Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Sun, Y., Z. Hou, M. Huang, F. Tian, and L.R. Leung, 2013: “Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model.” Hydrology and Earth System Sciences, v. 17, pp. 4995-5011, doi: 10.5194/hess-17-4995-2013.

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

Open Access

Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta

Mahat, V., and A. Anderson, 2013: “Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta.” Hydrology and Earth System Sciences, v. 17, pp. 4941-4956, doi: 10.5194/hess-17-4941-2013.

Rivers in Southern Alberta are vulnerable to climate change because much of the river water originates as snow in the eastern slopes of the Rocky Mountains. Changes in likelihood of forest disturbance (wildfire, insects, logging, etc.) may also have impacts that are compounded by climate change. This study evaluates the impacts of climate and forest changes on streamflow in the upper parts of the Oldman River in Southern Alberta using a conceptual hydrological model, HBV-EC (Hydrologiska Byråns attenbalansavdelning, Environment Canada), in combination with a stochastic weather generator (LARS-WG) driven by GCM (global climate model) output climate data. Three climate change scenarios (A1B, A2 and B1) are selected to cover the range of possible future climate conditions (2020s, 2050s, and 2080s). The GCM projected less than a 10% increase in precipitation in winter and a similar amount of precipitation decrease in summer. These changes in projected precipitation resulted in up to a 200% (9.3 mm) increase in winter streamflow in February and up to a 63% (31.2 mm) decrease in summer flow in June. Flow also decreased in July and August, when irrigation is important; these reduced river flows during this season could impact agriculture production. The amplification in the streamflow is mostly driven by the projected increase in temperature that is predicted to melt winter snow earlier, resulting in lower water availability during the summer. Uncertainty analysis was completed using a guided GLUE (generalized likelihood uncertainty estimation) approach to obtain the best 100 parameter sets and associated ranges of streamflows. The impacts of uncertainty in streamflows were higher in spring and summer than in winter and fall. Forest change compounded the climate change impact by increasing the winter flow; however, it did not reduce the summer flow.

Open Access

Assessing flood risk at the global scale: Model setup, results, and sensitivity

Ward, P.J., B. Jongman, F.S. Weiland, A. Bouwman, R. van Beek, M.F.P. Bierkens, W. Ligtvoet, and H.C. Winsemius, 2013: “Assessing flood risk at the global scale: Model setup, results, and sensitivity.” Environmental Research Letters, v. 8, paper no. 044019, doi: 10.1088/1748-9326/8/4/044019.

Globally, economic losses from flooding exceeded $19 billion in 2012, and are rising rapidly. Hence, there is an increasing need for global-scale flood risk assessments, also within the context of integrated global assessments. We have developed and validated a model cascade for producing global flood risk maps, based on numerous flood return-periods. Validation results indicate that the model simulates interannual fluctuations in flood impacts well. The cascade involves: hydrological and hydraulic modelling; extreme value statistics; inundation modelling; flood impact modelling; and estimating annual expected impacts. The initial results estimate global impacts for several indicators, for example annual expected exposed population (169 million); and annual expected exposed GDP ($1383 billion). These results are relatively insensitive to the extreme value distribution employed to estimate low frequency flood volumes. However, they are extremely sensitive to the assumed flood protection standard; developing a database of such standards should be a research priority. Also, results are sensitive to the use of two different climate forcing datasets. The impact model can easily accommodate new, user-defined, impact indicators. We envisage several applications, for example: identifying risk hotspots; calculating macro-scale risk for the insurance industry and large companies; and assessing potential benefits (and costs) of adaptation measures.

Open Access

Assessing ‘dangerous climate change’: Required reduction of carbon emissions to protect young people, future generations and nature

Hansen, J., P. Kharecha, M. Sato, V. Masson-Delmotte, F. Ackerman, D.J. Beerling, P.J. Hearty, O. Hoegh-Guldberg, S.-L. Hsu, C. Parmesan, J. Rockstrom, E. J. Rohling, J. Sachs, P. Smith, K. Steffen, L. Van Susteren, K. von Schuckmann, and J.C. Zachos, 2013: “Assessing ‘dangerous climate change’: Required reduction of carbon emissions to protect young people, future generations and nature.” PLOS One, doi: 10.1371/journal.pone.0081648.

