Hydrological Uncertainty Research Articles

Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty

With the rapid growth of population and economy, shortage and mismatch of land and water resources have deepened the need for cropping pattern optimization. In the context of the sustainable development of agriculture, cropping pattern optimization should not only pursue economic benefits, but the consequent environmental effects also deserve equal attention. Meanwhile, climate change increases the complexity of balancing conflicts of economic-environmental system by cropping pattern optimization. Therefore, this paper builds a multi-objective programming model for Economic-Environmental Synergistic Optimization for Cropping Pattern under Climate Change (EESO-CP-CC) model, with the goals of economic benefit increment and environmental pollutants emission reduction. The EESO-CP-CC model couples a non-point source pollution input-output model, a one-dimensional water quality model and an economic benefit function into an integrated framework. Fuzzy method was used to solve the optimization model, and the stochastic uncertainty of water supply under climate change was quantified by the integration of Bayesian approach and interval linear regression. The model was applied to Jinxi Irrigation District (JXID) in Heilongjiang Province, northeast of China. Results show that by adjusting the acreage of rice, corn and soybean, the harmony degree of economy-society-environment system increased by 10.7% compared to the current situation, indicating that the model tends to achieve the best possible economic benefits while ensuring the environmental effects. Compared with actual cropping pattern, the pollutants emissions reduced by 24.7% and 3% from corn and soybean, respectively. However, this led to a decrease of economic benefit by 8% in exchange, showing the trade-off between environmental pollution reduction and economic benefits improvement. The output coefficients of nitrogen and phosphorus pollutants were optimized, with the optimal output reducing by 20% compared to the standard. Cropping pattern and water resources allocation vary with different climate change conditions, however, the amplitude of variation is modest, indicating that the model can cope well with the changing environment. The developed model can help achieve synergistic development of economic benefits and environmental effects, and thus promote sustainable development of irrigation areas, and improve the coping capacity of agricultural water and land under climate change, by cropping pattern optimization and planning.

Multiple-steps scenario optimisation for pumping plants activation in water supply systems

Abstract Economic aspects concerning the high costs related to energy requirements for managing complex water supply systems need a robust strategy, particularly considering the activation of pumping plants. Considering hydrological uncertainties, the definition of strategic rules can ensure energy savings and the well-timed activation of costly water transfers for shortage risk alleviation. The modelling approach has been developed aiming at defining strategic rules of pumps activation thresholds. It considers the need for seasonal variations of activation and the different costs of energy in diverse time slots, according to the usual cost rules adopted by the authorities. Starting with the traditional scenario analysis approach, a new algorithm has been developed considering a multiple-steps scenario optimisation implemented using GAMS interfaced with CPLEX solvers. The results should allow the water authority to establish a robust strategy for pumping activation to guarantee the fulfilment of water demands and to ensure an energy-saving policy.

IGDT-Based Medium-Term Optimal Cascade Hydropower Operation in Multimarket with Hydrologic and Economic Uncertainties

Hydrologic uncertainty and economic uncertainty both affect market-oriented hydropower operation. For medium-term cascade hydropower operation, the uncertainties of daily reservoir inflow and market clearing power price can pose risks to operation, especially when power stations participate in multiple power markets. It is difficult to obtain the fluctuation range of uncertain variables for different expected power sale profits, and to plan the operation range of cascade hydropower stations. For a cascade hydropower system dispatching power to two markets, this work developed a risk assessment method for medium-term optimal cascade hydropower operation based on information gap decision-making theory (IGDT). We established a bilevel stochastic IGDT model for the main hydrologic and economic uncertainties in hydropower market participation. The lower-level model solves the minimum robustness profit or maximum opportunity profit from power sale, and the upper-level model solves the maximum robustness fluctuation range or minimum opportunity fluctuation range of the uncertain variables for different expected profits. Then we converted the bilevel model to two equivalent single-level models, and provided solutions. The proposed method does not require the probability distribution of uncertain variables, which is often unavailable; moreover, it reduces computational complexity while ensuring accuracy of results. The proposed method can provide decision makers with reasonable power dispatch options. This work used a cascade hydropower system in Southwest China as a case study. The proposed method provided acceptable fluctuation range of uncertain reservoir inflow and power price under different expected profits, and provided the operation range to guarantee the expected profit. Decision makers with different risk preferences can evaluate various strategies to optimize medium-term cascade hydropower operation to ensure the expected profit.

