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Floating islands in Lake Victoria, the world's second-largest fresh water lake, disrupt transportation, fisheries, irrigation, and water quality. Despite their impact, the dynamics of these islands remain unexplored. This study investigates island dynamics within the Nalubaale, Kiira, and Bujagali dams in Uganda, exploring the causes of their formation and the subsequent impact on hydropower production. The study collects data of Landsat imagery from 2000 to 2020, CHIRPS precipitation, and Lake Victoria's water level datasets from 2004, 2010, 2013, 2017, and 2020. The results reveal a strong correlation between precipitation, fluctuating water levels, and floating island formation, with nutrient-rich runoff from municipal waste and agriculture promoting island growth. In addition, rising water levels lead to the dislodging of rocks and soil, contributing to floating island formation, which may manifest with a lag time of up to one month. The analysis shows higher correlations between precipitation, water levels, and floating islands during the long (March–May) and short (September–November) rainy seasons as opposed to drier periods (June–August, December–February). The findings indicate that southeast monsoon winds, which transport floating vegetation, also are essential in influencing island dynamics. Consequently, the major drivers of floating islands in Lake Victoria are identified as precipitation, water level fluctuations and wind variations. Finally, a negative correlation between floating island eutrophication and power production at Kiira and Nalubaale stations suggests that the increased eutrophication caused by the presence of floating islands leads to reduced power output at both Kiira and Nalubaale power stations. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield. [ABSTRACT FROM AUTHOR]

Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. We present an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and k -means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representation of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures. [ABSTRACT FROM AUTHOR]

Changes in drought around the globe are among the most daunting potential effects of climate change. However, changes in droughts are often not well distinguished from changes in aridity levels. As drought constitutes conditions of aridity, the projected declines in mean precipitation tend to override changes in drought. This results in projections of more dire changes in drought than ever. The overestimate of changes can be attributed to the use of ‘static’ normal precipitation in the derivation of drought events. The failure in distinguishing drought from aridity is a conceptual problem of concern, particularly to drought policymakers. Given that the key objective of drought policies is to determine drought conditions, which are rare and so protracted that they are beyond the scope of normal risk management, for interventions. The main objective of this Case Study of Brazil is to demonstrate the differences between projections of changes in drought based on ‘static’ and ‘30-year dynamic’ precipitation normal conditions. First we demonstrate that the ‘static’ based projections suggest 4-fold changes in the probability of drought-year occurrences against changes by the dynamic normal precipitation. The ‘static-normal mean precipitation’ based projections tend to be monotonically increasing in magnitude, and were arguably considered unrealistic. Based on the ‘30-year dynamic’ normal precipitation conditions, the 13-member GCM ensemble median projection estimates of changes for 2050 under rcp4.5 1 1 rcp4.5: pathway of greenhouse gas emissions where total radiative forcing is stabilised (at 4.5 W m − 2 ) shortly after 2100, without overshooting the long-run radiative forcing target level. and rcp8.5 2 2 rcp8.5: pathway of greenhouse gas emissions which, assumes high population and slow income growth with modest rates of technological change and energy intensity improvements, leading to high energy demand and greenhouse gases emissions in absence of climate change policies. suggest: (i) Significant differences between changes associated with rcp4.5 and rcp8.5, and are more noticeable for droughts at long than short timescales in the 2070; (ii) Overall, the results demonstrate more realistic projections of changes in drought characteristics over Brazil than previous projections based on ‘static’ normal precipitation conditions. However, the uncertainty of response of droughts to climate change in CMIP5 simulations is still large, regardless of GCMs selection and translation processes undertaken. [ABSTRACT FROM AUTHOR]

The role of global sea surface temperature (SST) anomalies in modulating rainfall in the African region has been widely studied and is now less debated. However, their impacts and links to terrestrial water storage (TWS) in general, have not been studied. This study presents the pioneer results of canonical correlation analysis (CCA) of TWS derived from both global reanalysis data (1980–2015) and GRACE (Gravity Recovery and Climate Experiment) (2002–2014) with SST fields. The main issues discussed include, (i) oceanic hot spots that impact on TWS over tropical West Africa (TWA) based on CCA, (ii) long term changes in model and global reanalysis data (soil moisture, TWS, and groundwater) and the influence of climate variability on these hydrological indicators, and (iii) the hydrological characteristics of the Equatorial region of Africa (i.e., the Congo basin) based on GRACE-derived TWS, river discharge, and precipitation. Results of the CCA diagnostics show that El-Niño Southern Oscillation related equatorial Pacific SST fluctuations is a major index of climate variability identified in the main portion of the CCA procedure that indicates a significant association with long term TWS reanalysis data over TWA ( r  = 0.50, ρ < 0.05). Based on Mann-Kendall's statistics, the study found fairly large long term declines ( ρ < 0.05) in TWS and soil moisture (1982 − 2015), mostly over the Congo basin, which coincided with warming of the land surface and the surrounding oceans. Meanwhile, some parts of the Sahel show significant wetting (rainfall, soil moisture, groundwater, and TWS) trends during the same period (1982–2015) and aligns with the ongoing narratives of rainfall recovery in the region. Results of singular spectral analysis and regression confirm that multi-annual changes in the Congo River discharge explained a considerable proportion of variability in GRACE-hydrological signal over the Congo basin ( r  = 0.86 and R 2  = 0.70, ρ < 0.05). Finally, leading orthogonal modes of MERRA and GRACE-TWS over TWA show significant association with global SST anomalies. [ABSTRACT FROM AUTHOR]

