Physical dynamics of Lake Victoria over the past 34 years (1984-2018): Is the lake dying?

Developing meaningful water-energy-food-environment (WEFE) nexus indicators with stakeholders: An Upper White Nile case study

The Upper White Nile (UWN) basin plays a critical role in supporting essential ecosystem services and the livelihoods of millions of people in East Africa. The basin has been exposed to tremendous environmental pressures following high population growth, urbanisation, and land use change, all of which are compounded by the threats posed by climate change and insufficient financial and human resources. The water-energy-food-environment (WEFE) nexus provides a framework to assess solution options towards sustainable development by minimising the trade-offs between water, energy, and food resources. However, the majority of existing WEFE nexus indicators and tools tend to be developed without consideration of practitioners at the local level, thus constraining the practical application within real-world contexts. To try to address this gap and operationalise the WEFE nexus, we examined how local stakeholders frame the most pressing WEFE nexus challenges within the UWN basin, how these can be represented as indicators, and how existing WEFE nexus modelling tools could address this. The findings highlight the importance of declining water quality and aquatic ecosystem health as a result of deforestation and increasing agricultural intensity, with stakeholders expressing concerns for the uncertain impacts from climate change. Furthermore, a review of current WEFE nexus modelling tools reveals how they tend to be insufficient in addressing the most pressing environmental challenges within the basin, with a significant gap regarding the inclusion of water quality and aquatic ecosystem indicators. Subsequently, these findings are combined in order to guide the development of WEFE nexus indicators that have the potential to spatially model the trade-offs within the WEFE nexus in the UWN basin under climate change scenarios. This work provides an example of how incorporating local stakeholder's values and concerns can contribute to the development of meaningful indicators, that are fit-for-purpose and respond to the actual local needs.

A century of human-induced environmental changes and the combined roles of nutrients and land use in Lake Victoria catchment on eutrophication

Lake Victoria, a lifeline for millions of people in East Africa, is affected by anthropogenic activities resulting in eutrophication and impacting the aquatic life and water quality. Therefore, understanding the ongoing changes in the catchment is critical for its restoration. In this context, catchment and lake sediments are important archives in tracing nutrient inputs and their dominant sources to establish causality with human activities and productivity shifts. In this study, we determine the 1) changes in concentrations of total organic carbon (TOC), black carbon (BC), total nitrogen (TN), C/N ratio, and phosphorous (P) fractions in catchment sediments and the open lake, 2) distribution of diatom population in the lake, and 3) land use and land cover changes in the catchment. The distribution of TOC, BC, TN, C/N, and P correlate while showing spatial and temporal variations. In particular, the steady increase in BC confirms atmospheric inputs from anthropogenic activities in the catchment. However, lake sediments show more variations than catchment-derived sediments in geochemical trends. Notably, the catchment has undergone dramatic land use changes since the 1960s (post-independence). This change is most evident in satellite records from 1985 to 2014, which indicate accelerated human activities. For example, urban growth (666-1022%) and agricultural expansion (23-48%) increased sharply at the expense of a decline in forest cover, grassland, and woodlands in the catchment. Cities like Kisumu and Homa Bay expanded, coinciding with rapid population growth and urbanization. Consequently, nutrient inputs have increased since the 1960s, and this change corresponds with the divergence of diatom communities in the lake. In addition, the transition to Nitzschia and cyanobacteria mark increasing cultural eutrophication in the lake. The geochemical trends and statistical data support our inference(s) and provide insights into urban development and agriculture practices, which propelled increased nutrients from the catchment and productivity shifts in the lake.

Assessing Climate Influence on Spatiotemporal Dynamics of Macrophytes in Eutrophicated Reservoirs by Remotely Sensed Time Series

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.

