Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Abstract

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.     

The Sources of Uncertainty in Forecasting Caspian Sea Level and Estimating the Inundation Risk of Coastal Areas

Stochastic variations in the climate and hydrological regime, both natural and anthropogenic, are the main cause of uncertainty in long-term hydrological forecasts and hence increase the estimated risk of economic activity in the coastal zone of internal seas. Some sources of uncertainty, which appear during the hydrological analysis, are considered with the purpose to assess this risk. Digital relief models were used to determine the morphological characteristics (as functions of the sea level) and assess their contribution to variations in the level regime. To take into account the sample uncertainty in the parameter estimates of stochastic models of the “impellent” processes, it is proposed to use the existing methodology of probabilistic-deterministic prediction of water level variations in a closed water body in combination with the Bayesian approach.     

A methodology for simple and fast streamflow modelling

ABSTRACT

During the last few decades, hydrological models have become very powerful, capable of spatially analysing the hydrological information and accurately representing the geomorphological characteristics of the studied area. However, one of the drawbacks of this heightened intricacy is the amount of time required to set up a hydrological model. In this study, a simple methodology that requires only a minimum set-up time is presented. This methodology employs linear regression to combine the outputs of simple hydrological models to simulate hydrological responses. Two kinds of simple hydrological models are employed. The first one represents the characteristics of the streamflow attributed to overland flow, and the second the characteristics of the streamflow attributed to interflow and baseflow. The methodology was tested in 4 case studies, and the results were encouraging. The best performance was achieved in the case study with data of fine time step with significant length.     

Models as multiple working hypotheses: hydrological simulation of tropical alpine wetlands

Tropical alpine grasslands, locally known as páramos, are the water towers of the northern Andes. They are an essential water source for drinking water, irrigation schemes and hydropower plants. But despite their high socio‐economic relevance, their hydrological processes are very poorly understood. Since environmental change, ranging from small scale land‐use changes to global climate change, is expected to have a strong impact on the hydrological behaviour, a better understanding and hydrological prediction are urgently needed. In this paper, we apply a set of nine hydrological models of different complexity to a small, well monitored upland catchment in the Ecuadorian Andes. The models represent different hypotheses on the hydrological functioning of the páramo ecosystem at catchment scale. Interpretation of the results of the model prediction and uncertainty analysis of the model parameters reveals important insights in the evapotranspiration, surface runoff generation and base flow in the páramo. However, problems with boundary conditions, particularly spatial variability of precipitation, pose serious constraints on the differentiation between model representations. Copyright © 2010 John Wiley & Sons, Ltd.     

Testing the applicability of a kinematic wave-based distributed hydrological model in two climatically contrasting catchments

Abstract

Steep mountainous areas account for 70% of all river catchments in Japan. To predict river discharge for the mountainous catchments, many studies have applied distributed hydrological models based on a kinematic wave approximation with surface and subsurface flow components (DHM-KWSS). These models reproduce observed river discharge of catchments in Japan well; however, the applicability of a DHM-KWSS to catchments with different geographical and climatic conditions has not been sufficiently examined. This research applied a DHM-KWSS to two river basins that have different climatic conditions from basins in Japan to examine the transferability of the DHM-KWSS model structure. Our results show that the DHM-KWSS model structure explained flow regimes for a wet river basin as well as a large flood event in an arid basin; however, it was unable to explain long-term flow regimes for the arid basin case study.     

Hydrological modelling of temporally-varying catchments: facets of change and the value of information

Abstract

River basins are by definition temporally-varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different to those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibits significantly growing urbanization during the last 30 years. Alternative deterministic modelling options include a lumped water balance model with one time-varying parameter and a semi-distributed scheme based on the concept of hydrological response units. Model inputs and errors are respectively represented through linear and nonlinear stochastic models. The resulting nonlinear stochastic framework maximizes the exploitation of the existing information by taking advantage of the calibration protocol used in this issue.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

A two-stage time series model for monthly hydrological projections under climate change in the Lim River basin (southeast Europe)

ABSTRACT

Climate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.     