Drought-like humanitarian crises in the Greater Horn of Africa (GHA) are increasing despite recent progress in drought monitoring and prediction efforts. Notwithstanding these efforts, there remain challenges stemming from uncertainty in drought prediction, and the inflexibility and limited buffering capacity of the recurrent impacted systems. The complexity of the interactions of ENSO, IOD, IPO and NAO, arguably remains the main source of uncertainty in drought prediction. To develop practical drought risk parameters that potentially can guide investment strategies and risk-informed planning, this study quantifies, drought characteristics that underpin drought impacts management. Drought characteristics that include probability of drought-year occurrences, durations, areal-extent and their trends over 11 decades (1903–2012) were derived from the Standardized Precipitation Index (SPI).Transient probability of drought-year occurrences, modelled on Beta distribution, across the region ranges from 10 to 40%, although most fall within 20–30%. For more than half of the drought events, durations of up to 4, 7, 14 and 24 months for the 3-, 6-, 12- and 24-month timescales were evident, while 1 out of 10 events persisted for up to 18 months for the short timescales, and up to 36 months or more for the long timescales. Apparently, only drought areal-extent showed statistically significant trends of up to 3%, 1%, 3.7%, 2.4%, 0.7%, − 0.3% and − 0.6% per decade over Sudan, Eritrea, Ethiopia, Somalia, Kenya, Uganda and Tanzania, respectively. Since there is no evidence of significant changes in drought characteristics, the peculiarity of drought-like crises in the GHA can be attributed (at least in part) to unaccounted for systematic rainfall reduction. This highlights the importance of distinguishing drought impacts from those associated with new levels of aridity. In principle drought is a temporary phenomenon while aridity is permanent, a difference that managers and decision-makers should be more aware. [ABSTRACT FROM AUTHOR]

For Brazil, a country frequented by droughts and whose rural inhabitants largely depend on groundwater, reliance on isotope for its monitoring, though accurate, is expensive and limited in spatial coverage. We exploit total water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellites to analyse spatial-temporal groundwater changes in relation to geological characteristics. Large-scale groundwater changes are estimated using GRACE-derived TWS and altimetry observations in addition to GLDAS and WGHM model outputs. Additionally, TRMM precipitation data are used to infer impacts of climate variability on groundwater fluctuations. The results indicate that climate variability mainly controls groundwater change trends while geological properties control change rates, spatial distribution, and storage capacity. Granular rocks in the Amazon and Guarani aquifers are found to influence larger storage capability, higher permeability ( >10 -4 m/s) and faster response to rainfall (1 to 3 months' lag) compared to fractured rocks (permeability <10 -7 m/s and lags > 3 months) found only in Bambui aquifer. Groundwater in the Amazon region is found to rely not only on precipitation but also on inflow from other regions. Areas beyond the northern and southern Amazon basin depict a ‘dam-like’ pattern, with high inflow and slow outflow rates (recharge slope > 0.75, discharge slope < 0.45). This is due to two impermeable rock layer-like ‘walls' (permeability <10 -8 m/s) along the northern and southern Alter do Chão aquifer that help retain groundwater. The largest groundwater storage capacity in Brazil is the Amazon aquifer (with annual amplitudes of > 30 cm). Amazon's groundwater declined between 2002 and 2008 due to below normal precipitation (wet seasons lasted for about 36 to 47% of the time). The Guarani aquifer and adjacent coastline areas rank second in terms of storage capacity, while the northeast and southeast coastal regions indicate the smallest storage capacity due to lack of rainfall (annual average is rainfall <10 cm). [ABSTRACT FROM AUTHOR]

The Ganges-Brahmaputra-Meghna (GBM) River Basin presents a spatially diverse hydrological regime due to it's complex topography and escalating demand for freshwater resources. This presents a big challenge in applying the current state-of-the-art regional climate models (RCMs) for climate change impact studies in the GBM River Basin. In this study, several RCM simulations generated by RegCM4.4 and PRECIS are assessed for their seasonal and interannual variations, onset/withdrawal of the Indian monsoon, and long-term trends in precipitation and temperature from 1982 to 2012. The results indicate that in general, RegCM4.4 and PRECIS simulations appear to reasonably reproduce the mean seasonal distribution of precipitation and temperature across the GBM River Basin, although the two RCMs are integrated over a different domain size. On average, the RegCM4.4 simulations overestimate monsoon precipitation by $${\sim }26$$ and $${\sim }5\%$$ in the Ganges and Brahmaputra-Meghna River Basin, respectively, while PRECIS simulations underestimate (overestimate) the same by $${\sim }7\%$$ ( $${\sim }16\%$$ ). Both RegCM4.4 and PRECIS simulations indicate an intense cold bias (up to $$10\,^\circ \hbox {C}$$ ) in the Himalayas, and are generally stronger in the RegCM4.4 simulations. Additionally, they tend to produce high precipitation between April and May in the Ganges (RegCM4.4 simulations) and Brahmaputra-Meghna (PRECIS simulations) River Basins, resulting in early onset of the Indian monsoon in the Ganges River Basin. PRECIS simulations exhibit a delayed monsoon withdrawal in the Brahmaputra-Meghna River Basin. Despite large spatial variations in onset and withdrawal periods across the GBM River Basin, the basin-averaged results agree reasonably well with the observed periods. Although global climate model (GCM) driven simulations are generally poor in representing the interannual variability of precipitation and winter temperature variations, they tend to agree well with observed precipitation anomalies when driven by perfect boundary conditions. It is also seen that all GCM driven simulations feature significant positive surface temperature trends consistent with the observed datasets. [ABSTRACT FROM AUTHOR]

ABSTRACT Previous studies suggest that climate change impacts significantly on the hydro-climatic processes within the Ganges-Brahmaputra-Meghna ( GBM) River Basin ( RB). This study examines the observed climate characteristics and potential strengths and limitations of three global high-resolution reanalyses and satellite remote-sensing products over the GBM RB for period 1980-2013 by (1) estimating trends and interannual variations of precipitation and temperature, and (2) isolating precipitation variations likely associated with El Niño Southern Oscillation ( ENSO) and Indian Ocean Dipole ( IOD). The surface temperature trends show widespread warming across the basin with a maximum increase of 0.6 °C decade−1 over western Nepal and southern Tibet from 1980 to 2013. Rainfall changes from 1980 to 2013 indicate pronounced decline over high rainfall regions of northeast India, Bhutan, Nepal, and Bangladesh, especially from 1998 to 2013. Basin-wide averaged trends show rainfall decline of up to 39 mm decade−1 in June-August in the Brahmaputra-Meghna RB from 1998 to 2013. Temperature variability based on Principal Component Analysis indicates that the first mode is associated with sea surface temperature ( SST) warming in the Arabian Sea and the western tropical Pacific Ocean, while the second mode appears to be significantly correlated to SST anomalies in the western (eastern) tropical Indian (Pacific) Ocean. ENSO and IOD events are found to significantly influence rainfall variability contributing to about 10-20% ( ENSO) and 8-10% ( IOD) of the annual rainfall, mainly over Bhutan, Nepal, Bangladesh, and north-eastern India. Among the three reanalysis products: European Centre for Medium-Range Weather Forecasts ( ECMWF) retrospective analysis ( ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications ( MERRA), and Climate Forecast System Reanalysis ( CFSR), ERA-Interim (and MERRA) agrees well with the observed precipitation (temperature) data sets while, CFSR shows the least skills in representing the spatio-temporal variations of precipitation and temperature. [ABSTRACT FROM AUTHOR]

