No abstract

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall τ estimation method for copulas parameter estimation. The methods were employed to study joint severity-duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The Q 75 index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma-GEV, LN2-exponential, and LN2gamma were selected as the best paired drought severity-duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov-Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-τ is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall τ estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.

The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results.Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash-Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.2 of 20 models in the simulation of R-R non-linear process is more popular than conceptual models [5]. Data-driven models attempt to find a relationship between input and output parameters without considering the physical process [1]. Artificial Neural Networks (ANNs) are a type of data-driven model with a flexible mathematical structure which includes both linear and nonlinear concepts which operate within a dynamic input-output system [6].In the past several decades, there has been substantial growth in application of ANNs for R-R modelling [7][8][9][10][11][12][13][14][15] where which ANNs have been compared to other methods, including traditional statistical methods, conceptual models and other artificial intelligence models. Result of these studies have shown that ANN is more accurate than conventional and traditional statistical methods. For example, the ANN approach predicted more accurate runoff in the Mississippi River basin than the conventional autoregressive moving average with exogenous inputs (ARMAX) approach in [16]. Birikundavyi et al. [17] have demonstrated that streamflow forecasting using ANN models has better performance than conceptual model (PREVIS) and the conventional autoregressive model coupled with a Kalman filter (ARMAX-KF) models. Additionally, comparison of ANNs with other artificial intelligence models (e.g., support vector machine (SVM), genetic pr...

In separate analyses of the impacts of land use change and climate change, a scenario-based approach using remote sensing and hydro-climatological data was developed to assess changes in hydrological indices. The data comprised three Landsat TM images (1988, 1998, 2008) and meteorological and hydrological data for the Aligudarz and Doroud stations in the Marboreh watershed, Iran. The QUAC module and supervised classification maximum likelihood (ML) algorithm in ENVI 5.1 were used for remote sensing, the SWAT model for hydrological modelling and the Mann-Kendall and t test methods for statistical analysis. To create scenarios, the study period was divided into three decades (1983-1992, 1993-2002, 2003-2012) with clearly different land use/land cover (LULC). After hydrological modelling, 10 hydrological indices related to high and low flow indices (HDI and LDI) were analysed for seven scenarios developed by combining pre-defined climate periods and LULC maps. The major changes in land use were degradation of natural rangeland (− 18.49%) and increasing raid-fed farm area (+ 16.70%) and residential area (+ 0.80%). The Mann-Kendall test results showed a statistically significant (p < 0.05) decreasing trend in rainfall and flow during 1983-2012. In the scenarios evaluated, hydrological index trends were more sensitive to climate change than to LULC changes in the study area. Low flow indices were more affected than high flow indices in both land use and climate change scenarios. The results show little impact of land use change and indicate that climate change is the main driver of hydrological variations in the catchment. This is useful information in outlining future strategies for sustainable water resources management and policy decision-making in the Marboreh watershed.

Prioritization of watersheds has gained more importance in natural resources management, particularly in the context of watershed management. Flooding potential is one of the main indices used to prioritize watersheds. The objective of this study is to investigate the Pol‐Doab Shazand Watershed flooding conditions as a result of land use change and prioritization of its subwatersheds using rainfall – runoff data. For this purpose, after computation of design flow with different return periods, factor analysis technique was applied to determine the best flow with proper return period towards evaluation and prioritization of subwatersheds. The results showed that the design flow with 25‐year return period (Q25) is suitable. The results indicate that subwatershed 22 is the highest priority because of large changes in peak flow, and subwatershed 2 given the lowest priority since changes in peak flow is the lowest.