Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation

Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation

Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 32% groundwater RMSE reduction and soil moisture correlation increase from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 0.66 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 0.85) but also during the forecast period (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 14% groundwater RMSE reduction and soil moisture correlation increase from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 0.69 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼ 0.78) due to the effective impacts of the approach on both state and parameters.

The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981-2016)

The Australian and African continents, regions prone to hydroclimate extremes (e.g., droughts and floods), but with sparse distribution of rain-gauge that are limited in time, rely heavily on complementary satellite and reanalysis data to provide important crucial information necessary for informing policies and management. The problem, however, is that satellite products suffer from systematic biases while reanalysis products carry over uncertainties from their forcing parameters. Multi-Source Weighted-Ensemble Precipitation (MSWEP) is a new global rainfall-product that merges satellite, rain-gauge and re-analysis data to exploit their advantages and minimise their disadvantages. Although MSWEP has been validated globally, this product, together with its potential applications, e.g., in water storage fluxes, river discharge and climate impacts studies over Australia and Africa, regions with urgent need of reliable products, has however, not been verified. Using GRACE satellite products, GLDAS model data, GRDC runoff products, and ENSO/IOD climate indices; five rainfall products - FLUXNET, BoM, GPCC, CHIRPS, and AgCFSR; and a suite of statistical methods (Pearson, Kolmogorov-Smirnov, PCA and Three-Corner-Hat (TCH)), this study (i) evaluates monthly MSWEP-V2.1 data (1981-2016), and (ii), assesses its potential applications to water storage flux (within the water balance framework), river discharge analysis, and climate impacts studies. The results show good MSWEP correlations and cumulative distribution with BoM product over most of Australia except in regions with heavy monsoonal rainfall, e.g., northern and north-western Australia where it tends to underestimate. Over Africa, MSWEP has no obvious advantages compared to insitu-GPCC, satellite-CHIRPS or reanalysis-AgCFSR. Furthermore, it is unable to reflect on major hydro-climate extremes over west, east and southern Africa, where it underestimates compared to CHIRPS. Its potential applications to water storage flux, discharge and climate impacts over the two continents show better suitability for water storage flux in Africa, while no advantages are seen compared to other rainfall products on other aspects.

Improved remotely sensed satellite products for studying Lake Victoria's water storage changes

Lake Victoria (LV), the world's second largest freshwater lake, supports a livelihood of more than 42 million people and modulates the regional climate. Studying its changes resulting from impacts of climate variation/change and anthropogenic is, therefore, vital for its sustainable use. Owing to its shear size, however, it is a daunting task to undertake such study relying solely on in-situ measurements, which are sparse, either missing, inconsistent or restricted by governmental red tapes. Remotely sensed products provide a valuable alternative but come with a penalty of being mostly incoherent with each other as they originate from different sources, have different underlying assumptions and models. This study pioneers a procedure that uses a Simple Weighting approach to merge LV's multi-mission satellite precipitation and evaporation data from various sources and then improves them through a Postprocessing Filtering (PF) scheme to provide coherent datasets of precipitation (p), evaporation (e), water storage changes (Δs), and discharge (q) that accounts for its water budget closure. Principal component analysis (PCA) is then applied to the merged-improved products to analyze LV's spatio-temporal changes resulting from impacts of climate variation/change. Compared to the original unmerged data (0.62 and 0.37 average correlation for two samples), the merged-improved products are largely in agreement (0.91 average correlation). Furthermore, smaller imbalances between the merged-improved products are obtained with precipitation (37%) and water storage changes (35%) being the largest contributors to LV's water budget. This data improvement scheme could be applicable to any inland lake of a size similar to LV.

Understanding the global hydrological droughts of 2003-2016 and their relationships with teleconnections

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.

