Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting

High accuracy forecasting of medium and long-term hydrological runoff is beneficial to reservoir operation and management. A hybrid model is proposed for medium and long-term hydrological forecasting in this paper. The hybrid model consists of two methods, Singular Spectrum Analysis (SSA) and Auto Regressive Integrated Moving Average (ARIMA). In this model, the time series of annual runoff are first decomposed into several sub-series corresponding to some tendentious and periodic motions by using SSA and then each sub-series is predicted, respectively, through an appropriate ARIMA model, and lastly a correction procedure is conducted for the sum of the prediction results to ensure the superposed residual to be a pure random series. The annual runoff data of two reservoirs in China are analyzed as case studies. The results have been compared with the predictions made by ARIMA and Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF). It is shown that hybrid model has the best performance.     

Assessment of Sustainable Yield of Karst Water in Huaibei,China

This paper presents the assessment of sustainable yield in the Huaibei karst water area of Anhui province, China. A review of sustainable yield definition is introduced first in this paper, and sustainable development in karst areas is more difficult due to the complicated hydrogeologic conditions. General hydrogeology of the study area is provided to characterize hydraulic connections between the karst aquifer and an overlying porous aquifer. Groundwater level declines continuously due to over-exploitation of the karst groundwater, and two layers of groundwater dropping funnel were formed in Huaibei. These problems not only threaten the eco-geo-environment, but also compromise the water utilization which depends on the shallow porous water. A “critical water level” is proposed in this study to assess the sustainable yield, and it is determined by the historical exploitation data which represent the relationship between the karst water and the shallow porous water uses. A three layer Artificial Neural Network (ANN) model is used to understand the complex relationship of the karst water level and its influencing factors. Precipitation, exploitation and water level of latest period are chosen as the input nodes, seasonal records of water level are simulated by the ANN model. The sustainable yield is calculated by the trail-and-error adjusting method, and is equal to the pumping rate when the “critical water level” is maintained. The rate of 30.05 MCM/a is the sustainable yield for the Huaibei karst area in 2008, and it is less than the real pumping rate of 35.92 MCM/a. This assessment is meaningful to the management for the Huaibei karst water.     

Optimizing Safe Yield Policy Implementation

The presented method enhances groundwater-mandated safe yield management. It is useful for settings that prevent sustained yield or integrated management. To protect hydraulically connected surface water rights, the Utah government’s Cache Valley groundwater management plan proposes that total pumping increase not exceed 84,431 m³/day. To determine how best to spatially distribute additional allowable pumping, stakeholders quantified limits defining acceptable impacts on selected water resource indicators. A new simulation–optimization (S–O) algorithm used these limits while computing optimal spatially distributed perennial yield or safe yield groundwater pumping extraction strategies. The limits prevent unacceptable decreases in: head and net flow between aquifer and surface waters (rivers, surface/subsurface drains, springs, lakes). The optimization objective function maximizes weighted pumping to provide water for 18 growing municipalities. For 16 perennial yield scenarios, computed optimal pumping increases differ in protectiveness toward senior water rights, and range from 16% to 103% of the state plan-proposed increase. Implementing a protective strategy would achieve 90% of the storage changes needed to reach equilibrium within 23 years. Indicator potentiometric heads would reach equilibrium within 10–40 years. At equilibrium, an optimal Cache Valley perennial yield strategy acceptably minimizes net annual non-pumping discharges. By comparison, multi-period 20-year transient groundwater mining optimizations allow more pumping in early years. Pumping then must decline to satisfy seepage and head constraints through year 20. Adverse seepage impact would increase for years thereafter. For situations governed by safe or perennial yield policy, equilibrium-based (steady-state) optimization is very useful. It effectively develops optimal perennial yield strategies.     

Design of Barrages with Genetic Algorithm Based Embedded Simulation Optimization Approach

Barrages are hydraulic structures constructed across rivers to divert flow into irrigation canals or power generation channels. The most of these structures are founded on permeable foundation. The optimum cost of these structures is nonlinear function of factors that cause the seepage forces under the structure. There is, however, no procedure to ascertain the basic barrage parameters such as depth of sheet piles or cutoffs and the length and thickness of floor in a cost–effective manner. In this paper, a nonlinear optimization formulation (NLOF), which consists of an objective function of minimizing total cost, is solved using genetic algorithm (GA). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study shows drastic cost savings when the proposed NLOF is solved using GA than that of using classical optimization technique and conventional method. A parametric analysis has also been performed to study the effect of varying soil and hydrological conditions on design parameters and on over all cost.     

