Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamflow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948–1970 and 1971–2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human‐induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e.g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate‐ and human‐induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs.

Marginal agricultural land is estimated for biofuel production in Africa, China, Europe, India, South America, and the continental United States, which have major agricultural production capacities. These countries/regions can have 320-702 million hectares of land available if only abandoned and degraded cropland and mixed crop and vegetation land, which are usually of low quality, are accounted. If grassland, savanna, and shrubland with marginal productivity are considered for planting low-input high-diversity (LIHD) mixtures of native perennials as energy crops, the total land availability can increase from 1107-1411 million hectares, depending on if the pasture land is discounted. Planting the second generation of biofuel feedstocks on abandoned and degraded cropland and LIHD perennials on grassland with marginal productivity may fulfill 26-55% of the current world liquid fuel consumption, without affecting the use of land with regular productivity for conventional crops and without affecting the current pasture land. Under the various land use scenarios, Africa may have more than one-third, and Africa and Brazil, together, may have more than half of the total land available for biofuel production. These estimations are based on physical conditions such as soil productivity, land slope, and climate.

No abstract

Hydrologic models can be categorized as being either Newtonian or Darwinian in nature. The Newtonian approach requires a thorough understanding of the individual physical processes acting in a watershed in order to build a detailed hydrologic model based on the conservation equations. The Darwinian approach seeks to explain the behavior of a hydrologic system as a whole by identifying simple and robust temporal or spatial patterns that capture the relevant processes. Darwinian-based hydrologic models include the Soil Conservation Service (SCS) curve number model, the "abcd" model, and the Budyko-type models. However, these models were developed based on widely differing principles and assumptions and applied to distinct time scales. Here, we derive a one-parameter Budyko-type model for mean annual water balance which is based on a generalization of the proportionality hypothesis of the SCS model and therefore is independent of temporal scale. Furthermore, we show that the new model is equivalent to the key equation of the "abcd" model. Theoretical lower and upper bounds of the new model are identified and validated based on previous observations. Thus, we illustrate a temporal pattern of water balance amongst Darwinian hydrologic models, which allows for synthesis with the Newtonian approach and offers opportunities for progress in hydrologic modeling.

[1] Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons.Citation: Chen, X., N. Alimohammadi, and D. Wang (2013), Modeling interannual variability of seasonal evaporation and storage change based on the extended Budyko framework, Water Resour. Res., 49,[6067][6068][6069][6070][6071][6072][6073][6074][6075][6076][6077][6078]

Coastal responses to sea level rise (SLR) include inundation of wetlands, increased shoreline erosion, and increased flooding during storm events. Hydrodynamic parameters such as tidal ranges, tidal prisms, tidal asymmetries, increased flooding depths and inundation extents during storm events respond nonadditively to SLR. Coastal morphology continually adapts toward equilibrium as sea levels rise, inducing changes in the landscape. Marshes may struggle to keep pace with SLR and rely on sediment accumulation and the availability of suitable uplands for migration. Whether hydrodynamic, morphologic, or ecologic, the impacts of SLR are interrelated. To plan for changes under future sea levels, coastal managers need information and data regarding the potential effects of SLR to make informed decisions for managing human and natural communities. This review examines previous studies that have accounted for the dynamic, nonlinear responses of hydrodynamics, coastal morphology, and marsh ecology to SLR by implementing more complex approaches rather than the simplistic "bathtub" approach. These studies provide an improved understanding of the dynamic effects of SLR on coastal environments and contribute to an overall paradigm shift in how coastal scientists and engineers approach modeling the effects of SLR, transitioning away from implementing the "bathtub" approach. However, it is recommended that future studies implement a synergetic approach that integrates the dynamic interactions between physical and ecological environments to better predict the impacts of SLR on coastal systems.

[1] The annual water storage changes at 12 watersheds in Illinois are estimated based on the long-term soil moisture and groundwater level observations during . Storage change is usually ignored in mean annual and interannual water balance calculations. However, the interannual variability of storage change can be an important component in annual water balance during dry or wet years. Annual precipitation anomaly is partitioned into annual runoff anomaly, annual evaporation anomaly, and annual storage change. The estimated annual storage change ratios vary from À60% to 40% at the study watersheds. The interannual variability of evaporation is not strongly correlated with the interannual variability of precipitation, but is correlated with the interannual variations of effective precipitation. As a response to the interannual variability of precipitation, the interannual variation of evaporation is smaller than those of runoff and storage change. The effect of annual water storage change increases the correlation coefficients between annual evaporation ratio and climate dryness index. Therefore, interannual water storage changes need to be included in the estimation of evaporation and total water supply in the Budyko framework. Effective precipitation can be used as a substitute for precipitation when computing evaporation ratio and climate dryness index.

[1] This paper investigates the impact of climate change on drought by addressing two questions: (1) How reliable is the assessment of climate change impact on drought based on state-of-the-art climate change projections and downscaling techniques? and (2) Will the impact be at the same level from meteorological, agricultural, and hydrologic perspectives? Regional climate change projections based on dynamical downscaling through regional climate models (RCMs) are used to assess drought frequency, intensity, and duration, and the impact propagation from meteorological to agricultural to hydrological systems. The impact on a meteorological drought index (standardized precipitation index, SPI) is first assessed on the basis of daily climate inputs from RCMs driven by three general circulation models (GCMs). Two periods and two emission scenarios, i.e., 1991-2000 and 2091-2100 under B1 and A1Fi for Parallel Climate Model (PCM), 1990-1999 and 2090-2099 under A1B and A1Fi for Community Climate System Model, version 3.0 (CCSM3), 1980-1989 and 2090-2099 under B2 and A2 for Hadley Centre CGCM (HadCM3), are undertaken and dynamically downscaled through the RCMs. The climate projections are fed to a calibrated hydro-agronomic model at the watershed scale in Central Illinois, and agricultural drought indexed by the standardized soil water index (SSWI) and hydrological drought by the standardized runoff index (SRI) and crop yield impacts are assessed. SSWI, in particular with extreme droughts, is more sensitive to climate change than either SPI or SRI. The climate change impact on drought in terms of intensity, frequency, and duration grows from meteorological to agricultural to hydrological drought, especially for CCSM3-RCM. Significant changes of SSWI and SRI are found because of the temperature increase and precipitation decrease during the crop season, as well as the nonlinear hydrological response to precipitation and temperature change.