Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Soil moisture is an important parameter in landslides because of increased pore pressure and decreased shear strength. This research aims to derive soil moisture indicators from two hydrological models: the physically-based distributed hydrological model and the lumped model. Rainfall-Runoff-Inundation (RRI) Model is used to simulate the hydrological response of catchments to the rainfall-induced landslide in a distributed manner. Tank Model as a lumped hydrological model is also used in this study to simulate the dynamic of soil moisture. The study area is the upper Brantas River Basin, prone to landslides due to heavy rainfall and steep slope. Calibration of the model is conducted by tuning the model according to the river discharge data. The simulation indicates that acceptable performance is confirmed. Tank Model can provide the dynamic of the soil moisture. However, by using this approach, the spatial variation of the soil moisture cannot be presented. Regarding the quantitative amount of soil water content, RRI Model could make a reasonable simulation though the temporal variation is not adequately reproduced. Validation of this method with satellite soil moisture as well as ground measurement is also presented. The challenges of using these approaches to develop landslide hazard assessment are discussed.

A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

Numerical Assessment of Downward Incoming Solar Irradiance in Smoke Influenced Regions—A Case Study in Brazilian Amazon and Cerrado

Smoke aerosol plumes generated during the biomass burning season in Brazil suffer long-range transport, resulting in large aerosol optical depths over an extensive domain. As a consequence, downward surface solar irradiance, and in particular the direct component, can be significantly reduced. Accurate solar energy assessments considering the radiative contribution of biomass burning aerosols are required to support Brazil’s solar power sector. This work presents the 2nd generation of the radiative transfer model BRASIL-SR, developed to improve the aerosol representation and reduce the uncertainties in surface solar irradiance estimates in cloudless hazy conditions and clean conditions. Two numerical experiments allowed to assess the model’s skill using observational or regional MERRA-2 reanalysis AOD data in a region frequently affected by smoke. Four ground measurement sites provided data for the model output validation. Results for DNI obtained using δ-Eddington scaling and without scaling are compared, with the latter presenting the best skill in all sites and for both experiments. An increase in the relative error of DNI results obtained with δ-Eddington optical depth scaling as AOD increases is evidenced. For DNI, MBD deviations ranged from −2.3 to −0.5%, RMSD between 2.3 and 4.7% and OVER between 0 and 5.3% when using in-situ AOD data. Overall, our results indicate a good skill of BRASIL-SR for the estimation of both GHI and DNI.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Solar resource assessment in Ireland, comparison of satellite data versus ground measurement

<p>Accurate solar resource assessments are essential to project a solar photovoltaic (PV) plant’s energy production – and ultimately forecast its revenue.</p><p>Solar resource assessments are the bedrock of the ‘Revenue’ line in PV financial models. In today’s competitive financing environment, the assumptions underlying solar resource assessment often have make-or-break impact on project valuations. It’s critical that investors trust the numbers provided.</p><p>To quantify solar resource, industry typically compares different irradiation databases derived from multiple physical sources – whether measurements or satellite images. There is always some level of scatter; in Western Europe this is often around 3%, after excluding outliers.  Satellite database are never as good as accurate ground measurement.  And the rather narrow variation observed is due to past calibration of satellite derived model with data from weather stations.  The reality can be different when it comes to Ireland. </p><p>The solar sector is currently experiencing a rapid development in the Republic of Ireland, making the yield assessment and by extension the solar resource estimation key for the bankability of the projects.</p><p>The aim of our work was the validate the accuracy of different databases, available in Ireland.</p><p>The first step of this analysis will be to qualify our data sources. Everoze and Brightwind have access to measurement campaigns from multiple solar projects in Ireland. All the gathered dataset will be processed, applying state of the art quality control, to retain only trustable information.  The quality check will also include the sensors themselves, with a verification of the accuracy and calibration certificates of the different pieces of equipment.</p><p>In a second step, the qualified datasets will be used to compare satellite derived data.  We plan to use CAMS, SolarGIS and Meteonorm.  The intention is to categorise our results in regions, classified based on the difference in annual irradiation between different databases in order to reduce uncertainty – and ultimately boost investor confidence in energy yield assessments.</p>

Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements

This paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only requires near surface measurements for the prognosis once the neural network is trained. Another advantage is that it can be used to study the wind profile temporal evolution. This work uses data collected by a lidar sensor located at the Universidad de León (Spain). The neural network best configuration was determined using sensibility analyses. The result is a multilayer perceptron with three layers for each altitude: the input layer has six nodes for the last three measurements, the second has 128 nodes and the third consists of two nodes that provide u and v. The proposed method has better performance than traditional methods. The obtained wind profile information obtained is useful for multiple applications, such as preliminary calculations of the wind resource or CFD models.

First look at the urban carbon flux inversion system for Megacity CO2-Seoul

<p>To verify the urban fossil fuel carbon dioxide (FFCO<sub>2</sub>) flux over the Seoul Capital Area (SCA), we initiated the “Megacity CO<sub>2</sub>-Seoul” project in the year 2018. For the project, our research group established CO<sub>2</sub> and XCO<sub>2</sub> ground measurement stations deploying Seoul National University CO<sub>2</sub> Measurement instruments (SNUCO<sub>2</sub>M) and EM27/SUN. We also produced 1x1km urban biospheric flux with the CArbon Simulator from Space (CASS) and 1x1km FFCO<sub>2</sub> carbon emission inventory by employing machine learning techniques. The project comprises inverse modeling system using WRF-STILT. Under the Bayesian inverse model framework, we assess FFCO<sub>2</sub> inventory of Seoul, which are generated by the bottom-up approach, by paring the ground CO<sub>2</sub> measurement constraints. This is the first look at the verification of self-developed FFCO<sub>2</sub> inventory of Seoul. We are currently working on the improvement of the WRF-STILT inverse modeling system. In this presentation, we report verification of FFCO<sub>2</sub> emissions in SCA on February 2018. Our estimate reflects that our prior FFCO<sub>2</sub> inventory was overestimated in the comparison with results of the inverse model. Detailed results will be presented at the webinar. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT) (No. NRF-2019R1A2C3002868).</p>