Statistical Downscaling Model Research Articles

Analysis of Statistical Downscaling Model (SDSM) projected future rainfall in Northwestern, Western and Southern provinces of Sri Lanka

Ruhuna Journal of Science (RJS) is a multidisciplinary Science Journal published by the Faculty of Science, University of Ruhuna. It welcomes articles resulting from original research work covering many areas in Pure and Applied Sciences, and Technology, and scientific articles with interdisciplinary focus.Ruhuna Journal of Science is indexed in Web of Science (Master Journal List/ Zoological Records), and included on DOAJ and World Catalogue of Scientific Journals (WCOSJ).To submit your manuscript please go to http://rjs.ruh.ac.lk/index.php/rjs/indexCover Page ImageYala (යාල) National Park is the most visited and second largest national park in Sri Lanka, bordering the Indian Ocean. It is situated in the southeast region of the country with 124 km distance towards east from University of Ruhuna. Including Sri Lankan elephant, 44 species of mammals are resident in Yala National Park, and it has one of the highest leopard densities in the world. Shown in the cover image is the Patanangala, a rock outcrop in the Yala beach.Source: https://en.wikipedia.org/wiki/Yala_National_Park

Climate Change Assessment on Flood Occurrence in Kota Tinggi, Johor

Flood is associated with the climatic change where the climate variable has detrimental impact on hydrologic cycle. Johor is among the affected states by flood catastrophe for almost every year. Over the year, Johor has experienced increasing amount of rainfall. However, studies on future rainfall pattern in Johor are still quite limited. Thus, this study was carried out to identify climate change impacts by projecting rainfall data using Statistical Downscaling Model (SDSM) and predicting flood events utilizing the Standardised Precipitation Index (SPI). Through SDSM, the rainfall data was projected in corresponding with General Circulation Models (GCM) predictor under three different representative concentration pathway (RCP) of RCP2.6, RCP4.5 and RCP8.5 that consider different emission level in future. Future observation in the upcoming 30 years for both studied stations at Kota Tinggi, Johor recorded increasing trend in the projected mean annual rainfall under three scenarios where the highest change in projection was +12.2% under RCP8.5 at Ladang Telok Sengat station. From SPI analysis, it is predicted that there will be frequent occurrence of flooding in the upcoming 30 years due to more positive SPI values (> 1) indicated by the studied stations. From the study, the authorities can come up with better mitigation plan to supervise the flood event in the future.

Analisis Karakteristik Hujan Ekstrim Menggunakan Model Iklim di Wilayah Gunung Merapi

Extreme rainfall is one of the trigger factors for debris floods in stratovolcanos. It caused by volcanic materials will be easily eroded in large quantity with surface water flow as the result of extreme rainfall. Extreme rainfall is avnatural phenomenon which is often related with climate change. In the future, changes in extreme rainfall characteristics may occur. Therefore, it’s necessary to conduct extreme rainfall analysis for historical and future periods. In this study, the characteristics of rainfall analyzed were the variability of extreme rain as shown by trend analysis of extreme rain indices namely RTOT. Hourly rainfall data at eight rain stations used as input. Future rainfall data was projected using the global climate model CanESM2 (RCP4.5 and RCP8.5 and downscaling process using Statistical Downscaling Model (SDSM). Comparison of the projection rainfall with historical rainfall shows a different trend at each station. Increasing trend occurred at four stations including Plosokerep, Pucanganom, Sopalan, and Talun stations, with the highest increasing trend occurring at Sopalan stations. In addition, there was also a decreasing trend that occurred at Ngandong station for both scenarios and at Sorasan station in the RCP8.5 scenario. The Jrakah and Randugunting stations show a steady trend.