HUP‐BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting

AbstractUncertainty quantification and providing probabilistic streamflow forecasts are of particular interest for water resource management. The hydrologic uncertainty processor (HUP) is a well‐known Bayesian approach used to quantify hydrologic uncertainty based on observations and deterministic forecasts. This uncertainty quantification is model‐specific; however, utilizing information from multiple hydrologic models should be advantageous and should lead to better probabilistic forecasts. Using seven, structurally different, conceptual models, this study first aims at evaluating the effects of implementing different hydrologic models on HUP performance. Second, using the concepts of the Bayesian Model Averaging (BMA) approach, a multimodel HUP‐based Bayesian postprocessor (HUP‐BMA) is proposed where the combination of posterior distributions derived from HUP with different hydrologic models are used to better quantify the hydrologic uncertainty. All postprocessing approaches are applied for medium‐range daily streamflow forecasting (1–14 days ahead) in two watersheds located in Ontario, Canada. The results indicate that the HUP forecasts for short lead‐times are negligibly affected by implementing different hydrologic models, while with increasing lead‐time and flow magnitude, they significantly depend on the quality of the deterministic forecast. Moreover, the superiority of the proposed HUP‐BMA method over HUP is demonstrated based on various verification metrics in both watersheds. Additionally, HUP‐BMA outperformed the original BMA in quantifying hydrologic uncertainty for short lead‐times. However, by increasing lead‐time, considering the effects of initially observed flow on HUP‐BMA formulation may not be beneficial. So, its modified version unconditioned on initial observations is preferred.

Uncertainties and their interaction in flood hazard assessment with climate change

Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four catchments, incorporating within our modelling chain uncertainties associated with 12 global climate models contained in the Coupled Model Intercomparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and the use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood hazard in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding consistent with changes in annual maximum precipitation. We use additive chains and analysis of variance (ANOVA) to quantify and decompose uncertainties and their interactions in estimating selected flood quantiles for each catchment. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis. While uncertainties in future projections are widely assumed to be dominated by the ensemble of climate models used, we find that in one of our catchments uncertainties associated with bias correction methods dominate, while in another the uncertainty associated with the use of different extreme value distributions outweighs the uncertainty associated with the ensemble of climate models. These findings highlight the inability to generalise a priori about the importance of different components of the cascade of uncertainty in future flood hazard at the catchment scale. Moreover, we find that the interaction of components of the modelling chain employed are substantial (> 20 % of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall–runoff processes. Future work that seeks to further explore the characteristics of the uncertainty cascade as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be targeted in climate change impact assessments.

Propagating reliable estimates of hydrological forecast uncertainty to many lead times

We propose a revised version of the ERRIS (error reduction and representation in stages) error model capable of reliably propagating hydrological uncertainty to long lead times (>150 time steps). ERRIS employs four stages: a transformation to handle heteroscedasticity, a moving average bias-correction, an autoregressive model and two mixture Gaussian distributions. To propagate uncertainty through multiple lead times, ERRIS makes use of a technique termed ‘stochastic updating’. Ensemble spread at long lead times is partly controlled by the interplay of the autoregression coefficient ρ and the width of the error distribution. When ρ approaches 1 and the error distribution is wide, this causes over-wide ensemble distributions at long lead times. We control this interplay with the moving average bias-correction, which reduces the value of ρ and the width of the error distribution, resulting in reliable ensembles at long lead times. An additional control on the width of the ensemble at longer lead times is a restriction applied to the autoregressive model. This restriction guards against large overcorrections, which can lead to very poor forecasts. Applying the restriction when parameters are inferred can result in over-wide residual distributions. We propose the simple expedient of applying the restriction only when forecasts are generated, not when parameters are inferred. We show through a comparison with an earlier version of ERRIS that this produces more reliable ensemble distributions at long lead times, whilst still guarding against overcorrection. The resulting error model is simple and computationally efficient, and thus suitable for deployment in operational streamflow forecasting systems.

