Modelling river temperature from air temperature: case of the River Drava (Croatia)

Abstract

Measurements made in the past few decades undeniably indicate change in the climate. The most visible sign of global climate change is air temperature, while less visible indicators include changes in river water temperatures. Changes in river temperature can significantly affect the environment, primarily the biosphere. The physical, biological and chemical characteristics of the river are directly affected by water temperature, although estimation of this relationship presents a complex problem. Although river temperature is influenced by hydrological and meteorological factors, the purpose of this study is to model daily water temperature using only one known parameter, mean air temperature. The relationship between the daily mean air and daily water temperature of the River Drava in Croatia is analysed using linear regression, stochastic modelling or nonlinear regression and multilayer perceptron (MLP) feed-forward neural networks. The results indicate that the MLP models are much better models which can be used for the estimation and prediction of daily mean river temperature.

Editor D. Koutsoyiannis; Associate editor M. Acreman     

Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm

ABSTRACT

An integrated data-intelligence model based on multilayer perceptron (MLP) and krill herd optimization – the MLP-KH model – is presented for the estimation of daily pan evaporation. Daily climatological information collected from two meteorological stations in the northern region of Iran is used to compare the potential of the proposed model against classical MLP and support vector machine models. The integrated and the classical models were assessed based on different error and goodness-of-fit metrics. The quantitative results evidenced the capacity of the proposed MLP-KH model to estimate daily pan evaporation compared to the classical ones. For both weather stations, the lowest root mean square error (RMSE) of 0.725 and 0.855 mm/d, respectively, was obtained from the integrated model, while the RMSE for MLP was 1.088 and 1.197, and for SVM it was 1.096 and 1.290, respectively.     

Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa

Abstract

The increasing demand for water in southern Africa necessitates adequate quantification of current freshwater resources. Watershed models are the standard tool used to generate continuous estimates of streamflow and other hydrological variables. However, the accuracy of the results is often not quantified, and model assessment is hindered by a scarcity of historical observations. Quantifying the uncertainty in hydrological estimates would increase the value and credibility of predictions. A model-independent framework aimed at achieving consistency in incorporating and analysing uncertainty within water resources estimation tools in gauged and ungauged basins is presented. Uncertainty estimation in ungauged basins is achieved via two strategies: a local approach for a priori model parameter estimation from physical catchment characteristics, and a regional approach to regionalize signatures of catchment behaviour that can be used to constrain model outputs. We compare these two sources of information in the data-scarce region of South Africa. The results show that both approaches are capable of uncertainty reduction, but that their relative values vary.

Editor D. Koutsoyiannis

Citation Kapangaziwiri, E., Hughes, D.A., and Wagener, T., 2012. Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa. Hydrological Sciences Journal, 57 (5), 1000–1019.     

Historical water resources in South Asia: the hydrological background

Abstract

The development of historical water resources in the South Asian subcontinent has been largely dependent on the hydrological background. The runoff patterns are derived from climate statistics and the historical developments in different areas are related to these patterns.

Citation Sutcliffe, J., Shaw, J. & Brown, E. (2011) Historical water resources in South Asia: the hydrological background. Hydrol. Sci. J. 56(5), 775–788.     

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.     

Impacts of climate change on water resources in Spain

Abstract

Impacts on water resources produced by climate change can be exacerbated when occurring in regions already presenting low water resources levels and frequent droughts, and subject to imbalances between water demands and available resources. Within Europe, according to existing climate change scenarios, water resources will be severely affected in Spain. However, the detection of those effects is not simple, because the natural variability of the water cycle and the effects of water abstractions on flow discharges complicate the establishment of clear trends. Therefore, there is a need to improve the assessment of climate change impacts by using hydrological simulation models. This paper reviews water resources and their variability in Spain, the recent modelling studies on hydrological effects of climate change, expected impacts on water resources, the implications in river basins and the current policy actions.

Editor Z.W. Kundzewicz

Citation Estrela, T., Pérez-Martin, M.A., and Vargas, E., 2012. Impacts of climate change on water resources in Spain. Hydrological Sciences Journal, 57 (6), 1154–1167.     

A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows

Abstract

A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Wei, S., Yang, H., Song, J.X., Abbaspour, K., and Xu, Z.X., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389.     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

Regional models of flow-duration curves of perennial and intermittent streams and their use for calibrating the parameters of a rainfall–runoff model

Abstract

One of the main challenges faced by hydrologists and water engineers is the estimation of variables needed for water resources planning and management in ungauged river basins. To this end, techniques for transposing information, such as hydrological regional analyses, are widely employed. A method is presented for regionalizing flow-duration curves (FDCs) in perennial, intermittent and ephemeral rivers, based on the extended Burr XII probability distribution. This distribution shows great flexibility to fit data, with accurate reproduction of flow extremes. The performance analysis showed that, in general, the regional models are able to synthesize FDCs in ungauged basins, with a few possible drawbacks in the application of the method to intermittent and ephemeral rivers. In addition to the regional models, we summarize the experience of using synthetic FDCs for the indirect calibration of the Rio Grande rainfall–runoff model parameters in ungauged basins.

