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Soil volumetric water content (VWC) is one of the key factors in hydrological cycles and responsible for inducing droughts and floods. Therefore, the precise prediction of VWC is crucial for the effective management of water resources. However, the complexity in structural characteristics and interaction with several other external meteorological factors cause difficulty in establishing a mathematical model which can predict soil VWC accurately. This study demonstrates the applicability of Convolution Neural Network-Long short-term memory (CNN-LSTM) hybrid model to predict soil VWC (%), concentrating specifically on optimizing the predictors combination using recursive feature elimination (RFE) which results in a more interpretable model with less complexity. The model was developed using the data collected from Benton County of Washington, USA, and generalization capacity of the model was tested in other counties of Washington. To verify the improved prediction ability of the proposed model, the results were compared with the established CNN, LSTM and MLR models. The results reflected that the proposed CNN-LSTM model predicted better than the individual CNN, LSTM and MLR models for the training site as well as for the five testing sites, proving its good generalization capacity. [ABSTRACT FROM AUTHOR]

Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/m3, respectively. [ABSTRACT FROM AUTHOR]

Drought in Afghanistan has many impacts from the interaction between environmental and socio-economic factors in the agriculture and water supply sectors. The complex topography and climate change impacts cause high spatial and temporal variation in the precipitation pattern and pose several challenges in managing water resources. Therefore, this paper aimed to quantify the sustainability of water resources against the progression of drought. Observed monthly precipitation data monitored by 23 stations located across five river basins covering the entire country for the period 1970–2016 were used to demonstrate the potential impacts of drought on water resources sustainability. Based on severe drought estimation, the year 2000 and 2001 were identified as change points. Accordingly, datasets pertaining to before and after the change points were partitioned to analyze the long-term temporal shift of drought. The spatiotemporal variability of sustainability indicators was estimated using reliability resiliency and vulnerability concept. The results indicated a positive trend of precipitation in most of the river basins after the change points leading to an increase in sustainability. However, the major portion in the upstream of the Kabul River basin showed a decrease in sustainability of around 15% due to a reduction in precipitation. As the Kabul city has high population density, it needs immediate attention in effectively planning and managing available water resources. Furthermore, the comprehensive analyses reported in this paper discuss the possible implications of drought risk assessment and its impact on largely varying sustainability both spatially and temporally in Afghanistan. [ABSTRACT FROM AUTHOR]

Accurate forecasting of soil moisture (SM) is crucial for managing the irrigation demands effectively. The dynamics of SM is largely controlled by interaction between land and atmosphere. As an alternate to physics based models, the machine learning based tools have been shown to yield better accuracy in forecasting SM. However, the complexity in the process that controls SM at largely varying scale (from small size porous media to continental level climate) influence the forecast to have uncertainty. Hence, this paper aims at developing a modelling framework for forecasting daily SM up to 5 days lead time along with associated uncertainty. In this modeling, the uncertainty in initial point estimates of artificial neural network (ANN) parameters are re-estimated in a probabilistic framework using Particle filter. In order to reduce the high dimensionality of such problems, most sensitive parameters of the ANN model were identified through Sobol's sensitivity analysis. The SM and weather data collected from the R.J. Cook Agronomy Farm experimental field near Pullman, Washington, USA were used to demonstrate the proposed method. The overall results of the models were highly encouraging in terms of having a Nash-Sutcliffe efficiency of more than 0.90 in calibration and validation. Further, the parametric uncertainty of ANN model parameters have helped quantifying the uncertainty in the SM forecast and found within acceptable limits. The proposed framework, in turn, helped providing useful information when the models are used in decision making supported with associated uncertainty information. [ABSTRACT FROM AUTHOR]

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R2) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021–2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data. [ABSTRACT FROM AUTHOR]

A copper condenser dashpot in a refrigeration plant failed prematurely. The dashpot was a long tubular component with a cup brazed at each end. Stereomicroscopic examination of the fracture surface at low magnification revealed a typical ductile mode of failure. The failure was attributed to insufficient component thickness, which made the dashpot unable to withstand internal operating pressure, and to extensive annealing in the heat-affected zones of the brazed joints. It was recommended that the condenser dashpot design take into account the annealing effects of brazing. Hydrostatic testing at a pressure times greater than the maximum operating pressure prior to placing the component in service was also suggested. [ABSTRACT FROM AUTHOR]

The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial–temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor. [ABSTRACT FROM AUTHOR]

Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model's input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation–optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making. [ABSTRACT FROM AUTHOR]

