A document by Ankit Ghanghas. Click on the document to view its contents.

The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias-corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias-corrected lateral and lower boundary conditions. Results show that the RCMs with bias-corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub-daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short-term intense precipitation occurs.

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 product that minimizes the prediction errors against a prediction target, there is no reason to insist that the merged product be an average of the parent datasets. A more disciplined approach based on mathematical optimization would be to minimize prediction errors. However, formulating merging as an optimization problem is insufficient by itself as the statistics needed for optimization, e.g. signal-to-noise ratio (SNR) of parent products, are often unavailable in practice and must be estimated jointly. In this paper, we address both of these problems for data merging. We first formulate optimal merging of satellite data as a SNR optimization (SNR-opt), and propose an estimation method to jointly estimate the required SNRs. This SNR-based approach has a natural interpretation as a multi-input single-output Wiener filter. Through extensive experimental validation on three global- scale satellite-derived soil moisture and land surface temperature products, we demonstrate that our SNR optimization significantly improves merging results over weighted averaging schemes.

The effect of climate change on precipitation intensity is well documented. However, findings regarding changes in spatial extent of extreme precipitation events are still ambiguous as previous studies focused on particular regions and time domains. This study addresses this ambiguity by investigating the pattern of changes in the spatial extent of short duration extreme precipitation events globally. A grid-based indicator termed Spatial-Homogeneity (SH) is proposed and used to assess the changes of spatial extent in Global Precipitation Measurement (GPM) records. This study shows that i) rising temperature causes significant shrinking of precipitation extent in tropics, but an expansion of precipitation extent in arid regions, ii) storms with higher precipitation intensity show a faster decrease in spatial extent, iii) larger spatial extent storms are associated with higher total precipitable water. Results imply that in a warming climate, tropics may experience severe floods as storms may become more intense and spatially concentrated.

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 is designed to compensate for issues with hydrologic model structures, particularly those relevant to the soil moisture state in a rainfall-runoff model. In the context of water resource planning and management, errors in the model states have often been overlooked as an important source of uncertainty and have the potential to significantly degrade model simulations. A synthetic study shows that a classical parameter estimation approach will produce biased distributions when state errors exist, and that the proposed state and parameter uncertainty estimation (SPUE) can remove the bias in parameter estimates for improved model simulations. In a real case study, SPUE and the classical approach are implemented in 46 sites around Australia. The results show that hydrologic parameter distributions for a selected conceptual model can be significantly different when accounting for state uncertainty. This has large implications for scenario modelling since it puts into dispute how to determine appropriate parameters for such studies. SPUE parameters outperform the classical approach in a range of metrics including the Akaike Information Criterion. Additionally, SPUE parameters performed better during validation periods. Future work involves testing SPUE with different hydrologic model and likelihood formulations, and enhancing rigor by explicitly accounting for observational data uncertainty.

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 QDM-spatial bias-correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross‐validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias-corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered.

Satellite-derived data provide useful information about the rationale of Earth’s functioning. While satellite remote sensing has been regarded as the almost only means for observing the entire Earth in near-real-time, errors in satellite observations have limited their direct usage in applications. Merging two or more data sources has been regarded as a simple but effective way to decrease such errors (e. g. minimizing mean square errors between the observation and truth). The principle of data merging is to combine independent information of each data source, improving over each individual product by canceling out random errors, with effectiveness by the degree of independence over the data sources. In the case of linearly combining data, qualitative assessments of the error (i.e. error variance/covariance and data-truth correlation) are essential to calculate the optimal weight for each candidate product. However, such reference “truth” is rarely available in practical. To overcome this limitation, a triple collocation (TC) technique is often used to estimate data error by using a data triplet without the truth. Despite the usefulness and simplicity of the TC-based error estimation, the inherent assumptions (e.g. error independence) in the approach tend to induce sub-optimal results in the error estimation and/or data combination. There have been also further efforts to address the limitation such as quadruple collocation (QC) using a data quadruple to partially estimate error cross-correlation and single/double instrumental variable methods to lessen the difficulty in obtaining multiple datasets. In this presentation, we review the status of error estimation and data merging approaches based on the collocation methods and then present challenges to be addressed through future research.

We present herein a new basis for measuring river discharge in ungauged catchments. Surrogate runoff (SR) is created using remotely sensed data to compensate for the absence of ground streamflow measurements. Because of their widespread availability, remotely sensed SR products are attractive, with approaches such as satellite-derived measurement-calibration ratio (C/M ratio). However, the use of the C/M ratio suffers from its limited penetration through ground vegetation canopies. While a microwave signal with a longer wavelength has been used to enhance the penetration capability, the coarseness of the spatial resolution of the microwave signal offsets its improvement due to the inherent assumptions in the C/M ratio, i.e., selecting two contrasting pixels (i.e., measurement and calibration) at the same time. To address both issues, this study proposes a new SR formulation using a longer wavelength (L-band microwave) with a better assumption for handling coarse grids, whereby the temporal variability of dryness against the driest state in each grid is used. The performance of the new SR is assessed for 467 Australian Hydrologic Reference Station catchments. Results show considerable improvements in the Pearson linear correlation (R) between the proposed SR and streamflow: 44% of the study areas show R higher than 0.4 with the new approach, whereas only 13% of the study areas show R higher than 0.4 with the currently used alternative (C/M ratio derived from Ka-band microwave). Overall, the resulting SR is dramatically improved by using the newly designed SR approach with the L-band microwave signal.