A statistical downscaling prediction model for winter temperature over Xinjiang based on the CFSv2 and sea ice forcing

AbstractBased on a year‐to‐year increment approach, a statistical downscaling model is developed for winter temperature prediction over Xinjiang of northwest China by using the predicted 200‐hPa westerly wind in winter over the Ural Mountains from the Climate Forecast System version 2 (CFSv2) as well as the observed sea ice over the Barents Sea–Laptev Sea in the preceding September. The statistical downscaling hindcasts on the 1983–2018 winter temperature over Xinjiang show that the statistical downscaling method has significantly improved the prediction capability compared with the original CFSv2. Specifically, the temporal correlation coefficients increase from negative to positive in most of Xinjiang, with the values passing the significance test at 90% confidence level at 81% of the stations. The regional averaged root‐mean‐square error reduces by more than 20%. In addition, the anomaly correlation coefficient increases from 0.08 to 0.26, passing the significance test at 95% confidence level. For two typical cases of the extreme cold winter in 2011 and the extreme warm winter in 2016, the spatial distribution characteristics of temperature anomaly are well reproduced, which are generally consistent with the observations. Overall, the statistical downscaling model based on the year‐to‐year increment strategy is a relatively effective method for predicting the winter temperature in Xinjiang.

Climate change impacts on the flow regime and water quality indicators using an artificial neural network (ANN): a case study in Saskatchewan, Canada

AbstractIn this study, the artificial neural network (ANN) method was applied to investigate the impacts of climate change on the water quantity and quality of the Qu'Appelle River in Saskatchewan, Canada. First, the second-generation Canadian earth system model (CanESM2) was adopted to predict future climate conditions. The Statistical DownScaling Model (SDSM) was then applied to downscale the generated data. To analyze the water quality of the river, concentrations of dissolved oxygen (DO) and total dissolved solids (TDSs) from the river were collected. Using the collected climate and hydrometric data, the ANNs were trained to simulate (i) the ratio of snowfall-to-total precipitation based on the temperature, (ii) the river flow rate based on the temperature and precipitation; and (iii) DO and TDS concentrations based on the river flow and temperature. Finally, the generated climate change data were used as inputs to the ANN model to investigate the climate change impacts on the river flow as well as DO and TDS concentrations within the selected region. Hydrologic alteration of the river was evaluated via the Range of Variability Approach (RVA) under historical and climate change scenarios. The results under climate change scenarios were compared with those under historical scenarios and indicated that climate change would lead to a heterogeneous change in precipitation and temperature patterns. These changes would have serious degrading impacts on the river discharge as well as DO and TDS concentration levels, causing deterioration in the sustainability of the river system and ecological health of the region.

A Hybrid Statistical Downscaling Framework Based on Nonstationary Time Series Decomposition and Machine Learning

AbstractDownscaling techniques are effective to bridge the scale gap between global circulation models and regional studies. Statistical downscaling methods are prevalent due to their advantages in high computational efficiency and accuracy. However, an implicit assumption of most statistical techniques is that time series should be relatively stationary after certain function transformations. Otherwise, statistics of nonstationary time series may be meaningless for describing future behavior. In this study, a hybrid statistical downscaling framework was developed through integrating bivariate empirical mode decomposition (BEMD) and a machine learning method to extract multi‐timescale features from nonstationary data to enhance downscaling performance. The proposed framework can reduce the effects of non‐stationarity in data‐driven models by using the BEMD method, which can decompose time series into independent and stationary components at multiple time‐frequency resolutions. It was applied to downscale monthly precipitation and temperature of Canadian Earth System Model in multiple stations with different climate types in the Central Valley of California, USA, to verify its accuracy and generalization ability. The performance of downscaling maximum and minimum temperatures (R2 > 0.9) was more accurate than that of precipitation. The potential reason is that precipitation is more sensitive to transient weather phenomena, which can only be extracted from data with higher temporal resolution. The proposed model was further compared with models based on discrete wavelet transform and models without time series decomposition. The results showed that a decomposition strategy of the proposed framework can improve the downscaling accuracy, potentially providing a viable option to deal with the nonstationary of data in statistical downscaling models.