Drought is a function of time as well as climate variables such as temperature and precipitation. The process of drought forming is slow, and it manifests at different time scales, which adversely affects the economy of a country. The identification and characterization of droughts at various spatiotemporal scales are of great importance. It helps in the planning and management of water resources, policymaking, and agribusiness industries. In the present paper, the Cauvery River basin is chosen as a study area to analyze the changes in the frequency distribution of extreme droughts and duration, with the combined effect of evapotranspiration and rainfall. The drought indices such as Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) are implemented on monthly rainfall data and potential evapotranspiration of resolution 0.25° × 0.25° long./lat. for the period 1931–2010. The results reveal that the frequency of the extreme droughts over the basin has significantly increased over the post-era of global warming. The increased rate of extreme droughts is particularly evident in downstream of the basin, mainly due to the increase in temperature and deficit rainfall. Further, the implementation of continuous wavelet transform reveals that SPI at 3-(SPI-3) and 12-(SPI-12) month scale are associated with extended reconstruction of sea surface temperature (ERSST) in anti-phase and in-phase, respectively. It is concluded that the in-phase association of SPI-12 and ERSST enhances the drought situation compared to the anti-phase link of SPI-3 and ERSST. [ABSTRACT FROM AUTHOR]

This paper presents a single-objective optimization-based perturbation analysis to quantify model prediction uncertainty. A new index named coverage width index (CWI), which combines two commonly used uncertainty indices, the percentage of coverage (POC) and the average width (AW), was proposed to facilitate the optimization. Considering the outperformance of the wavelet neural network (WNN) among various data-driven modeling approaches in hydrogeological modeling, the proposed approach was integrated into WNN (called OPWNN). A case study was conducted to demonstrate the application of OPWNN in groundwater level forecasting at two wells in the Amaravathi River Basin, India. The sensitivity analysis of the effect of initial perturbation range on CWI suggested that uncertainty is sensitive to the selected perturbation range and a small perturbation does not guarantee an acceptable prediction interval (PI). The modeling results demonstrated that the OPWNN can optimize the PI effectively with minimized AW corresponding to an expected high POC. Therefore, this approach can yield more reliable predictions/forecasts for water resources management. [ABSTRACT FROM AUTHOR]

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement. [ABSTRACT FROM AUTHOR]

The meaningful quantification of uncertainty in hydrological model outputs is a challenging task since complete knowledge about the hydrologic system is still lacking. Owing to the nonlinearity and complexity associated with the hydrological processes, Artificial neural network (ANN) based models have gained lot of attention for its effectiveness in function approximation characteristics. However, only a few studies have been reported for assessment of uncertainty associated with ANN outputs. This study uses a simple method for quantifying predictive uncertainty of ANN model output through first order Taylor series expansion. The first order partial differential equations of non-linear function approximated by the ANN with respect to weights and biases of the ANN model are derived. A bootstrap technique is employed in estimating the values of the mean and the standard deviation of ANN parameters, and is used to quantify the predictive uncertainty. The method is demonstrated through the case study of Upper White watershed located in the United States. The quantitative assessment of uncertainty is carried out with two measures such as percentage of coverage and average width. In order to show the magnitude of uncertainty in different flow domains, the values are statistically categorized into low-, medium- and high-flow series. The results suggest that the uncertainty bounds of ANN outputs can be effectively quantified using the proposed method. It is observed that the level of uncertainty is directly proportional to the magnitude of the flow and hence varies along time. A comparison of the uncertainty assessment shows that the proposed method effectively quantifies the uncertainty than bootstrap method. [ABSTRACT FROM AUTHOR]

Sol-gel based fuel fabrication processes have the potential to be the nuclear fuel fabrication processes in the future. Hence development of sol-gel technology for nuclear fuel fabrication is being the pursued in the Department of Atomic Energy in India. As a part of the efforts, a laboratory scale facility for fabrication of test fuel pins for irradiation in the Fast Breeder Reactor (FBTR), Kalpakkam has been set up at the Indira Gandhi Centre for Atomic Research, Kalpakkam, India. These fuel pins will be vibropacked with sol-gel derived microspheres or stacked with pellets obtained by compaction of sol-gel derived microspheres. The facility is aimed at demonstration of the remote operation of the fuel pin fabrication process through the sol-gel route. A capsule containing three test pins from this facility will be irradiated in FBTR. The design features of the facility and the test fuel pins are described in this paper. [ABSTRACT FROM AUTHOR]

A detailed failure analysis was conducted on an ammonia refrigerant condenser tube component that failed catastrophically during its initial hours of operation. Evidence collected clearly demonstrated that the weld between a pipe and a dished end contained a sharp unfused region at its root (lack of penetration). Component failure had started from this weld defect. The hydrogen absorbed during welding facilitated crack initiation from this weld defect during storage of the component after welding. Poor weld toughness at the low operating temperature facilitated crack growth during startup, culminating in catastrophic failure as soon as the crack exceeded critical length. [ABSTRACT FROM AUTHOR]