The study area located in SW Spitsbergen, has experienced a warming of 4.5 °C in the last 40 years along with a large increase in autumn rainfall. The changes in flow regime were determined based on available hydro-meteorological data and simulations of a semi-distributed rainfall-runoff model, which allowed reconstruction of streamflow in the period 1979–2020 and trend analyses for each calendar day. Similar trends of change were detected in all studied catchments. These include earlier onset of snowmelt driven floods (by two weeks over the period of analysis), large increases in autumn flows, prolongation of the hydrologically active season (starts earlier and lasts longer), decrease in flows in the latter half of June and the early part of August (except for the Breelva catchment). As a result of increases in autumn precipitation, the flood regime in these catchments has changed from snowmelt-dominated to the bi-modal with peaks in both July/August and September. The results also indicate differences in the magnitude of hydrological response depending on the percentage of glacial coverage in the catchments. The larger the glacierized area is, the larger the changes in the flow regime.The hydrological regime of SW Spitsbergen catchments has undergone dramatic changes over the last four decades. Such a shift in hydro-climatic conditions is larger than that observed in lower latitudes and has significant environmental implications.

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

Abstract. 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.

Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response

Soil water content (SWC) is a key variable in many land surface processes, such as runoff generation, thus knowledge about its spatiotemporal dynamics at the catchment scale can be useful for constraining and evaluating hydrological models. Cosmic ray neutron sensor (CRNS) technology provides hectare scale SWC data, and with recent advances in mobile CRNS such information value can be extended to the catchment scale, although challenges in calibration remain, especially in wet environments. This study presents a new methodology suited for humid environments to explore spatio-temporal variability in near-surface soil water storage (SNS) dynamics at the catchment scale and its value in semi-distributed rainfall-runoff modelling calibration. For a humid mixed-agricultural catchment (~10 km2) in Scotland, we combined ~ 4-years of SWC data from a static CRNS at a landscape-representative location with “snapshots” at four key soil-land use (SLU) units to produce SWC timeseries for each one of those units. The SLU units involved a mixture of freely draining mineral and poorly draining organic-rich soils, supporting crop and livestock farming and moorland, respectively. We also explored the suitability of the standard CRNS calibration approach in the SLU units and found that the organic-rich soils required an adapted parameter calibration for SWC. The moorland SLU unit had the greatest difference in SWC dynamics from the other agricultural SLU units. To explore the additional information generated by the combined CRNS approach, we calibrated a semi-distributed rainfall-runoff model (HBV-light) by using SNS dynamics in individual SLU units in addition to streamflow. Compared to a lumped approach, the semi-distributed SWC information and model structure helped produce better constrained stream flows and further improved the representation of catchment internal storage dynamics. Ultimately, the value of the SWC time series for different SLU units in rainfall-runoff modelling will depend on model structure and the degree to which SNS dynamics vary within the landscape. This study showed the potential of expanding the information value of permanently installed CRNS sensors using portable CRNS surveys while addressing the various challenges related to organic-rich soils and wetter environments, although testing in different environments would be required to evaluate the wider applicability.

Can hydrological drought be efficiently predicted by conceptual rainfall-runoff models with global data products?

The seasonally-dry tropics of northern Costa Rica are characterized by recurrent drought events with negative socio-economic impacts on a vulnerable population. Scarce hydroclimatic observational data constraints reasonable water management and often results in water scarcity issues.This study analyses hydrological drought situations using freely available Global Precipitation Products (GPP) and a Regional Climate Model (RCM) that drive a relatively simple, semi-distributed rainfall-runoff model (HBV-Light). Firstly, the GPP and observed rainfall were used to calibrate the model simulating streamflow dynamics. Secondly, drought detection and estimates of drought duration, intensity and severity were determined with a daily variable threshold approach. Thirdly, we developed future hydrological drought scenarios based on a RCM. Generally, the GPP CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data) resulted in the best streamflow simulations (KGE > 0.6) compared with the model driven by observed rainfall (KGE > 0.7). CHIRPS also correctly identified the observed streamflow drought periods of 1994, 1997-1998 and 2001-2002.Average observed streamflow drought severity was 27.9 mm compared to the CHIRPS-derived severity of 20.6 mm (error estimate of ±7.3 mm). The model in combination with global data can be successfully used to identify drought periods and their duration, but model uncertainty currently prevents from forecasting streamflow deficit with volume errors below 50%. The future hydrological drought scenario showed more severe drought periods between 2039-2041 and 2042-2043.This study responds to the need for drought assessments in the seasonally-dry tropics with scarce observations as a tool for adaptation to climate change and water resource management.

