HYDRODYNAMIC AND EXPERIMENTAL STUDY OF UNCONFINED GROUND WATER BALANCE ELEMENTS IN REPRESENTATIVE AND EXPERIMENTAL BASINS

Abstract

A mathematical model is built for monthly river flows of the Orinoco river. The model consists of a cyclic part which explains up to 93% of the total variance of the series of monthly water levels, and a stochastic part which is shown to follow a 1st order autoregressive scheme with a primary variable or random component that behaves as “white noise” and appears to have near 60% chance of coming from a normal population. A time series of flow anomalies is obtained from long term monthly means. Statistical techniques are applied to the series of flow anomalies in order to obtain the mean number of crossings at an arbitrary level during an arbitrary time. There are also studied the mean length of an upward excursion over an arbitrary level and the mean time between successive upcrossings. The actual results for the Orinoco river are in good agreement with the statistical theory showing that this kind of analysis can be extremely useful in the design and planning of reservoir operations. (Key words: hydrology, runoff, statistical analysis.)     

Rainfall–interception–evaporation–runoff relationships in a semi-arid catchment,northern Limpopo basin,Zimbabwe

Abstract

Two types of monthly water balance models at basin scale are used: PE models use precipitation and potential evapotranspiration (PET) as their observed input data, whereas P models need only precipitation. Calibration proceeds by comparing model runoff and observed runoff. Calibration is entirely automatic with the exclusion of subjective elements. All models differ only by their actual evapotranspiration equations. PE models from previous papers are generalized essentially by replacing the constant evapotranspiration parameter by a periodic one, thus increasing the number of parameters by two (a “parameter” is an unknown constant to be estimated, and which is a characteristic of the river basin to be described). P models use a periodic “driving force”, which is intended to represent periodicity of hydrological phenomena, normally originating in the (unavailable) PET time series. These eight PE models and three P models are then applied to 55 river basins in 10 countries with widely diverging climates and soil conditions. A marked improvement of model performance in about one third of the basins is due to the introduction of the above mentioned periodic functions. Even when PET data are available it is sometimes useful to consider P models. P models scarcely perform less well than PE models. An engineer, wanting to try out as few models as possible on a given river basin, can restrict his attention to the optimization of two or three models. The paper is an extension of a long effort towards monthly water balance models, and is believed to give a solution in most circumstances.     

Dynamic-stochastic models of rainfall and snowmelt runoff formation

Abstract

Possibilities for the development of dynamic-stochastic models of runoff formation with random inputs are discussed. Two models are described: the first allows the calculation of the statistical distribution of the maximum discharges of rainfall floods, and the second the statistical distribution of snowmelt flood volumes. Meteorological inputs are generated by the Monte- Carlo method. Physically-based models are used for the transformation of input data into runoff. The various models are applied to observation data from two watersheds.     

Analysis of cross-correlated chaotic streamflows

Abstract

The 1911–2010 variability in monthly runoff and the effect of 1995–2005 summer water temperatures in a highly productive salmon system, the Fraser River Basin (FRB) of British Columbia, Canada are explored. Hydrometric data from 141 FRB gauges provide variations in monthly runoff including their extremes and months of occurrences, as well as trends in their variability. Stream temperatures and their relationships to runoff are also assessed. There is a gradual increase of monthly runoff ranges from the central plateau of the FRB towards higher altitudes with maxima in glacier-fed alpine streams. Maximum and minimum monthly runoff across the FRB typically occur during May–June and February, respectively. There is a tendency towards greater FRB variability in July runoff. Water temperatures show high variability in the unregulated North and South Thompson rivers and low variability in the regulated Nechako River. FRB low flows are associated with higher water temperatures, while high flows are associated with cooler ones, both of which may have a negative impact on salmon.

Editor Z.W. Kundzewicz; Associate editor S. Yue     

Identification of trends in Malaysian monthly runoff under the scaling hypothesis

Abstract

Statistical tests have been widely used for several decades to identify and test the significance of trends in runoff and other hydrological data. The Mann-Kendall (M-K) trend test is commonly used in trend analysis. The M-K test was originally proposed for random data. Several variations of the M-K test, as well as pre-processing of data for use with it, have been developed and used. The M-K test under the scaling hypothesis has been developed recently. The basic objective of the research presented in this paper is to investigate the trends in Malaysian monthly runoff data. Identification of trends in runoff data is useful for planning water resources projects. Existence of statistically significant trends would also lead to identification of possible effects of climate change. Monthly runoff data for Malaysian rivers from the past three decades are analysed, in both five-year segments and entire data sequences. The five-year segments are analysed to investigate the variability in trends from one segment to another in three steps: (1) the M-K tests are conducted under random and correlation assumptions; (2) the Hurst scaling parameter is estimated and tested for significance; and (3) the M-K test under the scaling hypothesis is conducted. Thus the tests cover both correlation and scaling. The results show that the number of significant segments in Malaysian runoff data would be the same as those found under the assumption that the river flow sequences are random. The results are also the same for entire sequences. Thus, monthly Malaysian runoff data do not have statistically significant trends. Hence there are no indications of climate change in Malaysian runoff data.

Citation Rao, A. R., Azli, M. & Pae, L. J. (2011) Identification of trends in Malaysian monthly runoff under the scaling hypothesis. Hydrol. Sci. J. 56(6), 917–929.     

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.     

A comparative study on a simple two-parameter monthly water balance model and the Kajiyama formula for monthly runoff estimation

ABSTRACT

A two-parameter monthly water balance model to simulate runoff can be used for a water resources planning programme and climate impact studies. However, the model estimates two parameters of transformation of time scale (c) and of the field capacity (SC) by a trial-and-error method. This study suggests a modified methodology to estimate the parameters c and SC using the meteorological and geological conditions. The modified model is compared with the Kajiyama formula to simulate the runoff in the Han River and International Hydrological Programme representative basins in South Korea. We show that the estimated c and SC can be used as the initial or optimal values for the monthly runoff simulation study in the model.

EDITOR M.C. Acreman; ASSOCIATE EDITOR S. Kanae     

Modelling streamflow trends for a watershed with limited data: case of the Litani basin,Lebanon

Abstract

Streamflow variability in the Upper and Lower Litani basin, Lebanon was modelled as there is a lack of long-term measured runoff data. To simulate runoff and streamflow, daily rainfall was derived using a stochastic rainfall generation model and monthly rainfall data. Two distinct synthetic rainfall models were developed based on a two-part probabilistic distribution approach. The rainfall occurrence was described by a Markov chain process, while the rainfall distribution on wet days was represented by two different distributions (i.e. gamma and mixed exponential distributions). Both distributions yielded similar results. The rainfall data were then processed using water balance and routing models to generate daily and monthly streamflow. Compared with measured data, the model results were generally reasonable (mean errors ranging from 0.1 to 0.8?m³/s at select locations). Finally, the simulated monthly streamflow data were used to investigate discharge trends in the Litani basin during the 20th century using the Mann-Kendall and Sen slope nonparametric trend detection methods. A significant drying trend of the basin was detected, reaching a streamflow reduction of 0.8 and 0.7 m³/s per decade in January for the Upper and Lower basin, respectively.

Editor D. Koutsoyiannis; Associate editor Sheng Yue

Citation Ramadan, H.H., Beighley, R.E., and Ramamurthy, A.S., 2012. Modelling streamflow trends for a watershed with limited data: case of the Litani basin, Lebanon. Hydrological Sciences Journal, 57 (8), 1516–1529.     

Validation of a Hydrological Model Intended for Impact Study: Problem Statement and Solution Example for Selenga River Basin

The study is aimed to evaluate a hydrological simulation model intended for assessing climate change impact. A new test was suggested and applied to evaluate the performance of a physically based model of Selenga River runoff generation. In this test, to calibrate the model, an enhanced Nash–and-Sutcliffe efficiency (NSE) criterion was used, including trend-oriented reference (benchmark) models instead of the simple reference model used in the original NSE criterion. Next, modifications were made in the Differential Split Sample test (DSS-test) of V. Klemeš (1986), focused on differences in the model performance criteria for climatically contrasting periods, and a new statistical measure was proposed to estimate the significance of these differences. After that, model performance was evaluated for four sites within the catchment, three indicators of interest (daily, monthly, and annual discharge series), and the model ability to reproduce the observed trends in annual and seasonal discharge values was assessed. The model proved robust enough to be applied to assessing climate change impact on the annual and monthly runoff in different parts of the Selenga River basin.

