Estimating rainfall and water balance over the Okavango River Basin for hydrological applications

A historical database for use in rainfall-runoff modeling of the Okavango River Basin in Southwest Africa is presented. The work has relevance for similar data-sparse regions. The parameters of main concern are rainfall and catchment water balance which are key variables for subsequent studies of the hydrological impacts of development and climate change. Rainfall estimates are based on a combination of in-situ gauges and satellite sources. Rain gauge measurements are most extensive from 1955 to 1972, after which they are drastically reduced due to the Angolan civil war. The sensitivity of the rainfall fields to spatial interpolation techniques and the density of gauges was evaluated. Satellite based rainfall estimates for the basin are developed for the period from 1991 onwards, based on the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) data sets. The consistency between the gauges and satellite estimates was considered. A methodology was developed to allow calibration of the rainfall-runoff hydrological model against rain gauge data from 1960-1972, with the prerequisite that the model should be driven by satellite derived rainfall products for the 1990s onwards. With the rain gauge data, addition of a single rainfall station (Longa) in regions where stations earlier were lacking was more important than the chosen interpolation method. Comparison of satellite and gauge rainfall outside the basin indicated that the satellite overestimates rainfall by 20%. A non-linear correction was derived used by fitting the rainfall frequency characteristics to those of the historical rainfall data. This satellite rainfall dataset was found satisfactory when using the Pitman rainfall-runoff model (Hughes et al., this issue). Intensive monitoring in the region is recommended to increase accuracy of the comprehensive satellite rainfall estimate calibration procedur

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

Evaluation of runoff estimation from GRACE coupled with different meteorological gridded products over the Upper Blue Nile Basin

Study Region: The Upper Blue Nile (UBN) basin, Ethiopia. Study Focus: In efforts to accurately close the water balance equation for the UBN basin using remote sensing products, river runoff is calculated as a residual from the water balance equation by incorporating Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and remote sensing products for precipitation (P) and evapotranspiration (ET). The calculated river runoff is then compared to the gauge records located at the basin's outlet. The best performing combination among the various combinations is chosen by aggregating rankings attributed to both error and linear fit metrics. The errors associated with each satellite product were assessed by forcing the In-Situ runoff to estimate the P, ET, and TWS. This methodology helps in addressing the uncertainty linked with each hydrological component. New Hydrological Insights for the Region: The best P, ET, and TWS combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE Spherical Harmonic products, respectively. The statistical results for the six metrics are R2 = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The uncertainty from each hydrological component was quantified and showed that improving the accuracy of P and ET estimation is a crucial step to successfully close the water balance

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

Multi-scale actual evapotranspiration mapping in South America with remote sensing data and the geeSEBAL model

O monitoramento preciso da evapotranspiração (ET) é crucial para gerenciar os recursos hídricos, garantir a segurança alimentar e avaliar os impactos das mudanças climáticas. Modelos de Balanço de Energia da Superfície (SEB) que usam dados de sensoriamento remoto são os mais confiáveis para estimar a ET, mas muitas vezes são difíceis de aplicar em grande escala devido ao longo tempo de processamento, necessidade de calibração local, entre outros obstáculos. Esta tese tem como foco a melhoria do geeSEBAL, uma implementação do modelo Surface Energy Balance Algorithm for Land (SEBAL) na plataforma Google Earth Engine (GEE), adaptando-o para modelagem em escala continental, usando imagens do Moderate Resolution Imaging Spectroradiometer (MODIS). O novo modelo, chamado geeSEBALMODIS, foi usado para gerar uma série temporal de ET a cada 8 dias para a América do Sul com pixels de 500 m. Estudos de validação mostram que o geeSEBAL-MODIS é mais preciso do que outros produtos globais de ET, com uma redução do erro de 13% na escala de campo e 30% na escala de bacia hidrográfica. O conjunto de dados está disponível para o público e pode ser usado para monitorar tanto mudanças climáticas em grande escala quanto as variações locais de ET relacionadas às atividades humanas. A análise de tendências mostra um aumento de 8,4% na ET sobre a América do Sul, associado ao aumento da demanda atmosférica, e à redução da precipitação e disponibilidade de água. Esses resultados destacam a importância de informações precisas sobre os processos do ciclo hidrológico para auxiliar no planejamento e gerenciamento dos recursos hídricos em um cenário de maior escassez. Nesse contexto, projetos como o OpenET, que fornece dados confiáveis e de alta resolução espacial de ET nos Estados Unidos, são cruciais para monitorar o consumo de água e auxiliar no desenvolvimento sustentável. Este trabalho também apresenta uma reprodução parcial do processo do OpenET para a intercomparação de modelos de sensoriamento remoto com dados de torres de fluxo, usando torres micrometeorológicas na América do Sul. Os resultados são promissores e abrem caminho para a expansão do OpenET além dos Estados Unidos e em direção a uma aplicação global.Accurately monitoring evapotranspiration (ET) is crucial for managing water resources, ensuring food security, and assessing the impacts of climate change. Surface Energy Balance (SEB) models that use remote sensing data are the most reliable for estimating ET, but they are often challenging to apply on a large scale due to long processing times, and local calibration requirements, among other obstacles. This dissertation focuses on improving geeSEBAL, an implementation of the Surface Energy Balance Algorithm for Land (SEBAL) model on the Google Earth Engine (GEE) platform, by adapting it for continental-scale modeling using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The new model, called geeSEBAL-MODIS, was used to generate a temporal series of ET every 8 days for South America with pixels of 500 m. Validation studies show that geeSEBAL-MODIS is more accurate than other global ET products, with a reduction in error of 13% at the field scale and 30% at the basin scale. The dataset is publicly available and can be used to monitor both largescale climate change and local ET variations related to human activities. Trend analysis shows an 8.4% increase in ET over South America, associated with increased atmospheric demand, and reductions in precipitation and water availability. These findings underscore the importance of accurate information on hydrological cycle processes to assist in planning and managing water resources in a scenario of greater scarcity. In this context, projects like OpenET, which provides reliable and high spatial-resolution ET data in the United States, are crucial for monitoring water consumption and aiding in sustainable development. This work also presents a partial reproduction of the OpenET process for intercomparing remote sensing models with flux tower data, using micrometeorological towers in South America. The results are promising and pave the way for expanding OpenET beyond the United States and toward global application

Integrating remote sensing information into a distributed hydrological model for improved water budget predictions in large - scale basins through data assimilation

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems

Amazon hydrology from space : scientific advances and future challenges

As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure