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The Upper and Lower Zab Rivers are two of main and most important tributaries of Tigris River in Northern Iraq region. They supply Tigris River with more than 40 % of its yield. The forecasting of flows for these rivers is very important in operation of the existing Dokan Dam on the Lower Zab River and the proposed Bakhma Dam on the Upper Zab River for flood mitigation and also in drought periods. Three types of Artificial Neural Networks (ANNs) are investigated and evaluated for flow forecasting of both rivers. The ANN techniques are the feedforward neural networks (FFNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBF). The networks’ performance varied with different cases involved in the study; however, the FFNN was almost better than other networks. The effect of including a time index within the inputs of the networks is investigated. In addition, the ANNs’ performance is investigated in forecasting the high and low peaks and in forecasting river flows using the data of the other river.

There has been a growing concern on temporal variations on drought characteristics due to climate change. This study compares meteorological drought characteristics for two different periods to quantify the temporal changes in seasonal droughts of 18 weather stations of the country. Fifty-five years rainfall and temperature data are divided into two different thirty-year periods, 1961–1990 and 1985–2014 and standardized precipitation evapotranspiration index (SPEI) for those periods are calculated to assess the changes. Four seasons in this study are selected as two major crop growing seasons namely, Rabi (November to April) and Kharif (May to October) and two critical periods for crop growth in term of water supply namely critical Rabi (March–April) and critical Kharif (May). Results show that moderate, extreme, and severe Rabi droughts has increased in 11, 9, and 4 stations out of 18 stations, respectively, and Kharif severe and extreme droughts has increased in 8 and 9 stations, respectively, In addition, the frequency analysis shows that the return periods have decreased during 1985–2014 at the stations where it was high during 1961–1990 and vice versa. This has made the spatial distribution of return periods of droughts more uniform over the country for most of the seasons. Increased return period of droughts in highly drought prone north and northwest Bangladesh has caused decrease in average frequency of droughts. Consequently, this result corresponds that Bangladesh experiences fewer droughts in recent years. Trend analysis of rainfall and temperature data reveals that significant increase of mean temperature and no significant change in rainfall in almost all months have increased the frequency of droughts in the regions where droughts were less frequent.

Dams accumulate sediment by interrupting the continuity of rivers, resulting in a loss of reservoir water storage capacity and decreased productive life. These issues raise a growing concern about the decreasing benefits of projects. This paper contributes to the implementation of sediment transit strategies and operating rules of reservoirs to reduce overflows and recover the technical–economic viability of sedimented reservoirs by maintaining ecological flow. The main difficulty lies in the fact that sedimentation of the reservoir limits the mobility of dredging equipment and blocks the intake. To regain the viability of the reservoir, the commonly used strategies to manage water resources and reservoir sedimentation were analyzed. To control reservoir sedimentation and restore the generation capacity, different sediment management strategies were implemented and evaluated at the entrance, body of the reservoir and intake; these strategies included reduction of the entry of sediments, restoration of the storage capacity, clearing of the water intake for the turbines to restore power generation, trash rack cleaning during the power generation process and modification of the hydroelectric power plant operating rules to optimize the economic income. The implemented strategies successfully reduced overflows from 88 to 40% in 3 years and stabilized the reservoir storage capacity by balancing the inflow and removal of sediments. Although the water intake for the turbines was cleaned, accumulation increased in other areas of the reservoir. Finally, root cause analysis (RCA) was employed, and solutions were proposed to increase the capacity of the reservoir and reduce overflows to 15%.

Reservoir inflow is a major component of the reservoir operations management system. It becomes highly essential to predict the accurate reservoir inflow. The lumped models and semi-distributed or fully distributed model implemented to solve a range of specific problems in the prediction of reservoir inflow. The findings in this paper compare a conceptual semi distributed Hydrologic Engineering Centre Hydrologic Modelling System (HEC-HMS) model and an ANN (Artificial Neural Network) based model for the prediction of inflow in the Koyna reservoir catchment, Maharashtra. The performance of the models is assessed using different statistical indicators such as Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Mean Absolute Error (MAE). The results confirmed the ability of the semi distributed (rHEC-HMS = 0.92, RMSEHEC-HMS = 129.37 m3/s, MAEHEC-HMS = 21.66 m3/s, NSEHEC-HMS = 0.82 and RSRHEC-HMS = 0.42) and ANN model (rANN = 0.85, RMSEANN = 176.29 m3/s, MAEANN = 14.62 m3/s, NSEANN = 0.69 and RSRANN = 0.55) to capture the effect of the complex hydrological phenomenon, variations of land use and soils of watershed. The study illustrates that the semi distributed HEC-HMS model shows moderately better results compared to ANN model. It may be noted that the ANN predicts the reservoir inflow using only one input i.e., rainfall, whereas the HEC-HMS requires exogenous input parameters and plenty of time for model building compared to ANN. This work will have a significant contribution for planning of reservoir operations within the catchment of Koyna reservoir.