We assess climate impacts of global warming using ongoing observations and paleoclimate data. We use Earth’s measured energy imbalance, paleoclimate data, and simple representations of the global carbon cycle and temperature to define emission reductions needed to stabilize climate and avoid potentially disastrous impacts on today’s young people, future generations, and nature. A cumulative industrial-era limit of ~500 GtC fossil fuel emissions and 100 GtC storage in the biosphere and soil would keep climate close to the Holocene range to which humanity and other species are adapted. Cumulative emissions of ~1000 GtC, sometimes associated with 2°C global warming, would spur “slow” feedbacks and eventual warming of 3–4°C with disastrous consequences. Rapid emissions reduction is required to restore Earth’s energy balance and avoid ocean heat uptake that would practically guarantee irreversible effects. Continuation of high fossil fuel emissions, given current knowledge of the consequences, would be an act of extraordinary witting intergenerational injustice. Responsible policymaking requires a rising price on carbon emissions that would preclude emissions from most remaining coal and unconventional fossil fuels and phase down emissions from conventional fossil fuels.

Open Access

Developing predictive insight into changing water systems: Use-inspired hydrologic science for the Anthropocene

Thompson, S.E., M. Sivapalan, C.J. Harman, V. Srinivasan, M.R. Hipsey, P. Reed, A. Montanari, and G. Bloschl, 2013: “Developing predictive insight into changing water systems: Use-inspired hydrologic science for the Anthropocene.” Hydrology and Earth System Sciences, v. 17, pp. 5013-5039, doi: 10.5194/hess-17-5013-2013.

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.

Open Access

Identifying external influences on global precipitation

Marvel, K., and C. Bonfils, 2013: “Identifying external influences on global precipitation.” Proceedings of the National Academy of Sciences, v. 110, pp. 19,301-19,306, doi: 10.1073/pnas.1314382110.

Changes in global (ocean and land) precipitation are among the most important and least well-understood consequences of climate change. Increasing greenhouse gas concentrations are thought to affect the zonal-mean distribution of precipitation through two basic mechanisms. First, increasing temperatures will lead to an intensification of the hydrological cycle (“thermodynamic” changes). Second, changes in atmospheric circulation patterns will lead to poleward displacement of the storm tracks and subtropical dry zones and to a widening of the tropical belt (“dynamic” changes). We demonstrate that both these changes are occurring simultaneously in global precipitation, that this behavior cannot be explained by internal variability alone, and that external influences are responsible for the observed precipitation changes. Whereas existing model experiments are not of sufficient length to differentiate between natural and anthropogenic forcing terms at the 95% confidence level, we present evidence that the observed trends result from human activities.

Open Access

Historic maps as a data source for socio-hydrology: a case study of the Lake Balaton wetland system, Hungary

Zlinszky, A., and G. Timár, 2013: “Historic maps as a data source for socio-hydrology: a case study of the Lake Balaton wetland system, Hungary.” Hydrology and Earth System Sciences, v. 17, pp. 4589-4606, doi: 10.5194/hess-17-4589-2013.

Socio-hydrology is the science of human influence on hydrology and the influence of the water cycle on human social systems. This newly emerging discipline inherently involves a historic perspective, often focusing on timescales of several centuries. While data on human history is typically available for this time frame, gathering information on the hydrological situation during such a period can prove difficult: measured hydrological data for such long periods are rare, while models and secondary data sets from geomorphology, pedology or archaeology are typically not accurate enough over such a short time. In the first part of this study, the use of historic maps in hydrology is reviewed. Major breakthroughs were the acceptance of historic map content as valid data, the use of preserved features for investigating situations earlier than the map, and the onset of digital georeferencing and data integration. Historic maps can be primary quantitative sources of hydro-geomorphological information, they can provide a context for point-based measurements over larger areas, and they can deliver time series for a better understanding of change scenarios.

In the second part, a case study is presented: water level fluctuations of Lake Balaton were reconstructed from maps, levelling logs and other documents. An 18th century map system of the whole 5700 km2 catchment was georeferenced, integrated with two 19th century map systems, and wetlands, forests and open water digitized. Changes in wetland area were compared with lake water level changes in a 220 yr time series. Historic maps show that the water level of the lake was closer to present-day levels than expected, and that wetland loss pre-dates drainage of the lake.

The present and future role of historic maps is discussed. Historic hydrological data has to be treated with caution: while it is possible to learn form the past, the assumption that future changes will be like past changes does not always hold. Nevertheless, old maps are relatively accessible data sets and the knowledge base for using them is rapidly growing, and it can be expected that long-term time series will be established by integrating georeferenced map systems over large areas.

In the Appendix, a step-by-step guide to using historic maps in hydrology is given, starting from finding a map, through georeferencing and processing the map to publication of the results.

