The modulation of electron density is an effective option for efficient alternative electrocatalysts. Here, p‐n junctions are constructed in 3D free‐standing FeNi‐LDH/CoP/carbon cloth (CC) electrode ...(LDH=layered double hydroxide). The positively charged FeNi‐LDH in the space‐charge region can significantly boost oxygen evolution reaction. Therefore, the j at 1.485 V (vs. RHE) of FeNi‐LDH/CoP/CC achieves ca. 10‐fold and ca. 100‐fold increases compared to those of FeNi‐LDH/CC and CoP/CC, respectively. Density functional theory calculation reveals OH− has a stronger trend to adsorb on the surface of FeNi‐LDH side in the p‐n junction compared to individual FeNi‐LDH further verifying the synergistic effect in the p‐n junction. Additionally, it represents excellent activity toward water splitting. The utilization of heterojunctions would open up an entirely new possibility to purposefully regulate the electronic structure of active sites and promote their catalytic activities.
The p‐n junction in FeNi‐LDH/CoP with positively charged FeNi‐LDH in the space‐charge region exhibits superior electro‐catalytical activity for the oxygen evolution reaction (OER). LDH=layered double hydroxide.
Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to ...sparse or non‐existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite‐based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite‐based rainfall products with relatively high spatial and temporal resolutions and quasi‐global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10‐day) and monthly time‐scales. The evaluation was done by comparing the satellite products with rain‐gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time‐scales, while TAMSAT performed better at the daily time‐scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.
The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are evaluated over East Africa by comparing with rain‐gauge data from about 1,200 stations as well as with other similar satellite products (the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT)). The above figure compares the skill (Eff) for different satellite products. The grey scale in the background is elevations in metres.
Abstract
Aqueous polysulfide/iodide redox flow batteries are attractive for scalable energy storage due to their high energy density and low cost. However, their energy efficiency and power density ...are usually limited by poor electrochemical kinetics of the redox reactions of polysulfide/iodide ions on graphite electrodes, which has become the main obstacle for their practical applications. Here, CoS
2
/CoS heterojunction nanoparticles with uneven charge distribution, which are synthesized in situ on graphite felt by a one-step solvothermal process, can significantly boost electrocatalytic activities of I
−
/I
3
−
and S
2−
/S
x
2−
redox reactions by improving absorptivity of charged ions and promoting charge transfer. The polysulfide/iodide flow battery with the graphene felt-CoS
2
/CoS heterojunction can deliver a high energy efficiency of 84.5% at a current density of 10 mA cm
−2
, a power density of 86.2 mW cm
−2
and a stable energy efficiency retention of 96% after approximately 1000 h of continuous operation.
Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. These increases are particularly ...hazardous for developing countries. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000–2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. For each of the 758 census units considered, the time series of the response and the predictor variables were averaged for agricultural areas resulting in a 17-season time series per unit for each variable. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean Rcv2 values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.
•Variability in seasonal biomass productivity was predicted for 758 units in Chile.•Anomalies of vegetation indices, rainfall, and climate indices were used as input.•We compared unit-specific linear regression with a global deep learning approach.•Overall, deep learning and linear regression have similar results for all lead times.•Our approach can be easily replicated across the globe.
•We examined the evolution of several drought metrics during the 2012 flash drought.•The Evaporative Stress Index (ESI) provided early warning of drought development.•NLDAS models sometimes depicted ...very different soil moisture anomalies.•Crop yield departures consistent with drought severity depicted by the ESI.
This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).
Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4wk), whereas corn conditions were more closely related to longer-range (8–12wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.