To provide information useful in policy formulation and management of drought impacts in Brazil, in this study, a sequence of drought events based on monthly rainfall of 1901-2013 on ~ 25 km x 25 km grid are derived at 4 timescales that include short-timescales (3-month and 6-month) and medium to long-timescales (12-month and 24-month). Subsequently, probability of drought occurrences, intensity, duration and areal-extent are calculated. The probabilities of occurrence of severe and extreme droughts at short-timescales are 1 in 12 and 1 in 66 years, respectively, all over the country. At medium to long-timescales, the probability of severe droughts is about 1 in 20 years in northern Brazil, and 1 in 10 years in the south. The probabilities of extreme droughts are 1 in 9 and 1 in 12 years over northern Brazil and in the south, respectively. In general, no evidence of significant (α = 0.05) trend is detected in drought frequency, intensity, and duration over the last 11 decades (since 1901) at all the 4 timescales. The drought areal-extent show increasing trends of 3.4%/decade over Brazil for both 3-month and 6-month timescales. However, the trend increases for the 12-month and 24-month timescales are relatively smaller, i.e., 2.4%/decade and 0.5%/decade, respectively. [ABSTRACT FROM AUTHOR]

Multiple drought episodes over the Volta basin in recent reports may lead to food insecurity and loss of revenue. However, drought studies over the Volta basin are rather generalised and largely undocumented due to sparse ground observations and unsuitable framework to determine their space-time occurrence. In this study, we examined the utility of standardised indicators (standardised precipitation index (SPI), standardised runoff index (SRI), standardised soil moisture index (SSI), and multivariate standardised drought index (MSDI)) and Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage to assess hydrological drought characteristics over the basin. In order to determine the space-time patterns of hydrological drought in the basin, Independent Component Analysis (ICA), a higher order statistical technique was employed. The results show that SPI and SRI exhibit inconsistent behaviour in observed wet years presupposing a non-linear relationship that reflects the slow response of river discharge to precipitation especially after a previous extreme dry period. While the SPI and SSI show a linear relationship with a correlation of 0.63, the correlation between the MSDIs derived from combining precipitation/river discharge and precipitation/soil moisture indicates a significant value of 0.70 and shows an improved skill in hydrological drought monitoring over the Volta basin during the study period. The ICA-derived spatio-temporal hydrological drought patterns show Burkina Faso and the Lake Volta areas as predominantly drought zones. Further, the statistically significant negative correlations of pacific decadal oscillations (0.39 and 0.25) with temporal evolutions of drought in Burkina Faso and Ghana suggest the possible influence of low frequency large scale oscillations in the observed wet and dry regimes over the basin. Finally, our approach in drought assessment over the Volta basin contributes to a broad framework for hydrological drought monitoring that will complement existing methods while looking forward to a longer record of GRACE observations. [ABSTRACT FROM AUTHOR]

Poor reliability of radiosonde records across South Asia imposes serious challenges in understanding the structure of upper-tropospheric and lower-stratospheric (UTLS) region. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 has overcome many observational limitations inherent in conventional atmospheric sounding instruments. This study examines the interannual variability of UTLS temperature over the Ganges-Brahmaputra-Meghna (GBM) river basin in South Asia using monthly averaged COSMIC radio occultation (RO) data, together with two global reanalyses. Comparisons between August 2006 and December 2013 indicate that MERRA (Modern-Era Retrospective Analysis for Research Application) and ERA-Interim (European Centre for Medium-Range Weather Forecasts reanalysis) are warmer than COSMIC RO data by 2 °C between 200 and 50 hPa levels. These warm biases with respect to COSMIC RO data are found to be consistent over time. The UTLS temperature show considerable interannual variability from 2006 to 2013 in addition to warming (cooling) trends in the troposphere (stratosphere). The cold (warm) anomalies in the upper troposphere (tropopause region) are found to be associated with warm ENSO (El Niño-Southern Oscillation) phase, while quasi-biennial oscillation (QBO) is negatively (positively) correlated with temperature anomalies at 70 hPa (50 hPa) level. PCA (principal component analysis) decomposition of tropopause temperatures and heights over the basin indicate that ENSO accounts for 73% of the interannual (non-seasonal) variability with a correlation of 0.77 with Niño3.4 index whereas the QBO explains about 10% of the variability. The largest tropopause anomaly associated with ENSO occurs during the winter, when ENSO reaches its peak. The tropopause temperature (height) increased (decreased) by about 1.5 °C (300 m) during the last major El Niño event of 2009/2010. In general, we find decreasing (increasing) trend in tropopause temperature (height) between 2006 and 2013. [ABSTRACT FROM AUTHOR]

