Daniel Viviroli

The potential for snow to supply human water demand in the present and future

Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins—which together have a present population of ~2 billion people—are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of >300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the projected future trends in snow resource potential, with about 90% of snow-sensitive basins showing potential for either increases or decreases over the near-term decades. Our results emphasize the importance of snow for fulfilling human water demand in many NH basins, and highlight the need to account for the full range of internal climate variability in developing robust climate risk management decisions.

Over the hills and further away from coast: global geospatial patterns of human and environment over the 20th–21st centuries

Proximity to the coast and elevation are important geographical considerations for human settlement. Little is known, however, about how spatial variation in these factors exactly relates to human settlements and activities, and how this has developed over time. Such knowledge is important for identifying vulnerable regions that are at risk from phenomena such as food shortages and water stress. Human activities are a key driving force in global change, and thus detailed information on population distribution is an important input to any research framework on global change. In this paper we assess the global geospatial patterns of the distribution of human population and related factors, with regard to the altitude above sea level and proximity to the coast. The investigated factors are physical conditions, urbanisation, agricultural practices, economy, and environmental stress. An important novel element in this study, is that we included the temporal evolution in various factors related to human settlements and agricultural practices over the 20th century, and used projections for some of these factors up to the year 2050. We found population pressure in the proximity of the coast to be somewhat greater than was found in other studies. Yet, the distribution of population, urbanisation and wealth are evolving to become more evenly spread across the globe than they were in the past. Therefore, the commonly believed tendency of accumulation of people and wealth along coasts is not supported by our results. At the same time, food production is becoming increasingly decoupled from the trends in population density. Croplands are spreading from highly populated coastal zones towards inland zones. Our results thus indicate that even though people and wealth continue to accumulate in proximity to the coast, population densities and economic productivity are becoming less diverse in relation to elevation and distance from the coast.

Flood-type classification in mountainous catchments using crisp and fuzzy decision trees

Floods are governed by largely varying processes and thus exhibit various behaviors. Classification of flood events into flood types and the determination of their respective frequency is therefore important for a better understanding and prediction of floods. This study presents a flood classification for identifying flood patterns at a catchment scale by means of a fuzzy decision tree. Hence, events are represented as a spectrum of six main possible flood types that are attributed with their degree of acceptance. Considered types are flash, short rainfall, long rainfall, snow-melt, rainfall on snow and, in high alpine catchments, glacier-melt floods. The fuzzy decision tree also makes it possible to acknowledge the uncertainty present in the identification of flood processes and thus allows for more reliable flood class estimates than using a crisp decision tree, which identifies one flood type per event. Based on the data set in nine Swiss mountainous catchments, it was demonstrated that this approach is less sensitive to uncertainties in the classification attributes than the classical crisp approach. These results show that the fuzzy approach bears additional potential for analyses of flood patterns at a catchment scale and thereby it provides more realistic representation of flood processes.

Influence of internal variability on population exposure to hydroclimatic changes

Future freshwater supply, human water demand, and people’s exposure to water stress are subject to multiple sources of uncertainty, including unknown future pathways of fossil fuel and water consumption, and ‘irreducible’ uncertainty arising from internal climate system variability. Such internal variability can conceal forced hydroclimatic changes on multi-decadal timescales and near-continental spatial-scales. Using three projections of population growth, a large ensemble from a single Earth system model, and assuming stationary per capita water consumption, we quantify the likelihoods of future population exposure to increased hydroclimatic deficits, which we define as the average duration and magnitude by which evapotranspiration exceeds precipitation in a basin. We calculate that by 2060, ~31%–35% of the global population will be exposed to >50% probability of hydroclimatic deficit increases that exceed existing hydrological storage, with up to 9% of people exposed to >90% probability. However, internal variability, which is an irreducible uncertainty in climate model predictions that is under-sampled in water resource projections, creates substantial uncertainty in predicted exposure: ~86%–91% of people will reside where irreducible uncertainty spans the potential for both increases and decreases in sub-annual water deficits. In one population scenario, changes in exposure to large hydroclimate deficits vary from −3% to +6% of global population, a range arising entirely from internal variability. The uncertainty in risk arising from irreducible uncertainty in the precise pattern of hydroclimatic change, which is typically conflated with other uncertainties in projections, is critical for climate risk management that seeks to optimize adaptations that are robust to the full set of potential real-world outcomes.

Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods

Design flood estimates for a given return period are required in both gauged and ungauged catchments for hydraulic design and risk assessments. Contrary to classical design estimates, synthetic design hydrographs provide not only information on the peak magnitude of events but also on the corresponding hydrograph volumes together with the hydrograph shapes. In this study, we tested different regionalization approaches to transfer parameters of synthetic design hydrographs from gauged to ungauged catchments. These approaches include classical regionalization methods such as linear regression techniques, spatial methods, and methods based on the formation of homogeneous regions. In addition to these classical approaches, we tested nonlinear regression models not commonly used in hydrological regionalization studies, such as random forest, bagging, and boosting. We found that parameters related to the magnitude of the design event can be regionalized well using both linear and nonlinear regression techniques using catchment area, length of the main channel, maximum precipitation intensity, and relief energy as explanatory variables. The hydrograph shape, however, was found to be more difficult to regionalize due to its high variability within a catchment. Such variability might be better represented by looking at flood-type specific synthetic design hydrographs.