Ashish Sharma

Ashish Sharma
UNSW Sydney | UNSW · School of Civil and Environmental Engineering

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399
Publications
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Publications

Publications (399)
Article
Urban flooding is one of the greatest threats to life and property, further exacerbated by the impacts of a warming climate. Balancing the increasing demand for liveable space with infrastructure for urban stormwater management presents a planning conundrum. Traditional thinking and design in urban stormwater is reaching limits in adequacy, or have...
Presentation
Full-text available
Forecasting hydrologic extremes across a range of timescales is critical for minimizing the socioeconomic costs of these events. Recognizing the remarkedly differences in the frequency spectrum between the response and the predictor variables in a modeling system, e.g., rainfall and streamflow in a hydrological system, we propose a wavelet-based va...
Article
There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying paramet...
Article
With rising concerns for water security and increasing interest in water resource development, accounting for river transmission losses in arid/semi‐arid region water budgets is a crucial yet challenging task. Transmission losses are usually confounded with many different processes and exacerbated by hydrologic and climatic non‐stationarity. A comm...
Article
Climate change is expected to have a significant impact on water security, with higher temperatures causing both enhanced droughts and flood extremes. Here, using global flow data from non-urban catchments, we investigate the sensitivity of flood volume to changes in concurrent surface air temperature. We find most of the world shows decreases in f...
Article
Understanding the hydrological processes in the Tibetan Plateau (TP) is increasingly important as the snow resources feed the demand for freshwater for a vast downstream population. Given the limited information available, the task of formulating a hydrological model that characterises future streamflow at downstream locations is challenging. A fle...
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Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments [...]
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The snowpack is a critical component of the hydrologic cycle in cold regions, the change in which becomes important for proper planning and management of water. The Tibetan Plateau provides significant amount of water to most Asian rivers, and consequently the downstream population is dependent on its availability. Despite its importance, potential...
Article
Uncertainty in input can significantly impair parameter estimation in water quality modeling, necessitating the accurate quantification of input errors. However, decomposing the input error from the model residual error is still challenging. This study develops a new algorithm, referred to as the Bayesian Error Analysis with Reordering (BEAR), to a...
Article
Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to auto...
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The relationship between extreme precipitation (EP) and precipitable water (W) is useful to assess design extremes and speculate on their expected changes with rising global temperatures. This study investigates the relationship between daily and longer‐duration EP and corresponding W at a global scale by analyzing remote‐sensed and reanalysis data...
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Availability of water resources is significantly affected by changes in seasonal rainfall, with water often in short supply when most needed. The majority of current research focuses on the impacts of multiyear drought, using monthly or annual average rainfall to investigate impacts to water resources. Here, we use daily rainfall to evaluate change...
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We present a new approach to predict streamflow using satellite data and a hydrological model. Remotely sensed data provide an attractive alternative to address the absence of streamflow data in hydrological model calibration. One observable signal holds particular appeal; the satellite-derived calibration ratio- measurement (C/M ratio) has been wi...
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The presence of error in water quality and hydrologic variables can significantly impair the calibration of water quality models. Precise and reliable identification of observational errors can have a significant impact on improving model parameter estimation. This study develops the Bayesian Error Analysis with Reordering (BEAR) method to accommod...
Presentation
Full-text available
Forecasting of hydrologic extremes across a range of timescales is critical for minimizing the socio-economic costs of these events. Consider, for instance, El Niño-Southern Oscillation (ENSO) is perhaps the strongest interannual signal in the global climate system with worldwide climatic, ecological, and societal impacts. Regression-based predicti...
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Climate warming has increased grassland productivity on the Qinghai-Tibet Plateau, while intensified grazing has brought increasing direct negative effects. To understand the effects of climate change and make sustainable management decisions, it is crucial to identify the combined effects. Here, we separate the grazing effects with a climate-drive...
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Plain Language Summary Climate models can simulate biased representations of atmospheric processes, necessitating procedures for correction before use in hydrological applications. Such spatial bias can be caused for many reasons, one of which is the use of point data in establishing a spatial reference field to compare model simulations against. T...
Article
Multi-model ensembles enable assessment of model structural uncertainty across multiple disciplines. Bayesian Model Averaging (BMA) is one of the most popular ensemble averaging approaches in hydrology but its predictions are easily impacted by the type of ensemble members selected. Here, we propose a regression-based ensemble approach, namely a Va...
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The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted pro...
Preprint
Full-text available
This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed Q...
Article
Observational studies of extreme daily and subdaily precipitation-temperature sensitivities (apparent scaling) aim to provide evidence and improved understanding of how extreme precipitation will respond to a warming climate. However, interpretation of apparent scaling results is hindered by large variations in derived scaling rates and divergence...
Article
Merging of multiple satellite datasets is a simple yet effective way to reduce prediction error. However, most merging methods for satellite data today are based on weighted averaging first proposed in 1969 for economic forecasting, which does not provide optimal outcomes when applied to satellite data. If our aim is to produce a merged data produc...
Article
Satellite-derived geophysical variables provide valuable information about the Earth’s functioning, but there are errors that limit their direct uses. Linearly combining two or more data sources is a simple and efficient method to reduce the uncertainty between the truth and observations. However, calculating the optimal weight for such a linear co...
Article
Errors in hydrological simulations have impacted their applications in flood prediction and water resources management. Properly characterizing the properties of errors such as heteroscedasticity and autocorrelation can provide improved hydrological predictions. Here, we present a probabilistic Long Short-Term Memory (LSTM) network for modeling hyd...
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Significant attention has recently been paid to deep learning as a method for improved catchment modeling. Compared with process-based models, deep learning is often criticized for its lack of interpretability. One solution is to combine a process-based hydrological model with a residual error model based on deep learning to give full scope to thei...
Article
Modelling the impact of climate change on streamflow for remote or data sparse regions is a challenge for hydrologists, as large datasets are often needed to adequately characterise the processes that dominate. The Tibetan Plateau, which forms the headwaters of the Brahmaputra and many other major rivers in the Indo-China region, is not closely mon...
Article
Forecasting of hydrologic extremes across a range of timescales is critical for minimizing the socio-economic costs of these events. Regression-based prediction is commonly adopted even in operational forecasting systems, often necessitating the use of distributional transformations to improve model specifications. One of the issues in such predict...
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A method is presented to address model state uncertainty in hydrologic model simulation. This is achieved by introducing tuneable parameters that allow adjustments to the model states. Excessive dimensionality is avoided by introducing only a limited number of parameters that control the index (timing) and size of the state adjustments. The method...
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The recent increase in the frequency of urban flooding in Bangkok has led to speculation that global warming may be to blame. Assessing this, however, is challenging, as Bangkok represents an ever-changing environment with changing storm drainage infrastructure, limited flood and precipitation data, and a tropical setting that complicates the relat...
Article
Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on...
Article
Cyanobacterial blooms are expected to be more frequent over time as more favourable environmental conditions are created in a warming climate. This study proposes strategies to effectively mitigate/reduce the concentration of cyanobacterial blooms based on a probabilistic modelling. The model adopted in this study is a probabilistic forecasting mod...
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Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing-based approach for correcting systematic biases in the time-varying trend of GCM simulations is proposed. The approach...
Article
Increases in the magnitude of storm and flood related catastrophes due to climate change are predicted to increase associated economic losses. There exists, however, conflicting evidence for greater economic losses despite well acknowledged increases in the severity of observed extreme events in recent decades. Here, using a worldwide catastrophe i...
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Soil moisture plays an important role in the hydrologic water cycle. Relative to in-situ soil moisture measurements, remote sensing has been the only means of monitoring global scale soil moisture in near real-time over the past 40 years. Among these, soil moisture products from radiometry sensors operating at L-band, e.g., SMAP, SMOS, and SMOS-IC,...
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Correction of atmospheric variables to remove systematic biases in GCM simulations prior to downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these tec...
Article
It is now well established that our warming planet is experiencing changes in extreme storms and floods, resulting in a need to better specify hydrologic design guidelines that can be projected into the future. This paper attempts to summarize the nature of changes occurring and the impact they are having on the design flood magnitude, with a focus...
Article
A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchan...
Article
Since 1901, global temperatures have risen by 0.89 °C, seriously impacting precipitation patterns and flowpeaks. However, few assessments of changes in global water balance have been conducted. Here we investigate the effect of rising temperatures on water recharge for 31 major river basins across the world using satellite derived terrestrial water...
Preprint
Full-text available
Merging of multiple satellite datasets is a simple yet effective way to reduce prediction error. However, most merging methods for satellite data today are based on weighted averaging first proposed in 1969 for economic forecasting, which does not provide optimal outcomes when applied to satellite data. If our aim is to produce a merged data produc...
Article
The existing bias correction (BC) methods used in impact studies are routinely based on a fixed model structure and often ignore the nature and magnitude of biases, and their variations into the future. As a calibrated model is applied to bias correct the future time series, there is no feedback mechanism to assess the impact of model complexity on...
Article
Input error in hydrologic models, mainly arising from observed precipitation, can impair model calibration. Precise and reliable identification of input error is important for improved model parameter estimation. However, limited information about the nature of the error and the memory of the hydrologic system make it challenging to disentangle inp...
Article
Toxic cyanobacteria blooms such as Anabaena, Aphanizomenon, Microcystis and Oscillatoria are of critical concern for public health and environmental system globally. An algal bloom is largely influenced by factors that jointly characterize the climatology (e.g., water temperature), hydraulics (e.g., water velocity) and nutrient concentrations (e.g....
Article
https://rdcu.be/cdCei Short-duration (1–3 h) rainfall extremes can cause serious damage to societies through rapidly developing (flash) flooding and are determined by complex, multifaceted processes that are altering as Earth’s climate warms. In this Review, we examine evidence from observational, theoretical and modelling studies for the intensif...