Editor D. Koutsoyiannis

Citation Costa, V., Fernandes, W., and Naghettini, M., 2013. Regional models of flow-duration curves of perennial and intermittent streams and their use for calibrating the parameters of a rainfall–runoff model. Hydrological Sciences Journal, 59 (2), 262–277.     

Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

ABSTRACT

Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Estimating the uncertainty in simulating the impacts of small farm dams on streamflow regimes in South Africa

Abstract

Small dams represent an important local-scale resource designed to increase water supply reliability in many parts of the world where hydrological variability is high. There is evidence that the number of farm dams has increased substantially over the last few decades. These developments can have a substantial impact on downstream flow volumes and patterns, water use and ecological functioning. The study reports on the application of a hydrological modelling approach to investigate the uncertainty associated with simulating the impacts of farm dams in several South African catchments. The focus of the study is on sensitivity analysis and the limitations of the data that would be typically available for water resources assessments. The uncertainty mainly arises from the methods and information that are available to estimate the dam properties and the water use from the dams. The impacts are not only related to the number and size of dams, but also the extent to which they are used for water supply as well as the nature of the climate and the natural hydrological regimes. The biggest source of uncertainty in South Africa appears to be associated with a lack of reliable information on volumes and patterns of water abstraction from the dams.

Citation Hughes, D. A. & Mantel, S. K. (2010) Estimating the uncertainty in simulating the impacts of small farm dams on streamflow regimes in South Africa. Hydrol. Sci. J. 55(4), 578–592.     

Flood simulation using different sources of rainfall in the Huong River,Vietnam / Simulation d'inondation à l'aide de différentes sources d'information pluviométrique dans le bassin de la Rivière Huong,Vietnam

Abstract

The possibility of simulating flooding in the Huong River basin, Vietnam, was examined using quantitative precipitation forecasts at regional and global scales. Raingauge and satellite products were used for observed rainfall. To make maximum use of the spatial heterogeneity of the different types of rainfall data, a distributed hydrological model was set up to represent the hydrological processes. In this way, streamflow simulated using the rainfall data was compared with that observed in situ. The forecast on a global scale showed better performance during normal flow peak simulations than during extreme events. In contrast, it was found that during an extreme flood peak, the use of regional forecasts and satellite data gives results that are in close agreement with results using raingauge data. Using the simulated overflow volumes recorded at the control point downstream, inundation areas were then estimated using topographic characteristics. This study is the first step in developing a future efficient early warning system and evacuation strategy.     

Nonlinear analysis of seasonality and stochasticity of the Indus River

Abstract

One of the world's largest irrigation networks, based on the Indus River system in Pakistan, faces serious scarcity of water in one season and disastrous floods in another. The system is dominated both by monsoon and by snow and glacier dynamics, which confer strong seasonal and inter-annual variability. In this paper two different forecasting methods are utilized to analyse the long-term seasonal behaviour of the Indus River. The study also assesses whether the strong seasonal behaviour is dominated by the presence of low-dimensional nonlinear dynamics, or whether the periodic behaviour is simply immersed in random fluctuations. Forecasts obtained by nonlinear prediction (NLP) and the seasonal autoregressive integrated moving average (SARIMA) methods show that the performance of NLP is relatively better than the SARIMA method. This, along with the low values of the correlation dimension, is indicative of low-dimensional nonlinear behaviour of the hydrological dynamics. A relatively better performance of NLP, using an inverse technique, may also be indicative of the low-dimensional behaviour. Moreover, the embedding dimension of the best NLP forecasts is in good agreement with the estimated correlation dimension. This provides evidence that the nonlinearity inherent in the monthly river flow due to the snowmelt and the monsoon variations dominate over the high-dimensional components and might be exploited for prediction and modelling of the complex hydrological system.

Citation Hassan, S. A. & Ansari, M. R. K. (2010) Nonlinear analysis of seasonality and stochasticity of the Indus River. Hydrol. Sci. J. 55(2), 250–265.     

Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow

Abstract

The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional pre-processing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data pre-processing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models.

Citation Jothiprakash, V. & Kote, A. S. (2011) Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol. Sci. J. 56(1), 168–186.     

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Abstract

Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore     

The effective management of national hydrometric data: experiences from the United Kingdom

Abstract

Access to hydrometric information underpins many areas of effective water management. This paper explores the operational practices of one national hydrological information service, the UK National River Flow Archive, in collating, managing and providing access to river flow data. An information lifecycle approach to hydrometric data management is advocated, with the paper detailing current UK procedures in the areas of: monitoring network design and development; data sensing and recording; validation and archival; synthesis and analysis; and data dissemination. The methods and policies outlined herein are widely transferable to other hydrological data archives around the world.

Editor D. Koutsoyiannis

Citation Dixon, H., Hannaford, J., and Fry, M.J., 2013. The effective management of national hydrometric data: experiences from the United Kingdom. Hydrological Sciences Journal, 58 (7), 1383–1399.     

Water resources and climate change impact modelling on a daily time scale in the Peruvian Andes

Abstract

Estimating water resources is important for adequate water management in the future, but suitable data are often scarce. We estimated water resources in the Vilcanota basin (Peru) for the 1998–2009 period with the semi-distributed hydrological model PREVAH using: (a) raingauge measurements; (b) satellite rainfall estimates from the TRMM Multi-satellite Precipitation Analysis (TMPA); and (c) ERA-Interim re-analysis data. Multiplicative shift and quantile mapping were applied to post-process the TMPA estimates and ERA-Interim data. This resulted in improved low-flow simulations. High-flow simulations could only be improved with quantile mapping. Furthermore, we adopted temperature and rainfall anomalies obtained from three GCMs for three future periods to make estimations of climate change impacts (Delta-change approach) on water resources. Our results show more total runoff during the rainy season from January to March, and temporary storages indicate that less water will be available in this Andean region, which has an effect on water supply, especially during dry season.

Editor Z.W. Kundzewicz; Associate editor D. Gerten     

High-resolution simulation of the spatial pattern of water use in continental China

ABSTRACT

High-resolution data on the spatial pattern of water use are a prerequisite for appropriate and sustainable water management. Based on one well-validated hydrological model, the Distributed Time Variant Gains Model (DTVGM), this paper obtains reliable high-resolution spatial patterns of irrigation, industrial and domestic water use in continental China. During the validation periods, ranges of correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) coefficient are 0.67–0.96 and 0.51–0.84, respectively, between the observed and simulated streamflow of six hydrological stations, indicating model applicability to simulate the distribution of water use. The simulated water use quantities have relative errors (RE) less than 5% compared with the observed. In addition, the changes in streamflow discharge were also correctly simulated by our model, such as the Zhangjiafen station in the Hai River basin with a dramatic decrease in streamflow, and the Makou station in the Pearl River basin with no significant changes. These changes are combined results of basin available water resources and water use. The obtained high-resolution spatial pattern of water use could decrease uncertainty of hydrological simulation and guide water management efficiently.

Editor M.C. Acreman; Associate editor X. Fang     

Distinct effects of temperature change on discharge and non-point pollution in subtropical southern China by SWAT simulation

Abstract

Climate change impacts on the availability of water resources. Projection of hydrological response to temperature change is valuable for water management. Such response may be complex and uncertain at the watershed scale and differences may exist between low and high latitudes. A simulation experiment was achieved by using SWAT modelling in the upstream watershed of Dongjiang River, South China. After calibration, the model was found appropriate for hydrological simulation in the study area and was run from 1995 to 2004 under a series of temperature change scenarios to reveal the response of streamflow and loads of sediment and nutrients. For a temperature increase of 3°C, streamflow, sediment and total phosphorus decreased by 5.2, 7.7 and 2.2%, respectively. Linear temperature change seemed to have a linear hydrological response. Nutrient deficiency was still the primary vegetation stress compared with water availability and temperature stress under rising temperatures. Comparison with previous research showed that two southern subtropical watersheds (one upstream and one downstream) gave different hydrological responses. Sediment and inorganic nitrogen loads decreased in the upstream watershed, but increased in the downstream one, when temperature increased. Under the warming scenarios, streamflow and sediment loads decreased throughout the year, especially during the wet season, which is different from results at high latitudes. Nutrient export decreased in April–June, but increased in the remaining months. Simulation results should be applied with caution in water resources management, as simulated climate change had variable hydrological influence in different regions and seasons.

Citation Xu, H. and Peng, S.L., 2013. Distinct effects of temperature change on discharge and non-point pollution in subtropical southern China by SWAT simulation. Hydrological Sciences Journal, 58 (5), 1032–1046.

Editor Z.W. Kundzewicz; Associate editor C.-Y. Xu