The application of multi-mission satellite data assimilation for studying water storage changes over South America

Constant monitoring of total water storage (TWS; surface, groundwater, and soil moisture) is essential for water management and policy decisions, especially due to the impacts of climate change and anthropogenic factors. Moreover, for most countries in Africa, Asia, and South America that depend on soil moisture and groundwater for agricultural productivity, monitoring of climate change and anthropogenic impacts on TWS becomes crucial. Hydrological models are widely being used to monitor water storage changes in various regions around the world. Such models, however, comes with uncertainties mainly due to data limitations that warrant enhancement from remotely sensed satellite products. In this study over South America, remotely sensed TWS from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is used to constrain the World-Wide Water Resources Assessment (W3RA) model estimates in order to improve their reliabilities. To this end, GRACE-derived TWS and soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) in order to separately analyze groundwater and soil moisture changes for the period 2002-2013. Following the assimilation analysis, Tropical Rainfall Measuring Mission (TRMM)'s rainfall data over 15 major basins of South America and El Niño/Southern Oscillation (ENSO) data are employed to demonstrate the advantages gained by the model from the assimilation of GRACE TWS and satellite soil moisture products in studying climatically induced TWS changes. From the results, it can be seen that assimilating these observations improves the performance of W3RA hydrological model. Significant improvements are also achieved as seen from increased correlations between TWS products and both precipitation and ENSO over a majority of basins. The improved knowledge of sub-surface water storages, especially groundwater and soil moisture variations, can be largely helpful for agricultural productivity over South America.

Assessing data assimilation frameworks for using multi-mission satellite products in a hydrological context

With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.

Understanding the association between climate variability and the Nile's water level fluctuations and water storage changes during 1992-2016

With the construction of the largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) along the Blue Nile, the Nile is back in the news. This, combined with Bujagali Dam on the White Nile are expected to bring ramification to the downstream countries. A comprehensive analysis of the Nile's waters (surface, soil moisture and groundwater) is, therefore, essential to inform its management. Owing to its shear size, however, obtaining in-situ data from "boots on the ground" is practically impossible, paving way to the use of satellite remotely sensed and models' products. The present study employs multi-mission satellites and surface models' products to provide, for the first time, a comprehensive analysis of the changes in Nile's stored waters' compartments; surface, soil moisture and groundwater, and their association to climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) over the period 1992-2016. In this regard, remotely sensed altimetry data from TOPEX/Poseidon (T/P), Jason-1, and Jason-2 satellites along with the Gravity Recovery And Climate Experiment (GRACE) mission, and the Tropical Rainfall Measuring Mission Project (TRMM) rainfall products are applied to analyze the compartmental changes over the Nile River Basin (NRB). This is achieved through the creation of 62 virtual gauge stations distributed throughout the Nile River that generate water levels, which are used to compute surface water storage changes. Using GRACE total water storage (TWS), soil moisture data from multi-models based on the Triple Collocation Analysis (TCA) method, and altimetry derived surface water storage, Nile basin's groundwater variations are estimated. The impacts of climate variability on the compartmental changes are examined using TRMM precipitation and large-scale ocean-atmosphere ENSO and IOD indices. The results indicate a strong correlation between the river level variations and precipitation changes in the central part of the basin (0.77 on average) in comparison to the northern (0.64 on average) and southern parts (0.72 on average). Larger water storages and rainfall variations are observed in the Upper Nile in contrast to the Lower Nile. A negative groundwater trend is also found over the Lower Nile, which could be attributed to a significantly lower amount of rainfall in the last decade and extensive irrigation over the region.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products

Africa, a continent endowed with huge water resources that sustain its agricultural activities is increasingly coming under threat from impacts of climate extremes (droughts and floods), which puts the very precious water resource into jeopardy. Understanding the relationship between climate variability and water storage over the continent, therefore, is paramount in order to inform future water management strategies. This study employs Gravity Recovery And Climate Experiment (GRACE) satellite data and the higher order (fourth order cumulant) statistical independent component analysis (ICA) method to study the relationship between terrestrial water storage (TWS) changes and five global climate-teleconnection indices; El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO), Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over Africa for the period 2003-2014. Pearson correlation analysis is applied to extract the connections between these climate indices (CIs) and TWS, from which some known strong CI-rainfall relationships (e.g., over equatorial eastern Africa) are found. Results indicate unique linear-relationships and regions that exhibit strong linkages between CIs and TWS. Moreover, unique regions having strong CI-TWS connections that are completely different from the typical ENSO-rainfall connections over eastern and southern Africa are also identified. Furthermore, the results indicate that the first dominant independent components (IC) of the CIs are linked to NAO, and are characterized by significant reductions of TWS over southern Africa. The second dominant ICs are associated with IOD and are characterized by significant increases in TWS over equatorial eastern Africa, while the combined ENSO and MJO are apparently linked to the third ICs, which are also associated with significant increase in TWS changes over both southern Africa, as well as equatorial eastern Africa.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.