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Drought events and their impacts vary spatially and temporally due to diverse pedo-climatic and hydrologic conditions, as well as variations in exposure and vulnerability, such as demographics and response actions. While hazardous severity and frequency of past drought events have been studied in detail, little is known about the effect of drought management strategies on the actual impacts, and how the hazard is perceived by relevant stakeholders for inducing action. In a continental study, we characterised and assessed the impacts and the perceptions of two recent drought events (2018 and 2019) in Europe and examined the relationship between management strategies and drought perception, hazard and impacts. The study was based on a pan-European survey involving national representatives from 28 countries and relevant stakeholders responding to a standard questionnaire. The survey focused on collecting information on stakeholders’ perceptions of drought, impacts on water resources and beyond, water availability and current drought management strategies at national and regional scales. The survey results were compared with the actual drought hazard information registered by the European Drought Observatory (EDO) for 2018 and 2019. The results highlighted high diversity in drought perceptions across different countries and in values of implemented drought management strategies to alleviate impacts by increasing national and sub-national awareness and resilience. The study concludes with an urgent need to further reduce drought impacts by constructing and implementing a European macro-level drought governance approach, such as a directive, which would strengthen national drought management and lessen harm to human and natural potentials.

Invited commentary.

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters, with the computational burden of calculating distributed estimates of predictive uncertainty, and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.

The aim of this paper is the estimation of uncertainty in an online data assimilation model applied to a sequential, multiple-step-ahead flood forecasting system. The main aim of the forecasting system under consideration is the derivation of real-time forecasts of the water levels with the maximum possible lead-time. This is achieved through a two-level, sequential data assimilation procedure. In order to extend the maximum lead-time, we incorporate the forecasts obtained from the earlier stages of the forecasting system, both rainfall-water level and water level routing processes. The updating of the gain of each of the subsystems introduces nonlinearity into the system performance. The Generalized Likelihood Uncertainty Estimation (GLUE) technique is used to estimate the uncertainty of model predictions in the decomposed online forecasting system.

A parametrically parsimonious, data-based model was built on observations at hillslope and catchment scale to simulate the distribution of overland flow within a small East African Highlands catchment (Kwalei, Tanzania). A rainfall-flow Data Based Mechanistic model identified catchment effective rainfall and separated the discharge quick flow, interpreted as the combination of overland flow plus reinfiltration along the slopes, and the slow flow, interpreted as ground water displacement. Observations of overland flow occurrence along the slopes were used to derive probability distribution functions (pdfs) of overland flow in relation to effective rainfall for two pre-defined hydrologic response units (HRUs): perennial (HRU_1) versus other crops (HRU_2). At low effective rainfall, overland flow was more frequent in HRU_2, while at high effective rainfall overland flow in the two HRUs was similar. The pdfs were employed to disaggregate the quick flow into HRU overland flow depth. Reinfiltration was accounted for by assuming that only the overland flow generated in the lower part of the field would drain downslope. Effective reinfiltration length was about 4 m. Comparison of model simulations versus Gerlach trough measurements indicated that rainfall intensity was not accounted for sufficiently. The use of smaller time steps or, alternatively, of a rainfall intensity threshold could improve model performance. However, given the high variability of overland flow observed along the slopes and the limited dataset, model simulations were considered satisfactory. Though the model needs further testing on other datasets, the disaggregating approach represents an inductive alternative to the use of infiltration equations to model overland flow within a catchment. (c) 2005 Elsevier B.V. All rights reserved.

Distributed erosion models are potentially good tools for locating soil sediment sources and guiding efficient Soil and Water Conservation (SWC) planning, but the uncertainty of model predictions may be high. In this study, the distribution of erosion within a catchment was predicted with a semi-empirical erosion model that combined a semi-distributed hydrological model with the Morgan, Morgan and Finney (MMF) empirical erosion model. The model was tested in a small catchment of the West Usambara Mountains (Kwalei catchment, Tanzania). Soil detachability rates measured in splash cups (0.48-1.16gJ(-1)) were close to model simulations (0.30-0.35gJ(-1)). Net erosion rates measured in Gerlach troughs (0.01-1.05kgm(-2) per event) were used to calibrate the sediment transport capacity of overland flow. Uncertainties of model simulations due to parameterisation of overland flow sediment transport capacity were assessed with the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The quality of the spatial predictions was assessed by comparing the simulated erosion pattern with the field-observed erosion pattern, measuring the agreement with the weighted Kappa coefficient of the contingency table. Behavioural parameter sets (weighted Kappa > 0.50) were those with short reinfiltration length (< 1.5m) and ratio of overland flow power a to local topography power close to 0.5. In the dynamic Hortonian hydrologic regime and the dissected terrain of Kwalei catchment, topography controlled the distribution of erosion more than overland flow. Simulated erosion rates varied from -4 to +2kgm(-2) per season. The model simulated correctly around 75% of erosion pattern. The uncertainty of model predictions due to sediment transport capacity was high; around 10% of the fields were attributed to either slight or severe erosion. The difficult characterisation of catchment-scale effective sediment transport capacity parameters poses a major limit to distributed erosion m

