Guidance on evaluating parametric model uncertainty at decision-relevant scales

Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

Tracer-aided modelling reveals quick runoff generation and groundwater losses producing young streamflow ages in a tropical rainforest catchment

There is still limited understanding of how waters mix, where waters come from and for how long they reside in tropical catchments. In this study, we used a tracer-aided model (TAM) and a gamma convolution integral model (GM) to assess runoff generation, mixing processes, water ages and transit times (TT) in the pristine humid tropical rainforest Quebrada Grande catchment in central Costa Rica. Models are based on a four-year data record (2016 to 2019) of continuous hydrometric and stable isotope observations. Both models agreed on a young water component of fewer than 95 days in age for 75% of the study period. The streamflow water ages ranged from around two months for wetter years (2017) and up to 9.5 months for drier (2019) years with a better agreement between the GM estimated TTs and TAM water ages for younger waters. Such short TTs and water ages result from high annual rainfall volumes even during drier years with 4,300 mm of annual precipitation (2019) indicating consistent quick near-surface runoff generation with limited mixing of waters and a supra-regional groundwater flow of likely unmeasured older waters. The TAM in addition to the GM allowed simulating streamflow (KGE > 0.78), suggesting an average groundwater contribution of less than 40% to streamflow. The model parameter uncertainty was constrained in calibration using stable water isotopes (δH), justifying the higher TAM model parameterization. We conclude that the multi-model analysis provided consistent water age estimates of a young water dominated catchment. This study represents an outlier compared to the globally predominant old water paradox, exhibiting a tropical rainforest catchment with higher new water fractions than older water.

Hydrological modeling in data sparse environments

<p>We demonstrate that in data sparse environments, model parameter uncertainty is not the only cause of concern. To get a meaningful outcome, input data uncertainty has to be taken into account as well. The procedure involved calibration of a hydrological model using recent daily data rich time period along with validation. A historical flood was simulated (after warmup) for which the input data were relatively sparse in space, namely precipitation and temperature, using the calibrated model parameters. Precipitation was assumed to be the main driver of this event. Results showed that by only using interpolated precipitation (e.g. IDW or Kriging), the magnitude and timing of the peak were incorrect, even after using very many different parameter vectors that performed equally well for the recent times. Subsequently, the model was inverted for precipitation i.e. input fields that produced the correct timing, magnitude, dependence in space and distributions were searched for. This was done using a previously developed simulation algorithm. The new fields showed that the same hydrograph could have been produced by two main types of conditions, namely, early snow cover that melted and heavy rain. The plausibility of the simulated fields was also assessed by comparing their structure in space to events in recent times.</p>

CNRD v1.0: a high-quality natural runoff dataset for hydrological and climate studies in China

Capsule summaryA long-term spatiotemporally continuous naturalized runoff record, CNRD v1.0, is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model.

Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study

Whether and how CO2 and nitrogen (N) availability interact to influence carbon (C) cycling processes such as soil respiration remains a question of considerable uncertainty in projecting future C–climate feedbacks, which are strongly influenced by multiple global change drivers, including elevated atmospheric CO2 concentrations (eCO2) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO2 efflux remain unresolved. Here, we show that in a multifactor free-air CO2 enrichment experiment, BioCON (Biodiversity, CO2, and N deposition) in Minnesota, the positive response of soil respiration to eCO2 gradually strengthened at ambient (low) N supply but not enriched (high) N supply for the 12-y experimental period from 1998 to 2009. In contrast to earlier years, eCO2 stimulated soil respiration twice as much at low than at high N supply from 2006 to 2009. In parallel, microbial C degradation genes were significantly boosted by eCO2 at low but not high N supply. Incorporating those functional genes into a coupled C–N ecosystem model reduced model parameter uncertainty and improved the projections of the effects of different CO2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO2 on soil respiration even in infertile systems.

Uncertainties and their interaction in flood risk assessment with climate change

The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local scale changes. Understanding and quantifying this cascade is essential to developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four Irish catchments, incorporating within our modelling chain uncertainties associated with 12 Global Climate Models contained in the Coupled Model Inter-comparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood risk in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding related to changes in annual maximum precipitation. We use a sensitivity test based on the analysis of variance (ANOVA) to decompose uncertainties and their interactions in estimating selected flood quantiles in the 2080s for each catchment. We find that the dominant sources of uncertainty vary between catchments, calling into question the ability to generalise about the importance of different components of the cascade of uncertainty in future flood risk. For two of our catchments, uncertainties associated with bias correction methods and extreme value distributions outweigh the uncertainty associated with the ensemble of climate models. For all catchments and flood quantiles examined, hydrological model parameter uncertainty is the least important component of our modelling chain, while the uncertainties derived from the interaction of components are substantial (>20 percent of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall runoff processes. Future work that seeks to further explore the dominant components of uncertainty as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be included in climate change impact assessments.

Impact of methane and black carbon mitigation on forcing and temperature: a multi-model scenario analysis

The relatively short atmospheric lifetimes of methane (CH4) and black carbon (BC) have focused attention on the potential for reducing anthropogenic climate change by reducing Short-Lived Climate Forcer (SLCF) emissions. This paper examines radiative forcing and global mean temperature results from the Energy Modeling Forum (EMF)-30 multi-model suite of scenarios addressing CH4 and BC mitigation, the two major short-lived climate forcers. Central estimates of temperature reductions in 2040 from an idealized scenario focused on reductions in methane and black carbon emissions ranged from 0.18–0.26 °C across the nine participating models. Reductions in methane emissions drive 60% or more of these temperature reductions by 2040, although the methane impact also depends on auxiliary reductions that depend on the economic structure of the model. Climate model parameter uncertainty has a large impact on results, with SLCF reductions resulting in as much as 0.3–0.7 °C by 2040. We find that the substantial overlap between a SLCF-focused policy and a stringent and comprehensive climate policy that reduces greenhouse gas emissions means that additional SLCF emission reductions result in, at most, a small additional benefit of ~ 0.1 °C in the 2030–2040 time frame.