Probabilistic Event Based Rainfall-Runoff Modeling Using Copula Functions

Multivariate probability analysis of hydrological elements using copula functions can significantly improve the modeling of complex phenomena by considering several dependent variables simultaneously. The main objectives of this study were to: (i) develop a stand-alone and event-based rainfall-runoff (RR) model using the common bivariate copula functions (i.e. the BCRR model); (ii) improve the structure of the developed copula-based RR model by using a trivariate version of fully-nested Archimedean copulas (i.e. the FCRR model); and (iii) compare the performance of the developed copula-based RR models in an Iranian watershed. Results showed that both of the developed models had acceptable performance. However, the FCRR model outperformed the BCRR model and provided more reliable estimations, especially for lower joint probabilities. For example, when joint probabilities were increased from 0.5 to 0.8 for the peak discharge (q_p) variable, the reliability criteria value increased from 0.0039 to 0.8000 in the FCRR model, but only from 0.0010 to 0.6400 in the BCRR model. This is likely because the FCRR considers more than one rainfall predictor, while the BCRR considers only one.

     

Evidence of Climate Change in the Senegal River Basin

International development policy makers are recognizing climate change and desertification as fundamental obstacles to the social and economic development of the Third World. Sub-Saharan Africa, particularly the Sahel region, has been severely impacted by the compounding effects of drought, deforestation and desertification. The Senegal River Basin in the West Africa is a prime example of a region where development objectives are seriously undermined by the drought-induced desertification process. The basic hydrologic constraint on development is revealed in a time series decompositionof Senegal River annual flow volumes, which strongly suggests that water resources availability has been substantially curtailed since 1960. Two alternative time series mechanisms are hypothesized to account for the decreased flow volumes in recent decades. The first time series model suggests the presence of a long-term periodicity, while the second model hypothesizes an ARMA(1,1,) process. The second hypothesis provides a superior model fit. The stationary ARMA(1,1) model can be fitted successfully, however, only after explicitly removing a non-stationary component by linearly detrending after 1960. The implication of non-stationarity in Senegal River hydrology provides additional analytic evidence that the landscape degradation and desertification processes observed in Sahelian Africa can be in part attributed to climate change effects. Efforts to redress desertification should be at once conscious of complex socioeconomic forces exacerbating the desertification process and fundamental hydrologic constraints to river basin development.     

Regionalization of Tank Model Using Landscape Metrics of Catchments

To address the challenges inherent in accessing spatiotemporal hydrological data, water resources professionals have developed various regionalization tools. The present study examines the possibility that changes in landscape metrics including mean shape index, mean perimeter-area ratio, mean patch size and patch density of land use/ land cover could result in variations in the optimized parameters of the conceptual rainfall-runoff Tank model. Data from 30 catchments that are geographically distributed in Germany was used to develop the procedure. Regression analysis-based modeling indicated that four out of twelve model parameters (r²?≥?0.40) can be explained by changes in catchment geometrics along with a set of landscape metrics of land use/land cover. They include: coefficient of infiltration flow (r²?=?0.48, p?<?0.03), intermediate flow (r²?=?0.77, p?<?0.02), water storage level for sub-surface flow (r²?=?0.57, p?<?0.05) and water storage level for intermediate flow (r²?=?0.85, p?<?0.01). Despite developing fairly reliable regression models, uncertainty analysis also revealed that uncertainty induced unreliability of the regionalized models is of significant importance.     

Assessing the Impacts of Four Land Use Types on the Water Quality of Wetlands in Japan

This study examined how changes in the composition of land use can affect wetland water quality. Twenty-four wetlands located in Hiroshima prefecture in the western part of Japan were selected for this purpose. The water quality parameters that were explored include: pH, electrical conductivity, turbidity, dissolved oxygen, total dissolved solid, temperature and different forms of nitrogen. These important indicators of the water quality in the study area were measured from December 2005 to December 2006. The composition of land uses was determined for the catchments of the wetlands. They were then categorized into three classes, including non-disturbed, moderately-disturbed and highly-disturbed wetlands, based on the extent of urban area (as the most disruptive land use type within the catchment of the wetlands). The relationship between land use types and water quality parameters for the wetlands was statistically examined. The findings indicated that there were significant positive relationships between the proportion (%) of urban areas within catchments of the wetlands and EC (r?=?0.67, p?<?0.01), TDS (r?=?0.69, p?<?0.01), TN (r?=?0.92, p?<?0.01), DON (r?=?0.6, p?<?0.01), NH₄ ⁺(r?=?0.47, p?<?0.05), NO₂ ^? (r?=?0.50, p?<?0.05), while negative relationships were observed between the proportion (%) of forest area in these wetlands and EC (r?=??0.62, p?<?0.01), TDS (r?=??0.68, p?<?0.01), TN (r?=??0.68, p?<?0.01), DON (r?=?-0.43, p?<?0.05), and NH₄ ⁺ (r?=??0.55, p?<?0.01). Analysis of the variance also revealed significant differences within the wetland groups in terms of the annual mean of electrical conductivity, total dissolved solids, total nitrogen, nitrite, dissolved inorganic nitrogen and dissolved organic nitrogen in the study area. Moreover, the study also indicated that the forest area plays a significant role in withholding nutrient loads from the wetlands, and hence, it can act as a sink for surface/subsurface nutrient inputs flowing into such water bodies from the watersheds.     

