SEHR-ECHO v1.0: A spatially explicit hydrologic response model for ecohydrologic applications

This paper presents the Spatially Explicit Hydrologic Response (SEHR) model developed at the Laboratory of Ecohydrology of the Ecole Polytechnique Fédérale de Lausanne for the simulation of hydrological processes at the catchment scale. The key concept of the model is the formulation of water transport by geomorphologic travel time distributions through gravity-driven transitions among geomorphic states: the mobilization of water (and possibly dissolved solutes) is simulated at the subcatchment scale and the resulting responses are convolved with the travel paths distribution within the river network to obtain the hydrologic response at the catchment outlet. The model thus breaks down the complexity of the hydrologic response into an explicit geomorphological combination of dominant spatial patterns of precipitation input and of hydrologic process controls. Nonstationarity and nonlinearity effects are tackled through soil moisture dynamics in the active soil layer. We present here the basic model set-up for precipitation-runoff simulation and a detailed discussion of its parameter estimation and of its performance for the Dischma River (Switzerland), a snow-dominated catchment with a small glacier cover

Potential climatic transitions with profound impact on Europe

We discuss potential transitions of six climatic subsystems with large-scale impact on Europe, sometimes denoted as tipping elements. These are the ice sheets on Greenland and West Antarctica, the Atlantic thermohaline circulation, Arctic sea ice, Alpine glaciers and northern hemisphere stratospheric ozone. Each system is represented by co-authors actively publishing in the corresponding field. For each subsystem we summarize the mechanism of a potential transition in a warmer climate along with its impact on Europe and assess the likelihood for such a transition based on published scientific literature. As a summary, the ‘tipping’ potential for each system is provided as a function of global mean temperature increase which required some subjective interpretation of scientific facts by the authors and should be considered as a snapshot of our current understanding. <br/

How do I know if my forecasts are better? Using benchmarks in Hydrological ensemble prediction

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are ‘toughest to beat’ and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better

Are current dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P

Catchment-scale water quality models are becoming increasingly popular tools for exploring the potential effects of land management, land use change and climate change on water quality. However, the dynamic, catchment-scale nutrient models in common usage are complex, with many uncertain parameters requiring calibration, limiting their usability and robustness. A key question is whether this complexity is justified. To explore this, we have developed a parsimonious P model, SimplyP, incorporating a coupled rainfall-runoff model and a biogeochemical model able to simulate streamflow, suspended sediment, particulate and dissolved P dynamics. The model’s complexity is compared in a small rural catchment in northeast Scotland. For three land use classes, less than six SimplyP model parameters must be determined through calibration alone, the rest may be based on measurements; INCA-P has around 40 unmeasurable parameters. Despite simpler process-representation, SimplyP produced a slightly better dissolved P simulation during both calibration and validation, and produced similar long-term projections in response to changes in land management. Results support the hypothesis that INCA-P is overly complex for the study catchment. We hope our findings will help prompt wider model comparison exercises, as well as debate amongst the water quality modelling community as to whether today's models are fit for purpose. Simpler models such as SimplyP have the potential to be useful management and research tools, building blocks for future model development (prototype code is freely available), or benchmarks against which more complex models could be evaluated

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Climate-Induced Changes in Spring Snowmelt Impact Ecosystem Metabolism and Carbon Fluxes in an Alpine Stream Network

Although stream ecosystems are recognized as an important component of the global carbon cycle, the impacts of climate-induced hydrological extremes on carbon fluxes in stream networks remain unclear. Using continuous measurements of ecosystem metabolism, we report on the effects of changes in snowmelt hydrology during the anomalously warm winter 2013/2014 on gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) in an Alpine stream network. We estimated ecosystem metabolism across 12 study reaches of the 254 km2 subalpine Ybbs River Network (YRN), Austria, for 18 months. During spring snowmelt, GPP peaked in 10 of our 12 study reaches, which appeared to be driven by PAR and catchment area. In contrast, the winter precipitation shift from snow to rain following the low-snow winter in 2013/2014 increased spring ER in upper elevation catchments, causing spring NEP to shift from autotrophy to heterotrophy. Our findings suggest that the YRN transitioned from a transient sink to a source of carbon dioxide (CO2) in spring as snowmelt hydrology differed following the high-snow versus low-snow winter. This shift toward increased heterotrophy during spring snowmelt following a warm winter has potential consequences for annual ecosystem metabolism, as spring GPP contributed on average 33% to annual GPP fluxes compared to spring ER, which averaged 21% of annual ER fluxes. We propose that Alpine headwaters will emit more within-stream respiratory CO2 to the atmosphere while providing less autochthonous organic energy to downstream ecosystems as the climate gets warmer

Hydrological extremes: controls, spatial & temporal variability and regional patterns (Session HS36, EGU General Assembly 2007)

Floods and droughts are the major weather related hydrological disasters and recent events have demonstrated Europe\u2019s continuing exposure to these natural hazards. High and low flows and associated floods and droughts are natural phenomena caused by meteorological anomalies and modified by the physical characteristics of catchments. The knowledge about the quantity, timing and risk of extreme discharges during floods and droughts is the basic requirement for a broad range of purposes in hydrology and water resources management. This knowledge has ideally to be based on the understanding the controls of the genesis of hydrological extremes (like the importance of landscape heterogeneities, threshold processes, scaling issues), finding appropriate ways how to characterize them (spatial and temporal variability, regional patterns) and designing reliable methods of predicting. Hydrological models based on physical concepts, statistical or regional approaches are used to define relationships between extreme flows, their characteristics and basin properties in order to predict characteristics of extremes at gauged and ungauged sites. The knowledge of the dominant processes of a study area is essential to pursue this task, and there is consensus among modellers that the parsimony of models is one important factor of predictive performance. The main objectives of this session are (a) to foster the understanding of the main governing processes of floods and droughts, (b) to discuss modeling approaches and concepts how to include process understanding in predictive models, (c) to shed light on the regional characteristics and spatial patterns of floods and droughts and the processes that give rise to such patterns including climate forcing, extreme precipitation and catchment response, (d) to present methods for estimating and/or predicting floods, droughts and low flows at a regional scale as well as regional methods for making predictions at the local scale, (e) to learn from practical applications of such models, (f) to profit from the similarity and differences of modeling concepts for both extremes. Specifically, papers are solicited that address one or more of the following questions: - what are the important mechanisms producing regional extremes, and how the use of diverse hydrological methods can contribute to highlighting the governing processes - what is the role of climatic forcing and catchment properties in the regional distribution of extremes, - which hydro-meteorological and catchment characteristics and indices can be used to describe regional patterns of extremes, - how do anthropogenic impacts and different land use patterns affect the spatial patterns of regional extremes, - how effective and reliable are the multivariate statistical techniques (e.g., cluster analysis, artificial neural networks, copulas, etc.) for identification of watersheds with similar dominant hydrological characteristics The scope of the session includes both general methodological contributions and case studies in different regions

Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alterna- tive intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Sec- ondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with clas- sical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration meth- ods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit