Evaluation of nonlinear models for precipitation forecasting

Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.     

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatial modelling of palsa mires in relation to climate in northern Europe

Palsa mires are mire complexes that occur in the Northern Hemisphere, representing one of the most marginal permafrost features at the outer limit of the permafrost zone. A climate‐based spatial model is presented for the distribution of palsa mires in northern Europe. The model is based on an extensive spatial data of palsa mires and climatological variables from 1913 grid cells in an area of c. 240 000 km². Generalized linear modelling (GLM) with curvilinear and interaction terms is used to derive the palsa mire–climate relationships. The ?nal model correctly classi?ed 77·6 per cent of the palsa mire presence squares. The results indicate a positive association of the distribution of palsa mires with increasing frost number and continentality, whereas precipitation and temperature showed a negative correlation with the distribution of palsa mires. Additionally, interaction of thawing degree days and summer time precipitation showed a negative association. Climatologically, the optimum areas of palsa mires occur in areas of low precipitation (<450 mm) and a mean annual temperature between ?3 °C and ?5 °C. Potential reasons for the performance of the model and the sensitivity of palsa mires to climate change are discussed. The application of a GIS‐based generalized linear modelling as used here provides a versatile method to study the distribution of different geomorphological phenomena across climatological gradients. Copyright © 2004 John Wiley & Sons, Ltd.     

Using response surfaces to estimate impacts of climate change on flood peaks: assessment of uncertainty

The potential impacts of climate change are an increasing focus of research, and ever‐larger climate projection ensembles are available, making standard impact assessments more onerous. An alternative way of estimating impacts involves response surfaces, which present the change in a given indicator for a large number of plausible climatic changes defined on a regular sensitivity domain. Sets of climate change projections can then be overlaid on the response surface and impacts estimated from the nearest corresponding points of the sensitivity domain, providing a powerful method for fast impact estimation for multiple projections and locations. However, the effect of assumptions necessary for initial response surface development must be assessed. This paper assesses the uncertainty introduced by use of a sensitivity framework for estimating changes in 20‐year return period flood peaks in Britain. This sensitivity domain involves mean annual and seasonal precipitation changes, and a number of simplifications were necessary for consistency and to reduce dimensionality. The effect of these is investigated for nine catchments across Britain, representing nine typical response surfaces (response types), using three sets of climate projections. The results show that catchments can have different causes of uncertainty and some catchments have an overall higher level of uncertainty than others. These differences are compatible with the underlying climatological and hydrological differences between the response types, giving confidence in generalization of the results. This enables the development of uncertainty allowances by response type, to be used alongside the response surfaces to provide more robust impact estimates. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Improving the reliability of groundwater monitoring networks using combined numerical,geostatistical and neural network-based simulation models

ABSTRACT

A new methodology is proposed for improving the accuracy of groundwater-level estimations and increasing the efficiency of groundwater-level monitoring networks. Three spatio-temporal (S-T) simulation models, numerical groundwater flow, artificial neural network and S-T kriging, are implemented to simulate water-table level variations. Individual models are combined using model fusion techniques and the more accurate of the individual and combined simulation models is selected for the estimation. Leave-one-out cross-validation shows that the estimation error of the best fusion model is significantly less than that of the three individual models. The selected fusion model is then considered for optimal S-T redesign of the groundwater monitoring network of the Dehgolan Plain (Iran). Using a Bayesian maximum entropy interpolation technique, soft data are included in the geostatistical analyses. Different scenarios are defined to incorporate economic considerations and different levels of precision in selecting the best monitoring network; a network of 37 wells is proposed as the best configuration. The mean variance estimation errors of all scenarios decrease significantly compared to that of the existing monitoring network. A reduction in equivalent uniform annual costs of different scenarios is achieved.     

Group-based estimation of missing hydrological data: II. Application to streamflows

Abstract

The group approach that treats hydrological data as groups rather than as single-valued observations was proposed in a companion paper. Various models representing four techniques are briefly presented and applied to single series and bi-series cases, respectively, in this paper. The techniques represented by these models are regression, time series analysis, partitioning modelling, and artificial neural networks. The utility of the models for estimating missing streamflow data using the group approach is investigated. It turns out that the group approach is valid for estimating missing values, and possibly other applications, when data are significantly auto-correlated.     

Spatial distribution of rainfall trends in Sicily (1921-2000)

The feared global climate change could have important effects on various environmental variables including rainfall in many countries around the world. Changes in precipitation regime directly affect water resources management, agriculture, hydrology and ecosystems. For this reason it is important to investigate the changes in the spatial and temporal rainfall pattern in order to improve water management strategies.In this study a non-parametric statistical method (Mann-Kendall rank correlation method) is employed in order to verify the existence of trend in annual, seasonal and monthly rainfall and the distribution of the rainfall during the year. This test is applied to about 250 rain gauge stations in Sicily (Italy) after a series of procedures finalized to the estimation of missing records and to the verification of data consistency.In order to understand the regional pattern of precipitation in Sicily, the detected trends are spatially interpolated using spatial analysis techniques in a GIS environment.The results show the existence of a generalized negative trend for the entire region.     

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.     

Assessment of eight reference evapotranspiration (ETo) methods considering Köppen climate class in Iran

ABSTRACT

The performance of eight empirical equations for estimating ET_o at 80 weather stations in Iran is evaluated. The equations assessed are Hargreaves (HGS), Trajkovic (TKC), Berti (BTI), Ravazzani (RZI), Irmak (IMK), Turc (TRC) and two Valiantzas methods (VTS1 and VTS2). The FAO56 reference crop Penman-Monteith (PM) equation is used as a baseline to evaluate their performance. Also, a Köppen climate classification map for Iran is developed and the best ET_o method for each climate type identified. The updated Köppen climate map shows six climate sub-classes; BWh, BWk, BSh, BSk, Csa and Dsa in Iran with a percentage of land area covered by each sub-class of 43, 17, 7, 9, 11 and 13%, respectively. The best performing ET_o equation for each climate class in Iran was HGS for BSh, VTS1 for BWk, and VTS2 for BSk, BWh, Csa and Dsa.     

