A holy grail of hydrology is to understand catchment processes well enough that models can provide detailed simulations across a variety of hydrologic settings at multiple spatiotemporal scales, and ...under changing environmental conditions. Clearly, this cannot be achieved only through intensive place-based investigation at a small number of heavily instrumented catchments, or by empirical methods that do not fully exploit our understanding of hydrology. In this opinion paper, we discuss the need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall-runoff process, thereby balancing depth with breadth. We examine the history of such investigations, discuss the benefits (improved process understanding resulting in robustness of prediction at ungauged locations and under change), examine some practical challenges to implementation and, finally, provide perspectives on issues that need to be taken into account as we move forward. Ultimately, our objective is to provoke further discussion and participation, and to promote a potentially important theme for the upcoming Scientific Decade of the International Association of Hydrological Sciences entitled Panta Rhei.
Droughts may have tremendous impacts on humans. However, the space‐time characteristics of droughts are not very well understood, as case studies usually focus on individual drought events. Here we ...investigate the spatiotemporal drought characteristics of a large sample of events over the past 210 years in the Greater Alpine Region of Central Europe. We use monthly precipitation data, and flag, for each grid point, time steps with precipitation below a 20% percentile. We then propose a new method that detects drought events by connecting the flagged elements to space‐time drought regions. In contrast to the traditional drought indices that are based on a fixed, prescribed time window, this method is able to identify droughts of different durations in an objective way. The data show multidecadal variations of drought frequency, duration, intensity, and severity, but no consistent trends over the 210 year period. The top 5% of events in terms of their severity show a shift in seasonality from winter/spring events in the late nineteenth century toward autumn events during the last decades of the twentieth century. The most severe events center either in the Northwest or in the Southeast of the region analyzed. We found no significant correlations of drought frequency, duration, intensity, and severity with the temperature increases in the past three decades. Dry springs significantly enhance temperatures during summer droughts, suggesting a soil moisture‐temperature feedback.
Key Points
A new method is proposed for detecting atmospheric drought events in terms of their frequency, duration, intensity, and severity
Multidecadal variations of drought frequency, duration, intensity, and severity over the past 210 years exist in the GAR, but no trends
Most severe events center either around the Northwest or the Southeast of the GAR
In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, ...awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200 years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.
Key Points
Socio‐hydrological flood modeling and empirical data are combined to quantitatively describe the human‐flood system of the Elbe in Dresden
The approach presented here allows for understanding the feedbacks and dynamics of specific human‐flood systems
With this approach it is possible to quantify parameters, like societal forgetfulness, that cannot be easily obtained with other methods
MODIS snow cover products are appealing for hydrological applications because of their good accuracy and daily availability. Their main limitation, however, is cloud obscuration. In this study we ...evaluate simple mapping methods, termed temporal and spatial filters, that reduce cloud coverage by using information from neighboring non-cloud covered pixels in time or space, and by combining MODIS data from the Terra and Aqua satellites. The accuracy of the filter methods is evaluated over Austria, using daily snow depth observations at 754 climate stations and daily MODIS images in the period 2003-2005. The results indicate that the filtering techniques are remarkably efficient in cloud reduction, and the resulting snow maps are still in good agreement with the ground snow observations. There exists a clear, seasonally dependent, trade off between accuracy and cloud coverage for the various filtering methods. An average of 63% cloud coverage of the Aqua images is reduced to 52% for combined Aqua-Terra images, 46% for the spatial filter, 34% for the 1-day temporal filter and 4% for the 7-day temporal filter, and the corresponding overall accuracies are 95.5%, 94.9%, 94.2%, 94.4% and 92.1%, respectively.
Key documents such as the European Water Framework Directive and the U.S. Clean Water Act state that public and stakeholder participation in water resource management is required. Participation aims ...to enhance resource management and involve individuals and groups in a democratic way. Evaluation of participatory programs and projects is necessary to assess whether these objectives are being achieved and to identify how participatory programs and projects can be improved. The different methods of evaluation can be classified into three groups: (i) process evaluation assesses the quality of participation process, for example, whether it is legitimate and promotes equal power between participants, (ii) intermediary outcome evaluation assesses the achievement of mainly nontangible outcomes, such as trust and communication, as well as short‐ to medium‐term tangible outcomes, such as agreements and institutional change, and (iii) resource management outcome evaluation assesses the achievement of changes in resource management, such as water quality improvements. Process evaluation forms a major component of the literature but can rarely indicate whether a participation program improves water resource management. Resource management outcome evaluation is challenging because resource changes often emerge beyond the typical period covered by the evaluation and because changes cannot always be clearly related to participation activities. Intermediary outcome evaluation has been given less attention than process evaluation but can identify some real achievements and side benefits that emerge through participation. This review suggests that intermediary outcome evaluation should play a more important role in evaluating participation in water resource management.