Poor reliability of radiosonde records across South Asia imposes serious challenges in understanding the structure of upper-tropospheric and lower-stratospheric (UTLS) region. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 has overcome many observational limitations inherent in conventional atmospheric sounding instruments. This study examines the interannual variability of UTLS temperature over the Ganges-Brahmaputra-Meghna (GBM) river basin in South Asia using monthly averaged COSMIC radio occultation (RO) data, together with two global reanalyses. Comparisons between August 2006 and December 2013 indicate that MERRA (Modern-Era Retrospective Analysis for Research Application) and ERA-Interim (European Centre for Medium-Range Weather Forecasts reanalysis) are warmer than COSMIC RO data by 2°C between 200 and 50 hPa levels. These warm biases with respect to COSMIC RO data are found to be consistent over time. The UTLS temperature show considerable interannual variability from 2006 to 2013 in addition to warming (cooling) trends in the troposphere (stratosphere). The cold (warm) anomalies in the upper troposphere (tropopause region) are found to be associated with warm ENSO (El Niño-Southern Oscillation) phase, while quasi-biennial oscillation (QBO) is negatively (positively) correlated with temperature anomalies at 70 hPa (50 hPa) level. PCA (principal component analysis) decomposition of tropopause temperatures and heights over the basin indicate that ENSO accounts for 73% of the interannual (non-seasonal) variability with a correlation of 0.77 with Niño3.4 index whereas the QBO explains about 10% of the variability. The largest tropopause anomaly associated with ENSO occurs during the winter, when ENSO reaches its peak. The tropopause temperature (height) increased (decreased) by about 1.5°C (300 m) during the last major El Niño event of 2009/2010. In general, we find decreasing (increasing) trend in tropopause temperature (height) between 2006 and 2013. [ABSTRACT FROM AUTHOR]

With the vast water resources of West Africa coming under threat due to the impacts of climate variability and human influence, the need to understand its terrestrial water storage (TWS) changes becomes very important. Due to the lack of consistent in-situ hydrological data to assist in the monitoring of changes in TWS, this study takes advantage of the Gravity Recovery and Climate Experiment (GRACE) monthly gravity fields to provide estimates of vertically integrated changes in TWS over the period 2002–2014, in addition to satellite altimetry data for the period 1993–2014. In order to understand TWS variability over West Africa, Principal Component Analysis (PCA), a second order statistical technique, and Multiple Linear Regression Analysis (MLRA) are employed. Results show that dominant patterns of GRACE-derived TWS changes are observed mostly in the West Sahel, Guinea Coast, and Middle Belt regions of West Africa. This is probably caused by high precipitation rates at seasonal and inter-annual time scales induced by ocean circulations, altitude and physiographic features. While the linear trend for the spatially averaged GRACE-derived TWS changes over West Africa for the study period shows an increase of 6.85 ± 1.67 mm/yr, the PCA result indicates a significant increase of 20.2 ± 5.78 mm/yr in Guinea, a region with large inter-annual variability in seasonal rainfall, heavy river discharge, and huge groundwater potentials. The increase in GRACE-derived TWS during this period in Guinea, though inconsistent with the lack of a significant positive linear trend in TRMM based precipitation, is attributed to a large water surplus from prolonged wet seasons and lower evapotranspiration rates, leading to an increase in storage and inundated areas over the Guinea region. This increase in storage, which is also the aftermath of cumulative increase in the volume of water not involved in surface runoff, forms the huge freshwater availability in this region. However, the relatively low maximum water levels of Kainji reservoir in recent times (i.e., 2004/2005, 2007/2008, and 2011/2012) as observed in the satellite altimetry-derived water levels might predispose the Kainji dam to changes that probably may have a negative impact on the socio-economic potentials of the region. GRACE-derived TWS is not well correlated with TRMM-based precipitation in some countries of West Africa and apparently indicates a lag of two months over much of the region. On the other hand, the regression fit between GLDAS-derived TWS and GRACE-derived TWS shows R 2 of 0.85, indicating that trends and variability have been well modeled. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent special reports on climate extremes have shown evidences of changes in the patterns of climate extremes at global, regional and local scales. Understanding the characteristics of climate extremes at regional and local levels is critical not only for the development of preparedness and early warning systems, but is also fundamental in the development of any adaptation strategies. There is still very limited knowledge regarding the past, present and future patterns of climate extremes in the Greater Horn of Africa (GHA). This study, which was supported by the World Bank Global Facility for Disaster Reduction and Recovery (WB-GFDRR) and implemented by the World Meteorological Organization, was organized in terms of three workshops with three main objectives; (1) analysis of daily rainfall and temperature extremes for ten countries in the GHA region using observed in situ data running from 1971 to 2006, (2) assessing whether the United Kingdom Met-office and Hadley centre Providing REgional Climates for Impact Studies (UK-PRECIS) modelling system can provide realistic representation of the past and present climate extremes as observed by available in situ data, and (3) studying the future regional climate extremes under different scenarios to further assess the expected changes in climate extremes. This paper, therefore, uses the outputs of these workshops and also includes post-workshop analyses to assess the changes of climate extremes within the GHA. The results showed a significant decrease in total precipitation in wet days greater than 1 mm and increasing warm extremes, particularly at night, while cold extremes are decreasing. Considering a combination of geophysical models and satellite gravimetry observations from the Gravity Recovery and Climate Experiment (GRACE) mission in the frame of GRACE daily Kalman-smoothing models, for the years 2002 to 2010, we explored a decline in total water storage variations over the GHA. [ABSTRACT FROM AUTHOR]

Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station's (CHIRPS) precipitation and the Global Land Data Assimilation System's (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016–2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period. [ABSTRACT FROM AUTHOR]

Accurate crop yield forecasting is essential in the food industry's decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI's spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models' output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems' radio occultation (GNSS-RO) provides the upper troposphere-lower stratosphere (UTLS) vertical atmospheric profiles that are complementing radiosonde and reanalysis data. Such data are employed in the numerical weather prediction (NWP) models used to forecast global weather as well as in climate change studies. Typically, GNSS-RO operates by remotely sensing the bending angles of an occulting GNSS signal measured by larger low Earth orbit (LEO) satellites. However, these satellites are faced with complexities in their design and costs. CubeSats, on the other hand, are emerging small and cheap satellites; the low prices of building them and the advancements in their components make them favorable for the GNSS-RO. In order to be compatible with GNSS-RO requirements, the clocks of the onboard receivers that are estimated through the precise orbit determination (POD) should have short-term stabilities. This is essential to correctly time tag the excess phase observations used in the derivation of the GNSS-RO UTLS atmospheric profiles. In this study, the stabilities of estimated clocks of a set of CubeSats launched for GNSS-RO in the Spire Global constellation are rigorously analysed and evaluated in comparison to the ultra-stable oscillators (USOs) onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites. Methods for improving their clock stabilities are proposed and tested. The results (i) show improvement of the estimated clocks at the level of several microseconds, which increases their short-term stabilities, (ii) indicate that the quality of the frequency oscillator plays a dominant role in CubeSats' clock instabilities, and (iii) show that CubeSats' derived UTLS (i.e., tropopause) atmospheric profiles are comparable to those of COSMIC-2 products and in situ radiosonde observations, which provided external validation products. Different comparisons confirm that CubeSats, even those with unstable onboard clocks, provide high-quality RO profiles, comparable to those of COSMIC-2. The proposed remedies in POD and the advancements of the COTS components, such as chip-scale atomic clocks and better onboard processing units, also present a brighter future for real-time applications that require precise orbits and stable clocks. [ABSTRACT FROM AUTHOR]

