An Integrated Modeling System for Estimating Glacier and Snow Melt Driven Streamflow from Remote Sensing and Earth System Data Products in the Himalayas

Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo- SFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010), the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification of water availability

A Tool for Downscaling Weather Data from Large-grid Reanalysis Products to Finer Spatial Scales for Distributed Hydrological Applications

A downscaling tool was developed to provide sub-daily high spatial resolution surfaces of weather variables for distributed hydrologic modeling from NASA Modern Era Retrospective-Analysis for Research and Applications reanalysis products. The tool uses spatial interpolation and physically based relationships between the weather variables and elevation to provide inputs at the scale of a gridded hydrologic model, typically smaller (∼100 m) than the scale of weather reanalysis data (∼20–200 km). Nash-Sutcliffe efficiency (NSE) measures greater than 0.70 were obtained for direct tests of downscaled daily temperature and monthly precipitation at 173 SNOTEL sites. In an integrated test driving the Utah Energy Balance (UEB) snowmelt model, 80% of these sites gave NSE \u3e 0.6 for snow water equivalent. These findings motivate use of this tool in data sparse regions where ground based observations are not available and downscaled global reanalysis products may be the only option for model inputs

Improving the Physical Processes and Model Integration Functionality of an Energy Balance Model for Snow and Glacier Melt

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available dat

The Enigmatic Quaternary Shell Bed Deposits of Lake Tanganyika, Tanzania: Linking Anthropogenic Land-Use with Nearshore Sedimentation

Extensive deposits of carbonate shell-rich sediments in the nearshore environment ( 100 km2). Here, we suggest that observed onshore watershed disturbances result in sediments that are muddier with a higher percentage of clay in the area offshore of the Lagosa and Rukoma river watersheds compared to the area offshore of the largely unaltered Katumbi river watershed. Widespread burning of land, agricultural land-use, and the torrential rains of the wet season are causal factors that result in significant increases in fine-grained clastic sedimentation, increased fluvio-deltaic sediment plume size, and increased delta shoreline progradation. In areas most affected by sedimentation, littoral sponges are largely absent, and we speculate that other important benthic organisms that inhabit the shell beds are also negatively impacted by the influx of fine-grained clastic sediment. The discovery of a small population of Neothauma tanganyicense offshore of the relatively unaltered Katumbi river watershed suggests that the shell beds’ heterogeneity could be related to anthropogenic land-use and its effect on the occurrence of shell-bed forming gastropods

Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Assessment of satellite derived rainfall and its use in the ACRU hydrological model.

Master of Science in Hydrology. University of KwaZulu-Natal, Pietermaritzburg, 2017.Many parts of southern Africa are considered water scarce regions. Therefore, sound management and decision making is important to achieve maximum usage with sustainability of the precious resource. Hydrological models are often used to inform management decisions; however model performance is directly linked to the quality of data that is input. Rainfall is a key aspect of hydrological systems. Understanding the spatial and temporal variations of rainfall is of paramount importance to make key management decisions within a management area. Rainfall is traditionally measured through the use of in-situ rain gauge measurements. However, rain gauge measurements poorly represent the spatial variations of rainfall and rain gauge networks are diminishing, especially in southern Africa. Due to the sparse distribution of rain gauges and the spatial problems associated with rain gauge measurements, the use of satellite derived rainfall is being increasingly advocated. The overall aim of this research study was to investigate the use of satellite derived rainfall into the ACRU hydrological model to simulate streamflow. Key objectives of the study included (i) the validation of satellite derived rainfall with rain gauge measurements, (ii) generation of time series of satellite derived rainfall to drive the ACRU hydrological model, and (iii) validation of simulated streamflow with measured streamflow. The products were evaluated in the upper uMngeni, upper uThukela (summer rainfall) as well as the upper and central Breede catchments (winter rainfall). The satellite rainfall products chosen for investigation in this study included TRMM 3B42, FEWS ARC2, FEWS RFE2, TAMSAT-3 and GPM. The satellite rainfall products were validated using rain gauges in and around the study sites from 1 January 2010 to 30 April 2017. The rainfall products performed differently at each location with high variation in daily magnitudes of rainfall. Total rainfall volumes over the period of analysis were generally in better agreement with rain gauge volumes with TRMM 3B42 tending to overestimate rainfall volumes whereas the other products underestimated rainfall volumes. The ACRU model was applied using satellite rainfall and rain gauge measurements in the aforementioned study catchments from 1 October 2007 to 30 September 2016. Streamflow results were generally poor and variable amongst products. Daily correlations of streamflow were poor. Total streamflow volumes were in better agreement with total volumes of observed streamflow. TRMM 3B42 and rain gauge driven simulations produced the best results in the summer rainfall region, whereas the FEWS driven simulations produced the best results in the winter rainfall region

Evaluating uncertainty in water resources estimation in Southern Africa : a case study of South Africa

