Adapting dryland agriculture to climate change: Farming implications and research and development needs in Western Australia

The Western Australian wheat-belt has experienced more rainfall decline than any other wheat-cropping region in Australia. Future climate change scenarios suggest that the Western Australian wheat-belt is likely to see greater future reductions in rainfall than other regions, together with a further increase in temperatures. While these changes appear adverse for water-limited rain-fed agriculture, a close analysis of the changes and their impacts reveals a more complex story. Twentieth century changes in rainfall, temperature and atmospheric CO₂ concentration have had little or no overall impact on wheat yields. Changes in agricultural technology and farming systems have had much larger impacts. Contrary to some claims, there is no scientific or economic justification for any immediate actions by farmers to adapt to long-term climate change in the Western Australian wheat-belt, beyond normal responses to short-term variations in weather. Rather than promoting current change, the most important policy response is research and development to enable farmers to facilitate future adaptation to climate change. Research priorities are proposed.     

Multi-factor impact analysis of agricultural production in Bangladesh with climate change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.     

From desert to Mediterranean rangelands: will increasing drought and inter-annual rainfall variability affect herbaceous annual primary productivity?

Climate change is predicted to alter the rainfall regime in the Eastern Mediterranean Basin: total annual rainfall will decrease, while seasonal and inter-annual variation in rainfall will increase. Such changes in the rainfall regime could potentially lead to large-scale changes in aboveground net primary productivity (ANPP) in the region. We conducted a data-driven evaluation of herbaceous ANPP along an entire regional rainfall gradient, from desert (90 mm MAR [Mean Annual Rainfall]) to Mesic-Mediterranean (780 mm MAR) ecosystems, using the largest database ever collated for herbaceous ANPP in Israel, with the aim of predicting consequences of climate change for rangeland productivity. This research revealed that herbaceous ANPP increases with increasing rainfall along the gradient, but strong dependence on rainfall was only apparent within dry sites. Rain Use Efficiency peaks at mid-gradient in Mediterranean sites without woody vegetation (560 and 610 mm MAR). Inter-annual coefficients of variation in rainfall and herbaceous ANPP decrease along the rainfall gradient up to ca. 500 mm MAR. Climate change is more likely to affect herbaceous ANPP of rangelands in the arid end of the rainfall gradient, requiring adaptation of rangeland management, while ANPP of rangelands in more mesic ecosystems is less responsive to variation in rainfall. We conclude that herbaceous ANPP in most Mediterranean rangelands is less vulnerable to climate change than generally predicted.     

Winter‐time near‐surface currents in the strait of Juan de Fuca

Abstract

During November 1976 to February 1977 near‐surface wind, current and temperature measurements were made at three sites along the Strait of Juan de Fuca. Strong tidal currents and major intrusions of warmer, fresher offshore coastal water were superimposed upon the estuarine circulation of near‐surface seaward flow. The r.m.s. amplitudes of the diurnal and semidiurnal tidal currents were ～30 cms^‐1 and 30–47 cm s^‐1, respectively. The vector‐mean flow at 4 m‐depth was seaward and decreased in speed from 28 cm s^‐1 at 74 km from the entrance to 9 cm s^‐1 at 11 km from the entrance. On five occasions intrusions of 1–3 C warmer northeast Pacific coastal water occurred for durations of 1–10 days. The 25 cm s^‐1 up‐strait speed of the intrusive lens agreed to within 20% of the gravity current speed computed from Benjamin's (1968) hydraulic model. The near‐surface currents associated with the intrusions and the southerly coastal winds were significantly correlated, indicating that the intrusions were initiated when shoreward Ekman currents advected Pacific coastal water into the Strait. The reversals were not significantly coherent with the along‐strait sea surface slope measured along the north side of the Strait nor were they strongly related to local wind forcing.     