     

Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed,Canada

ABSTRACT

To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlation matrix. Precipitation series from weather generators are used as input to the hydrological model, the soil and water assessment tool (SWAT), to generate runoff over the Red Deer watershed, Canada for further runoff analysis. The results indicate that the SWAT model using SWG-generated data accurately represents the mean monthly streamflow for most of the months. The multi-site generators were capable of better representing the monthly streamflow variability, which was notably underestimated by the single-site version. In terms of extreme flows, the proposed method reproduced the observed extreme flow with smaller bias than MBFA, while the single-site generator significantly underestimated the annual maximum flows due to its poor capability in addressing partial precipitation correlations.     

Effect of modelling scale on the assessment of climate change impact on river runoff

Abstract

The effect of using two distributed hydrological models with different degrees of spatial aggregation on the assessment of climate change impact on river runoff was investigated. Analyses were conducted in the Narew River basin situated in northeast Poland using a global hydrological model (WaterGAP) and a catchment-scale hydrological model (SWAT). Climate change was represented in both models by projected changes in monthly temperature and precipitation between the period 2040–2069 and the baseline period, resulting from two general circulation models: IPSL-CM4 and MIROC3.2, both coupled with the SRES A2 emissions scenario. The degree of consistency between the global and the catchment model was very high for mean annual runoff, and medium for indicators of high and low runoff. It was observed that SWAT generally suggests changes of larger magnitude than WaterGAP for both climate models, but SWAT and WaterGAP were consistent as regards the direction of change in monthly runoff. The results indicate that a global model can be used in Central and Eastern European lowlands to identify hot-spots where a catchment-scale model should be applied to evaluate, e.g. the effectiveness of management options.

Editor D. Koutsoyiannis; Associate editor F.F. Hattermann

Citation Piniewski, M., Voss, F., Bärlund, I., Okruszko, T., and Kundzewicz. Z.W., 2013. Effect of modelling scale on the assessment of climate change impact on river runoff. Hydrological Sciences Journal, 58 (4), 737–754.     

Improved estimators of correlation and R2 for skewed hydrologic data

ABSTRACT

The coefficient of determination R² and Pearson correlation coefficient ρ = R are standard metrics in hydrology for the evaluation of the goodness of fit between model simulations and observations, and as measures of the degree of dependence of one variable upon another. We show that the standard product moment estimator of ρ, termed r, while well-behaved for bivariate normal data, is upward biased and highly variable for bivariate non-normal data. We introduce three alternative estimators of ρ which are nearly unbiased and exhibit much less variability than r for non-normal data. We also document remarkable upward bias and tremendous increases in variability associated with r using both synthetic data and daily streamflow simulations from 905 calibrated rainfall–runoff models. We show that estimators of ρ = R accounting for skewness are needed for daily streamflow series because they exhibit high variability and skewness compared to, for example, monthly/annual series, where r should perform well.     

Analysis of hydrological seasonality across northern catchments using monthly precipitation–runoff polygon metrics

Abstract

Seasonality is an important hydrological signature for catchment comparison. Here, the relevance of monthly precipitation–runoff polygons (defined as scatter points of 12 monthly average precipitation–runoff value pairs connected in the chronological monthly sequence) for characterizing seasonality patterns was investigated to describe the hydrological behaviour of 10 catchments spanning a climatic gradient across the northern temperate region. Specifically, the research objectives were to: (a) discuss the extent to which monthly precipitation–runoff polygons can be used to infer active hydrological processes in contrasting catchments; (b) test the ability of quantitative metrics describing the shape, orientation and surface area of monthly precipitation–runoff polygons to discriminate between different seasonality patterns; and (c) examine the value of precipitation–runoff polygons as a basis for catchment grouping and comparison. This study showed that some polygon metrics were as effective as monthly average runoff coefficients for illustrating differences between the 10 catchments. The use of precipitation–runoff polygons was especially helpful to look at the dynamics prevailing in specific months and better assess the coupling between precipitation and runoff and their relative degree of seasonality. This polygon methodology, linked with a range of quantitative metrics, could therefore provide a new simple tool for understanding and comparing seasonality among catchments.