Soil Moisture Simulation in Selected Austrian Catchements With Use of the Tuw Conceptual Semi-Distributed Rainfall Runoff Model

Nowadays, hydrological rainfall-runoff models are routinely used for modelling hydrological balances, the generation of floods, or droughts. For decades numerous rainfallrunoff models have been tested with many calibration procedures and different model structures. However, for rainfall-runoff, it is challenging to find a particular model structure and parameter set that can correctly describe the complicated flow formation processes in diverse physiographic conditions. Improvements to existing rainfall-runoff modelling concepts and data assimilation are therefore continuously being tested. In recent years, remote sensing has played an increasing role in the surveying of hydrological phenomena. Remote sensing of soil moisture data can be very helpful because soil moisture measurements in field conditions are not always straightforward. The quality of remotely sensed data is rising; nowadays, we can routinely start using data with proper spatial and temporal resolutions. In this paper, we have focused on an evaluation of the parametrisation of the soil moisture submodel of the TUW rainfall-runoff model by remotely sensed soil moisture data. We calibrated the TUW model for three selected catchments in Austria with flat hypsometric characteristics using discharges as a criterion. For the calibration, we used both the lumped and semi-distributed model versions of the model and compared the quality of the soil moisture of both versions. Both the lumped and semi-distributed versions performed well in the discharge simulation. In the case of the soil moisture simulation, we achieved slightly better results with the semi-distributed version of the model. Difficulties with accessing the data from the remote sensing are discussed since remote sensing sensors still have problems when clouds and snow cover the catchments.

Daily discharge simulation: combining semi-distributed GIS-based and artificial intelligence models

Developing highly accurate semi-distributed rainfall runoff models are still a big challenge in streamflow simulation. In this paper, a new technique using ANN to improve the accuracy of TOPMODEL is presented. TOPMODEL contains three sub-models, which are root storage, gravity storage and saturated storage. The proposed scheme is to replace one of the sub-models by artificial neural networks (ANN) model. A medium catchment located in tropical Malaysia known as Rantau Panjang catchment (RPC) is used. Two years, 1998-1999, are used for calibration, and 2000-2001 are used for validation process using daily data sets. Model results are evaluated by Nash-Sutcliffe model (NS), relative volume error (RVE) and correlation coefficient (CoC) which have been improved from 0.63 to 0.86, 0.92 to 0.93 and 40.91 to 14.12 respectively demonstrate the ability of ANN to improve the accuracy of TOPMODEL. It is concluded that the scheme can improve performance in terms of streamflow simulation.

Streamflow regionalization using a similarity approach in ungauged basins: Application of the geo-environmental signatures in the Karkheh River Basin, Iran

Reliable estimation of streamflow especially in ungauged basins is most important for environmental management and planning. Regionalization of streamflow using either the physical attributes or spatial proximities of linked gauged and ungauged basins are considered to be standard techniques for estimating streamflow in ungauged basins. Unfortunately, regionalization of modeling parameters based solely on physical similarities or spatial proximity between gauged and ungauged basins is not always adequate. The hydrologic similarities between basins are related to the basin's response to precipitation, which it is referred to various conditions of basins, so considering the comprehensive characteristics from basins is very important to achieve a knowledge of the hydrology of the basin. Therefore, this study aimed to consider a comprehensive set of signatures with the fuzzy clustering approach. Similar basins were determined by considering key geo-environmental signatures, including topographical/morphological characteristics, soil properties, land use types, remote sensing (RS)-based indices, and climate indices. Redundancy in the selected geo-environmental signatures was assessed using factor analysis. Parameters of lumped and semi-distributed rainfall-runoff models were calibrated in the most similar donor basin (DB) to the ungauged basin (UB) and validated by matching them to the pseudo ungauged basin (PUB). Streamflow estimation results from the pseudo ungauged basins demonstrated that the applied procedure and semi-distributed rainfall-runoff model was an efficient method to estimate streamflow in the ungauged basins.

