Rainfall and streamflow response to El Niño Southern Oscillation: a case study in a semiarid catchment, Australia.

This paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0-3 month lead time, compared to rainfall distribution.

Mahanadi streamflow: climate change impact assessment and adaptive strategies

Impacts of climate change on hydrology are assessed by downscaling large scale general circulation model (GCM) outputs of climate variables to local scale hydrologic variables. This modelling approach is characterized by uncertainties resulting from the use of different models, different scenarios, etc. Modelling uncertainty in climate change impact assessment includes assigning weights to GCMs and scenarios, based on their performances, and providing weighted mean projection for the future. This projection is further used for water resources planning and adaptation to combat the adverse impacts of climate change. The present article summarizes the recent published work of the authors on uncertainty modelling and development of adaptation strategies to climate change for the Mahanadi river in India.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system’s Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system's Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Isolating the impacts of land use and climate change on streamflow

Quantifying the isolated and integrated impacts of land use (LU) and climate change on streamflow is challenging as well as crucial to optimally manage water resources in river basins. This paper presents a simple hydrologic modeling-based approach to segregate the impacts of land use and climate change on the streamflow of a river basin. The upper Ganga basin (UGB) in India is selected as the case study to carry out the analysis. Streamflow in the river basin is modeled using a calibrated variable infiltration capacity (VIC) hydrologic model. The approach involves development of three scenarios to understand the influence of land use and climate on streamflow. The first scenario assesses the sensitivity of streamflow to land use changes under invariant climate. The second scenario determines the change in streamflow due to change in climate assuming constant land use. The third scenario estimates the combined effect of changing land use and climate over the streamflow of the basin. Based on the results obtained from the three scenarios, quantification of isolated impacts of land use and climate change on streamflow is addressed. Future projections of climate are obtained from dynamically downscaled simulations of six general circulation models (GCMs) available from the Coordinated Regional Downscaling Experiment (CORDEX) project. Uncertainties associated with the GCMs and emission scenarios are quantified in the analysis. Results for the case study indicate that streamflow is highly sensitive to change in urban areas and moderately sensitive to change in cropland areas. However, variations in streamflow generally reproduce the variations in precipitation. The combined effect of land use and climate on streamflow is observed to be more pronounced compared to their individual impacts in the basin. It is observed from the isolated effects of land use and climate change that climate has a more dominant impact on streamflow in the region. The approach proposed in this paper is applicable to any river basin to isolate the impacts of land use change and climate change on the streamflow.

Summary of Precipitation and Streamflow for Potrero and San Clemente Creeks in Water-Year 2007, Santa Lucia Preserve, Monterey County, California

In March 2007 CSU-Monterey Bay began hydrologic monitoring of Santa Lucia Preserve for the Santa Lucia Conservancy. This project is a continuation of monitoring begun by Balance Hydrologics as part of the permit requirements for land development. The purpose of this annual report is to present data summaries for the 2007 water year (October 1, 2006 to September 31, 2007). Rainfall in water year 2007 was very low, representing the 15 year drought rainfall. Streamflow was relatively low as well as indicated by baseflow conditions approaching the drought conditions of water-year 1991 (Croyle and Smith, 2007). Document contains 30 pages)

Low streamflow in the Myakka River Basin Area in Florida

The Sarasota-Manatee area is a water-short area and the study was undertaken in 1963 in order to determine the storage capability and discharge rates of the Myakka water shed. It was found that many of the streams of the water shed were virtually dry during part of every year. However, the basins of the Myakka lakes, through which the river flows offer some storage potential, that if properly developed would provide a continuance drift of about seven million gallons of water per day of good quality water that would be high in color and temperature upon occasion. With reasonable treatment some of this water could be used to meet the present needs of the rapidly expanding coastal areas. (PDF contains 40 pages.)

Streamflow response of a small forested catchment on different timescales

The hydrological response of a catchment to rainfall on different timescales is result of a complex system involving a range of physical processes which may operate simultaneously and have different spatial and temporal influences. This paper presents the analysis of streamflow response of a small humid-temperate catchment (Aixola, 4.8 km(2)) in the Basque Country on different timescales and discusses the role of the controlling factors. Firstly, daily time series analysis was used to establish a hypothesis on the general functioning of the catchment through the relationship between precipitation and discharge on an annual and multiannual scale (2003-2008). Second, rainfall-runoff relationships and relationships among several hydrological variables, including catchment antecedent conditions, were explored at the event scale (222 events) to check and improve the hypothesis. Finally, the evolution of electrical conductivity (EC) during some of the monitored storm events (28 events) was examined to identify the time origin of waters. Quick response of the catchment to almost all the rainfall events as well as a considerable regulation capacity was deduced from the correlation and spectral analyses. These results agree with runoff event scale data analysis; however, the event analysis revealed the non-linearity of the system, as antecedent conditions play a significant role in this catchment. Further, analysis at the event scale made possible to clarify factors controlling (precipitation, precipitation intensity and initial discharge) the different aspects of the runoff response (runoff coefficient and discharge increase) for this catchment. Finally, the evolution of EC of the waters enabled the time origin (event or pre-event waters) of the quickflow to be established; specifically, the conductivity showed that pre-event waters usually represent a high percentage of the total discharge during runoff peaks. The importance of soil waters in the catchment is being studied more deeply.