Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological‐hydraulic models are at the core of flood forecasting and risk assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byråns Vattenbalansavdelning and the hydraulic model LISFLOOD‐FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time‐continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event‐based modeling approach were outlined as pragmatic solutions for more accurate prediction of large‐scale spatiotemporal patterns of flood dynamics, particularly in the presence of low‐accuracy elevation data.

Among all remote sensing missions, the Gravity Recovery and Climate Experiment (GRACE) was unique as it measured the change in total water content across all terrestrial water storages (TWS) including subsurface, deep soil moisture, and groundwater. However, its coarse resolution is a major challenge for practical applications. Ensemble Kalman filters (EnKFs) are useful tools to combine observations with models to reduce prediction errors. But due to the coarse resolution of the GRACE products, the EnKF does not work well in its usual form. Accordingly, different EnKF structures have been proposed and employed but a comparison between them has not yet been attempted. Here we assessed these structures using a synthetic problem. Alternative structures were formed using different increment calculation and updating strategies, observation operators, and the types of observation fed to the filter. It was found that all available structures led to an improvement in model performance when measured against a synthetic reference. However, the degree of improvement was strongly dependent on the assimilation strategy. Assimilating absolute TWS values (the summation of the TWS anomalies and an unbiased baseline) gave the best model performance when combined with an increment calculation strategy in which the increments are calculated and applied to all days of the month. However, without an unbiased baseline, assimilating TWS changes still leads to an acceptable improvement in model performance. Among the observation operators, those that predict the observations as an average of multiple days had the best performance.

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This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980-2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020-2056 period. Based on the NRMSE, Sn, and NS evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe droughts and longer will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it decresed over extreme droughts in the future.