Comparing Statistical and Semi-Distributed Rainfall–Runoff Models for a Large Subtropical Watershed: A Case Study of Jiulong River Catchment, China

In this contribution, the authors present their preliminary investigations into modeling the rainfall–runoff generation relation in a large subtropical catchment (Jiulong River catchment) on the southeast coast of China. Previous studies have mostly focused on modeling the streamflow and water quality of its small rural subcatchments. However, daily runoff on the scale of the whole catchment has not been modeled before, and hourly runoff data are desirable for some oceanographic applications. Three methods are proposed for modeling streamflow using rainfall outputted by the Weather Research Forecast (WRF) model, calculated potential evaporation (PET), and land cover type: (i) a ridge regression model; (ii) NPRED-KNN: a nonparametric k-nearest neighbor model (KNN) employing a parameter selection method (NPRED) based on partial information coefficient; (iii) the Hydrological Simulation Program-Fortran (HSPF) model with an hourly time step. Results show that the NPRED-KNN approach is the most unsuitable method of those tested. The HSPF model was manually calibrated, and ridge regression performs no worse than HSPF based on daily verification, whilst HSPF can produce realist daily flow time series, of which ridge regression is incapable. The HSPF model seems less prone to systematic underprediction when replicating monthly-annual water balance, and it successfully replicates the baseflow index (the flow intensity) of the Jiulong River catchment system.

SEMI-DISTRIBUTED NEURAL NETWORK MODELS FOR STREAMFLOW PREDICTION IN A SMALL CATCHMENT PINANG

This paper applied an artificial intelligence methodology for streamflow prediction in a flash flood in Pinang catchment based on TOPMODEL input and output data sets. TOPMODEL is a semi-distributed rainfall runoff model widely used in numerous water resource applications. However, literature has indicated relative weakness in TOPMODEL performances in streamflow prediction. Thus, radial basis function neural network (RBF-NN) has been employed to improve the accuracy of streamflow prediction and then compared with TOPMODEL and multilayer perceptron neural network (MLP-NN) performances. Four years of daily hydro-meteorological data sets (for the period between 2007 to 2010) were used for calibration and validation analysis. The results have shown an improvement from 0.749 and -19.2 of the calibration period to 0.957 and 0.001, and from 0.774 and -19.84 of the validation period to 0.956 and -3.611 of Nash-Sutcliffe model (NS) and Relative Volume Error (RVE), respectively. RBF-NN performance has been established to improve the daily streamflow prediction; however, the MLP-NN was better in contrast with the involved method in the study. It can be concluded that TOPMODEL performance showed a high ability to simulate the peaks compared with both AI methodologies.

Contribution of isotopic research techniques to characterize high-mountain-Mediterranean karst aquifers: The Port del Comte (Eastern Pyrenees) aquifer