Editor Z.W. Kundzewicz; Associate editor K. Heal

Citation Ali, G., Tetzlaff, D., Kruitbos, L., Soulsby, C., Carey, S., McDonnell, J., Buttle, J., Laudon, H., Seibert, J., McGuire, K., and Shanley, J., 2013. Analysis of hydrological seasonality across northern catchments using monthly precipitation–runoff polygon metrics. Hydrological Sciences Journal, 59 (1), 56–72.     

A Bayesian analysis of Generalized Pareto Distribution of runoff minima

Global climate change models have predicted the intensification of extreme events, and these predictions are already occurring. For disaster management and adaptation of extreme events, it is essential to improve the accuracy of extreme value statistical models. In this study, Bayes' Theorem is introduced to estimate parameters in Generalized Pareto Distribution (GPD), and then the GPD is applied to simulate the distribution of minimum monthly runoff during dry periods in mountain areas of the Ürümqi River, Northwest China. Bayes' Theorem treats parameters as random variables and provides a robust way to convert the prior distribution of parameters into a posterior distribution. Statistical inferences based on posterior distribution can provide a more comprehensive representation of the parameters. An improved Markov Chain Monte Carlo (MCMC) method, which can solve high‐dimensional integral computation in the Bayes equation, is used to generate parameter simulations from the posterior distribution. Model diagnosis plots are made to guarantee the fitted GPD is appropriate. Then based on the GPD with Bayesian parameter estimates, monthly runoff minima corresponding to different return periods can be calculated. The results show that the improved MCMC method is able to make Markov chains converge faster. The monthly runoff minima corresponding to 10a, 25a, 50a and 100a return periods are 0.60 m³/s, 0.44 m³/s, 0.32 m³/s and 0.20 m³/s respectively. The lower boundary of 95% confidence interval of 100a return level is below zero, which implies that the Ürümqi River is likely to cease to flow when 100a return level appears in dry periods. Copyright © 2015 John Wiley & Sons, Ltd.     

The antecedent soil moisture condition of the curve number procedure

Abstract

Estimation of direct runoff, peak discharge or hydrographs is often necessary in small to medium-sized ungauged basins. Different models are used in practice for these purposes, depending on the type of problem, the available data and the prevailing runoff mechanisms in the study basin. This paper discusses the applicability of the curve number procedure developed by the US Soil Conservation Service (SCS) to estimate direct runoff in basins characterized by small to gentle undulating slopes mainly covered with natural grasslands. Rainfall and runoff data measured in the Cañada de Los Chanchos basin in Uruguay is used to fit the curve numbers and to analyse the antecedent soil moisture condition proposed by the SCS.     

SUR LA PUISSANCE DES CRUES AU JAPON A MADAGASCAR ET A LA REUNION

Abstract

Hydrological modelling has faced the problem of ungauged basins for many years: how does one estimate hydrological characteristics for a river for which there are no data? Whatever the kind of model, it needs at least hydroclimatic input data and discharge data for calibration. However, the Yates model does not need any discharge data for calibration: it is a pre-calibrated model from a vegetation—climate classification map. In the specific context of West and Central Africa, where data are often of poor quality and very scarce, it is interesting to compare the performance of such a model with those of calibrated models, and with observed data. For this study, a platform including different semi-global rainfall—runoff models which allow the estimation of monthly runoff at a spatial resolution of 0.5° × 0.5° was used. The performance of the Yates model is very close to those of calibrated models, so that one can say that this simple model, based simply on a vegetation—climate classification, can be a very useful prediction tool in regions of scarce and unreliable data, such as those of interest to the International Association of Hydrological Sciences (IAHS) initiative on prediction in ungauged basins (PUB). Therefore, this model was applied to a period covering the last 30 years, and to a data set covering the first decades of the 21st century, from a climatic scenario of doubling the CO₂ concentration in the atmosphere. The results show that, in West Africa, where drought conditions have now prevailed for 35 years, water resources should still be decreasing in the future, following the general decreasing trend of rainfall projected by the climatic scenarios.     