Function-Based Troposphere Tomography Technique for Optimal Downscaling of Precipitation

Precipitation is an important meteorological indicator that has a direct and significant impact on ecology, agriculture, hydrology, and other vital areas of human health and life. It is therefore essential to monitor variations of this parameter at a global and local scale. To monitor and predict long-term changes in climate elements, Global Circulation Models (GCMs) can provide simulated global-scale climatic processes. Due to the low spatial resolution of these models, downscaling methods are required to convert such large-scale information to regional-scale data for local applications. Among the downscaling methods, the Statistical DownScaling Model (SDSM) and the Artificial Neural Networks (ANNs) are widely used due to their low computational volume and suitable output. These models mainly require training data, and generally, the reanalysis data obtained from the National Center for Environmental Prediction (NCEP) and European Centre for Medium-range Weather Forecasts (ECMWF) are used for this purpose. With an optimal downscaling method, instead of applying the humidity indices extracted from ECMWF data, the outputs of the function-based tropospheric tomography technique obtained from the Global Navigation Satellite System (GNSS) will be used. The reconstructed function-based tropospheric data is then fed to the SDSM and ANN methods used for downscaling. The results of both methods indicate that the tomography can increase the accuracy of the downscaling process by about 20 mm in the wet months of the year. This corresponds to an average improvement of 38% with regard to the root mean square error (RMSE) of the monthly precipitation.

Influence of Climate Change and Land-Use Alteration on Water Resources in Multan, Pakistan

This study presents an evaluation of climate and land-use changes induced impacts on water resources of Multan City, Pakistan. Statistical Down Scaling Model (SDSM) and Geographical Information System (GIS) are used for climate change scenario and spatial analyses. Hydrologic Engineering Center’s Hydraulic Modeling System (HEC-HMS) model is used for rainfall-runoff simulation. The investigated results show significant changes in climatological parameters, i.e., an increase in temperature and decrease in precipitation over the last 40 years, and a significant urban expansion is also observed from 2000 to 2020. The increase in temperature and urbanization has reduced the infiltration rate into the soil and increased the runoff flows. The HEC-HMS results indicate that surface runoff gradually increased over the last two decades. Consequently, the depth of the water table in the shallow aquifer has declined by about 0.3 m/year. Projected climate indices stipulate that groundwater depletion will occur in the future. Arsenic levels have exceeded the permissible limit owing to unplanned urban expansion and open dumping of industrial effluents. The results can help an efficient water resources management in Multan.

Potential climate change impact assessment on the hydrology of the Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

The recent climate change is manifested by human activities, such as increasing global temperature, anomalous rainfall, and extreme fluctuations in the hydrological cycle noticed in different regions of the earth. This study assesses the hydrological changes of Lake Tana Basin under future climatic changes. The study used Statistical Downscaling Model (SDSM) to project future climate change. The basin's hydrology was assessed using the Soil and Water Assessment Tool (SWAT). The SWAT was driven by the outputs of SDSM and calibrated against observed river flow. The results indicate that the maximum and minimum temperature trends are generally in line with the global trends, whereas the precipitation shows an inconsistent pattern. The predictive results from the RCP8.5 radiative forcing scenarios indicate that during the wet season (dry season), the evapotranspiration, streamflow, and base flow will tend to increase by 14.9%, 9.7%, and 20.9% (70.1%, 2.8%, and 16.7%) respectively by the year 2090s. The streamflow curve for the Lake Tana basin's under future climate change shows a steep slope, and stream discharge is highly variable, particularly the surface runoff. This study indicates that although climate change seems uncertain for the study area, preparedness is warranted to avoid the aggravation of soil erosion, water stress and scarcity, and often floods/droughts.

New downscaling prediction models for spring drought in China

AbstractAccurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.

Modelling climate change impact on soil loss and erosion vulnerability in a watershed of Shiwalik Himalayas

Understanding the impact of climate change on soil erosion is required to plan effective soil conservation and management practices in hilly and mountainous landscape. Though, few studies have been executed in Indian Himalayas, much less in Shiwalik Himalayas. Thus, keeping this in view, the aim of the study is to estimate the possible impact of projected climate change scenarios on soil loss and erosion vulnerability using Statistical Downscaling Model (SDSM), MarkSim Weather Generator, Agricultural Policy Environmental eXtender (APEX) model and AHP method. The present study downscaled four climate scenarios on the near-future, noted the 2020 s (2011–2040), mid-future, noted 2050 s (2011–2070) and far-future, noted 2080 s (2071–2100/2095) under two Special Report Emission Scenarios (SRES), A2 and B2 with two Representative Concentration Pathway (RCP), 4.5 and 8.5 scenarios. Both SRES and RCP scenarios predicted an increase in temperature and annual rainfall depth during 21st century. Calibrated APEX model was used to simulate future soil loss. The study exposed that the average annual soil loss might increase up to 46.94%, 38.80%, 35.34% and 25.93% in the A2, B2, RCP 4.5 and RCP 8.5 scenarios respectively from the base period (1985–2014). The vulnerability assessment indicates, most of the areas might be under risk from slightly to highly vulnerable and was found that forest land cover could resist this erosion vulnerability to a certain extent. The findings of the study showed a possibility for climate change to increase the rate of soil loss unless conservation strategies or proper land use plans are remunerated. This study offers scientists and policymakers to a detailed understanding of the future impact of rainfall on soil loss and erosion vulnerability in the Shiwalik Himalayas.

Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region-Specific Compound Ozone and Temperature Burden Over Europe.

High ground‐level ozone concentrations and high air temperatures present two health‐relevant natural hazards. The most severe health outcomes are generally associated with concurrent elevated levels of both variables, representing so‐called compound ozone and temperature (o‐t‐) events. These o‐t‐events, their relationship with identified main meteorological and synoptic drivers, as well as ozone and temperature levels themselves and the linkage between both variables, vary temporally and with the location of sites. Due to the serious health burden and its spatiotemporal variations, the analysis of o‐t‐events across the European domain represents the focus of the current work. The main objective is to model and project present and future o‐t‐events, taking region‐specific differences into account. Thus, a division of the European domain into six o‐t‐regions with homogeneous, similar ground‐level ozone and temperature characteristics and patterns built the basis of the study. In order to assess region‐specific main meteorological and synoptic drivers of o‐t‐events, statistical downscaling models were developed for selected representative stations per o‐t‐region. Statistical climate change projections for all central European o‐t‐regions were generated to assess potential frequency shifts of o‐t‐events until the end of the 21st century. The output of eight Earth System Models from the sixth phase of the Coupled Model Intercomparison Project considering SSP245 and SSP370 scenario assumptions was applied. By comparing midcentury (2041–2060) and late century (2081–2100) time slice differences with respect to a historical base period (1995–2014), substantial increases of the health‐relevant compound o‐t‐events were projected across all central European regions.

To Accurately and Lightly Downscale the Temperature Field by Deep Learning

Abstract Downscaling is essential in atmospheric science, aiming to infer the fine-scale field from the coarse-scale field. To obtain the high-resolution temperature field, our team proposed a deep learning–based model, the China Meteorological Administration land data assimilation system statistical downscaling model (CLDASSD). Inspired by some works in computer vision, we proposed the improved version, Light-CLDASSD, which is a lightweight model with fewer parameters. The modified model has the characteristics of light training and fewer parameters. What is more, we introduced station observation data in the model to make the downscaling results more accurate. Taking temperature as the research object, we performed experiments in the Beijing–Tianjin–Hebei region and downscaled the temperature field from 1/16° (0.0625°) to 0.01°. Experiments show that Light-CLDASSD can get robust results. As for spatial distribution, Light-CLDASSD can reconstruct fine and accurate spatial distribution on complex mountains and reconstruct small-scale characteristics in plain areas that other models cannot achieve. As for temporal change, Light-CLDASSD performs better at local noon and warm seasons. Furthermore, Light-CLDASSD achieves better performance than other models and is comparable with High-Resolution China Meteorological Administration’s Land Assimilation System (HRCLDAS). The root-mean-square error (RMSE) of Light-CLDASSD is 0.08°C lower than HRCLDAS, and the bias distribution is more concentrated at 0°C. This article is an upgrade of the CLDASSD model and preliminary exploration of the back-calculation for high-resolution historical data. Significance Statement This work proposes a deep learning–based statistical downscaling model named Light China Meteorological Administration land data assimilation system statistical downscaling model (Light-CLDASSD), which can downscale the temperature field generated by CLDAS from 1/16° (0.0625°) to 0.01°. Introducing observation data improves the performance, and the model results are comparable to HRCLDAS products. Our research is of great significance to developing high-resolution data and the back-calculation of historical assimilation data.

Future short-term estimation of flowrate of the Euphrates river catchment located in Al-Najaf Governorate, Iraq through using weather data and statistical downscaling model

Abstract The issue of global warming has negatively affected the global climate, which caused a decrease in the amount of precipitation and a significant increase in temperatures and evaporation rates. Iraq suffers from poor rainfall, which exacerbates the problem of declining water levels in rivers and the drying up of some tributaries. The Euphrates river, which passes through the eastern side of the Al-Najaf Governorate, is the case study in the current research. A statistical downscaling model is created for rainfall data from 2010 to 2020 with the aim of predicting the future precipitation biennially from 2022 to 2028. The model is calibrated and reflects close results with observations with high accuracy and reliability. The future precipitation levels are transformed after multiplying these levels by the total surface areas of the Euphrates river and its tributaries, and the value of future flowrates to be added to the Euphrates river is estimated. The results showed that the Euphrates river will receive a flowrate of 1,535,945 m3 in 2026, which is the highest value when compared with the values of flowrate for the years 2022, 2024, and 2028, which are estimated at 1,475,072, 1,456,037, and 1,378,403 m3, respectively.

Evaluation and projection of the annual maximum streamflow in response to anthropogenic and climatic effects under nonstationary conditions in the Hanjiang River Basin, China

Abstract The flood regimes have been changing due to the climate change and human activities. Evaluating the flood risk under nonstationarity is critical to water resource management authorities in disaster reduction. In this study, the annual maximum streamflow (AMS) was used to analyze the nonstationarity in flood frequency in the Hanjiang River (HJR) Basin. A Generalized Additive Model for Location, Scale and Shape (GAMLSS) is employed to characterize nonstationarity in the AMS with time, as well as climatic and anthropogenic factors. Additionally, changes of the AMS in response to future climate change and human activity are also investigated. Results indicate that flood behavior can be better described by the nonstationary model with physically based covariates than that with time and the stationary model, implying that flood regimes of the HJR Basin are mainly influenced by anthropogenic and climatic factors including reservoir, precipitation and temperature. The precipitation and temperature projected by the Statistical Downscaling Model (SDSM) under two climate scenarios (RCP2.6 and RCP4.5) in the HJR Basin are characterized by an increasing trend over the period of 2006–2100. Furthermore, an increasing trend was found in the AMS during 2051–2100, indicating that flood risk is likely to increase in the future in the HJR Basin due to the climate change alone without further changes in hydrological engineering and flood management. The results quantified the flood frequency under nonstationarity conditions with physically based covariates and provided information to the decision-makers to address the potential risks posed to the HJR Basin.

Spatial–temporal distribution and forecasting model of precipitation using dynamic-statistical information fusion

Abstract In order to explore the evolvement mechanism of hydrometeorological elements, spatial–temporal distribution of precipitation in the Huai river basin is studied by statistical drawing and empirical orthogonal function decomposition. How to make an objective combination for the predictive results of precipitation? Information fusion in data assimilation is introduced to merge the improved National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) with the multilinear regression model. Firstly, in terms of time, the annual precipitation is apt to decline at most stations within 30 years, and precipitation mainly concentrates in the flood season. The characteristics of spatial distribution are similar to topographic features. It can also be found that precipitation gradually decreases from south to north. Secondly, from statistical forecasting, the relationship between precipitation and global sea surface temperature (SST) is explored. Prediction equation is established with SST and the average precipitation. Thirdly, from dynamic model forecasting, the CFSv2 original model and the CFSv2 statistical downscaling model are used to analyze the influence of model deviation on fusion prediction. The optimum interpolation assimilation method is applied for realizing the optimal integration of statistical and dynamic model prediction. Finally, the standardized precipitation index (SPI) is calculated by the combined forecasting of annual precipitation to evaluate drought conditions. The results show that SST is an important factor affecting precipitation, which may be applied as a forecasting direction with other factors. The merged precipitation prediction skill by the CFSv2 original model and the statistical model do not have the great promotion, which is still lower than the prediction skill only by the statistical model. However, the merged precipitation prediction skill by the CFSv2 statistical downscaling model and the statistical model is better than the prediction skills by the two models mentioned above, respectively. These indicate that when the prediction difference between the models is large, the merged prediction error cannot be minimized. When the prediction skill levels are equal, there is an improvement in the merged result. So, it is necessary to revise the climate dynamic model by downscaling. What is more, the obtained drought levels match the actual disaster conditions, providing theoretical support of hydrology and meteorology for the prevention of natural disasters.

ASSESSMENT OF FUTURE CLIMATE PROJECTIONS IN ALGERIA USING STATISTICAL DOWNSCALING MODEL

In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.

Analysing Historical and Modelling Future Soil Temperature at Kuujjuaq, Quebec (Canada): Implications on Aviation Infrastructure

The impact of climate change on soil temperatures at Kuujjuaq, Quebec in northern Canada is assessed. First, long-term historical soil temperature records (1967–1995) are statistically analyzed to provide a climatological baseline for soils at 5 to 150 cm depths. Next, the nature of the relationship between atmospheric variables and soil temperature are determined using a statistical downscaling model (SDSM) and National Centers for Environmental Prediction (NCEP), a climatological data set. SDSM was found to replicate historic soil temperatures well and used to project soil temperatures for the remainder of the century using climate model output Canadian Second Generation Earth System Model (CanESM2). Three Representative Concentration Pathway scenarios (RCP 2.6, 4.5 and 8.5) were used from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This study found that the soil temperature at this location may warm at 0.9 to 1.2 °C per decade at various depths. Annual soil temperatures at all depths are projected to rise to above 0 °C for the 1997–2026 period for all climate scenarios. The melting soil poses a hazard to the airport infrastructure and will require adaptation measures.

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Evapotranspiration is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island using their two input parameters: daily maximum temperature (Tmax), and daily minimum temperature (Tmin). The Tmax and Tmin were downscaled with the help of statistical downscaling model (SDSM) for three future periods 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations. For RCP8.5, it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers in future quantification of the availability of water for the island and to optimize the use of island water resources on a sustainable basis.

Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco

Soil erosion is an increasingly issue worldwide, due to several factors including climate variations and humans’ activities, especially in Mediterranean ecosystems. Therefore, the aim of this paper is: (i) to quantify and to predict soil erosion rate for the baseline period (2000–2013) and a future period (2014–2027), using the Revised Universal Soil Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT) model in the R’Dom watershed in Morocco, based on the opportunities of Remote Sensing (RS) techniques and Geographical Information System (GIS) geospatial tools. (ii) we based on classical statistical downscaling model (SDSM) for rainfall prediction. Due to the lack of field data, the model results are validated by expert knowledge. As a result of this study, it is found that both agricultural lands and bare lands are most affected by soil erosion. Moreover, it is showed that soil erosion in the watershed was dominated by very low and low erosion. Although the area of very low erosion and low erosion continued to decrease. Hence, we hereby envisage that our contribution will provide a more complete understanding of the soil degradation in this study area and the results of this research could be a crucial reference in soil erosion studies and also may serve as a valuable guidance for watershed management strategies.