Water resources in high mountain karst aquifers are usually characterized by high rainfall, recharge and discharge that lead to the sustainability of the downstream ecosystems. Nevertheless, these hydrological systems are vulnerable to the global change impact. The mean transit time (MTT) is a key parameter to describe the behavior of these hydrologic systems and also to assess their vulnerability. This work is focused on estimating MTT by using environmental tracers in the framework of high-mountain karst systems with a very thick unsaturated zone (USZ). To this end, it is adapted to alpine zones a methodology that combines a semi-distributed rainfall-runoff model to estimate recharge time series, and a lumped-parameter model to obtain ΜΤΤ. The methodology has been applied to the Port del Comte Massif (PCM) hydrological system (Southeastern Pyrenees, NE Spain), a karst aquifer system with an overlying 1000 m thick USZ. Six catchment areas corresponding to most important springs of the system are considered. The obtained results show that hydrologically the behavior of the system can be described by an exponential flow model (EM), with MTT ranging between 1.9 and 2.9 years. These ΜΤΤ values are shorter than those obtained by considering a constant recharge rate along time, which is the easiest and most applied aquifer recharge hypothesis when estimating ΜΤΤ through lumped-parameter models.

Rainfall-runoff modelling using river-stage time series in the absence of reliable discharge information: a case study in the semi-arid Mara River basin

Abstract. Hydrological models play an important role in water resources management. These models generally rely on discharge data for calibration. Discharge time series are normally derived from observed water levels by using a rating curve. However, this method suffers from many uncertainties due to insufficient observations, inadequate rating curve fitting procedures, rating curve extrapolation, and temporal changes in the river geometry. Unfortunately, this problem is prominent in many African river basins. In this study, an alternative calibration method is presented using water-level time series instead of discharge, applied to a semi-distributed rainfall-runoff model for the semi-arid and poorly gauged Mara River basin in Kenya. The modelled discharges were converted into water levels using the Strickler–Manning formula. This method produces an additional model output; this is a “geometric rating curve equation” that relates the modelled discharge to the observed water level using the Strickler–Manning formula and a calibrated slope-roughness parameter. This procedure resulted in good and consistent model results during calibration and validation. The hydrological model was able to reproduce the water levels for the entire basin as well as for the Nyangores sub-catchment in the north. The newly derived geometric rating curves were subsequently compared to the existing rating curves. At the catchment outlet of the Mara, these differed significantly, most likely due to uncertainties in the recorded discharge time series. However, at the “Nyangores” sub-catchment, the geometric and recorded discharge were almost identical. In conclusion, the results obtained for the Mara River basin illustrate that with the proposed calibration method, the water-level time series can be simulated well, and that the discharge-water-level relation can also be derived, even in catchments with uncertain or lacking rating curve information.

Validating Rainfall-Runoff Modelling Using Satellite-Based and Reanalysis Precipitation Products in the Sre Pok Catchment, the Mekong River Basin

The limitation in quantity and quality of climate data, in particular rainfall measurements, directly affects the reliability of hydrological models, which negatively impacts on the quality of water resources planning and management. This is obviously true in poorly-gauged or in transboundary river catchments with limited practice of data sharing among the riparian states. Satellite-based and reanalysis rainfall products are increasingly recognised as a promising information source to supplement ground observations. This research aims to study the feasibility of using satellite-based and reanalysis rainfall data for rainfall-runoff modelling in the Sre Pok catchment-a poorly gauged catchment located in Vietnam and Cambodia. First, three selected rainfall products (Tropical Rainfall Measuring Mission-TRMM, CPC MORPHing Technique-CMORPH, and European Reanalysis-ERA-Interim) were compared against ground rainfall data of 13 rain-gauges, located inside and in the vicinity of the study catchment. The validation results show that the studied global datasets have a reasonably good correlation with ground observation at monthly time scale (NSE = 0.3 to 0.8, R2 = 0.4 to 0.8), but relatively poor correlation at daily scale (NSE = −0.4 to 0.3, R2 = 0.1 to 0.4). Next, a semi-distributed rainfall-runoff model based on HBV-Light was developed to validate these datasets in the Krong Ana sub-catchment of the Sre Pok. The results illustrate satisfactory to very good model performances at monthly time scale for the satellite-based and reanalysis rainfall inputs (NSE = 0.58 to 0.77, R2 = 0.59 to 0.78 for the calibration period from 2004 to 2007, while NSE = 0.59 to 0.80, R2 = 0.64 to 0.84 for the validation period from 2008 to 2010). However, only TRMM and ERA-Interim give acceptable results at daily scale with NSE = 0.52 to 0.67, R2 = 0.53 to 0.67 for the calibration period, and NSE = 0.41 to 0.61, R2 = 0.45 to 0.65 for the validation period. The study concludes that the tested satellite-based and reanalysis rainfall products, especially TRMM and ERA-Interim, could mimic reasonably well the monthly rainfall pattern in the study region. It also demonstrates a great opportunity to use satellite-based and reanalysis rainfall for runoff modelling at monthly time scale in the Sre Pok catchment and other poorly-gauged (transboundary) catchments in the region, for example in the Mekong and Red River Basins.

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. One of the main challenges of setting up such a system is finding appropriate model parameter values for ungauged catchments. Previous studies have shown that the transfer of parameter sets from hydrologically similar gauged catchments is one of the best performing regionalization methods. However, a remaining key issue is the identification of suitable descriptors of similarity. In this study, we use decision tree learning to explore parameter set transferability in the full space of catchment descriptors. For this purpose, a semi-distributed rainfall-runoff model is set up for 35 catchments in ten Chinese provinces. Hourly runoff data from in total 858 storm events are used to calibrate the model and to evaluate the performance of parameter set transfers between catchments. We then present a novel technique that uses the splitting rules of classification and regression trees (CART) for finding suitable donor catchments for ungauged target catchments. The ability of the model to detect flood events in assumed ungauged catchments is evaluated in series of leave-one-out tests. We show that CART analysis increases the probability of detection of 10-year flood events in comparison to a conventional measure of physiographic-climatic similarity by up to 20%. Decision tree learning can outperform other regionalization approaches because it generates rules that optimally consider spatial proximity and physical similarity. Spatial proximity can be used as a selection criteria but is skipped in the case where no similar gauged catchments are in the vicinity. We conclude that the CART regionalization concept is particularly suitable for implementation in sparsely gauged and topographically complex environments where a proximity-based regionalization concept is not applicable.

GIS-Based Rainfall-Runoff Neuro Model for Streamflow Prediction

TOPMODEL is a semi-distributed rainfall runoff model that has been widely used in numerous water resources’ applications in the last few decades. However, literature has identified the weakness in the TOPMODEL performances in streamflow prediction. In this paper, a multilayer perceptron neural network (MLP-NN) has been adopted to improve the accuracy of streamflow prediction in a flash flood in Pinang catchment area. Two daily hydro-meteorological datasets of year 2007-2008 and 2009-2010 were used for calibration and validation periods, respectively. The new method presented in this study uses the TOPMODEL input-output datasets during the calibration period to train the MLP-NN to predict the output. Then, the trained MLP-NN model structure is used to predict the streamflow based on validation period datasets. The three efficiencies considered to evaluate the model performances are the Nash-Sutcliffe model (NS), the Relative Volume Error (RVE) and the Correlation Coefficient (CoC). The results indicated an improvement from 0.749, -19.2 and 0.893 of NS, RVE and CoC of the calibration period to 0.978, 0.364 and 0.989, respectively. Moreover, for the validation periods, the performance has been improved from 0.774, -19.84 and 0.933 of NS, RVE and CoC to 0.975, -0.029 and 0.789, respectively. The ability of MLP-NN to improve TOPMODEL daily streamflow prediction has been demonstrated in this study.

Evaluation of three semi-distributed hydrological models in simulating discharge from a small forest and arable dominated catchment

AbstractCatchment scale hydrological models are promising tools for simulating the effect of catchment-specific processes and management on soil and water resources. Here, we present a model intercomparison study of runoff simulations using three different semi-distributed rainfall-runoff catchment models. The objective of this study was to demonstrate the applicability of the Hydrologiska Byrans Vattenavdelning (HBV-Light); Precipitation, Evapotranspiration and Runoff Simulator for Solute Transport (PERSiST); and INtegrated CAtchment (INCA) models on Somogybabod Catchment, near Lake Balaton, Hungary.The models were calibrated and validated against observed discharge data at the outlet of the catchment for the period of January 1, 2006 –July 12, 2015. Model performance was evaluated using graphical representations, e.g. daily and monthly hydrographs and Flow Duration Curves (FDC) and model evaluation statistic; Nash–Sutcliffe efficiency (NSE) and coefficient of determination (

