Dingbao Wang

Land availability for biofuel production.

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.

Climate change impacts on crop production in Iran's Zayandeh-Rud River Basin.

This study evaluates climate change impacts on crop production and water productivity of four major crops (wheat, barley, rice, and corn) in Irans Zayandeh-Rud River Basin. Multi-model ensemble scenarios are used to deal with uncertainties in climate change projections for the study period (2015-2044). On average, monthly temperature will increase by 1.1 to 1.5°C under climate change. Monthly precipitation changes may be positive or negative in different months of the year. Nevertheless, on the annual basis, precipitation will decrease by 11 to 31% with climate change. While warming can potentially shorten the crop growth period, crop production and water productivity of all crops are expected to decrease due to lower precipitation and higher water requirements under higher temperature. Out of the four studied crops, rice and corn are more vulnerable to climate change due to their high irrigation water demand. So, their continued production can be compromised under climate change. This finding is of particular importance, given the locally high economic and food value of these crops in central Iran.

A one-parameter Budyko model for water balance captures emergent behavior in darwinian hydrologic models

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.

The dynamic effects of sea level rise on low‐gradient coastal landscapes: A review

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.

From channelization to restoration: Sociohydrologic modeling with changing community preferences in the Kissimmee River Basin, Florida

The Kissimmee River Basin (Florida, USA) underwent river channelization in the 1960s and subsequent restoration in the 1990s, revealing a shift in management emphasis from flood protection to wetland health. In this paper, this shift is hypothesized to result from changing human values and preferences, and a power differential between the more numerous and affluent upstream urban residents (who prioritize wetland restoration) and downstream rural residents (who prioritize flood protection). We develop a conceptual sociohydrologic model to simulate the interactions between community interests and hydrology. The modeling results show that flood intensity decreased after channelization, which reduced concern about flooding. However, channelization also led to a decrease in wetland storage, which caused an increase of wetland concern, especially among the urban residents. Eventually, the community sensitivity switched from favoring flood protection to favoring wetlands, and subsequent management strategies switched from channelization to restoration. Using the model, we project that the wetlands will be recovering for the next 20 years and community sensitivity will slowly go back to a neutral state. However, possible rainfall intensification in the future could return the community sensitivity to favoring flood protection again. The preferential increase of upstream population growth will raise the communitys concern about wetlands and the preferential increase of downstream population growth will magnify concern about flooding. This study provides insight into the driving forces behind human-water interactions in the Kissimmee River Basin while simultaneously demonstrating the potential of sociohydrologic modeling to describe complex human-water coupled systems with simple concepts and equations.

Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration’s (NOAA’s) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1–7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers’ practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years u...

Impact of Climate Change on Crop Yield: A Case Study of Rainfed Corn in Central Illinois

This paper assesses the effect of climate change on crop yield from a soil water balance perspective. The uncertainties of regional-scale climate models, local-scale climate variability, emissions scenarios, and crop growth models are combined to explore the possible range of climate change effects on rainfed corn yield in central Illinois in 2055. The results show that a drier and warmer summer during the corn growth season and wetter and warmer precrop and postcrop seasons will likely occur. Greater temperature and precipitation variability may lead to more variable soil moisture and crop yield, and larger soil moisture deficit and crop yield reduction are likely to occur more frequently. The increased water stress is likely to be most pronounced during the flowering and yield formation stages. The expected rainfed corn yield in 2055 is likely to decline by 23%‐34%, and the probability that the yield may not reach 50% of the potential yield ranges from 32% to 70% if no adaptation measures are instituted. Among the multiple uncertainty sources, the greenhouse gas emissions projection may have the strongest effect on the risk estimate of crop yield reduction. The effects from the various uncertainties can be offset to some degree when the uncertainties are considered jointly. An ensemble of GCMs with an equal weight may overestimate the risk of soil moisture deficits and crop yield reduction in comparison with an ensemble of GCMs with different weight determined by the root-meansquare error minimization method. The risk estimate presented in this paper implies that climate change adaptation is needed to avoid reduced corn yields and the resulting profit losses in central Illinois.

The response of runoff and sediment loading in the Apalachicola River, Florida to climate and land use land cover change

The response of runoff and sediment loading in the Apalachicola River under projected climate change scenarios and land use land cover (LULC) change is evaluated. A hydrologic model using the Soil and Water Assessment Tool was developed for the Apalachicola region to simulate daily runoff and sediment load under present (circa 2000) and future conditions (2100) to understand how parameters respond over a seasonal time frame to changes in climate, LULC, and coupled climate/LULC. The Long Ashton Research Station-Weather Generator was used to downscale temperature and precipitation from three general circulation models, each under Intergovernmental Panel on Climate Change (IPCC) carbon emission scenarios A2, A1B, and B1. Projected 2100 LULC data provided by the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center was incorporated for each corresponding IPCC scenario. Results indicate that climate change may induce seasonal shifts to both runoff and sediment loading. Changes in LULC showed that more sediment load was associated with increased agriculture and urban areas and decreased forested regions. A nonlinear response for both runoff and sediment loading was observed by coupling climate and LULC change, suggesting that both should be incorporated into hydrologic models when studying the future conditions. The outcomes from this research can be used to better guide management practices and mitigation strategies.

Climate Change Impact on Runoff and Sediment Loads to the Apalachicola River at Seasonal and Event Scales

ABSTRACT Chen, X.; Alizad, K.; Wang, D., and Hagen, S.C., 2014. Climate change impact on runoff and sediment loads to the Apalachicola River at seasonal and event scales. In this study, potential climate change impacts on runoff and sediment load in Apalachicola River basin in Florida are assessed using Soil and Water Assessment Tool (SWAT), a semi-distributed hydrologic model. The observed streamflow and sediment load from 1984 to 1994 are used for the model calibration and validation. The streamflow Nash-Sutcliffe Coefficients (NSEs) for the simulation and validation periods (1984–1989 and 1990–1994 years) are 0.92 and 0.88, respectively. The sediment NSEs for the simulation and validation periods are calculated to be 0.46 and 0.36, respectively, with excellent description of trend variability. Rainfall data under climate change effects is applied as the calibrated SWAT model input to estimate the streamflow and sediment load change. The rainfall and temperature data is prepared using two regional climate models (RCM); HRM3-HADCM3, and RCM3-GFDL. Results show that the average daily level of streamflow and sediment load will not vary significantly, but the peak flow and peak sediment load will increase dramatically due to the more intense and less frequent rainfall events. The impact of climate change during an extreme rainfall event is also investigated. A storm event with 25-year return period and 24-hour duration in 1991 is taken as the baseline event. Based on the projection using RCM3-GFDL scenario, the streamflow and sediment load may increase by 50% and 89%, respectively.

Unifying catchment water balance models for different time scales through the maximum entropy production principle

The paper presents a thermodynamic basis for water balance partitioning at the catchment scale, through formulation of flux-force relationships for the constituent hydrological processes, leading to the derivation of optimality conditions that satisfy the principle of Maximum Entropy Production (MEP). Application of these optimality principles at three different time scales leads to the derivation of water balance equations that mimic widely used, empirical models, i.e., Budyko-type model over long-term scale, the “abcd” model at monthly scale, and the SCS model at the event scale. The applicability of MEP in each case helps to draw connections between the water balances at the three different time scales, and to demonstrate a common thermodynamic basis for the otherwise empirical water balance models. In particular, it is concluded that the long time scale Budyko-type model and the event scale SCS model are both special cases of the monthly “abcd” model. This article is protected by copyright. All rights reserved.