Climate change is linked with change in precipitation, evapotranspiration, and other climatological parameters, and therefore the runoff of a river basin will be affected. The Gomati River basin is the largest in Tripura. The increased settlement in the Gomati River basin and climate change may threaten the natural flow patterns that enable its diversity. This study assesses the impact of climate change on total flow from a catchment in northeast India (the Gomati River catchment). For this assessment, the Group Method of Data Handling (GMDH) model was used to simulate the rainfall–runoff relationship in the catchment with respect to the observed data during 2008–2009. The statistically downscaled outputs of the Hadley Centre Global Environment Model version 2 (HadGEM2-ES) general circulation model scenario was used to assess the impacts of climate change on the Gomati River basin. Future projections were developed for the 2030s, 2040s, and 2050s. The results of this study may contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in northeast Tripura.

The predictability of unit hydrograph (UH) models that are based on the concepts of land morphology and isochrones to generate direct runoff hydrograph (DRH) were evaluated in this paper. The intention of this study was to evaluate the models for accurate runoff prediction from ungauged watershed using the ArcGIS® tool. Three models such as exponential distributed geomorphologic instantaneous unit hydrograph (ED-GIUH) model, GIUH based Clark model, and spatially distributed unit hydrograph (SDUH) model, were used to generate the DRHs for the St. Esprit watershed, Quebec, Canada. Predictability of these models was evaluated by comparing the generated DRHs versus the observed DRH at the watershed outlet. The model input data, including natural drainage network and Horton's morphological parameters (e.g. isochrone and instantaneous unit hydrograph), were prepared using a watershed morphological estimation tool (WMET) on ArcGIS® platform. The isochrone feature class was generated in ArcGIS® using the time of concentration concepts for overland and channel flow and the instantaneous unit hydrograph was generated using the Clark's reservoir routing and S-hydrograph methods. An accounting procedure was used to estimate UH and DRHs from rainfall events of the watershed. The variable slope method and phi-index method were used for base flow separation and rainfall excess estimation, respectively. It was revealed that the ED-GIUH models performed better for prediction of DRHs for short duration (≤6 h) storm events more accurately (prediction error as low as 4.6–22.8%) for the study watershed, than the GIUH and SDUH models. Thus, facilitated by using ArcGIS®, the ED-GIUH model could be used as a potential tool to predict DRHs for ungauged watersheds that have similar geomorphology as that of the St. Esprit watershed.

The calibration of an event based rainfall-runoff model for steam flow forecasting is challenging because, it is difficult to measure the parameters physically on the field for each rainfall event. In the present study, Fuzzy rule based Multi-objective Genetic Algorithm (MGA) is developed to optimize the infiltration and roughness parameters of an event based rainfall-runoff model. Nash Sutcliffe Efficiency (NSE), Coefficient of Determination (R2) and transformed volume difference (f(V)) are used as the objective functions of the MGA and all Pareto optimal solutions are identified using Nondominated Sorting method. As three objective functions are included in the calibration, the number of Pareto optimal solutions are also increases and hence, the optimization problem now becomes a decision making problem. Therefore, to select the best solution from all Pareto optimal solutions, a Fuzzy Rule-Based Model (FRBM) is developed to get alternative values of each Pareto optimal solution. First, the Fuzzy rule based MGA is developed by integrating the FRBM with the MGA. Then the Fuzzy rule based MGA is integrated with an event based runoff model. The developed Fuzzy-MGA based runoff model is tested on three different watersheds and the simulation results of Fuzzy-MGA based runoff model are compared with observed data and previous study results. From the simulated events of three watersheds using Fuzzy-MGA based runoff model, it is observed that the mean percentage error in any criteria (i.e. volume of runoff, peak runoff, and time to peak) of the developed model for a watershed is less than 16.33%. It is also noted that the developed Fuzzy-MGA based runoff model is able to produce hydrographs that are much closer to the measured hydrographs.

The need for credible, salient and legitimate climate change adaptation options in the water sector, which target location specific adaptation requirements, is well recognized. In developing countries, the low-hanging fruit; no-regret options, should be identified with stakeholders and assessed against future changes in water availability and demand, for comparing effectiveness and robustness. Such integrated basin-scale assessments, including reservoir catchment and command areas, can suitably inform adaptation decision-making. In this study, we integrate participatory and modelling approaches for evaluation of reservoir catchment and command area no-regret options addressing water availability and demand in the Kangsabati river basin. Through multi-level stakeholder workshops we identify and prioritize options, followed by evaluation of two reservoir catchment options; check dams and increasing forest cover and three reservoir command options; changing cropping pattern, traditional ponds and waste water reuse, using the Water Evaluation And Planning (WEAP) model. We use four high resolution (~25 km) regional climate model simulations of future climatic factors, along with non-climatic factors affecting water demand, for forcing WEAP. We find that options have varied ability in addressing adaptation requirements. Amongst catchment options, increasing forest cover addresses adaptation requirements more suitably than check dams, while in the command areas we observe mixed abilities of options, leading to the inference that combining complementary options may be a more useful strategy. We conclude by discussing our experiences with this approach in a developing country context, in terms of benefits, limitations, lessons learnt and future research directions.