Variability effect of hydrological regime on river quality pattern and its uncertainties: case study of Zarjoob River in Iran

Abstract River water quality assessment, affected by pollution load, and river regime changes in various climate conditions, is an implementation that simplifies water resources management, and justifies terms for increases or decreases in human activities. The current paper aims to offer a water quality model of a river considering parametric, hydrologic, and pollution load uncertainty by using uncertainty indexes like Plevel, ARIL, and NUE. These indexes were used to analyze the influences of the model's parameters and the river's regime alternations on the results. A Qual2K model, calibrated with PSO algorithm, is presented and connected to GLUE algorithm to assess the model's uncertainties like effective input parameters on the modeled variations, headwater flow, and input pollutions. Zarjoob River, in the north of Iran, was chosen as the case study. The results illustrate that the interaction among parameters, hydrologic and pollutant discharge data should be considered in river water quality simulation. The presented methodology can analyze the influences of parametric uncertainty, parametric and hydrologic uncertainty, and pollution input load uncertainty according to any quantity of observations and the modeled results of any river.

A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties

A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties

A comprehensive framework for HSPF hydrological parameter sensitivity, optimization and uncertainty evaluation based on SVM surrogate model- A case study in Qinglong River watershed, China

To improve HSPF hydrological and water quality simulation, a new SVM surrogate modeling method was investigated and a comprehensive framework for hydrological parameter sensitivity, optimization and uncertainty analysis was established for Qinglong River watershed, Hebei Province, China. SVM surrogate model was set up based on pairs of parameter sets and Nash-Sutcliffe efficiency coefficients. It was concluded that: (1) SVM surrogate model performs well in both reliability and efficiency. (2) Sensitivity of eleven parameters was evaluated: AGWRC is extremely sensitive parameters, AGWETP, DEEPFR, BASETP are sensitive parameters and UZSN, LZSN, LZETP, INTFW, CEPSC, IRC, INFILT are not influential parameters. (3) Recommended parameter intervals were: LZSN [2.0,5.82], INFILT [0.21,0.47], AGWRC [0.85,0.87], DEEPFR [0.001,0.17], BASETP [0.001,0.09], AGWETP [0.0011,0.13], CEPSC [0.01,0.29], UZSN [0.05,1.20], IRC [0.3,0.62], LZETP [0.34,0.85], INTFW [1.0,5.77] and the optima were obtained respectively. (4) Posterior distributions of eleven parameters were obtained.

Overall uncertainty of climate change impacts on watershed hydrology in China

AbstractThe hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies.

An integrated simulation-optimization modeling system for water resources management under coupled impacts of climate and land use variabilities with priority in ecological protection

An integrated simulation-optimization modeling system (ISOMS) approach was developed for assessing adaptive strategies in response to coupled impacts of climate and landuse variations. The ISOMS can not only reflect future hydrological trends under changing environment, but also provide water-allocation plans under various uncertainties expressed as random or fuzzy feature systematically. A case study of Anning River Basin with dry and warm characteristics in the upper Yangtze River was applied to inspect the model's applicability. A range of alternatives to adaptive strategies were generated under combinations of climate and landuse scenarios with different satisfaction levels. Results reveal that: (i) there exist increasing trend of temperature and precipitation in future period (2021–2050); (ii) the streamflow variation is more sensitive to climate change than landuse change; (iii) the hydrologic system uncertainties would lead to changes in water resources allocation; (iv) a low level of uncertainty satisfaction or a high level of violation risk would reduce the system reliability for the water resources system. The ISOMS approach has tremendous significance for evaluating hydrologic variations with complicated uncertainties, and providing optimal water allocation schemes responding to the coupled impacts of climate and landuse variations among society, economy and environment.