Land use change is the second most important anthropogenic influence on climate beside the emission of greenhouse gases. Urbanization is leading to significant land use changes in Africa since the ...continent is undergoing rapid urbanization and population growth in recent decades. Addis Ababa is one of these fast growing cities in the continent. Therefore, detection of land use change is very important to identify its impact on climate and sustainable land use management of the city. The study used Landsat images to generate land use/land cover change map for the city. The normalized difference vegetation index (NDVI) is used to detect the major changes of vegetation cover occurred between 1986 and 2011 as a result of land use and land cover change. Downscaled HadCM3 simulations under A2 and B2 emission scenarios is used to investigate future urban heat island (UHI) over the city of Addis Ababa. In the city, the analysis of Landsat images has shown that the built-up areas have increased by 121.88 km2 within the last 25 years. This finding is consistent with NDVI images taken over the same period that reveal a decline in vegetation cover. The impact of the urbanization-driven land use/cover change has resulted in notable nocturnal urban heat island (UHI) as revealed from an average increase in minimum temperature of 1.5°C at the centre of the city relative to rural site over the 1960–2001 period. The mean of the 2006–2010 spatial minimum temperature anomaly with respect to the base period mean of 1981–2005 is consistent with the observed UHI. The temperature in the central areas (both commercial and residential sectors) of Addis Ababa is warmer than the surrounding areas. The thermal gradient increase from about 1.44°C at the centre (Arada, Addis Ketema, Lideta and Kirkos) to 0.21°C at the peripheral parts of the city (Gulele, Bole, Nefasilk-Lafto, Kolfe Keranio and east of Yeka sub-cities) transecting across the hot (high-density urban) to moderately warm to cool (non-built-up) areas. However, the maximum temperature and rainfall exhibit variability that follows topographic differences. Future urban climate change projections of urban heat island formation under A2 and B2 emission scenarios show that the nocturnal UHI will be intense in winter or dry season episodes in the city. The highest urban warming is from October to December (2.5°C to 3.2°C) during 2050s and 2080s.
•Landsat images is used to generate land use/land cover change map.•Notable nocturnal urban heat island (UHI) is present over Addis Ababa.•The thermal gradient decreases from the centre to suburban sites.•Temperature and rainfall exhibit variability that follows topographic differences.•Projection shows that UHI will be intense during dry season episodes in the city.
Broad absorption spectra with efficient generation and separation of available charge carriers are indispensable requirements for promising semiconductor-based photocatalysts to achieve the ultimate ...goal of solar-to-fuel conversion. Here, Cu
3−
x
SnS
4
(
x
= 0−0.8) with copper vacancies have been prepared and fabricated via solvothermal process. The obtained copper vacancy materials have extended light absorption from ultraviolet to near-infrared-II region for its significant plasmonic effects. Time-resolved photoluminescence shows that the vacancies can simultaneously optimize charge carrier dynamics to boost the generation of long-lived active electrons for photocatalytic reduction. Density functional theory calculations and electrochemical characterizations further revealed that copper vacancies in Cu
3−
x
SnS
4
tend to enhance hydrogen’s adsorption energy with an obvious decrease in its H
2
evolution reaction (HER) overpotential. Furthermore, without any loadings, the H
2
production rate was measured to be 9.5 mmol·h
−1
·g
−1
. The apparent quantum yield was measured to be 27% for wavelength
λ
> 380 nm. The solar energy conversion efficiency was measured to be 6.5% under visible-near infrared (vis-NIR) (
λ
> 420 nm).
In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is ...crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents.
In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage ...of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.
This study first assesses the performance of regional climate models in the Coordinated Regional Climate Downscaling Experiment (CORDEX) in reproducing observed extreme precipitation indices over the ...Greater Horn of Africa (GHA) region during 1989-2005. The study then assesses projected changes in these extremes during 2069-2098 compared to 1976-2005. The Regional Climate Model (RCM) simulations are made using two RCMs, with large-scale forcing from four CMIP5 Global limate Models(GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). We found that RCM simulations have reasonably captured observed patterns of moderate precipitation extreme indices (MPEI). Pattern correlation coefficients between simulated and observed MPEI exceed 0.5 for all except the Simple Daily Intensity Index (SDII). However, significant overestimations or underestimations exist over localized areas in the region. Projected changes in Total annual Precipitation (PRCPTOT) and the annual number of heavy (>10 mm) and very heavy (>20 mm) precipitation days by 2069-2098 show a general north-south pattern, with decreases over the southern half and increases over the northern half of the GHA. These changes are often greatest over parts of Somalia, Eritrea, the Ethiopian highlands and southern Tanzania. Maximum one- and five-day precipitation totals over a year and SDII (ratio of PRCPTOT to rainy days) are projected to increase over a majority of the GHA, including areas where PRCPTOT is projected to decrease, suggesting fewer, but heavier rainy days in the future. Changes in the annual sum of daily precipitation above the 95th and 99th percentiles are statistically significant over a few locations, with the largest projected decrease/increase over Eritrea and northwestern Sudan/Somalia. Projected changes in Consecutive Dry Days (CDD) suggest longer periods of dryness over the majority of the GHA, except the central portions covering northern Uganda, southern South Sudan, southeastern Ethiopia and Somalia. Substantial increases in CDD are located over southern Tanzania and the Ethiopian highlands. The magnitude and the spatial extent of statistically-significant changes in all MPEI increase from RCP4.5 to RCP8.5, and the separation between positive and negative changes becomes clearer under RCP8.5.
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