During the period 2019–2020, Lake Victoria water levels rose at an alarming rate that has caused various problems in the region. The influence of this phenomena on surface and subsurface water resources has not yet been investigated, largely due to lack of enough in situ measurements compounded by the spatial coverage of the lake's basin, incomplete/inconsistent hydrometeorological data, and unavailable governmental data. Within the framework of joint data assimilation into a land surface model from multi-mission satellite remote sensing, this study employs the state-of-art Gravity Recovery and Climate Experiment follow-on (GRACE-FO) time-variable terrestrial water storage (TWS), newly released ERA-5 reanalysis, and satellite radar altimetry products to understand the cause of the rise of Lake Victoria on the one hand, and the associated impacts of the rise on the total water storage compartments (surface and groundwater) triggered by the extreme climatic event on the other hand. In addition, the study investigates the impacts of large-scale ocean–atmosphere indices on the water storage changes. The results indicate a considerable increase in water storage over the past two years, with multiple subsequent positive trends mainly induced by the Indian Ocean Dipole (IOD). Significant storage increase is also quantified in various water components such as surface water and water discharge, where the results show the lake's water level rose by ∼ 1.4 m, leading to approximately 1750 gigatonne volume increase. Multiple positive trends are observed in the past two years in the lake's water storage increase with two major events in April–May 2019 and December 2019–January 2020, with the rainfall occurring during the short rainy season of September to November (SON) having had a dominant effect on the lake's rise. [ABSTRACT FROM AUTHOR]

Global hydrological models facilitate studying of water resources and their variations over time. The accuracies of these models are enhanced when combined with ever-increasing satellite remotely sensed data. Traditionally, these combinations are done via data assimilation approach, which permits the use of improved hydrological outputs to study regions with limited in-situ measurements such as the Nile Basin. This study aims at using the state-of-art satellite radar altimetry data to enhance a land-based hydrological model for studying water storage changes over the Nile Basin. Altimetry-derived surface water storage, for the first time, is assimilated into the model using the ensemble Kalman filter (EnKF) for the period of 2003 to 2016. Multiple datasets from ground measurements, as well as space observations, are used to evaluate the performance of the assimilated satellite altimetry data. Results indicate that the assimilation successfully improves model outputs, especially the surface water component. The process increases the correlation between surface water storage changes and water level variations from satellite radar altimetry by 0.44 and reduces the surface water discharge root-mean-square errors (RMSE) by approximately 33%. Unlabelled Image • Altimetry-derived water storage observation are used across the Nile River. • Observations are assimilated into a hydrologic model to improve its estimates. • Data assimilation successfully improves model surface water simulations. • We also find that the process enhanced other water storage components. • Results better reflect hydroclimate variabilities than model without assimilation. [ABSTRACT FROM AUTHOR]

The carbon isotope composition (δ13C Leaf) of C 3 plant leaves provides valuable information on the carbon-water cycle of vegetation and their responses to climate change within terrestrial ecosystems. However, global applications of δ13C Leaf are hindered by a lack of global long-term spatial maps (isoscapes) that capture vegetation δ13C Leaf variations. The ways in which δ13C Leaf varies over time and across regions are still unknown. In this study, we collected leaf carbon isotope samples across the globe and selected the optimal predictive model from three machine learning algorithms to construct long-term annual global δ13C Leaf isoscapes at a spatial resolution of 0.05° for natural C 3 plants between 2001 and 2020. We also assessed the potential of remotely sensed spectral bands, atmospheric CO 2 characteristics, geographic, and physiological information to estimate the δ13C Leaf of the global C 3 plants. Our results show that the random forest (RF) algorithm can more accurately construct the δ13C Leaf isoscape (R2 = 0.61, Nash Sutcliffe = 0.61, RMSE = 1.21‰, MAE = 0.91‰) than the multilayer perceptron (MLP) and support vector machine (SVM). The inclusion of atmospheric CO 2 characteristics, geographical, and physiological information greatly improves prediction compared to relying only on spectral bands. Among the variables, elevation, band 3 spectral reflectance, and solar-induced chlorophyll fluorescence were the three most important variables for constructing the isoscape model, and their relative importance all exceeded 85%. The predicted isoscape revealed strong spatial heterogeneity of δ13C Leaf for C 3 plants at a global scale between different continental regions, with enriched values occurring in high-altitude cold and arid regions, and depleted values occurring in warm, humid, or tropical regions. For the first time, we estimated the global depleted rate of δ13C Leaf in C 3 plant (−0.0491 ± 0.07 ‰ year−1 from 2001 to 2020). Over the past two decades, 86.49% and 1.00% of global grid cells showed more depleted and more enriched trends in δ13C Leaf of C 3 plants, respectively. Our results demonstrated the potential for establishing isoscapes by combining remote sensing, atmospheric CO 2 characteristics, physiological, and geographic variables using the RF machine learning algorithm. The isoscape products generated in this study are valuable for assessing carbon-water coupling of terrestrial ecosystems and for improving land surface models. • Global mapping of δ13C of leaves of natural C 3 plants at 0.05° resolution. • Combining remote sensing and environmental information with machine learning. • Obtain global spatial and temporal patterns of δ13C in the leaves of C 3 plants. • Isoscapes such as these will benefit biogeochemical cycling studies. [ABSTRACT FROM AUTHOR]