A method to employ the spatial organization of catchments into semi-distributed rainfall–runoff models

Abstract. A distributed or semi-distributed deterministic hydrological model should consider the hydrologically most relevant catchment characteristics. These are heterogeneously distributed within a watershed but often interrelated and subject to a certain spatial organization which results in archetypes of combined characteristics. In order to reproduce the natural rainfall–runoff response the reduction of variance of catchment properties as well as the incorporation of the spatial organization of the catchment are desirable. In this study the width-function approach is utilized as a basic characteristic to analyse the succession of catchment characteristics. By applying this technique we were able to assess the context of catchment properties like soil or topology along the streamflow length and the network geomorphology, giving indications of the spatial organization of a catchment. Moreover, this information and this technique have been implemented in an algorithm for automated sub-basin ascertainment, which included the definition of zones within the newly defined sub-basins. The objective was to provide sub-basins that were less heterogeneous than common separation schemes. The algorithm was applied to two parameters characterizing the topology and soil of four mid-European watersheds. Resulting partitions indicated a wide range of applicability for the method and the algorithm. Additionally, the intersection of derived zones for different catchment characteristics could give insights into sub-basin similarities. Finally, a HBV96 case study demonstrated the potential benefits of modelling with the new subdivision technique.

A Fast Semi Distributed Rainfall Runoff Model for Engineering Applications in Arid and Semi-Arid Regions

A new GIS based rainfall runoff model is developed for engineering applications, achieving a highly automated watershed analysis process starting from watershed delineation and up to the runoff hydrograph calculation. The model can be classified as a semi-distributed time area model that adopts an improved grid based approach for calculation of watershed response. The model deals with each grid cell in the digital elevation model as an independent hydrologic unit. Travel time through each grid cell is estimated using Manning’s formula and a stream power formula that relates the hydraulic radius at the cell to the characteristics of its upstream watershed area and excess rainfall depth. The watershed response at its outlet is estimated by routing the response of each grid cell using a flow path response function that is defined for that cell. The routed responses of all watershed cells are then convoluted to produce the outflow hydrograph. Model advantages include accuracy improvements due to the incorporation of grid-based routing calculations (both translation and attenuation), fully automated model structure, and fast ability to model many watersheds simultaneously. The combination of these advantages constitutes the novelty of the model that makes it very suitable for engineering design as well as for real-time applications. The model was tested using the data of the experimental watershed, Walnut Gulch, Arizona, USA, gauged by 88 rainfall stations and several discharge recording flumes. The results show that the model can accurately predict the runoff hydrograph where suitable input is available.

Integrating Local Scale Drainage Measures in Meso Scale Catchment Modelling

This article presents a methodology to optimize the integration of local scale drainage measures in catchment modelling. The methodology enables to zoom into the processes (physically, spatially and temporally) where detailed physical based computation is required and to zoom out where lumped conceptualized approaches are applied. It allows the definition of parameters and computation procedures on different spatial and temporal scales. Three methods are developed to integrate features of local scale drainage measures in catchment modelling: (1) different types of local drainage measures are spatially integrated in catchment modelling by a data mapping; (2) interlinked drainage features between data objects are enabled on the meso, local and micro scale; (3) a method for modelling multiple interlinked layers on the micro scale is developed. For the computation of flow routing on the meso scale, the results of the local scale measures are aggregated according to their contributing inlet in the network structure. The implementation of the methods is realized in a semi-distributed rainfall-runoff model. The implemented micro scale approach is validated with a laboratory physical model to confirm the credibility of the model. A study of a river catchment of 88 km2 illustrated the applicability of the model on the regional scale.