Hydro-climatic variability and forest fires in Mexico’s northern temperate forests

Global warming is likely modifying the hydrological cycle of forested watersheds. This report set as objectives to: a) assess the hydrological variables interception loss, I, potential and actual evapo-transpiration, E, Et, runoff, Q, and soil moisture content, θ; b) evaluate whether these variables are presenting consistent trends or oscillations that can be associated to global warming or climate variability; and c) relate θ to the number of wildfires and the burned area in Durango, Mexico. A mass balance approach estimated daily variables of the water cycle using sub-models for I and Et to calculate Q and θ for a time series from 1945 to 2007. Regression and auto-regressive and moving averaging (ARIMA) techniques evaluated the statistical significance of trends. The cumulative standardized z value magnified and ARIMA models projected statistically similar monthly and annual time series data of all variables of the water cycle. Regression analysis and ARIMA models showed monthly and annual P, I, E, and Et, Q, and θ do not follow consistent up or downward linear tendencies over time with statistical significance; they rather follow oscillations that could be adequately predicted by ARIMA models (r² ≥ 0.70). There was a consistent statistical association (p ≤ 0.05) of θ with the number of wildfires and the area burned regardless of the different spatial scales used in evaluating these variables. The analysis shows seasonal variability is increasing over time as magnifying pulses of dryness and wetness, which may be the response of the hydrological cycle to climate change. Further research must center on using longer time series data, testing seasonal variability with additional statistical analysis, and incorporating new variables in the analysis.     

Disaggregation modelling of monthly streamflows using a new approach of the method of fragments

Abstract

The method of fragments is applied to the generation of synthetic monthly streamflow series using streamflow data from 34 gauging stations in mainland Portugal. A generation model based on the random sampling of the log-Pearson Type III distribution was applied to each sample to generate 1200 synthetic series of annual streamflow with an equal length to that of the sample. The synthetic annual streamflow series were then disaggregated into monthly streamflows using the method of fragments, by three approaches that differed in terms of the establishment of classes and the selection of fragments. The results of the application of such approaches were compared in terms of the capacity of the method to preserve the main monthly statistical parameters of the historical samples.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Silva, A.T. and Portela, M.M., 2012. Disaggregation modelling of monthly streamflows using a new approach of the method of fragments. Hydrological Sciences Journal, 57 (5), 942–955.     

Annual and monthly rainfall data generation schemes

Synthetic annual and monthly rainfall data series are generated by using autoregressive (AR) processes, Thomas-Fiering (TF) model, method of fragments (F) and its modified version (MF), two-tier (TT) model, and a newly developed wavelet (W) approach. It is seen that the W approach is as well in preserving the statistical behavior of the observed data series as the classical annual and monthly hydrological data generation schemes used in this study. The W approach is found even better in replacing some particular characteristics such as the mean of the sequence and correlation between the successive months in the series. It is, therefore, proposed as a new annual and monthly hydrological data generation scheme.     

Rainfall—runoff response,event-based runoff coefficients and hydrograph separation

Abstract

Event-based runoff coefficients can provide information on watershed response. They are useful for catchment comparison to understand how different landscapes “filter” rainfall into event-based runoff and to explain the observed differences with catchment characteristics and related runoff mechanisms. However, the big drawback of this important parameter is the lack of a standard hydrograph separation method preceding its calculation. Event-based runoff coefficients determined with four well-established separation methods, as well as a newly developed separation method, are compared and are shown to differ considerably. This signifies that runoff coefficients reported in the literature often convey less information than required to allow for catchment classification. The new separation technique (constant-k method) is based on the theory of linear storage. Its advantages are that it is theoretically based in determining the end point of an event and that it can also be applied to events with multiple peaks. Furthermore, it is shown that event-based runoff coefficients in combination with simple statistical models improve our understanding of rainfall—runoff response of catchments with sparse data.