Modelling the water level of the alluvial aquifer of an ephemeral river in south-western Zimbabwe

ABSTRACT

Water from the alluvium of ephemeral rivers in Zimbabwe is increasingly being used. These alluvial aquifers are recharged annually from infiltrating floodwater. Nonetheless, the size of this water resource is not without limit and an understanding of the hydrological processes of an alluvial aquifer is required for its sustainable management. This paper presents the development of a water balance model, which estimates the water level in an alluvial aquifer recharged by surface flow and rainfall, while allowing for abstraction, evaporation and other losses. The model is coupled with a watershed model, which generates inflows from upland catchment areas and tributaries. Climate, hydrological, land cover and geomorphological data were collected as inputs to both models as well as observed flow and water levels for model calibration and validation. The sand river model was found to be good at simulating the observed water level and was most sensitive to porosity and seepage.     

Conceptual examination of climate–soil controls upon rainfall partitioning in an open-fractured soil II: Response to a population of storms

A simple two-domain bucket model of fractured soil was coupled with a stochastic model of rainfall variability, in order to investigate the climate and soil controls upon the stochastic properties of the triggering of fracture flow and surface runoff, and the partitioning of rainfall between the matrix and fracture domains and surface runoff. Conventionally, soils are regarded as time domain filters between rainfall and hydrological response. This investigation highlights an additional type of threshold filtering especially important in understanding the infiltration behaviour of fractured soils, for which an event-based characterisation of rainfall in modelling is crucial. A priori-definable indices were derived which are capable of describing elements of this threshold filtering, by allowing the statistical properties of fracture flow- and surface runoff-triggering storms (i.e., mean and variance of storm duration, intensity and effective inter-storm period, as well as cumulative partitioning of rainfall), to be inferred directly from average storm and soil properties. Using these indices, the long-term response of fractured soils, including the long-term hydrological importance of fractures, can be estimated without simulation.     

River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.     

Improving flood forecasting using an input correction method in urban models in poorly gauged areas

ABSTRACT

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.     

Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi‐method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL‐2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.     

Study of discontinuities in hydrological data using catastrophe theory

Abstract

Modelling and prediction of hydrological processes (e.g. rainfall–runoff) can be influenced by discontinuities in observed data, and one particular case may arise when the time scale (i.e. resolution) is coarse (e.g. monthly). This study investigates the application of catastrophe theory to examine its suitability to identify possible discontinuities in the rainfall–runoff process. A stochastic cusp catastrophe model is used to study possible discontinuities in the monthly rainfall–runoff process at the Aji River basin in Azerbaijan, Iran. Monthly-averaged rainfall and flow data observed over a period of 20 years (1981–2000) are analysed using the Cuspfit program. In this model, rainfall serves as a control variable and runoff as a behavioural variable. The performance of this model is evaluated using four measures: correlation coefficient, log-likelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results indicate the presence of discontinuities in the rainfall–runoff process, with a significant sudden jump in flow (cusp signal) when rainfall reaches a threshold value. The performance of the model is also found to be better than that of linear and logistic models. The present results, though preliminary, are promising in the sense that catastrophe theory can play a possible role in the study of hydrological systems and processes, especially when the data are noisy.

Citation Ghorbani, M. A., Khatibi, R., Sivakumar, B. & Cobb, L. (2010) Study of discontinuities in hydrological data using catastrophe theory. Hydrol. Sci. J. 55(7), 1137–1151.     

Stochastic Forecasting Models of the Monthly Streamflow for the Blue Nile at Eldiem Station

Egypt is almost totally dependent on the River Nile for satisfying about 95% of its water requirements. The River Nile has three main tributaries: White Nile, Blue Nile, and River Atbara. The Blue Nile contributes about 60% of total annual flow reached the River Nile at Aswan High Dam. The goal of this research is to develop a reliable stochastic model for the monthly streamflow of the Blue Nile at Eldiem station, where the Grand Ethiopian Renaissance Dam (GERD) is currently under construction with a storage capacity of about 74 billion m³. The developed model may help to carry out a reliable study on the filling scenarios of GERD reservoir and to minimize its expected negative side effects on Sudan and Egypt. The linear models: Deseasonalized AutoRegressive Moving Average (DARMA) model, Periodic AutoRegressive Moving Average (PARMA) model and Seasonal AutoRegressive Integrated Moving Average (SARIMA) model; and the nonlinear Artificial Neural Network (ANN) model are selected for modeling monthly streamflow at Eldiem station. The performance of various models during calibration and validation were evaluated using the statistical indices: Mean Absolute Error, Root Mean Square Error and coefficient of determination (R2) which indicate the strength of fitting between observed and forecasted values. The results show that the performance of the nonlinear model (ANN) was much better than all investigated linear models (DARMA, PARMA and SARIMA) in forecasting the monthly flow discharges at Eldiem station.     