Water quality variations in different climates of Iran: toward modeling total dissolved solid using soft computing techniques

In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L^?1) and Cl (meq L^?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.     

Conditional bias-penalized kriging (CBPK)

Simple and ordinary kriging, or SK and OK, respectively, represent the best linear unbiased estimator in the unconditional sense in that they minimize the unconditional (on the unknown truth) error variance and are unbiased in the unconditional mean. However, because the above properties hold only in the unconditional sense, kriging estimates are generally subject to conditional biases that, depending on the application, may be unacceptably large. For example, when used for precipitation estimation using rain gauge data, kriging tends to significantly underestimate large precipitation and, albeit less consequentially, overestimate small precipitation. In this work, we describe an extremely simple extension to SK or OK, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes conditional bias in addition to unconditional error variance. For comparative evaluation of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the kriging estimates are cross-validated. For generalization and potential application in other optimal estimation techniques, we also derive CBPK in the framework of classical optimal linear estimation theory.     

Comparison of five snow water equivalent estimation methods across categories

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R² of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R² = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R² = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R² = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.     

Artificial neural network models for forecasting monthly precipitation in Jordan

Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the performance criterion under consideration.     

Hydrological Sciences Journal Contents 2002

Abstract

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.

Editor D. Koutsoyiannis     

Estimating temperature and salinity profiles using empirical orthogonal functions and clustering on historical measurements

Oceanographic climatology is normally estimated by dividing the world’s oceans into geographical boxes of fixed shape and size, where each box is represented by a climatological salinity and temperature profile. The climatological profile is typically an average of historical measurements from that region. Since an arbitrarily chosen box may contain different types of water masses both in space and time, an averaged profile may be a statistically improbable or even non-physical representation. This paper proposes a new approach that employs empirical orthogonal functions in combination with a clustering technique to divide the world’s oceans into climatological regions. Each region is represented by a cluster that is determined by minimising the variance of the state variables within each cluster. All profiles contained in a cluster are statistically similar to each other and statistically different from profiles in other clusters. Each cluster is then represented by mean temperature and salinity profiles and a mean position. Methods for estimating climatological profiles from the cluster information are examined, and their performances are compared to a conventional method of estimating climatology. The comparisons show that the new methods outperform conventional methods and are particularly effective in areas where oceanographic fronts are present.     

Annual runoff estimation—an example of karstic aquifers in the transboundary region of Croatia and Slovenia

Abstract

An approach to the global estimation of water balance elements and their spatial distribution using GIS is presented. It is primarily related to the catchments where measured data are scarce and the spatial differentiation of the hydrological characteristics is not possible without climatological data. Emphasis is placed on estimating the water balance of transboundary karstic aquifers, where problems concerning the hydrometeorological data, catchment boundaries and determination of water balance elements in general are far more complex. The runoff estimation was done using the Turc and Langbein methods, which are the most frequently applied in this region. The years 1961–1990 were used as the reference period. Based on comparison of the results, the applicability of the methods is discussed. The approach proposed is suitable for estimation of water balance in the study area and may also be applied in a wider region.     

Seasonalizing mountain system recharge in semi-arid basins-climate change impacts

Climate variability and change impact groundwater resources by altering recharge rates. In semi-arid Basin and Range systems, this impact is likely to be most pronounced in mountain system recharge (MSR), a process which constitutes a significant component of recharge in these basins. Despite its importance, the physical processes that control MSR have not been fully investigated because of limited observations and the complexity of recharge processes in mountainous catchments. As a result, empirical equations, that provide a basin-wide estimate of mean annual recharge using mean annual precipitation, are often used to estimate MSR. Here North American Regional Reanalysis data are used to develop seasonal recharge estimates using ratios of seasonal (winter vs. summer) precipitation to seasonal actual or potential evapotranspiration. These seasonal recharge estimates compared favorably to seasonal MSR estimates using the fraction of winter vs. summer recharge determined from isotopic data in the Upper San Pedro River Basin, Arizona. Development of hydrologically based seasonal ratios enhanced seasonal recharge predictions and notably allows evaluation of MSR response to changes in seasonal precipitation and temperature because of climate variability and change using Global Climate Model (GCM) climate projections. Results show that prospective variability in MSR depends on GCM precipitation predictions and on higher temperature. Lower seasonal MSR rates projected for 2050-2099 are associated with decreases in summer precipitation and increases in winter temperature. Uncertainty in seasonal MSR predictions arises from the potential evapotranspiration estimation method, the GCM downscaling technique and the exclusion of snowmelt processes.     

Plausible impact of global climate change on water resources in the Tarim River Basin

Combining the temperature and precipitation data from 77 climatological stations and the climatic and hydrological change data from three headstreams of the Tarim River: Hotan, Yarkant, and Aksu in the study area, the plausible association between climate change and the variability of water resources in the Tarim River Basin in recent years was investigated, the long-term trend of the hydrological time series including temperature, precipitation, and stream-flow was detected, and the possible association between the El Nino/Southern Oscillation (ENSO) and these three kinds of time series was tested. The results obtained in this study show that during the past years, the temperature experienced a significant monotonic increase at the speed of 5%, nearly 1℃rise; the precipitation showed a significant decrease in the 1970s, and a significant increase in the 1980s and 1990s, the average annual precipitation was increased with the magnitude of 6.8 mm per decade. A step change occurred in both temperature and