Springer Science and Business Media LLC

In this study, we present a long-term water management model which can be an important tool for the ordinary management of a reservoir. The model can be made simple and applicable in a graphic form. The key point of the model is that the sedimentation rate of solids can be modeled according to a power law. The basic equation of the model is tested using bathymetric surveys of different dams located in different parts of the world. Robustness and predictive power are evaluated both numerically and by comparison with satellite results. Then the model is transformed into the form of a management plot and applied to the real case of the Camastra dam.

Most long-term sampling regimes are calendar based, collecting one or two samples per month regardless of the stream conditions. Loads estimated with calendar-based sampling are often used for expensive water quality mitigation measures. In this paper, we have tested the differences between the calendar-based and extensive sampling methods for two watersheds of different sizes, and three parameters—total nitrogen, total phosphorus, and total suspended solids. Based on the results obtained and the costs associated with the remediation, a simple decision-making framework is proposed for watershed managers to decide on the applicability of a calendar-based sampling method. Direct loads (DL) were computed using a method based on an intensive sampling of flow and other water quality parameters. Weighted regression loads (WL) were estimated using the WRTDS model designed for modified calendar-based sampling. The results suggest that for trend analysis and planning on a larger scale, long-term loads obtained from a modified calendar-based sampling regime may be used as a reasonable substitute for loads obtained from intensive sampling. However, for purposes where accurate daily loads are needed (e.g., water quality model calibration) WL may not be an effective substitute for DL. Finally, we recommend that the costs of control measures should be assessed when deciding on a sampling regime.

This paper presents a technique to derive the unit impulse response functions (UIRF) used for determination of unit hydrograph by employing the Z-transform technique to the response function derived from the Auto Regressive Moving Average (ARMA) process of order (p, q). The proposed approach was applied to reproduce direct surface runoff for single storm event data registered over four watersheds of area ranging from 0.42 to 295 km2. It is observed that the UIRF based on ARMA (1, 2) and ARMA (2, 2) provides a better representation of the watershed response. Further, to test the superiority of the developed impulse response function form ARMA process, the direct runoff hydrographs were computed using the simple ARMA process and optimized Nash’s two parameter model and compared with the results obtained from UIRF’s of ARMA model. The performance of the models based on the graphical presentation as well as from the test statistics viz. RMSE and MAPE indicates that UIRF-ARMA (p, q) performs better than optimized Nash Model and mostly similar to simple ARMA (p,q) model. Further more, the ARMA process of order p ≤ 2 and q ≤ 2 is generally sufficient and less cumbersome than the Argand diagram based approach for UIRF derivation.

In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.

Reliability in flow prediction is key to designing water resources projects. Over prediction may result in overdesign whereas under prediction brings about insufficient capacity solutions. While the former means insufficient use of financial resources, the latter may result in some water demand unmet. Therefore, so many techniques have been developed and used to make better flow prediction. In this study, this traditional problem is revisited in an attempt to improve the modeling performance of long used conceptual hydrological models. This is attained by incorporating fuzzy systems into a presently used conceptual model. The fuzzy integration process is carried out through the replacement of the storage elements of conceptual model by fuzzy systems. The case study undertaken has proved that the fuzzy conceptual model developed is quite competitive with ordinary conceptual model and promises improved predictions.

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.

The paper introduces and applies a methodology to screen investments aimed at reducing water supply risks due to hydrologic failures in headwork systems for municipal use, based on the principles of cost-benefit analysis. As risk includes both the probability of a failure and its effect, the methodology combines a simulation module of the system, fed by a stochastic hydrologic input to reproduce the probability distribution of the failures, with a metric for supply failure damage provided by the price – demand relationship for municipal water. Benefits are assessed as the averted damage compared to a base case without investments. This approach is then combined with the classic discounted cashflow approach of cost – benefit analysis to allow for the dynamics of both water supply and demand due to trends in population growth, individual consumption and, above all, planned reduction of losses in water distribution networks. The methodology is applied to screen a number of different supply-side projects for the headwork system supplying Apulia, in southern Italy featuring both regulated surface and groundwater resources and providing drinking water to over 4,000,000 persons. The procedure allows both ranking of single projects by their economical performances and the economic evaluation of combinations of different projects. The study also aims to assess the impact of the selected time scale, of cross-correlation among production sites, and of the specification of the demand function on projects' economic indicators. Results show that each modelling assumption has a considerable impact on the value of the economic indicators in absolute terms, but ranking of the different projects seems to be less sensitive to such modelling aspects.