Regional Flood Risk Projections under Climate Change

AbstractFlood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.

Expression of Concern: Fuzzy-probabilistic modeling the flood characteristics using bivariate frequency analysis and α-cut decomposition

Abstract Water allocation in agricultural lands, optimal design of hydraulic structures and climatic phenomena are the events in water management science that face hydrological uncertainties. The purpose of this study is to estimate the characteristics of surface runoff based on probabilistic and fuzzy analysis. Separation and generation of basic hydrological information, probabilistic modeling, fuzzy analysis, and optimization to achieve the solution were the main steps of the decision-making problem. Long-term hydrological data of the study area were collected, analyzed and used as a basis for the simulation model. In this study, a copula-based stochastic method was developed to deal with uncertainties related to rainfall and runoff characteristics as well as to address the nonlinear dependence between multiple random variables. The relationship between rainfall variables and flood characteristics was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using the non-dominated sorting genetic algorithm to find the optimal extreme points. The obtained solutions were used as a fuzzy response to calculate the flood of the Baghmalek plain in Khuzestan province in southwestern Iran. The results showed that the maximum model error occurred in predicting rainfall depth and flood volume, and the maximum rainfall rate and runoff flow could be calculated more accurately. Moreover, the developed fuzzy-probabilistic model was able to predict more than 90% of flood events within the defined fuzzy range.

Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts

Reliable and accurate regional multistep-ahead flood forecasts during extreme events are crucial and beneficial to flood disaster management and preparedness. Hydrologic uncertainty associated with the nonlinear dependence structure of flood inundation dynamics makes flood inundation forecasting fundamentally challenging. This study proposes a novel machine learning-based model (SAE-RNN) that hybrids the stacked autoencoder (SAE) with a recurrent neural network (RNN) for providing accurate and timely information to support emergency management in areas impacted by flood hazards. The proposed SAE-RNN model uses SAE to compress (encode) the high-dimensional flood inundation depths in a wide region into a low-dimensional latent space representation (flood features), uses RNN to forecast multistep-ahead flood features based on regional rainfall patterns, and finally uses SAE to reconstruct (decode) the multistep-ahead forecasts of flood features into regional flood inundation depths. A large number of hourly datasets of flood inundation depths collected in Yilan County of Taiwan formed the case study, where each dataset contains 169,797 grids of inundation depth. The datasets were divided into three independent datasets for use in training, validating and testing stages. The models’ results showed that RMSE values were very small (<0.09 m) and R2 values were high (>0.95) in all the cases (1- up to 3-hour-ahead forecasts in three stages). We conclude that the reason why the proposed SAE-RNN models are capable of attaining favorable regional multistep-ahead flood inundation forecasts could be owing to two core strategies: the effective continual extraction of the nonlinear dependence structure from flood inundation dynamics for lessening hydrologic uncertainty by virtue of SAE; and the nonlinear conversion of rainfall sequences into future flood features by virtue of RNN.

