Uncertainty plays a key role in hydrological modeling and forecasting, which can have tremendous environmental, economic, and social impacts. Therefore, it is crucial to comprehend the nature of this uncertainty and identify its scope and effects in a way that enhances hydrological modeling and forecasting. During recent decades, hydrological researchers investigated several approaches for reducing inherent uncertainty considering the limitations of sensor measurement, calibration, parameter setting, model conceptualization, and validation. Nevertheless, the scope and diversity of applications and methodologies, sometimes brought from other disciplines, call for an extensive review of the state-of-the-art in this field in a way that promotes a holistic view of the proposed concepts and provides textbook-like guidelines to hydrology researchers and the community. This paper contributes to this goal where a systematic review of the last decade's research (2010 onward) is carried out. It aims to synthesize the theories and tools for uncertainty reduction in surface hydrological forecasting, providing insights into the limitations of the current state-of-the-art and laying down foundations for future research. A special focus on remote sensing and multi-criteria-based approaches has been considered. In addition, the paper reviews the current state of uncertainty ontology in hydrological studies and provides new categorizations of the reviewed techniques. Finally, a set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate-change-sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021-2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961-2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one-dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm(3)/cm(3). Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.

Earlier impact studies have suggested that climate change may severely alter the hydrological cycle in alpine terrain. However, these studies were based on the use of a single or a few climate scenarios only, so that the uncertainties of the projections could not be quantified. The present study helps to remedy this deficiency. For 2 Alpine river basins, the Thur basin (1700 km(2)) and the Ticino basin (1515 km(2)), possible future changes in the natural water budget relative to the 1981-2000 (Thur) and 1991-2000 (Ticino) baselines were investigated by driving the distributed catchment model WaSiM-ETH with a set of 23 regional climate scenarios for monthly mean temperature (T) and precipitation (P). The scenarios referred to 2081-2100 and were constructed by applying a statistical-downscaling technique to outputs from 7 global climate models. The statistical-downscaling scenarios showed changes in annual mean T between +1.3 and +4.8degreesC and in annual total P between -11 and +11%, with substantial variability between months and catchments. The simulated overall changes in the hydrological water cycle were qualitatively robust and independent of the choice of a particular scenario. In all cases, the projections showed strongly decreased snow-pack and shortened duration of snow cover, resulting in time-shifted and reduced runoff peaks. Substantial reductions were also found in summer flows and soil-water availability, in particular at lower elevations. However, the magnitudes and certain aspects of the projected changes depended strongly on the choice of scenario. In particular, quantitative projections of soil moisture in the summer season and of the runoff in both the summer and autumn seasons were found to be quite uncertain, mainly because of the uncertainty present in the scenarios for P. Our findings clearly demonstrate that quantitative assessments of hydrological changes in the Alps using only a small number of scenarios may yield misleading results. This work strengthens our confidence in the overall results obtained in earlier studies and suggests distinct shifts in future Alpine hydrological regimes, with potentially dramatic implications for a wide range of sectors.