Key Points
Identifies key criteria for evaluating participation
Provides guidance on structuring the evaluation of participation activities
Shows that intermediary outcomes should play a greater role
Globally, many different kinds of water resources management issues call for policy- and infrastructure-based responses. Yet responsible decision-making about water resources management raises a ...fundamental challenge for hydrologists: making predictions about water resources on decadal- to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle - a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.
Four catchment grouping methods are compared in terms of their performance in predicting specific low flow discharges
q
95, i.e. the specific discharge that is exceeded on 95% of all days. The ...grouping methods are the residual pattern approach, weighted cluster analysis, regression trees and an approach based on seasonality regions. For each group, a regression model between catchment characteristics and
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95 is fitted independently. Data from 325 sub-catchments in Austria ranging in catchment area from 7 to 963
km
2 are used in the analysis. The performance of the methods is assessed by leave-one-out cross-validation of the regressionestimates, which emulates the case of ungauged catchments. Results indicate that the grouping based on seasonality regions performs best and explains 70% of the spatial variance of
q
95. The favourable performance of this grouping method is likely related to the striking differences in seasonal low flow processes in the study domain. Winter low flows are associated with the retention of solid precipitation in the seasonal snow pack while summer low flows are related to the relatively large moisture deficits in the lowland catchments during summer. The regression tree grouping performs second best (explained variance of 64%) and the performance of the residual pattern approach is similar (explained variance of 63%). The weighted cluster analysis only explains 59% of the spatial variance of
q
95, which is only a minor improvement over the global regression model, i.e. without using any grouping (explained variance of 57%). An analysis of the sample characteristics of all methods suggests that, again, the grouping method based on the seasonality regions has the most favourable characteristics although all methods tend to underestimate specific low flow discharges in the very wet catchments.
While it is known that farmers adopt different decision‐making behaviors to cope with stresses, it remains challenging to capture this diversity in formal model frameworks that are used to advance ...theory and inform policy. Guided by cognitive theory and the theory of bounded rationality, this research develops a novel, socio‐hydrological model framework that can explore how a farmer's perception of water availability impacts crop choice and water allocation. The model is informed by a rich empirical data set at the household level collected during 2013 in Kenya's Upper Ewaso Ng'iro basin that shows that the crop type cultivated is correlated with water availability. The model is able to simulate this pattern and shows that near‐optimal or “satisficing” crop patterns can emerge also when farmers were to make use of simple decision rules and have diverse perceptions on water availability. By focusing on farmer decision making it also captures the rebound effect, i.e., as additional water becomes available through the improvement of crop efficiencies it will be reallocated on the farm instead of flowing downstream, as a farmer will adjust his (her) water allocation and crop pattern to the new water conditions. This study is valuable as it is consistent with the theory of bounded rationality, and thus offers an alternative, descriptive model in addition to normative models. The framework can be used to understand the potential impact of climate change on the socio‐hydrological system, to simulate and test various assumptions regarding farmer behavior and to evaluate policy interventions.
Key Points
We present a socio‐hydrological model that captures a farmer's crop choice and water allocation given his perception of water availability
We show how “satisficing” crop patterns can emerge also when each farmer is assumed to follow simple decision rules guided by his perception
In line with bounded rationality theory, the model offers an alternative to normative models and can support climate adaptation research
We propose a framework for identifying types of causative mechanisms of floods. The types are long‐rain floods, short‐rain floods, flash floods, rain‐on‐snow floods, and snowmelt floods. We adopt a ...catchment perspective, i.e., the focus is on the catchment state and the atmospheric inputs rather than on atmospheric circulation patterns. We use a combination of a number of process indicators, including the timing of the floods, storm duration, rainfall depths, snowmelt, catchment state, runoff response dynamics, and spatial coherence. On the basis of these indicators and diagnostic regional plots we identify the process types of 11,518 maximum annual flood peaks in 490 Austrian catchments. Forty‐three percent of the flood peaks are long‐rain floods, only 3% are snowmelt floods, and the relative contribution of the types changes with the flood magnitude. There are pronounced spatial patterns in the frequency of flood type occurrence. For example, rain‐on‐snow floods most commonly occur in northern Austria. Runoff coefficients tend to increase with rainfall depth for long‐rain floods but are less dependent of rainfall depth and exhibit much larger scatter for flash floods. All types exhibit seasonal patterns, both in terms of flood magnitudes and catchment altitudes of flood occurrence. The coefficient of variation (CV) of the flood samples stratified by process type decreases with catchment area for most process types with the exception of flash floods for which CV increases with catchment area.
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