Article
This study evaluates the relative strengths of three remotely sensed soil moisture (SM) products to capture temporal variability at a global scale, the products being the Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC) and Advanced Scatterometer (ASCAT). For this, the conventional reference based Pearson corr...
Article
While broad consensus exists that temperatures are increasing, there is uncertainty surrounding the direction of change manifested in actual evapotranspiration (ET) worldwide. This study assessed trends in ET across the land surface using eleven widely used global datasets for a 32-year study period. To demonstrate the agreement and disagreement of...
Presentation
Full-text available
As of late July 2020, Australia has seen the most devastating bushfire and the world has been altered radically due to the coronavirus pandemic. Urgent actions should be taken to tackle poverty and inequality, health, education, education, biodiversity, and climate. As hydro-climatologists, we aim to resolve the challenge in hydro-climatological fo...
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Full-text available
An accurate description of changes in extreme rainfall events requires high resolution simulations. Regional climate models (RCMs), where GCM data are used to provide input boundary conditions, are widely used as a way to resolve finer spatial scale phenomena. A problem with this, however, is that the inherent systematic biases within the GCM simul...
Preprint
Full-text available
Uncertainty in inputs can significantly impair parameter estimation in water quality modeling, necessitating accurate quantification of input errors. However, decomposing input error from model residual error is still challenging. This study develops a new algorithm, referred to as Bayesian error analysis with reshuffling (BEAR), to address this pr...
Article
Full-text available
Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The lateral and lower boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). Thes...
Article
The frequency and severity of extreme weather events have been noted to be changing in both time and space as a result of rising global temperatures. In this regard, the analysis of joint occurrences of extreme climate events has gained considerable importance for planning and emergency management as such events often result in system failures that...
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Full-text available
The recent bushfires (2019-2020) in New South Wales (NSW) Australia were catastrophic by claiming human and animal lives, affecting ecosystems, destroying infrastructure, and more. Recent studies have investigated relationships between hydroclimatic signals and past bushfires, and very recently, a few commentary papers claimed drought and fuel mois...
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This work presents an open-source tool to predict natural system responses by transforming the frequency spectrum of predictor variables to create a response that better resembles observations. The R package, namely WAvelet System Prediction (WASP), is based on a discrete wavelet transform (DWT)-based variance transformation method. We further intr...
Code
Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies. Environmental Modelling & Software, 135, 104907. https://doi.org/10.1016/j.envsoft.2020.104907
Article
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Plain Language Summary Increasing global temperatures are likely to result in the intensification of extreme precipitation events with resultant flooding of great societal concern. Understanding the relationship between extreme precipitation and temperature provides valuable information for the design, operation, and risk assessment of high‐hazard...
Article
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Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into th...
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Anthropogenic climate change is leading to the intensification of extreme rainfall due to an increase in atmospheric water holding capacity at higher temperatures as governed by the Clausius-Clapeyron (C-C) relationship. However, the rainfall-temperature sensitivity (termed scaling) often deviates from the C-C relationship. This manuscript uses cla...
Article
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The present understanding of how changes in climate conditions will impact the flux of natural organic matter (NOM) from the terrestrial to aquatic environments and thus aquatic dissolved organic carbon (DOC) concentrations is limited. In this study, three machine learning algorithms were used to predict variations in DOC concentrations in an Austr...
Article
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Projection of extreme rainfall under climate change remains an area of considerable uncertainty. In the absence of geographically consistent simulations of extreme rainfall for the future, alternatives relying on physical relationships between a warmer atmosphere and its moisture carrying capacity are projected, scaling with a known atmospheric cov...
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Full-text available
Projections of extreme precipitation are of considerable interest in a range of design and management applications. These projections, however, can exhibit uncertainty that requires quantification to provide confidence to any application they are used in. This study assesses the uncertainty in projected extreme daily precipitation, separated into m...
Code
This is a new version based on original NPRED without calling Fortran codes. Source: Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights. Environmental Modelling & Software, 83, 271-275. Source code: https://github.com/zejiang-unsw/NP...
Article
Environmental measurements generate great volumes of high-dimensional data (often noisy and with missing values) from which meaningful messages may be extracted through appropriate organisation and summarisation. The self-organizing map (SOM) is an artificial neural network popular for recognizing patterns, relationships and clusters in such data....
Presentation
Full-text available
As we write this abstract, Australia is experiencing widespread forest fires, Sydney has declared significant water restriction measures curtailing demand, and the entire country is experiencing a drought that is amongst the worst on record. Formulating a stable and practical approach for predicting drought into the future is being realised as an i...
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A global challenge for water resource management in rivers worldwide is ensuring water supply reliability satisfies consumptive and environmental demands. High variability in water supply, water policy and management decisions, and uncertainty about the effects of climate change compound this challenge. Understanding factors driving water allocatio...
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Regional flood frequency analysis forms the basis for ascertaining design thresholds for extreme flow events for the purpose of resource management and design of hydraulic structures, especially at ungauged or partially gauged basins. A crucial step in this analysis is transferring available information from gauged sites to ungauged sites, which is...