Improvements in computational and observational technologies in geoinformatics, e.g., the use of laser scanners that produce huge point cloud data sets, or the proliferation of global navigation satellite systems (GNSS) and unmanned aircraft vehicles (UAVs), have brought with them the challenges of handling and processing this "big data". These call for improvement or development of better processing algorithms. One way to do that is integration of symbolically presolved sub-algorithms to speed up computations. Using examples of interest from real geoinformatic problems, we will discuss the Dixon-EDF resultant as an improved resultant method for the symbolic solution of parametric polynomial systems. We will briefly describe the method itself, then discuss geoinformatics problems arising in minimum distance mapping (MDM), parameter transformations, and pose estimation essential for resection. Dixon-EDF is then compared to older notions of "Dixon resultant", and to several respected implementations of Gröbner bases algorithms on several systems. The improved algorithm, Dixon-EDF, is found to be greatly superior, usually by orders of magnitude, in both CPU usage and RAM usage. It can solve geoinformatics problems on which the other methods fail, making symbolic solution of parametric systems feasible for many problems. [ABSTRACT FROM AUTHOR]

We developed a high-quality global integrated water vapour (IWV) dataset from 12 552 ground-based global positioning system (GPS) stations in 2020. It consists of 5 min GPS IWV estimates with a total number of 1 093 591 492 data points. The completeness rates of the IWV estimates are higher than 95 % at 7253 (58 %) stations. The dataset is an enhanced version of the existing operational GPS IWV dataset provided by the Nevada Geodetic Laboratory (NGL). The enhancement is reached by employing accurate meteorological information from the fifth generation of European ReAnalysis (ERA5) for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. A dedicated data screening algorithm is also implemented. The GPS IWV dataset has a good agreement with in situ radiosonde observations at 182 collocated stations worldwide. The IWV biases are within ±3.0 kg m -2 with a mean absolute bias (MAB) value of 0.69 kg m -2. The standard deviations (SD) of IWV differences are no larger than 3.4 kg m -2. In addition, the enhanced IWV product shows substantial improvements compared to NGL's operational version, and it is thus recommended for high-accuracy applications, such as research of extreme weather events and diurnal variations of IWV and intercomparisons with other IWV retrieval techniques. Taking the radiosonde-derived IWV as reference, the MAB and SD of IWV differences are reduced by 19.5 % and 6.2 % on average, respectively. The number of unrealistic negative GPS IWV estimates is also substantially reduced by 92.4 % owing to the accurate zenith hydrostatic delay (ZHD) derived by ERA5. The dataset is available at 10.5281/zenodo.6973528 (Yuan et al., 2022). [ABSTRACT FROM AUTHOR]

We developed a high-quality global Integrated Water Vapour (IWV) dataset from 12,552 ground-based Global Positioning System (GPS) stations in 2020. It consists of 5-min GPS IWV estimates with a total number of 1,093,591,492 datapoints. The completeness rates of the IWV estimates are higher than 95% at 7253 (58%) stations. The dataset is an enhanced version of the existing operational GPS IWV dataset provided by Nevada Geodetic Laboratory (NGL). The enhancement is reached by employing accurate meteorological information from the fifth generation of European ReAnalysis (ERA5) for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. A dedicated data screening algorithm is also implemented. The GPS IWV dataset has a good agreement with in-situ radiosonde observations at 182 collocated stations worldwide. The IWV biases are within ±3.0 kg m-2 with a Mean Absolute Bias (MAB) value of 0.69 kg m-2. The standard deviations (SD) of IWV differences are no larger than 3.4 kg m-2. In addition, the enhanced IWV product shows substantial improvements compared to NGL's operational version, and it is thus recommended for high-accuracy applications, such as research of extreme weather events and diurnal variations of IWV, and intercomparisons with other IWV retrieval techniques. Taking the radiosonde-derived IWV as reference, the MAB and SD of IWV differences are reduced by 19.5% and 6.2% on average, respectively. The number of unrealistic negative GPS IWV estimates are also substantially reduced by 92.4% owing to the accurate Zenith Hydrostatic Delay (ZHD) derived by ERA5. The dataset is available at: https://doi.org/10.5281/zenodo.6973528 (Yuan et al., 2022). [ABSTRACT FROM AUTHOR]

The overgrowth of macrophytes is a recurrent problem within reservoirs of urbanized and industrialized areas, a condition triggered by the damming of rivers and other human activities. Although the occurrence of aquatic plants in waterbodies has been widely monitored using remote sensing, the influence of climate variables on macrophyte spatiotemporal dynamics is rarely considered in studies developed for medium scales to long periods of time. We hypothesize that the spatial dispersion of macrophytes has its natural rhythms influenced by climate fluctuations, and, as such, its effects on the heterogeneous spatial distribution of this vegetation should be considered in the monitoring of water bodies. A eutrophic reservoir is selected for study, which uses the Normalized Difference Vegetation Index (NDVI) as a proxy for macrophytes. Landsat's NDVI long-term time series are constructed and matched with the Climate Variable (CV) from the National Oceanic and Atmospheric Administration (NOAA) to assess the spatiotemporal dynamics of aquatic plants and their associated climate triggers. The NDVI and CV time series and their seasonal and trend components are correlated for the entire reservoir, compartments, and segmented areas of the water body. Granger-causality of these climate variables show that they contribute to describe and predict the spatial dispersion of macrophytes. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and transport, thereby affecting the entire basin hydrology. In this study, we analyzed watershed-scale processes in the study area, where agriculture and natural shrub land are the dominant LCs. The objective of this study was to assess the time series and spatial patterns of LCC using remotely-sensed data from 1973 to 2018, for which we used six snapshots of satellite images. The LC distribution in relation to watershed characteristics such as topography and soils was also evaluated. For LCC detection analysis, we used Landsat datasets accessed from the United States Geological Survey (USGS) archive, which were processed using remote sensing and Geographic Information System (GIS) techniques. Using these data, four major LC types were identified. The findings of an LC with an overall accuracy above 90% indicates that the area experienced an increase in agricultural LC at the expense of other LC types such as bushland, grazing land, and mixed forest, which attests to the semi-continuous nature of deforestation between 1973 and 2018. In 1973, agricultural land covered only 10% of the watershed, which later expanded to 48.4% in 2018. Bush, forest, and grazing land types, which accounted for 59.7%, 16.7%, and 13.5% of the watershed in 1973, were reduced to 45.2%, 2.3%, and 4.1%, respectively in 2018. As a result, portions of land areas, which had once been covered by pasture, bush, and forest in 1973, were identified as mixed agricultural systems in 2018. Moreover, spatial variability and distribution in LCC is significantly affected by soil type, fertility, and slope. The findings showed the need to reconsider land-use decision tradeoffs between social, economic, and environmental demands. [ABSTRACT FROM AUTHOR]