Consideration of streamflow forecast uncertainty in the development of short-term hydropower station optimal operation schemes: A novel approach based on mean-variance theory

The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal theory represented by the stochastic dynamic programming model (SDPM) is commonly used for SHSOO, which is focus on the uncertainty during a single lead time and lack of an overall consideration of multiple independent lead times of differing length. Thus, this paper proposes a short-term hydropower station optimal operation model based on mean-variance theory (MV-SHSOOM) for reducing operational risk. The proposed multi-step model considers the forecasting uncertainty of different lead times as an integral component to guide a station’s optimal operation. Specifically, the forecast uncertainty was integrated by the joint probability distribution of forecast error using a multivariate hydrologic uncertainty processor (HUP) at first. Then, possible observed streamflow sequences were obtained using the joint probability distribution of forecast error and taken as input to the short-term optimal operation model of the hydropower station. Finally, the mean-variance theory was applied to construct the optimal operation model for balancing the power generation and operational risk. The proposed model was applied to the Jinxi hydropower station, China, to determine its performance. The results show that: 1) the dependent structure among the forecast errors at different lead times is significant in terms of Spearman correlation, and the joint probability distribution of forecast error can be effectively described by a multivariate HUP; 2) the proposed MV-SHSOOM is superior to the traditional deterministic dynamic programming model (DDPM) and SDPM, and can more effectively reduce operational risk while increasing power generation.

Drought Monitoring in Bivariate Probabilistic Framework for the Maximization of Water Use Efficiency

Hydrological uncertainties are considered as the major components of agricultural water management. Determining the drought effects as a meteorological phenomenon should be evaluated to investigate the groundwater exploitation strategies for irrigation. The main objective of this study was to show how copula functions are used in the bivariate analysis of drought and increase the water use efficiency in the Khanmirza plain, Chaharmahal and Bakhtiari province, Iran. Moreover, the water amounts estimated by probabilistic analysis in different return periods were allocated to the cropping pattern using particle swarm optimization algorithm. Therefore, the drought characteristics including severity and duration were extracted using normalized rainfall index. Then, the frequency distributions were fitted to the mentioned drought characteristics, and the best fitted marginal distribution was specified for every drought characteristics. The results showed that the gamma and generalized extreme values distributions had the best fitness on the drought severity and duration, respectively. Furthermore, the goodness-of-fit tests were considered for Clayton, Ali-Mikhail-Haq, Frank, Gamble, Gamble-Hougaard, and Joe using Akaike information criterion and Bayesian information criterion. Frank copula is the best function for constructing the multivariate distribution in the study area. Results showed that the developed plans increased the probabilistic values of soil moisture content in root zone for the cultivated crops in the study area. Groundwater resource index was deceased to negative amounts related to existing conditions in the five recent years. Furthermore, optimal irrigation scheduling increased the soil moisture content by the average of 60% at the peak point of water requirement curve.

Evaluating the economic impact of water scarcity in a changing world

Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

AbstractComparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA &gt; HM &gt; GC &gt; ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model

This work introduces a general multi-objective parameter estimation framework to exploit MODIS-based snow cover maps to reduce predictive streamflow uncertainty in snow-dominated catchments. The well-known GLUE methodology is applied with a multi-objective approach, combining streamflow observations recorded at the outlet section and satellite-derived snow cover maps, aggregated to fractional values of the catchment area. The hydrological model used in this study includes a snowpack routine which exploits a statistical representation of the distribution of clear sky potential solar radiation - a significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The study provides an assessment of this approach based on operational quality data from two medium-size mountainous basins (a nested one included in a larger parent basin) located in the eastern Italian Alps. The nested basin is considered as ungauged, thus allowing a spatial assessment of the multi-objective approach. Results show a positive feedback between streamflow and snow cover area likelihoods, highlighted by means of the Pareto plot. Moreover, a better identifiability of the parameters driving snowmelt rate is found and consequently a shrink of the predictive streamflow uncertainty is observed. A containing ratio of 0.54 and a mean sharpness of 0.11 are found at the outlet of the parent basin, while a containing ratio equal to 0.65 and a mean sharpness equal to 0.17 are estimated at the nested basin, used as a validation test. These results confirm the potential of MODIS snow cover maps as additional data to inform hydrological models leading to more reliable and sharper streamflow simulations. This approach might be also appealing when streamflow simulations are required for ungauged basins.