Integration of remote sensing data into hydrological models for reservoir management

Abstract

The purpose of this paper is to present the methodology set up to derive catchment soil moisture from Earth Observation (EO) data using microwave spaceborne Synthetic Aperture Radar (SAR) images from ERS satellites and to study the improvements brought about by an assimilation of this information into hydrological models. The methodology used to derive EO data is based on the appropriate selection of land cover types for which the radar signal is mainly sensitive to soil moisture variations. Then a hydrological model is chosen, which can take advantage of the new information brought by remote sensing. The assimilation of soil moisture deduced from EO data into hydrological models is based principally on model parameter updating. The main assumption of this method is that the better the model simulates the current hydrological system, the better the following forecast will be. Another methodology used is a sequential one based on Kalman filtering. These methods have been put forward for use in the European AIMWATER project on the Seine catchment upstream of Paris (France) where dams are operated to alleviate floods in the Paris area.     

Contemporary stochastic approach to water resource systems: The ARMP and feature prediction models

An autoregressive model with Markovian parameters (ARMP/a.r.m.p.) and a feature prediction scheme (FPM) are developed in this paper. The ARMP is physically based and adaptive in its implementation thus taking into consideration the inherent periodicities in hydrological time series. The FPM is motivated by the current inability to provide a suitable and sufficiently comprehensive yet simplified mathematical hydrological model. It is based on pattern analysis and is such that a system's dynamic feature is predicted using a priori data which can subsequently be used to simulate the missing data and forecast future hydrological parameters. The ARMP and FPM provide efficient alternatives to some other existing models which are not, in general, applicable to all classes of hydrological problems; with an added advantage of on-line forecasting. A comparative analysis of the techniques are undertaken using the discharge record data from the River Nile at Aswan Dam from 1870–1945. It is further proposed that in order to enhance the over-all performance of the orediction scheme, the FPM may be used as an input (training data) to the ARMP.     

Modelling basin-scale distribution of fish occurrence probability for assessment of flow and habitat conditions in rivers

Abstract

Flow regimes play an important role in sustaining biodiversity in river ecosystems. However, the effects of flow regimes on riverine fish have not been clearly described. Therefore, we propose a new methodology to quantitatively link habitat conditions (such as flow indices and physical habitat conditions) to the occurrence probability (OP) of fish species. We developed a basin-scale fish distribution model by integrating the concept of habitat suitability assessment with a distributed hydrological model in order to estimate the OP of fish, with particular attention to flow regime. A generalized linear model was used to evaluate the relationship between the probabilities of fish occurrence and major environmental factors in river sections. A geomorphology-based hydrological model was adopted to simulate river discharge, which was used to calculate 10 flow indices. The occurrence probabilities of 50 fish species in the Sagami River in Japan were modelled. For the prediction accuracy, field survey results that included at least five observations of both the presence and the absence of each species were required to obtain relatively reliable prediction (accuracy > 60%). Using the developed model, important habitat conditions for each species were identified, which showed the importance of low-flow events for more than 10 species, including Hypomesus nipponensis and Rhinogobius fluviatilis. The model also confirmed the positive effects of natural flow and the negative effect of river-crossing structures, such as dams and weirs, on the OP of most species. The suggested approach enables us to evaluate and project the ecological consequences of water resource management policy. The results demonstrate the applicability of the fish distribution model to provide quantitative information on the flow required to maintain fish communities.

Editor Z.W. Kundzewicz; Guest editor M. Acreman

Citation Sui, P., Iwasaki, A., Saavedra, V.O.C., and Yoshimura, C., 2013. Modelling basin-scale distribution of fish occurrence probability for assessment of flow and habitat conditions in rivers. Hydrological Sciences Journal, 59 (3–4), 618–628.     

Development of a modelling methodology for the investigation of riparian hydrological processes

The riparian zone is in intimate contact with the river and, as such, is a critical zone for understanding hydrological problems. This paper presents a general modelling methodology for the assessment of riparian hydrological processes. It is applicable to a wide range of riparian spaces and incorporates current expertise in numerical methods. A core part of the modelling methodology is the random walk particle method (RWPM). We develop an RWPM as part of the ESTEL2D subsurface flow model, test it against analytical solutions and apply it to the simulation of parcels of water as they move through the riparian zone. The modelling methodology provides a new opportunity to assess fundamental hydrological process issues such as the proportioning of pre‐event and event water storm runoff, and reversals of flow in floodplains. Copyright © 2005 John Wiley & Sons, Ltd.