Spatio-Temporal Analysis of Sawa Lake’s Physical Parameters between (1985–2020) and Drought Investigations Using Landsat Imageries

Lake Sawa located in Southwest Iraq is a unique natural landscape and without visible inflow and outflow from its surrounding regions. Investigating the environmental and physical dynamics and the hydrological changes in the lake is crucial to understanding the impact of hydrological changes, as well as to inform planning and management in extreme weather events or drought conditions. Lake Sawa is a saltwater lake, covering about 4.9 square kilometers at its largest in the 1980s. In the last decade, the lake has dried out, shrinking to less than 75% of its average size. This contribution focuses on calculating the bank erosion and accretion of Lake Sawa utilizing remote sensing data captured by Landsat platforms (1985–2020). The methodology was validated using higher-resolution Sentinel imagery and field surveys. The outcomes indicated that the area of accretion is significantly higher than erosion, especially of the lake’s banks in the far north and the south, in which 1.31 km2 are lost from its surface area. Further analysis of especially agricultural areas around the lake have been performed to better understand possible reasons causing droughts. Investigations revealed that one possible reason behind droughts is related to the rapid increase in agriculture areas surrounding the lake. It has been found that the agriculture lands have expanded by 475% in 2020 compared to 2010. Linear regression analysis revealed that there is a high correlation (69%) between the expanding of agriculture lands and the drought of Lake Sawa.

Precipitation and Soil Moisture Spatio-Temporal Variability and Extremes over Vietnam (1981-2019): Understanding Their Links to Rice Yield

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.

The 2019-2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products

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.

Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin

This study compares LSTM neural network and wavelet neural network (WNN) for spatio-temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic basins. Using long-term in situ observed data for 30 years (1980–2009) from ten rain gauge stations and three discharge measurement stations, the rainfall and runoff trends in the Nzoia River basin are predicted through satellite-based meteorological data comprising of: precipitation, mean temperature, relative humidity, wind speed and solar radiation. The prediction modelling was carried out in three sub-basins corresponding to the three discharge stations. LSTM and WNN were implemented with the same deep learning topological structure consisting of 4 hidden layers, each with 30 neurons. In the prediction of the basin runoff with the five meteorological parameters using LSTM and WNN, both models performed well with respective R2 values of 0.8967 and 0.8820. The MAE and RMSE measures for LSTM and WNN predictions ranged between 11–13 m3/s for the mean monthly runoff prediction. With the satellite-based meteorological data, LSTM predicted the mean monthly rainfall within the basin with R2 = 0.8610 as compared to R2 = 0.7825 using WNN. The MAE for mean monthly rainfall trend prediction was between 9 and 11 mm, while the RMSE varied between 15 and 21 mm. The performance of the models improved with increase in the number of input parameters, which corresponded to the size of the sub-basin. In terms of the computational time, both models converged at the lowest RMSE at nearly the same number of epochs, with WNN taking slightly longer to attain the minimum RMSE. The study shows that in hydrologic basins with scarce meteorological and hydrological monitoring networks, the use satellite-based meteorological data in deep learning neural network models are suitable for spatial and temporal analysis of rainfall and runoff trends.

An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes

Inland lakes are essential components of hydrological and biogeochemical water cycles, as well as indispensable water resources for human beings. To derive the long-term and continuous trajectory of lake inundation area changes is increasingly significant. Since it helps to understand how they function in the global water cycle and how they are impacted by climate change and human activities. Employing optical satellite images, as an important means of lake mapping, has been widely used in the monitoring of lakes. It is well known that one of the obvious difficulties of traditional remote sensing-based mapping methods lies in the tremendous labor and computing costs for delineating the large lakes (e.g., Caspian Sea). In this study, a novel approach of reconstructing long-term and high-frequency time series of inundation areas of large lakes is proposed. The general idea of this method is to obtain the lake inundation area at any specific observation date by referring to the mapping relationship of the water occurrence frequency (WOF) of the selected shoreline segment at relatively slight terrains and lake areas based on the pre-established lookup table. The lookup table to map the links of the WOF and lake areas is derived from the Joint Research Centre (JRC)Global Surface Water (GSW) dataset accessed in Google Earth Engine (GEE). We select five large lakes worldwide to reconstruct their long time series (1984-2018) of inundation areas using this method. The time series of lake volume variation are analyzed, and the qualitative investigations of these lake changes are eventually discussed by referring to previous studies. The results based on the case of North Aral Sea show that the mean relative error between estimated area and actually mapped value is about 0.85%. The mean R² of all the five lakes is 0.746, which indicates that the proposed method can produce the robust estimates of area time series for these large lakes. This research sheds new light on mapping large lakes at considerably deducted time and labor costs, and be effectively applicable in other large lakes in regional and global scales.