Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.

Using Synthetic Climate Datasets for Geotechnical and Geoenvironmental Design Problems

Location-specific climate datasets are required for the design and evaluation of a number of civil engineering projects. It requires huge effort to compile a multi-year quality-controlled climate dataset. In this paper, a method of generating simulated daily climate variables of interest from readily-available climate normal using a general purpose weather generator SIMETAW is presented. The accuracy of this method is assessed by comparing the climate datasets generated using SIMETAW with the recorded historical climate datasets for nine different sites across Canada with climates ranging from semi-arid to pre-humid. This comparison was done using visual presentations as well as statistical analyses of the two datasets. It was found that the multi-year daily climate datasets generated by SIMETAW using just 12 monthly climate normal values are fairly similar to the recorded historical climate datasets. The usefulness of SIMETAW-generated climate datasets was demonstrated by using them in numerical simulations of three different design problems, namely, infiltration into soils, swelling potential of an expansive soil, and soil cover design. From the results of these numerical simulations, it is concluded that the SIMETAW-generated multi-year daily climate datasets are satisfactory for use in the geotechnical and geoenvironmental problems of the kind simulated herein.

Separating the impacts of heat stress events from rising mean temperatures on winter wheat yield of China

Warming due to climate change has profound impacts on regional crop yields, and this includes impacts from rising mean growing season temperature and heat stress events. Adapting to these two impacts could be substantially different, and the overall contribution of these two factors on the effects of climate warming and crop yield is not known. This study used the improved WheatGrow model, which can reproduce the effects of temperature change and heat stress, along with detailed information from 19 location-specific cultivars and local agronomic management practices at 129 research stations across the main wheat-producing region of China, to quantify the regional impacts of temperature increase and heat stress separately on wheat in China. Historical climate, plus two future low-warming scenarios (1.5/2.0oC warming above pre-industrial) and one future high-warming scenario (RCP8.5), were applied using the crop model, without considering elevated CO2 effects. The results showed that heat stress and its yield impact were more severe in the cooler northern sub-regions than the warmer southern sub-regions with historical and future warming scenarios. Heat stress was estimated to reduce wheat yield in most of northern sub-regions by 2.0% - 4.0% (up to 29% in extreme years) under the historical climate. Climate warming is projected to increase heat stress events in frequency and extent, especially in northern sub-regions. Surprisingly, higher warming did not result in more yield-impacting heat stress compared to low-warming, due to advanced phenology with mean warming and finally avoiding heat stress events during grain filling in summer. Most negative impacts of climate warming are attributed to increasing mean growing-season temperature, while changes in heat stress are projected to reduce wheat yields by an additional 1.0% to 1.5% in northern sub-regions. Adapting to climate change in China must consider the different regional and temperature impacts to be effective.

Identifying the critical orientation of wood-frame walls in assessing moisture risks using hygrothermal simulation

Hygrothermal simulations can be used as a reliable tool in analysing moisture performance. For an efficient analysis, it is important to appropriately select the wall orientation in the simulations. ASHRAE 160 recommends to using orientation with highest amount of annual wind-driven rain (WDR) and the orientation with the least annual solar radiation. The objective of this work was to identify the orientation which leads to the worst moisture performance of different wall assemblies under historical climate in different Canadian cities. Four cardinal orientations (North, East, South, and West) and orientation receiving the highest amount of annual WDR (Default) were tested in this study. The simulations were carried out assuming three scenarios of moisture loads for four different wood-frame (2×6 wood stud) wall systems that differ by their claddings: brick, fibreboard, stucco, and vinyl. With an assumption of no WDR, north facing wall always leads to the worst moisture performance. In the presence of WDR, with and without water source, default orientation leads to the worst moisture performance with few exceptions. As default orientation was based on total sum of WDR, it sometimes may not lead to worst performance and hence hourly distribution of WDR should be taken into consideration.

Feminization of Hawksbill Turtle hatchlings in the Twenty-first Century at an important Regional Nesting Aggregation

Projected climate change is forecasted to have significant effects on biological systems worldwide. Marine turtles in particular may be vulnerable, as the sex of their offspring is determined by their incubating temperature. This study is aimed to estimate historical and forecast future, primary sex ratios of hawksbill turtle hatchlings, Eretmochelys imbricata, in Qatar. Incubation temperatures were measured over two nesting seasons. Climate data from same period was regressed with nest temperatures to estimate incubation temperatures and hatchling sex ratios for the site from 1993 to 2100. Historical climate data showed female-biased sex ratios of 73.2 ±12.1% from 1993 to 2017. Female biases from 2018 to 2100 averaged 85.7% ±6.7%. In addition, predicted female hatchling production was >90% from 2054. These results show that hawksbill primary sex ratios in Qatar are at risk of significant feminization by the year 2100.

The Readiness Index for Climate Change Adaptation in Africa: The Role of Climate and Adaptive Capacity Proxies

As global changes continue, the repercussions in Africa remain profound. This is reflected notably in food and water crises across Africa. This work examines the readiness of Africa to climate change adaptation through a newly developed readiness index (ClimAdaptCap Index). In fact, this work shifts the readiness debate from emotional descriptions that currently flood academic scholarship to a more pragmatic evidence-based approach in assessing readiness. Readiness for climate change adaptation is driven by the intensity of climate forcing and adaptive capacity. The historical climate score data or precipitation and temperature for the period 1991–2016 were culled from the World Bank Climate Portal. The historical adaptive capacity score data included proxies such as poverty and literacy rates from 1991 to 2016 were collected from the World Bank and Macrotrends. The climate data were normalized using the normalization function to enhance interpretation, comparison, and fusion into the index. Missing poverty and literacy rate data were estimated by linear interpolation of the poverty and literacy rate data. The ClimAdaptCap Index was developed to compute readiness. This index is the first of its kind and will serve as a flagship for assessing readiness for climate change adaptation as it is highly adaptable to different contexts. This work’s first-ever maps of readiness show that North and Southern Africa are the readiest for climate change adaptation under historical climate and literacy and poverty conditions. West Africa is the least ready while Middle and East Africa are in the middle. Consistent is that readiness has a positive correlation with literacy rates and an inverse one with poverty rates. In addition, with readiness scores of between 0.35 and 0.39 for all the regions with a maximum potential score of 1, this work has shown that the level of readiness in Africa is generally low, and there is a very small variation between the different regions. In addition, climate change adaptation will highly be influenced by both climatic and non-climatic indicators. The developed readiness index adequately simulates readiness to climate change adaptation in Africa and complements previous frameworks of adaptation preparedness.