Snowmelt Modelling Approaches in Watershed Models: Computation and Comparison of Efficiencies under Varying Climatic Conditions

Most of the watershed models contain snowmelt-computing options but there are modelling difficulties in snow-covered watersheds either due to paucity of data or in addressing snowmelt computation weakly. The temperature index (TI) and/or energy balance (EB algorithms of HEC-1, NWSRFS, PRMS, SHE, SRM, SSARR, SWAT, TANK, and UBC models have been investigated. The performance has been evaluated at the point (station specific) snowmelt computation with and without snowpack accounting. The computations have been performed for Solang station at 2?485 m altitude located in the western Himalayas. Springtime weekly snow and meteorological data of 1?983, 2003, and 2008 have been used. Data year 2008 has been used for weekly simulation with the observed snowpack ablation. The probability of success in simulating the snowmelt using TI/EB of all the models in average is 0.77. Nash-Sutcliffe (NS) efficiency coefficients for simulation with snowpack accounting are found to vary between 0.84 and 0.97. Although NS coefficients for verification year 2003 are satisfactory (0.5 to 0.88) but snowmelt prediction/verification efficiency at an interval of 25 years (1983) is below average. However, verification on probability criteria for data year 1983 in the case of TI/EB is 0.63/0.48. Results from EB approach show wind dependent fluctuations. Uncertainty arises due to inter-decadal variability of the snowpack/snowmelt. The approach applied in this paper is valuable in order to have a quick evaluation of snowmelt algorithm before integrating it with any operational watershed model.     

A Comparative Study of MLR,KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction

Water Resources Management - Reliable and precise prediction of the rivers flow is a major concern in hydrologic and water resources analysis. In this study, multi-linear regression (MLR) as a...     

A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

Over the past few decades, many numerical streamflow prediction techniques using observed time series (TS) have been developed and widely used in water resources planning and management. Recent advances in quantitative rainfall forecasting by numerical weather prediction (NWP) models have made it possible to produce improved streamflow forecasts using continuous rainfall-runoff (RR) models. In the absence of a suitable integrated system of NWP, RR and river system models, river operators in Australia mostly use spreadsheet-based tools to forecast streamflow using gauged records. The eWater Cooperative Research Centre of Australia has recently developed a new generation software package called eWater Source, which allows a seamless integration of continuous RR and river system models for operational and planning purposes. This paper presents the outcomes of a study that was carried out using Source for a comparative evaluation of streamflow forecasting by several well-known TS based linear techniques and RR models in two selected sub-basins in the upper Murray river system of the Murray-Darling Basin in Australia. The results were compared with the actual forecasts made by the Murray River operators and the observed data. The results show that while streamflow forecasts by the river operators were reasonably accurate up to day 3 and traditional TS based approaches were reasonably accurate up to 2?days. Well calibrated RR models can provide better forecasts for longer periods when using high quality quantitative precipitation forecasts. The river operators tended to underestimate large magnitude flows.     

Influence of Precipitation Changes on the SPI and Related Drought Severity. An Analysis Using Long-Term Data Series

Drought indices, such as the Standardized Precipitation Index (SPI) are used to quantify drought severity. Due to the SPI probabilistic and standardized nature, a given value of SPI computed in distinct time periods or locations indicates the same relative drought severity but corresponds to different amounts of precipitation. Thus, the present study aims at contributing for a comprehensive analysis of the influence of long-term precipitation variability on drought assessment by the SPI. Long records of monthly precipitation, spanning from 1863 to 2007 in several locations across Portugal, were divided into 30 years sub-periods and the SPI with 12-month time scale (SPI-12) was computed for each sub-period and for the entire period of records. The probability distributions adjusted to precipitation in those different time periods were compared envisaging to detect the SPI sensitivity to the reference period and, therefore, to changes in precipitation. Precipitation thresholds relative to the upper limits of SPI-12 drought categories were obtained and the influence of the time period was investigated. Results have shown that when SPI values derived from the full data record for a recent time period are lower/higher than the SPI values derived from data of the considered time period a recent downward/upward shift of precipitation has occurred. Coherently, a common pattern of drought aggravation from the initial until the more recent period was not detected. However, in southern locations, lower precipitation thresholds of the SPI drought categories were generally found in the more recent period, particularly for more severe drought categories, whereas in the northern locations Porto and Montalegre, an increase was detected. The impacts of the reference period on the computed SPI drought severity and frequency are shown, bringing to discussion the need for updating ´normal´ conditions when long term precipitation records are available and precipitation changes are observed.     

Performance Assessment of the Linear,Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.     