Hydrological models are widely used tools in water resources estimation, but they are simple representations of reality and are frequently based on inadequate input data and uncertainties in parameter values. Data observation networks are expensive to establish and maintain and often beyond the resources of most developing countries. Consequently, measurements are difficult to obtain and observation networks in many countries are shrinking, hence obtaining representative observations in space and time remains a challenge. This study presents some guidelines on the identification, quantification and reduction of sources of uncertainty in water resources estimation in southern Africa, a data scarce region. The analyses are based on example sub-basins drawn from South Africa and the application of the Pitman hydrological model. While it has always been recognised that estimates of water resources availability for the region are subject to possible errors, the quantification of these uncertainties has never been explicitly incorporated into the methods used in the region. The motivation for this study was therefore to contribute to the future development of a revised framework for water resources estimation that does include uncertainty. The focus was on uncertainties associated with climate input data, parameter estimation (and recognizing the uncertainty due model structure deficiencies) methods and water use data. In addition to variance based measures of uncertainty, this study also used a reservoir yield based statistic to evaluate model output uncertainty, which represents an integrated measure of flow regime variations and one that can be more easily understood by water resources managers. Through a sensitivity analysis approach, the results of the individual contribution of each source of uncertainty suggest regional differences and that clear statements about which source of uncertainty is likely to dominate are not generally possible. Parameter sensitivity analysis was used in identifying parameters which are important withinspecific sub-basins and therefore those to focus on in uncertainty analysis. The study used a simple framework for evaluating the combined contribution of uncertainty sources to model outputs that is consistent with the model limitations and data available, and that allows direct quantitative comparison between model outputs obtained by using different sources of information and methods within Spatial and Time Series Information Modelling (SPATSIM) software. The results from combining the sources of uncertainties showed that parameter uncertainty dominates the contribution to model output uncertainty. However, in some parts of the country especially those with complex topography, which tend to experience high rainfall spatial variability, rainfall uncertainty is equally dominant, while the contributions of evaporation and water use data uncertainty are relatively small. While the results of this study are encouraging, the weaknesses of the methods used to quantify uncertainty (especially subjectivity involved in evaluating parameter uncertainty) should not be neglected and require further evaluations. An effort to reduce data and parameter uncertainty shows that this can only be achieved if data access at appropriate scale and quality improves. Perhaps the focus should be on maintaining existing networks and concentrating research efforts on making the most out of the emerging data products derived from remote sensing platforms. While this study presents some initial guidelines for evaluating uncertainty in South Africa, there is need to overcome several constraints which are related to data availability and accuracy, the models used and the capacity or willingness to adopt new methods that incorporate uncertainty. The study has provided a starting point for the development of new approaches to modelling water resources in the region that include uncertain estimates

A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h−1) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation

Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

International audienceKnowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the TropicalRainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB

Satellite rainfall for food security on the African continent: performance and accuracy of seven rainfall products between 2001 and 2016

Food security is the most emerging issue for most countries on the African continent. The major share of the agricultural production in Africa is rain fed whereas many of the irrigated zones are in water scarce and/or transboundary river basins. Accurate and detailed information on rainfall distribution is essential to support food security and water accounting applications. However, the network of meteorological stations that may provide such information is sparse. Gridded precipitation products developed from satellite imagery sources have become increasingly available as an alternative or an addition to observed rainfall. The goal of this study was to evaluate and compare seven existing rainfall products for the Africa continent for the purpose of food security monitoring and water resources applications. We made a point-to-grid comparison for daily, dekadly, monthly and annual time steps of approximately 1,100 WMO stations that reported rainfall between 2001 and 2016. The rainfall products that were included are ARC, CHIRPS, PERSIANN, RFE, TAMSAT, TRMM 3B42 and MSWEP. A set of 5 continuous statistical indicators (Pearson correlation coefficient, Mean Erorr, Bias, Root Mean Square Error and Nash–Sutcliffe Efficiency coefficient) and two categorical indicators (Probability of Detection and False Alarm Ratio) were used. WMO stations are unevenly distributed across Africa; though the total number of daily precipitation observations in Africa has increased by 50% from around 400 in 2001 to 600 in 2016. In West-Africa the coverage has improved significantly with the exception of Guinea-Conakry, Liberia and Sierra Leone. In Eastern and Southern Africa less stations are reporting with the exception of Tanzania. “White areas” are the horn of Africa, the sparsely populated Sahel desert stretching from Mauritania to Sudan and a belt in central Africa from Sudan down south to Angola. Important high intensity rainfall zones in West Africa (Fouta Djallon highlands, Nigeria) and Central Africa (Congo Basin) as well as Ethiopia in East-Africa have poor observation records. Our analysis confirmed earlier findings that daily rainfall estimations of all products are weakly related with daily observation. We therefore focused on the cumulative values for dekads and months that we averaged for all stations in Africa. These values show that the most reliable products are MSWEP, CHIRPS, ARC and RFE, although the last two have the lowest ME equaling -0.80 and 1.22 respectively. TRMM has the highest ME value (0.11) but depicts poor scores for the other six indictors. Weakest products are TAMSAT and PERSIANN that were ranked lowest for almost all statistical indicators. Preliminary spatial results show that the performance of the products for coastal stations is lower than inland stations. The Bias of most products is high in north Africa compared to similar areas in Sub-Saharan Africa