Asymmetry in the time-lapse seismic response to injection and depletion

Large changes in seismic reflection amplitude have been observed around injectors, and result from the decrease in elastic‐wave velocity due to the increase in pore pressure in the reservoir. In contrast, the velocity change resulting from the decrease in pore pressure in depleting reservoirs is observed to be smaller in magnitude. Elastic‐wave velocities in sandstones vary with stress due to the presence of stress‐sensitive grain boundaries within the rock. Grain‐boundary stiffness increases non‐linearly with increasing compressive stress, due to increased contact between opposing faces of the boundary. This results in a change in velocity due to a decrease in pore pressure that is smaller than the change in velocity caused by an increase in pore pressure, in agreement with time‐lapse seismic observations. The decrease in porosity resulting from depletion is not fully recovered upon re‐pressurization, and this leads to an additional steepening of the velocity vs. effective stress curve for injection relative to depletion. This difference is enhanced by any breakage of cement or weakening of grain contacts that may occur during depletion and by the reopening or formation of fractures or joints and dilation of grain boundaries that may occur during injection.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Inertial instability of large Rossby number horizontal shear flows in a thin homogeneous layer

A horizontal shear flow having a Rossby number, Ro, greater than unity on a rotating plane can become unstable when its shear value is less than −f, the Coriolis frequency. In this paper, this instability is investigated for an O(10 km) submesoscale, sinusoidal shear flow in a thin homogeneous fluid layer as in an oceanic mixed layer or a shallow sea. The most unstable mode is shown by a linear analysis to occur in a narrow localized region centered around the maximum anticyclonic current shear. However, nonlinear numerical calculations show that the instability can grow to encompass both unstable and stable regions of the current. A consequence of this finite-amplitude evolution is the formation of surface convergence/shear fronts. The possibility that inertial instability mechanism is a source of some surface convergence/shear features seen in remote sensing images of the sea surface is discussed. A comparison is made with the shear-flow instability that can occur concurrently in a sinusoidal shear current, and inertial instability is shown to be the dominant instability mechanism in the immediate range above Ro=2.     

Local climatic drivers of changes in phenology at a boreal-temperate ecotone in eastern North America

Ecosystems in biogeographical transition zones, or ecotones, tend to be highly sensitive to climate and can provide early indications of future change. To evaluate recent climatic changes and their impacts in a boreal-temperate ecotone in eastern North America, we analyzed ice phenology records (1975?C2007) for five lakes in the Adirondack Mountains of northern New York State. We observed rapidly decreasing trends of up to 21?days less ice cover, mostly due to later freeze-up and partially due to earlier break-up. To evaluate the local drivers of these lake ice changes, we modeled ice phenology based on local climate data, derived climatic predictors from the models, and evaluated trends in those predictors to determine which were responsible for observed changes in lake ice. November and December temperature and snow depth consistently predicted ice-in, and recent trends of warming and decreasing snow during these months were consistent with later ice formation. March and April temperature and snow depth consistently predicted ice-out, but the absence of trends in snow depth during these months, despite concurrent warming, resulted in much weaker trends for ice-out. Recent rates of warming in the Adirondacks are among the highest regionally, although with a different seasonality of changes (early winter > late winter) that is consistent with other lake ice records in the surrounding area. Projected future declines in snow cover could create positive feedbacks and accelerate current rates of ice loss due to warming. Climate sensitivity was greatest for the larger lakes in our study, including Wolf Lake, considered one of the most ecologically intact ??wilderness lakes?? in eastern North America. Our study provides further evidence of climate sensitivity of the boreal-temperate ecotone of eastern North America and points to emergent conservation challenges posed by climate change in legally protected yet vulnerable landscapes like the Adirondack Park.     

Adaptation and poverty reduction in Mozambique: an opportunity for developing countries to lead§

Climate change disproportionately impacts the world’s poorest countries. A recent World Bank report highlighted that over 100 million people are at risk of falling into extreme poverty as a result of climate change. There is currently a lack of information about how to simultaneously address climate change and poverty. Climate change challenges provide an opportunity for those impacted most to come up with new and innovative technologies and solutions. This article uses an example from Mozambique where local and international partners are working side-by-side, to show how developing countries can simultaneously address climate change and poverty reduction using an ecosystem-based adaptation approach. Using ecosystem-based adaptation, a technique that uses the natural environment to help societies adapt to climate change, developing countries can lead the way to improve climate adaptation globally. This paradigm shift would help developing countries become leaders in ecosystem-based adaptation and green infrastructure techniques and has implications for climate policy worldwide.

POLICY RELEVANCE

The Paris Agreement resulting from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of Parties (COP 21) in December 2015 was rightly lauded for its global commitment to cut greenhouse gas emissions. However, COP 21 was also historic because of its call for non-party stakeholders to address climate change, inclusion of a global goal of ‘enhancing adaptive capacity, strengthening resilience and reducing vulnerability’, and the United States’ commitment of $800 million to adaptation funding. The combination of recognizing the need for new stakeholders to commit to climate change adaptation, the large impact climate change will have on the developing world, and providing access to funds for climate change adaptation creates a unique opportunity for developing countries to pave the way in adaptation policies in practices. Currently, developing countries are creating National Adaptation Plans (NAPs) for the UNFCCC. Through including a strong component of ecosystem-based adaptation in NAPs, developing countries can shape their countries’ policies, improve local institutions and governments, and facilitate a new generation of innovative leaders. Lessons learned in places like Mozambique can help lead the way in other regions facing similar climatic risks.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Building comparable global change vulnerability assessments: The vulnerability scoping diagram

Advancing vulnerability science depends in part on identifying common themes from multiple, independent vulnerability assessments. Such insights are difficult to produce when the assessments use dissimilar, often qualitative, measures. The Vulnerability Scoping Diagram is presented to facilitate the comparison of assessments with dissimilar measures. The diagram is illustrated with recent research on drought vulnerabilities, showing that common insights into vulnerability may emerge if independent research teams use a common structure for organizing information about exposure, sensitivity and adaptive capacity—even if the underlying measures differ between assessments. Broadly adopting this technique, which is grounded in the “Eight Steps” methodological protocol [Schröter, D., Polsky, C., Patt, A., 2005. Assessing vulnerabilities to the effects of global change: an eight step approach. Mitigation and Adaptation Strategies for Global Change 10(4), 573–595], will enable a vulnerability meta-analysis, the lessons from which may permit places to identify helpful adaptation or mitigation options without first having to conduct their own vulnerability assessments.     

