The Community Land Model (CLM3) Dynamic Global Vegetation Model (CLMDGVM) is used diagnostically to identify land and atmospheric model biases that lead to biases in the simulated vegetation. The CLM-DGVM driven with observed atmospheric data (offline simulation) underestimates global forest cover, overestimates grasslands, and underestimates global net primary production. These results are con...

Complex mathematical models requiring major computing power to tease out their implications are currently the main theoretical tool used to understand and predict the response of the climate system to external forcing, such as that resulting from the current human perturbation to greenhouse gas concentrations in the atmosphere. These models developed quickly over the last half of the 20 century...

Southwestern US irrigated landscapes are facing upheaval due to water scarcity and land use conversion associated with climate change, population growth, and changing economics. In the traditionally irrigated valleys of northern New Mexico, these stresses, as well as instances of community longevity in the face of these stresses, are apparent. Human systems have interacted with hydrologic proce...

Advances in computational tools and modelling techniques combined with enhanced process knowledge have, in recent decades, facilitated a rapid progress in hydrologic modelling. From the use of traditional lumped models, the hydrologic science has moved to the much more complex, fully distributed models that exude an enhanced knowledge of hydrologic processes. Despite this progress, uncertaintie...

Research on ecosystem and societal response to global environmental change typically considers the effects of shifts in mean climate conditions. There is, however, some evidence of ongoing changes also in the variance of hydrologic and climate fluctuations. A relatively high interannual variability is a distinctive feature of the hydrologic regime of dryland regions, particularly at the desert ...

A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644–651. A distance matrix that contains the hydrologic distance between any two sites in a study area is needed to fit a geostatistical model using the tail-up and tail-down autocovariance functions. However, the hydrologic distance information needed to model the covariance between flow-connected a...

Global climate is influenced by the Arctic hydrologic cycle, which is, in part, regulated by sea ice through its control on evaporation and precipitation. However, the quantitative link between precipitation and sea ice extent is poorly constrained. Here we present observational evidence for the response of precipitation to sea ice reduction and assess the sensitivity of the response. Changes i...

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly q...

[1] Lateral water flow in catchments can produce important patterns in water and nutrient fluxes and stores and also influences the long-term spatial development of forest ecosystems. Specifically, patterns of vegetation type and density along hydrologic flow paths can represent a signal of the redistribution of water and nitrogen mediated by lateral hydrologic flow. This study explores the use...

Hydrologic classification is one of the most widely applied tasks in ecohydrology. During the last two decades, a considerable effort has gone into analysis and development of methodological approaches to hydrologic classification. We reviewed the process of hydrologic classification, differentiating between an approach based on deductive reasoning using environmental regionalization, hydrologi...