Application assessment of GRACE and CHIRPS data in the Google Earth Engine to investigate their relation with groundwater resource changes(Northwestern region of Iran)

In recent years, drought has become a global issue, especially in arid and semi-arid areas. It is without doubt that the identification and monitoring of the drought phenomenon can help to reduce the damages that would occur. In addition, rain is one of the factors which directly affect the water levels of underground water reservoirs. This research applied a linear gradient regression method developed on the basis of GRACE, CHIRPS, and data from monitoring wells to investigate the groundwater storage changes.These data have been analyzed on the Google Earth Engine platform. In order to conduct temporal and spatial analyses, the water levels of the aquifer were generated from the monitoring wells and zoned into five classes. Also, the amount of water storage and rain from the year 2003 to 2017 in the West Azerbaijan Province were investigated using the GRACE satellite and the CHIRPS data, respectively. The results obtained from the GRACE satellite data show that the average water level in the underground reservoirs in Iran had started to decrease since 2008 and reached its peak in 2016 with an average decrease of 16 cm in that year. The average annual decline of groundwater level in the studied time period was 5 cm. A chart developed from the CHIRPS annual rainfall data indicates that the biggest decline in rainfall occurred in 2008, and the declining trend has remained steady. Linear analyses were made on GRACE with CHIRPS results and monitoring wells data separately, from which the correlation coefficients are between 86% and 97%, showing generally high correlations. Furthermore, the results obtained from the zoning of the aquifer showed that in the period of 2004 to 2016, due to the decrease in rainfall and the excessive withdrawal of groundwater, the water levels also decreased.     

Wetland cover change detection using multi-temporal remotely sensed data

Lake Urmia, located in northwest Iran, contains a number of wetlands significantly affecting the environmental, social, and economic conditions of the region. The ecological condition of Lake Urmia has degraded during the past decade, due to climate change, human activities, and unsustainable management. The poor condition of the lake has also affected the surrounding wetlands. This study analyzes the land cover change of one of the wetlands in the southern part of Lake Urmia, known as Ghara-Gheshlagh wetland, in the period 1989–2015 using post-classification change detection and machine learning image classification. For this analysis, three Landsat images, acquired in 1989 (TM), 2001 (TM), and 2015 (Landsat-8), were used for the classification and change detection. Support vector machine learning algorithm, a supervised learning method, is employed, and images are classified into four main land cover classes namely “water,” ”barren,” “salty land,” and “agriculture and grassland.” Change detection was carried out for pairs of years 1989 to 2001 and 2001 until 2015. The results of this classification show that there is a sharp increase in the area of salt-saturated land as well as a decrease in the area of water resources. Overall classification accuracy obtained were high for the individual years: 1989 (91.48%), 2001 (90.63%), and 2015 (88.6%). Also, the Kappa coefficients for individual maps were high: 1989 (0.89), 2001 (0.8742), and 2015 (0.84). After that, the land cover change map of the study area is obtained between 1989 to 2001 and then 2001 to 2015. The results of this analysis suggest that more efforts should be taken to effectively manage water resources in the region and point to potential locations for focused management actions within the wetland area.     

Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from?

There is a growing appreciation of the uncertainties in the estimation of snow-melt and glacier-melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046–2075 compared to 1976–2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world. We suggest that modellers of glaciated catchments should present results from at least the book-ends: models with low sensitivity to ice-melt and models with high sensitivity to ice-melt. This would reduce confusion among decision makers when they are faced with similar contrasting results.     

Modeling and mapping of solar radiation using geostatistical analysis methods in Iran

Because of economic and technical limitations, measuring solar energy received at ground level (R _s ) isn’t possible in all parts of the country, and in only 12% of synoptic stations is this parameter measured and recorded. Thus, it should be estimated and modeled spatially based on other climatic variables using mathematical methods. In this research, many attempts have been made to introduce an air temperature-based model for Rs estimation, and then, based on the output of the mentioned models, several geostatistical methods have been tested, and finally an elegant spatial model is proposed for (Rs) zoning in Iran. In this regard, the relationships between the measured amounts of monthly solar radiation and other climatic parameters, such as a monthly average, maximum and minimum temperature, precipitation, relative humidity, and the number of sunny hours during the period 1970–2010, are examined and modeled. It was revealed that based on the linear relationship between the monthly average air temperatures and solar radiation values recorded in each of the stations, that the best-fit linear model, with R ²  = 0.822, MAE = 1.81, RMSE = 2.51%, and MAPE = 10.08, can be introduced for Rs estimation. Then, using the outputs of the proposed model, the amounts of (R _s ) are estimated in another 171 meteorological stations (a total of 192 stations), and eight geostatistical methods (IDW, GPI, RBF, LPI, OK, SK, UK, and EBK) were investigated for zoning. Comparing the resulting variograms showed that in addition to proof of spatial correlation between solar radiation data, they can be applied for modeling changes in various directions. Analyzing the ratio of the nugget effect on the roof of the variograms showed that the Gaussian model with the lowest ratio (Co/Co + C = 0.883) and (R ²  = 0.972), could model the highest correlation between the data and, therefore, it was used for data interpolation. To select the best geostatistical model, R2, MAE, and RMSE were used. On this basis, it was found that the RBF method with R ²  = 0.904, MAE = 3.02, RMSE = 0.39% is the most effective. Also, the IDW method with R ²  = 0.90, MAE = 3.08, RMSE = 0.391%, compared to other methods is the most effective. In addition, for data validation, correlations between observed and estimated values of solar radiation were studied and found R ²  = 0.86.     

Geographically distributed hybrid testing & collaboration between geotechnical centrifuge and structures laboratories

Distributed Hybrid Testing (DHT) is an experimental technique designed to capitalise on advances in modern networking infrastructure to overcome traditional laboratory capacity limitations. By coupling the heterogeneous test apparatus and computational resources of geographically distributed laboratories, DHT provides the means to take on complex, multi-disciplinary challenges with new forms of communication and collaboration. To introduce the opportunity and practicability afforded by DHT, here an exemplar multi-site test is addressed in which a dedicated fibre network and suite of custom software is used to connect the geotechnical centrifuge at the University of Cambridge with a variety of structural dynamics loading apparatus at the University of Oxford and the University of Bristol. While centrifuge time-scaling prevents real-time rates of loading in this test, such experiments may be used to gain valuable insights into physical phenomena, test procedure and accuracy. These and other related experiments have led to the development of the real-time DHT technique and the creation of a flexible framework that aims to facilitate future distributed tests within the UK and beyond. As a further example, a real-time DHT experiment between structural labs using this framework for testing across the Internet is also presented.