An Entropy-Based Approach to Fuzzy Multi-objective Optimization of Reservoir Water Quality Monitoring Networks Considering Uncertainties

In this study, a new fuzzy methodology for a multi-objective optimization of reservoir Water Quality Monitoring Stations (WQMS) was developed, based on Transinformation Entropy (TE), the IRanian Water Quality Index (IRWQI), and fuzzy social choice considering uncertainties. The approach was utilized in the Karkheh Dam reservoir in Iran. The objective functions were: 1) minimizing costs, 2) minimizing redundant information and uncertainties, and 3) maximizing the spatial coverage of the network. A CE-QUAL-W2 model was used for the simulation of water quality variables. The IRWQI was computed to reveal a complete picture of the reservoir water quality. The TE quantities were calculated for each pair of potential stations. The TE values were plotted against the spatial distances among potential WQMS to obtain the TE–Distance (TE–D) curve, and minimize redundant information among stations, while providing coverage of the entire network. A multi-objective Genetic Algorithm (NSGA-II) was applied to obtain Pareto-optimal solutions taking stakeholder preference into account. The most preferred solution was then obtained using fuzzy social choice approaches to achieve a consensus. The fuzziness embedded in the decision-making procedure, the uncertainty in the value of mutual information, and the uncertainty in identifying the optimal distance among WQMS were also investigated. Results indicated that the three fuzzy social choice approaches (Borda Count, Minimax, and Approval Voting) led to the same number of optimized WQMS in each fuzzy alpha-cut. Based on the fuzzy linguistic quantifiers method, the number of optimized WQMS was increased.     

Exploring alternative sources of funding for deploying sustainable sanitation technologies and services in Mongolia

One of the major challenges for deploying sustainable sanitation technologies and services around the world is financing. The present study applied both qualitative (key informant interviews) and quantitative (household survey) methods to explore sources of alternative financing in Ulaanbaatar, Mongolia, towards deploying sustainable sanitation technologies and services. Microfinance organizations, government subsidies and mining industries may represent potential sources of financing for the implementation of sustainable sanitation technologies and services in Mongolia. Moreover, building social capital among Ger residents and reinventing the idea of ‘corporate WASH responsibility’ could constitute new directions for the future.     

Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy

Forecasting urban water demand can be of use in the management of water utilities. For example, activities such as water-budgeting, operation and maintenance of pumps, wells, reservoirs, and mains require quantitative estimations of water resources at specified future dates. In this study, we tackle the problem of forecasting urban water demand by means of back-propagation artificial neural networks (ANNs) coupled with wavelet-denoising. In addition, non-coupled ANN and Linear Multiple Regression were used as comparison models. We considered the case of the municipality of Syracuse, Italy; for this purpose, we used a 7?year-long time series of water demand without additional predictors. Six forecasting horizons were considered, from 1 to 6?months ahead. The main objective was to implement a forecasting model that may be readily used for municipal water budgeting. An additional objective was to explore the impact of wavelet-denoising on ANN generalization. For this purpose, we measured the impact of five different wavelet filter-banks (namely, Haar and Daubechies of type db2, db3, db4, and db5) on a single neural network. Empirical results show that neural networks coupled with Haar and Daubechies?? filter-banks of type db2 and db3 outperformed all of the following: non-coupled ANN, Multiple Linear Regression and ANN models coupled with Daubechies filters of type db4 and db5. The results of this study suggest that reduced variance in the training-set (by means of denoising) may improve forecasting accuracy; on the other hand, an oversimplification of the input-matrix may deteriorate forecasting accuracy and induce network instability.     

Uncertainty Estimation in Flood Inundation Mapping: An Application of Non‐parametric Bootstrapping

Disaster prevention planning is affected in a significant way by a lack of in‐depth understanding of the numerous uncertainties involved with flood delineation and related estimations. Currently, flood inundation extent is represented as a deterministic map without in‐depth consideration of the inherent uncertainties associated with variables such as precipitation, streamflow, topographic representation, modelling parameters and techniques, and geospatial operations. The motivation of this study is to estimate uncertainties in flood inundation mapping based on a non‐parametric bootstrapping method. The uncertainty is addressed through the application of non‐parametric bootstrap sampling to the hydrodynamic modelling software, HEC‐RAS, integrated with Geographic Information System (GIS). This approach was used to simulate different water levels and flow rates corresponding to different return periods from the available database. The study area was the Langat River Basin in Malaysia. The results revealed that the inundated land and infrastructure are subject to a flooding hazard of high‐frequency events and that the flood damage potential is increasing significantly for residential areas and valuable land‐use classes with higher return periods. The proposed methodology, as well as the study outcomes, of this paper could be beneficial to policymakers, water resources managers, insurance companies and other flood‐related stakeholders. Copyright © 2017 John Wiley & Sons, Ltd.     

Crow Algorithm for Irrigation Management: A Case Study

Water Resources Management - This study employed a new evolutionary algorithm namely, the crow algorithm (CA), to optimize reservoir operation and minimize irrigation water deficit. Comprehensive...