Downscaling land surface temperatures (LST) from satellite imagery is essential for many fine-scale applications. However, the accuracy of the downscaling is often limited by different environmental and geographical conditions. In this work, a novel LST downscaling framework is proposed to improve the accuracy, especially for heterogeneous areas with varying land covers and complex terrains. The framework focuses on downscaling the MODIS LST from 1 km to 100 m, using the proposed geographically and temporally neural network weighted autoregression (GTNNWAR) model with spatio-temporal fused scaling factors derived from Landsat 8 imagery and digital surface models (DSM). To tackle the issues of the non-stationarity of the scaling factors in heterogenous areas, a region-adaptive parameterization approach is first applied. Then, the GTNNWAR invokes a two-stage deep neural network to estimate the regression coefficients, resulting in the adaption of varying weights for the scaling factors to raise the prediction performance. Moreover, the GTNNWAR is incorporated with a spatial autoregressive model which intakes the neighbor effects so that the overall accuracy can be further improved. Prior to the actual downscaling with the GTNNWAR, a filter-based fusion method is applied to ensure the spatio-temporal consistency of scaling factors is high enough for the neural networks to converge. The results suggest that the proposed framework exhibits high accuracy at the boundaries of different land covers and complex terrains. Compared with several other downscaling algorithms in three case study areas (Beijing and Zhangye in China, Netherlands–Germany in Europe), the proposed framework outperforms with a 28% improved R-squared (R2) and a root mean square error (RMSE) of 1.02 K. In addition, the downscaled LST has R2 over 0.63 for the UAV observations (Guangdong). It is concluded that the proposed framework has high reliability and robustness to provide LST datasets with high spatio-temporal resolutions in a wide range of land types. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R2 improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process. • A novel PWV fusion framework embedded with super-resolution and autoregression. • The framework delivers seamless daily 0.01° PWV to support related applications. • Fused PWV is highly consistent with GPS data (r = 0.82–0.95 and RMSE = 2.21–4.01 mm). • The framework provides over 24% improvement compared to the traditional methods. [ABSTRACT FROM AUTHOR]

• The carbon and water cycles of frozen soil ecosystems on the Qinghai-Tibet Plateau were analyzed. • Water use efficiency and gross primary productivity decreased progressively with stronger freezing of the frozen soil. • Evapotranspiration increased and then decreased with increasing freezing gradient. • Vegetation dominated the change in water-carbon coupling over the largest area in each permafrost type except predominantly continuous and island permafrost. The carbon and water cycle patterns of frozen soil ecosystems in regions, such as the Qinghai-Tibet Plateau (QTP), which have fragile and sensitive ecologies, are possibly affected by climate change. However, these patterns along the gradients of varying degrees of freezing remain poorly understood. In this study, we examine the spatio-temporal patterns of water use efficiency (WUE), gross primary productivity (GPP), and evapotranspiration (ET) in six frozen soil zones of the Qinghai-Tibet Plateau (QTP) with varying degrees of frozen soil. The analysis is based on the frozen soil classification criteria and remote sensing data from 2001 to 2020. The influences of six climatic, nine edaphic, and two botanic parameters on these patterns is also investigated. The results indicate that 48.05%, 24.07%, and 20% of the regions in QTP exhibit significantly upward trends of GPP, ET, and WUE, respectively. The highest increase trends were observed in GPP in the middle-thick seasonally frozen ground zone (2.36 g C m−2 yr−1), ET in the mountain permafrost zone (2.35 mm yr−1), and WUE in the predominantly continuous permafrost zone (0.003 g C kg−1 H 2 O yr−1). Meanwhile, GPP and WUE decrease progressively with the increase of freezing degrees, from the short-time frozen ground zone (GPP: 604.99 ± 51.99 g C m−2; WUE: 2.34 ± 0.15 g C kg−1 H 2 O) to the predominantly continuous permafrost zone (GPP: 114.07 ± 11.77 g C m−2; WUE: 0.33 ± 0.04 g C kg−1 H 2 O), while ET does not show significant differences across the three permafrost zones. The enhanced vegetation index is the primary factor affecting GPP and WUE in most frozen soil zones, whereas climate variables mainly impact ET. Our findings emphasize the importance of considering the effects of different freezing degrees of frozen soil when analyzing the response of the carbon and water cycles in the QTP in the context of climate change. [ABSTRACT FROM AUTHOR]

Observation weighting is an essential component of GPS stochastic modeling and plays a key role in reliable outlier detection and parameter estimation. Nowadays, satellite elevation angle and SNR are used as quality indicators for GPS phase measurements in high-accuracy geodetic applications. In comparison with elevation-dependent models, SNR-based weighting schemes represent the reality better, but usually require greater implementation efforts. Relying upon a representative analysis of empirical SNR-based weights, this paper proposes the elevation-dependent exponential weighting function EXPZ, which benefits from realistic SNR-based weights and enables easy software implementation. To process GPS data from a regional network, this advanced weighting scheme is implemented in the Bernese GPS Software 5.0 and is compared with the conventional elevation-dependent COSZ model in terms of phase ambiguity resolution, troposphere parameter (TRP) estimation, and site coordinate determination. The results show that the proposed EXPZ model significantly attenuates the downweighting effects on low-elevation observations and improves the success rates of ambiguity resolution by about 10%, the standard deviations of site-specific TRPs by about 40%, and the repeatability of daily coordinate estimates by up to 2.3 mm (50%). [ABSTRACT FROM PUBLISHER]