The paper presents an application of the generalised likelihood uncertainty estimation methodology to the problem of estimating the uncertainty of predictions produced by environmental models. The methodology is placed in a wider context of different approaches to inverse modelling and, in particular, a comparison is made with Bayesian estimation techniques based on explicit structural assumptions about model error. Using a simple example of a rainfall-flow model, different evaluation measures and their influence on the prediction uncertainty and credibility intervals are demonstrated. (c) 2005 Elsevier Ltd. All rights reserved.

Scientists from different disciplines now routinely work together on com-plex, multi-interactive, geochemical and biochemical environmental processes and it has become necessary to define a common language that can be under-stood by all the relevant scientific community. This in turn will aid the transfer of expertise between scientists from different disciplines. This paper attempts to relate different modelling approaches dealing with uncertainty in environmental processes. The literature review is based on hydrological, geophysical and envi-ronmental risk modelling, but the conclusions are relevant to all scientists work-ing on environmental problems, which are almost invariably poorly defined. We argue that modelling of environmental processes should be stochastic rather than deterministic. After listing the basic definitions and possible sources of uncer-tainty in environmental models, we present the main approaches to the modelling of uncertainties in, and variability of, environmental processes. We propose a general methodology for dealing with uncertainty, using as an example aspects of uncertainty assessment in modelling of rainfall-flow process at a catchment scale.

Water quality predictions in an ungauged catchment require the development of a model that is able to capture the basic physical features of the process and depends only on variables that are easily available. From this point of view, the model has similar requirements to those used in future climate scenario analysis. The mechanistic water quality model, developed in GKSS, Germany, for the purpose of climate change analysis, uses only climatic variables, such as temperature, radiation and discharge, to predict the time variability of algae concentrations. This paper presents the development of a statistical analogue to this mechanistic model. The goal of this research is the derivation of a data-based model that has the minimum number of parameters required to explain the data and, at the same time, is able to represent the physical features of the process (a Data-Based Mechanistic or DBM model). The approximation of the mechanistic model is obtained by a statistical analysis of the relations between the model input and output variables, as well as the linearisation of the mechanistic algae equations, leading to the development of a statistically tractable model. The result of this analysis is a nonlinear, Multi Input Single Output (MISO) transfer function model that provides a statistical counterpart of the mechanistic algae model. The model is used to reconstruct hourly chlorophyll-a concentrations (a measure of algae concentrations) during the “pre-unification of Germany ” period (before 1990) in the River Elbe, Germany. The uncertainty of the predictions is assessed and the results are validated against available monthly chlorophyll-a measurements.

The problem of Prediction in Ungauged Basins (PUB) is intimately linked with the concept of regionalisation; namely the transfer of information from one catchment that is gauged to another that is not. But such regionalisation exercises can be dangerous and should be attempted only with great care. The present paper addresses what the authors believe to be one essential aspect of regionalisation: namely, the importance of considering only ‘top-down ’ models that are parametrically efficient (parsimonious) and fully ‘identifiable ’ from the available catchment data. We argue further that many mechanistic model parameters are more naturally defined in the context of continuous-time, differential equation models (normally derived by the application of natural ‘laws’, such as mass and energy conservation). As a result, there are advantages if such models are identified and estimated directly in this continuous-time, differential equation form, rather than being formulated and estimated as discrete-time models. The arguments presented in the paper are illustrated by an example in which the top-down, Data-Based Mechanistic (DBM) approach to modelling is applied to a set of precipitation-flow data. This involves the application of an advanced method of continuous-time transfer function identification and estimation; and the interpretation of this estimated model in physically meaningful terms, as required by the DBM modelling approach.