Open Access

Southeastern United States summer rainfall framework and its implication for seasonal prediction

Li, L., and W. Li, 2013: “Southeastern United States summer rainfall framework and its implication for seasonal prediction.” Environmental Research Letters, v. 8, paper no. 044017, doi: 10.1088/1748-9326/8/4/044017.

A new rainfall framework is constructed to describe the complex probability distribution of southeastern United States (SE US) summer (June–July–August) rainfall, which cannot be well represented by traditional kernel fitting methods. The new framework is based on the configuration of a three-cluster finite normal mixture model and is realized by Bayesian inference and a Markov Chain Monte Carlo (MCMC) algorithm. The three rainfall clusters reflect the probability distribution of light, moderate, and heavy rainfall in summer, and are linked to different climate factors. The variation of light rainfall intensity is likely associated with the combined effects of La Niña and the tri-pole sea surface temperature anomaly (SSTA) over the North Atlantic. Heavy rainfall concurs with a ‘horseshoe-like’ SSTA over the North Atlantic. In contrast, moderate rainfall is less correlated with the SSTA and likely caused by atmospheric internal dynamics. Rainfall characteristics and their linkages with SSTAs help improve seasonal predictions of regional climate. Such a new framework has an important implication in understanding the response of regional hydrology to climate variability and climate change; and our study suggest that it can be extended to other regions and seasons with similar climate.

Open Access

Comparative analysis of hydrologic signatures in two agricultural watersheds in east-central Illinois: Legacies of the past to inform the future

Yaeger, M.A., M. Sivapalan, G.F. McIsaac, and X. Cai, 2013: “Comparative analysis of hydrologic signatures in two agricultural watersheds in east-central Illinois: Legacies of the past to inform the future.” Hydrology and Earth System Sciences, v. 17, pp. 4607-4623, doi: 10.5194/hess-17-4607-2013.

Historically, the central Midwestern US has undergone drastic anthropogenic land use change, having been transformed, in part through government policy, from a natural grassland system to an artificially drained agricultural system devoted to row cropping corn and soybeans. Current federal policies are again influencing land use in this region with increased corn acreage and new biomass crops proposed as part of an energy initiative emphasizing biofuels. To better address these present and future challenges it is helpful to understand whether and how the legacies of past changes have shaped the current response of the system. To this end, a comparative analysis of the hydrologic signatures in both spatial and time series data from two central Illinois watersheds was undertaken. The past history of these catchments is reflected in their current hydrologic responses, which are highly heterogeneous due to differences in geologic history, artificial drainage patterns, and reservoir operation, and manifest temporally, from annual to daily timescales, and spatially, both within and between the watersheds. These differences are also apparent from analysis of the summer low flows, where the more tile-drained watershed shows greater variability overall than does the more naturally drained one. In addition, precipitation in this region is also spatially heterogeneous even at small scales, and this, interacting with and filtering through the historical modifications to the system, increases the complexity of the problem of predicting the catchment response to future changes.

Open Access

Inverse streamflow routing

Pan, M., and E.F. Wood, 2013: “Inverse streamflow routing.” Hydrology and Earth System Sciences, v. 17, pp. 4577-4588, doi: 10.5194/hess-17-4577-2013.

The process whereby the spatially distributed runoff (generated through saturation/infiltration excesses, subsurface flow, etc.) travels over the hillslope and river network and becomes streamflow is generally referred to as “routing”. In short, routing is a runoff-to-streamflow process, and the streamflow in rivers is the response to runoff integrated in both time and space. Here we develop a methodology to invert the routing process, i.e., to derive the spatially distributed runoff from streamflow (e.g., measured at gauge stations) by inverting an arbitrary linear routing model using fixed interval smoothing. We refer to this streamflow-to-runoff process as “inverse routing”. Inversion experiments are performed using both synthetically generated and real streamflow measurements over the Ohio River basin. Results show that inverse routing can effectively reproduce the spatial field of runoff and its temporal dynamics from sufficiently dense gauge measurements, and the inversion performance can also be strongly affected by low gauge density and poor data quality.

The runoff field is the only component in the terrestrial water budget that cannot be directly measured, and all previous studies used streamflow measurements in its place. Consequently, such studies are limited to scales where the spatial and temporal difference between the two can be ignored. Inverse routing provides a more sophisticated tool than traditional methods to bridge this gap and infer fine-scale (in both time and space) details of runoff from aggregated measurements. Improved handling of this final gap in terrestrial water budget analysis may potentially help us to use space-borne altimetry-based surface water measurements for cross-validating, cross-correcting, and assimilation with other space-borne water cycle observations.

Open Access