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management. Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162 days as well as a-hopefully-limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near-real-time GRACE forecast over the regions that exhibit strong teleconnections. [ABSTRACT FROM AUTHOR]

Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with realistic uncertainties remains a challenge. This study evaluates the feasibility of the IWV trends derived from the newly released fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) for climate change analysis in continental Europe. This is achieved by comparing the trends derived from in-situ ground-based Global Positioning System (GPS)'s daily IWV series from 1994 to 2019 at 109 stations. The realistic uncertainties and statistical significances of the IWV trends are evaluated with the time series analysis on their noise characteristics and proper noise models. Results show that autoregressive moving average ARMA(1,1) noise model is preferred rather than the commonly assumed white noise (WN) or first - order autoregressive AR(1) noise for about 68% of the ERA5 and GPS IWV series. An improper noise model would misevaluate the trend uncertainty of an IWV time series, compared with its specific preferred noise model. For example, ARMA(1,1) may misevaluate the standard deviations of their trend estimates (0.1–0.3 kg m−2 decade−1) by 10%. Nevertheless, ARMA(1,1) is recommended as the default noise model for the ERA5 and GPS IWV series. However, the preferred noise model for each ERA5 minus GPS (E -G) IWV series should be specifically determined, because the AR(1)-related models can result in an underestimation on its trend uncertainty by 90%. In contrast, power-law (PL) model can lead to an overestimation by up to nine times. The E -G IWV trends are within −0.2–0.4 kg m−2 decade−1, indicating that the ERA5 is a potential data source of IWV trends for climate change analysis in continental Europe. The ERA5 and GPS IWV trends are consistent in their magnitudes and geographical patterns, lower in Northwest Europe (0–0.4 kg m−2 decade−1) but higher around the Mediterranean Sea (0.6–1.4 kg m−2 decade−1). • Improper noise models would bias the uncertainties of Integrated Water Vapor trends. • ARMA(1,1) model is recommended for daily ERA5 and GPS IWV time series analysis. • Proper noise models for the daily ERA5 minus GPS IWV series analysis are varied. • ERA5 is a potential data source of IWV trends for climate change analysis in Europe. • The IWV trends are generally 0–1.4 kg m−2 decade−1 in Europe and 69% are significant. [ABSTRACT FROM AUTHOR]

River damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over-abundance of aquatic macrophytes resulting from the input of high concentration of nutrients in the ecosystem that can affect the health of the ecosystem. In these situations, macrophytes are treated as weeds that need to be continuously monitored and analysed over time. Historically, remote sensing has played a prominent role in change detection studies and, nowadays, considering the open data sources of multi-temporal images and the high computational performance that allows for larger volumes of historical images to be mined, water monitoring is a recurrent object of analysis. The Salto Grande reservoir is a small water body located in the metropolitan region of Campinas, São Paulo, Brazil, characterized by high rates of urbanization and industrialization. The intense anthropic occupation around the reservoir triggered the degradation of the landscape and the decrease of water quality. This study explored the potential of image-attributes' time series to monitor the spatio-temporal behavior of aquatic macrophytes in the Salto Grande Reservoir. Our assumption was that the combination of techniques for analyzing large multi-temporal datasets enables us to understand the trends and changes in the macrophytes occurrence in this small reservoir. To achieve this, quarterly Normalized Difference Vegetation Index (NDVI) time series based on Landsat data imagery from 1984 to 2017 were built to analyze the occurrence and persistence of these aquatic plants in the reservoir. A principal component analysis (PCA) was applied to the NDVI time series, which allowed us to identify typical years in the abundance of macrophytes and twelve regions of greater and lesser temporal variability in its abundance, by a K-means aggregation of the first principal component scores. For these regions, the Breaks for Additive and Seasonality Trend (BFAST) algorithm was used to analyze the trend, cyclic behaviour, and changes in the time series of the average NDVI. BFAST was able to detect gradual and abrupt changes for each of the twelve areas by searching for breakpoints in the temporal series. It was observed that the regions near the dam and where the conditions of the river are still maintained are most affected by the occurrence of macrophytes, characterized by an average NDVI greater than 0.4. Although subject to more subtle seasonal variations, all these regions defined at least one breakpoint, suggesting abrupt changes such as sharp interventions to control the overabundance of macrophytes at specific time. The regions located in the middle of the reservoir, with a more lacustrine influence, had lower average NDVI and small variations over time. Thus, it was possible to identify the critical regions of the studied reservoir with excess of growing macrophytes through the applied method, which also can be applied to similar areas. • PCA and K-means grouped spatiotemporal similar areas in an NDVI Landsat time series. • BFAST detected abrupt changes in macrophytes' growth trend in areas near the river. • Macrophyte's abundance and persistence occurred in areas near the dam and the river. [ABSTRACT FROM AUTHOR]

Current approaches for obtaining shoreline change rates suffer from inability to give a specialist interpretation of the numerical results represented by velocities (m/yr). This study proposes a fuzzy model for coastal zone human impact classification that integrates shoreline changes, NDVI, and settlement influences to enhance numerical-linguistic fuzzy classification through Geographical Information System (GIS)'s graphical visualization prowess. The model output representing scores are numbers ranging from zero to one, which are convertible into fuzzy linguistic classification variables; i.e., low , moderate , and high on the one hand. On the other hand, use of GIS through NDVI (Normalized Difference Vegetation Index) provides enhancement through graphic visualization. Using Itamaraca Island in Brazil as an example, multi-temporal satellite images are processed to provide all the required input variables. The resulting output divides the entire island into five sectors representing both quantitative and qualitative outcomes (i.e., fuzzy classification composed of both scores and maps), showcasing the capability of the proposed approach to complement shoreline change analysis through physical (map) interpretation in addition to the frequently used numbers. The proposed fuzzy model is validated using random in-situ samples and high resolution image data that has been classified by a coastal geomorphology specialist. The accuracy of the interpretation show 81% of matches are achievable compared to the results of the fuzzy model. The final results delivered by the proposed fuzzy approach show the complex behavior of the local dynamics, thereby adding useful and substantial information for environmental issues and Integrated Coastal Zone Management. [ABSTRACT FROM AUTHOR]