Simulating the effects of spatial configurations of agricultural ditch drainage networks on surface runoff from agricultural catchments

The study of runoff is a crucial issue because it is closely related to flooding, water quality and erosion. In cultivated catchments, agricultural ditch drainage networks are known to influence runoff. As anthropogenic elements, agricultural ditch drainage networks can therefore be altered to better manage surface runoff in cultivated catchments. However, the relationship between the spatial configuration, i.e. the density and the topology, of agricultural ditch drainage networks and surface runoff in cultivated catchments is not understood. We studied this relationship by using a random network simulator that was coupled to a distributed hydrological model. The simulations explored a large variety of spatial configurations corresponding to a thousand stochastic agricultural ditch drainage networks on a 6.4 km² Mediterranean cultivated catchment. Next, several distributed hydrological functions were used to compute water flow paths and runoff for each simulation. The results showed that (i) denser networks increased the drained volume and the peak discharge and decreased hillslopes runoff, (ii) greater network density did not affect the surface runoff any further above a given network density, (iii) the correlation between network density and runoff was weaker for small subcatchments (< 2 km²) where the variability in the drained area that resulted from changes in agricultural ditch drainage networks increased the variability of runoff and (iv) the actual agricultural ditch drainage network appeared to be well optimized for managing runoff as compared with the simulated networks. Finally, our results highlighted the role of agricultural ditch drainage networks in intercepting and decreasing overland flow on hillslopes and increasing runoff in drainage networks. Copyright © 2011 John Wiley & Sons, Ltd.     

Reconciling approaches to climate change adaptation for Colombian agriculture

The projected impact of climate change on agro-ecological systems is considered widespread and significant, particularly across the global tropics. As in many other countries, adaptation to climate change is likely to be an important challenge for Colombian agricultural systems. In a recent study, a national-level assessment of the likely future impacts of climate change on agriculture was performed (Ramirez-Villegas et al. Clim Chang 115:611–628, 2012, RV2012). The study diagnosed key challenges directly affecting major crops and regions within the Colombian agricultural system and suggested a number of actions thought to facilitate adaptation, while refraining from proposing specific strategies at local scales. Further insights on the study were published by Feola (2013) (F2013), who stressed the need for transformative adaptation processes to reduce vulnerability particularly of resource-limited farmers, and the benefits of a predominantly stakeholder-led approach to adaptation. We clarify that the recommendations outlined in RV2012 were not intended as a recipe for multi-scale adaptation, but rather a set of actions that are required to diagnose and develop adaptation actions particularly at governmental levels in coordination with national and international adaptation initiatives. Such adaptation actions ought to be, ideally, a product of inclusive sub-sectorial assessments, which can take different forms. We argue that Colombian agriculture as a whole would benefit from a better outlining of adaptation needs across temporal scales in sub-sectorial assessments that take into account both RV2012 and F2013 orientations to adaptation. We conclude with two case studies of research on climate change impacts and adaptation developed in Colombia that serve as examples of realistic, productive sectorial and sub-national assessments.     

Little River Experimental Watershed,a keystone in understanding of coastal plain watersheds

The US Department of Agriculture-Agricultural Research Service Southeast Watershed Research Laboratory (SEWRL) initiated a hydrologic research program on the Little River Experimental Watershed (LREW) in 1967. Long-term (52 years) streamflow data are available for nine sites, including rainfall-runoff relationships and hydrograph characteristics regularly used in research on interactive effects of climate, vegetation, soils, and land-use in low-gradient streams of the US EPA Level III Southeastern Plains ecoregion. A summary of prior research on the LREW illustrates the impact of the watershed on building a regional understanding of hydrology and water quality. Climatic and streamflow data were used to make comparisons of scale across the nine nested LREW watersheds (LRB, LRF, LRI, LRJ, LRK, LRO, LRN, LRM, and LRO3) and two regional watersheds (Alapaha and Little River at Adel). Annual rainfall for the largest LREW, LRB, was 1200 mm while average annual streamflow was 320 mm. Annual rainfall, streamflow, and the ratio between annual streamflow and rainfall (Sratio) were similar (α = 0.05) across LREWs LRB, LRF, LRI, LRJ, LRK, and LRO. While annual rainfall within the 275 ha LRO3 was found to be similar to LRO and LRM (α = 0.05), annual streamflow and Sratio were significantly different (α = 0.05). Comparisons of annual rainfall, streamflow, and Sratio between LRB and the regional watersheds indicated no differences (α = 0.05). Based upon this analysis, most regional watersheds shared similar hydrologic characteristics. LRO3 was an exception, where increases in row crops and decreases in forest coverage resulted in increased streamflow. LREW data have been instrumental in building considerable scientific understanding of flow and transport processes for these stream systems. Continued operation of the LREW hydrologic network will support hydrologic research as well as environmental quality and riparian research programs that address emerging and high priority natural resource and environmental issues.