Relationships Between Satellite Observed Lit Area and Water Footprints

Global water resources are vulnerable to depletion due to the increasing demand of an ever-increasing human population. A country’s water footprint is a measure of the total volume of water needed to produce the goods and services consumed by the country, including water originating beyond its own borders. The water footprint can be a critical indicator of global water resource use, but its practical application is hindered by a lack of comparable data across national boundaries. The purpose of this article is to test the applicability of the nighttime imagery products produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) for the assessment of the global water footprint. To accomplish this purpose, the average areal extent of nighttime lighting (lit area) is calculated from 1997 to 2001. Next, lit area is regressed on the total water footprint for each country, as indicated by the Water Footprint Network (WFN), to estimate that country’s total water footprint using nighttime imagery. Model residuals are analyzed at the national scale to understand the appropriateness of nighttime imagery for assessing water consumption. Results indicate strong positive correlations between lit area and total water footprint (TWF), domestic water withdrawal (DWW), and industrial water consumption (IWC) at the national scale. Overall, the analyses reveal that the rate of agricultural water consumption to total water footprint (AWCR) and population density can affect the precision of estimates when lit area is selected as a proxy to estimate water footprints.     

Streamflow Simulation by SWAT Using Different Precipitation Sources in Large Arid Basins with Scarce Raingauges

Streamflow from the mountains is the main source of water for the lower plain in arid regions. Accurate simulation of streamflow is of great importance to the arid ecosystem. However, many large arid drainage basins in northwestern China have low density of precipitation stations, which makes the streamflow modeling and prediction very difficult. Based on raingauge data and Tropical Rainfall Measuring Mission (TRMM) data combined with raingauge data, different approaches were explored for spatializing precipitation in large area with scarce raingauges. Spatialized precipitation was then input into Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate streamflow. Results from a case study in the Manas river basin showed that simulated hydrographs using both the approaches are able to reproduce the watershed hydrological behavior. Moreover, statistical assessment indicated that hydrological model driven by the spatialized precipitation based on radar combined with raingauge data performed better than that based on gauge data. Radar precipitation estimator can provide a practical data source for hydrological modeling at a basin scale where the raingauge network is sparse.     

Surveillance of Drinking Water Quality for Safe Water Supply—A Case Study from Shillong,India

To ascertain the quality of drinking water being supplied and maintained, it is necessary to conduct water quality surveillance for evolving suitable strategy for future planning. In the present investigation, water quality was monitored in treatment plants, service reservoirs, and at consumer ends in three seasons to assess the baseline water quality status at Shillong in Meghalaya. There are three water treatment plants at Shillong namely Umkhen, Mawlai and GSWS with design capacities of 1.5, 3.4 and 34 Million liter per day (MLD) respectively. Each treatment plant is having rapid sand filtration followed by disinfection. The study reveals that the physico-chemical parameters of water quality at consumer end meets Indian drinking water quality standards (BIS 1991) after conventional treatment followed by disinfection, whereas the bacteriological parameters for raw water sources exceed the permissible limit indicating the treatment need for drinking purposes. Throughout year the average feacal coliform contamination at service reservoir and to consumer end were found as 44 to 156 CFU/100 ml which may be attributed to the general management practices for maintenance of service reservoirs and the possibility of en route contamination.     

Effects of Bed Load Movement on Mean Flow Characteristics in Mobile Gravel Beds

The effects of bed-load movement on mean flow characteristics were experimentally investigated using particle image velocimetry (PIV). The gravel beds associated with open-channel flow are hydraulically rough, and two types of grain with different median diameters were used. The flow field and bed load motion images were simultaneously obtained via the particular experimental setup. The mass of sediment particles instantaneously moving on the bed, i.e., the sand-wave, was observed in the experiment. It was found that the sand-waves have an average friction angle of 11.2° and heights ranging from 2.2 d ₅₀ to 3.8 d ₅₀. The mean velocity profiles on the mobile bed can also be represented with logarithmic distribution with deviations occurred in the near wall region. Based on the Schultz–Grunow formula, a modified equation is developed in this study to better express the flow resistance factor in terms of the Reynolds number and the ratio of flow depth to roughness height.     

Drought Severity Assessment Based on Bivariate Probability Analysis

Conventionally drought severity is assessed based on drought indices. Recently the Reconnaissance Drought Index (RDI) was proposed to assess drought severity based on the precipitation to potential evapotranspiration ratio (P/PET). In this paper RDI is studied as a bivariate index under a set of assumptions and simplifications. The paper presents a simple computational procedure for estimating the P/PET ratio for selected reference periods varying from 3 to 12 months, for any return period of drought. Alternatively, based on this procedure, the severity of any drought episode is rationally assessed. A bivariate probability analysis is employed based on the assumption that P and PET values are normally distributed and often negatively correlated. Examples for the application of the proposed procedure are presented using data from several meteorological stations in Greece. It is shown that the assumption of normality of both P and PET holds for long periods at all examined stations.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.