Evaluating the Performance of Agricultural Water Distribution Systems Using FIS,ANN and ANFIS Intelligent Models

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

     

Recent Climate Trends and Drought Behavioral Assessment Based on Precipitation and Temperature Data Series in the Songhua River Basin of China

Precise analysis of spatiotemporal trends of temperature, precipitation and meteorological droughts plays a key role in the sustainable management of water resources in the given region. This study first aims to detect the long-term climate (monthly/seasonally and annually) trends from the historical temperature and precipitation data series by applying Spearmen’s Rho and Mann-Kendall test at 5 % significant level. The measurements of both climate variables for a total period of 49 years (1965–2013) were collected from the 11 different meteorological stations located in the Songhua River basin of China. Secondly, the two well-known meteorological drought indices including the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) were applied on normalize data to detect the drought hazards at 3, 6, 9 and 12 month time scale in the study area. The analysis of monthly precipitation showed significant (p < 0.05) increasing trends during the winter (November and December months) season. Similarly, the results of seasonal and annual air temperature showed a significant increase from 1 °C to 1.5 °C for the past 49 years in the basin. According to the Sen’s slope estimator, the rate of increment in seasonal temperature slope (0.26 °C/season) and precipitation (9.02 mm/season) were greater than annual air temperature (0.04 °C/year) and precipitation (1.36 mm/year). By comparing the results of SPI and RDI indices showed good performance at 9 (r = 0.96, p < 0.01) and 12 (r = 0.99, p < 0.01) month drought analysis. However, the yearly drought analysis at over all stations indicated that a 20 years were under dry conditions in entire study area during 49 years. We found the extreme dry and wet conditions in the study region were prevailing during the years of 2001 and 2007, and 1994 and 2013, respectively. Overall, the analysis and quantifications of this study provides a mechanism for the policy makers to mitigate the impact of extreme climate and drought conditions in order to improve local water resources management in the region.

     

Assessment of Contributions of Climatic Variation and Human Activities to Streamflow Changes in the Lancang River,China

The construction and operation of hydropower reservoirs on the Lancang River have drawn worldwide concern, because streamflow changes have occurred in this river since the introduction of dams. To address these concerns, it is necessary to quantitatively assess relative contributions of climatic variations and human activities to these changes. In this research, Mann-Kendall method was used to assess the trends in hydro-climatic data. The streamflow data were divided into a reference period (1956–1985) and a change period (1986–2008) based on hydropower reservoir construction timeline. The Back-Propagation Artificial Neural Network (BP-ANN) model was used to reconstruct natural streamflow. The contributions of climatic variations and human activities were investigated at the yearly, seasonal and monthly time scales. The results indicate that human activities exerted a slightly greater impact on flow changes than did climatic variations, at the yearly time scale (54.6 and 45.4 %, respectively). At the seasonal time scale, climatic variations made a greater contribution (65.8 %) during the wet season, while the contribution of human activities became the dominant factor during the dry season (85.3 %). At the monthly time scale, the contribution of climatic variations in January, June, August, and September was greater than that of human activities, while in the remaining eight months, human activities exerted a greater contribution than did climatic variation. The relative contributions of human activities and climatic variations (RCs) were also determined during the single- and cascade-dam periods; these did not always increase at the three time scales when dam system shifted from single-dam to cascade-dam.     

Drought Forecasting Using Neural Networks,Wavelet Neural Networks,and Stochastic Models: Case of the Algerois Basin in North Algeria

Drought forecasting is a major component of a drought preparedness and mitigation plan. This paper focuses on an investigation of artificial neural networks (ANN) models for drought forecasting in the algerois basin in Algeria in comparison with traditional stochastic models (ARIMA and SARIMA models). A wavelet pre-processing of input data (wavelet neural networks WANN) was used to improve the accuracy of ANN models for drought forecasting. The standard precipitation index (SPI), at three time scales (SPI-3, SPI-6 and SPI-12), was used as drought quantifying parameter for its multiple advantages. A number of different ANN and WANN models for all SPI have been tested. Moreover, the performance of WANN models was investigated using several mother wavelets including Haar wavelet (db1) and 16 daubechies wavelets (dbn, n varying between 2 and 17). The forecast results of all models were compared using three performance measures (NSE, RMSE and MAE). A comparison has been done between observed data and predictions, the results of this study indicate that the coupled wavelet neural network (WANN) models were the best models for drought forecasting for all SPI time series and over lead times varying between 1 and 6 months. The structure of the model was simplified in the WANN models, which makes them very convenient and parsimonious. The final forecasting models can be utilized for drought early warning.

     

Quantifying Whitewater Recreation Opportunities in Cataract Canyon of the Colorado River,Utah: Aggregating Acceptable Flows and Hydrologic Data to Identify Boatable Days

The structural norm approach was combined with the Potential for Conflict Index to define recreation streamflow needs for the Colorado River in Utah and Colorado. An online survey was completed by 128 commercial and non‐commercial boaters, who evaluated a range of flows for whitewater boating. For the Cataract Canyon reach, respondents rated the quality of their recreation experience of specific flows, describing the quality of boating opportunities across the full range of historical streamflows. Ranges for both acceptable and optimum flows were defined, as well as thresholds for unacceptable flows. These ranges were then evaluated against historical hydrologic records to quantify the timing, frequency, and duration of days when defined whitewater flows exist across different year types (i.e. average boatable days). Results indicated that on average, a total of 257 boatable days existed in dry years, and 353 total boatable days occurred in dry‐typical years. In wet and wet‐typical years, 362 and 365 total boatable days respectively, occurred on average. Results of the boatable days' analysis indicated that over the 23‐year period of record, whitewater boating opportunities occurred nearly every day of the year in all but the driest year types. Results from this study provide resource managers with information which can be used in the development of annual operating plans for the Colorado River Basin and help managers understand how changes in flow impact the quality of recreational opportunities. This application demonstrates the value of analysing boatable days on any river where recreation management is a priority. Copyright © 2016 John Wiley & Sons, Ltd.