The Euro-Atlantic Circulation Response to the Madden-Julian Oscillation Cycle of Tropical Heating: Coupled GCM Intervention Experiments

Abstract

Intervention experiments using the Coupled Forecast System model, version 2 (CFSv2), have been performed in which various Madden-Julian Oscillation (MJO) evolutions were added to the model’s internally generated heating: Slow Repeated Cycles, Slow Single Cycle, Fast Repeated Cycles, and Fast Single Cycle. In each experiment, one of these specified MJO evolutions of tropical diabatic heating was added in multiple ensemble reforecasts of boreal winter (1 November to 31 March for 31 winters: 1980–2010). Since in each experiment, multiple re-forecasts were made with the identical heating evolution added, predictable component analysis is used to identify modes with the highest signal-to-noise ratio. Traditional MJO-phase analysis of total model heating (dominated by internally generated heating) shows that the MJO-related heating structure compares well with heating estimated from observed fast and slow episodes; however, the model heating is larger by a factor of two. The evolution of Euro-Atlantic circulation regimes indicates a clear response due to the added heating, with a robust increase in the frequency of occurrence of the negative phase of the North Atlantic Oscillation (NAO?) after the heating crosses into the Pacific and a somewhat less robust increase in the positive phase of the NAO (NAO+) following Indian Ocean heating. In the Fast Cycle experiments, the model response is somewhat muted compared with the Slow Cycle experiments. The Scandinavian Blocking regime becomes more frequent prior to the NAO? regime. The two leading modes in the predictable component analysis of 300?hPa height (Z300), synoptic scale feedback (DZ300), and planetary wave diabatic heating in all experiments form an oscillatory pair with high statistical significance. The oscillatory pair represents the cyclic response to the particular MJO signal (Fast or Slow, Single, or Repeated Cycles) in each case. The period is about 64 days for the Slow Cycle and 36 days for the Fast Cycle, consistent with the imposed periods. The time series of one of the leading modes of Z300 is highly anti-correlated with the frequency of occurrence of the NAO– in the Repeated Cycle experiments. A clear cycle involving the Z300 and DZ300 leading modes is identified.     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

Accessibility Futures

This study uses accessibility as a performance measure to evaluate a matrix of future land use and network scenarios for planning purposes. The concept of accessibility dates to the 1950s, but this type of application to transportation planning is new. Previous research has established the coevolution of transportation and land use, demonstrated the dependence of accessibility on both, and made the case for the use of accessibility measures as a planning tool. This study builds off of these findings by demonstrating the use of accessibility‐based performance measures in the Twin Cities Metropolitan Area. This choice of performance measure also allows for transit and highway networks to be compared side‐by‐side. For roadway modeling, zone‐to‐zone travel time matrix was computed using stochastic user equilibrium (SUE) assignment with travel time feedback to trip distribution. A database of schedules was used on the transit networks to assign transit routes. This travel time data was joined with the land use data from each scenario to obtain the employment, population, and labor accessibility from each traffic analysis zone (TAZ) within specified time ranges. Tables of person‐weighted accessibility were computed for 20 minutes with zone population as the weight for employment accessibility and zone employment as the weight for population and labor accessibility. Maps of accessibility by zone were produced to show the spatial distribution of accessibility across the region. The results show that a scenario where population and employment growth are concentrated in the center of the metropolitan area would produce the highest accessibility no matter which transportation network changes are made. However, another scenario which concentrates population growth in the center of the metropolitan area and shifts employment growth to the periphery consistently outperforms the scenario representing the projected 2030 land use without any growth management strategy.     

Earth observations during space shuttle flight STS‐41: Discovery's mission to planet earth

Abstract

Multi‐temporal ERS‐1 SAR data acquired over a large agricultural region in West Bengal was used to classify kharif crops like rice, jute and sugarcane. Rice crop grown under lowland management practice showed a temporal characteristic. The dynamic range of backscatter was highest for this crop in temporal SAR data. This was used to classify rice using temporal SAR data. Such temporal character was not observed for the other study crops, which may be due to the difference in cultivation practice and crop calendar. Significant increase in backscatter from the ploughed fields was used to derive information on onset and duration of land preparations. Synergistic use of optical remote sensing data and SAR data increased the separability of rice crop from homesteads and permanent vegetation classes.     

Near real-time high-resolution airborne camera,AEROCam, for precision agriculture

Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 × 1080 CCD array (7.4-μm detector) with 8-bit quantization. When operated at ～2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.