Improving rainfall erosivity estimates using merged TRMM and gauge data

Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are often unavailable in many areas of the world. Satellite remote sensing allows provision of the continuous gridded estimates of rainfall, yet it is generally characterized by significant bias. Here we present a methodology that merges daily rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data using collocated cokriging (ColCOK) to quantify the spatial distribution of rainfall and thereby to estimate rainfall erosivity across China. This study also used block kriging (BK) and TRMM to estimate rainfall and rainfall erosivity. The methodologies are evaluated based on the individual rain gauge stations. The results from the present study generally indicate that the ColCOK technique, in combination with TRMM and gauge data, provides merged rainfall fields with good agreement with rain gauges and with the best accuracy with rainfall erosivity estimates, when compared with BK gauges and TRMM alone

Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPMIMERG and Comprehensive Assessment (2000–2020)

In soil erosion estimation models, the variable with the greatest impact is rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000-2020. By using this method, a correlation of 0.7 was found between the PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 4831 M Jmmha−1h −1 was estimated with a general increase towards the lowland Amazon regions and high values are found on the north-coast Pacific area of Peru. The spatial identification of the most risk areas of erosion, was carried out through a relationship between the ED and rainfall. Both erosivity data sets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision

Improving usability of weather radar data in environmental sciences : potentials, challenges, uncertainties and applications

Precipitation is a crucial driver for many environmental processes and exhibits a high spatiotemporal variability. The traditional, widely-used point-scale measurements by rain gauges are not able to detect the spatial rainfall distribution in a comprehensive way. Throughout the last decades, weather radars have emerged as a new measurement technique that is capable of providing areal precipitation information with high spatial and temporal resolution and put precipitation monitoring on a new level. However, radar is an indirect remote sensing technique. Rain rates and distributions are inferred from measured reflectivities, which are subject to a series of potential error sources. In the last years, several operational national radar data archives exceeded a time series length of ten years and several new radar climatology datasets have been derived, which provide largely consistent, well-documented radar quantitative precipitation estimate (QPE) products and open up new climatological application fields for radar data. However, beside uncertainties regarding data quality and precipitation quantification, several technical barriers exist that can prevent potential users from working with radar data. Challenges include for instance different proprietary data formats, the processing of large data volumes and a scarcity of easy-to-use and free-of-charge software, additional effort for data quality evaluation and difficulties concerning data georeferencing. This thesis provides a contribution to improve the usability of radar-based QPE products, to raise awareness on their potentials and uncertainties and to bridge the gap between the radar community and other scientific disciplines which are still rather reluctant to use these highly resolved data. First, a GIS-compatible Python package was developed to facilitate weather radar data processing. The package uses an efficient workflow based on widely used tools and data structures to automate raw data processing and data clipping for the operational German radar-based and gauge-adjusted QPE called RADOLAN (“RADar OnLine Aneichung”) and the reanalysed radar climatology dataset named RADKLIM. Moreover, the package provides functions for temporal aggregation, heavy rainfall detection and data exchange with ArcGIS. The Python package was published as an Open Source Software called radproc. It was used as a basis for all subsequent analyses conducted in this study and has already been applied successfully by several scientific working groups and students conducting heavy rainfall analysis and data aggregation tasks. Second, this study explored the development, uncertainties and potentials of the hourly RADOLAN and RADKLIM QPE products in comparison to ground-truth rain gauge data. Results revealed that both QPE products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, the analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation and range-dependent attenuation. The applicability of the evaluation results was underpinned by the publication of a rainfall inter-comparison geodataset for the RADOLAN, RADKLIM and rain gauge datasets. The intercomparison dataset is a collection of precipitation statistics and several parameters that can potentially influence radar data quality. It allows for a straightforward comparison and analysis of the different precipitation datasets and can support a user’s decision on which dataset is best suited for the given application and study area. The data processing workflow for the derivation of the intercomparison dataset is described in detail and can serve as a guideline for individual data processing tasks and as a case study for the application of the radproc library. Finally, in a case study on radar composite data application for rainfall erosivity estimation, RADKLIM data with a 5-minute temporal resolution were used alongside rain gauge data to compare different erosivity estimation methods used in erosion control practice. The aim was to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Moreover, correction factors proposed in other studies were tested with regard to their ability to compensate for different temporal resolutions of rainfall input data and the underestimation of precipitation by radar data. The results clearly showed that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. The radar climatology data showed a high potential to improve rainfall erosivity estimations, but also a certain bias in the spatial distribution of the R-factor due to the rather short time series and a few radar artefacts. The application of correction factors to compensate for the underestimation of the radar led to an improvement of the results, but a possible overcorrection could not be excluded, which indicated a need for further research on data correction approaches. This thesis concludes with a discussion of the role of open source software, open data and of the implementation of the FAIR (Findable, Accessible, Interoperable, Re-usable) principles for the German radar QPE products in order to improve data usability. Finally, practical recommendations on how to approach the assessment of QPE quality in a specific study area are provided and potential future research developments are pointed out.Niederschlag ist ein wesentlicher Antrieb vieler Umweltprozesse und weist eine hohe räumliche und zeitliche Variabilität auf. Die traditionellen, weit verbreiteten punktuellen Messungen mit Ombrometern sind nicht in der Lage, die räumliche Niederschlagsverteilung flächendeckend zu erfassen. Im Laufe der letzten Jahrzehnte hat sich mit dem Wetterradar eine neue Messtechnik etabliert, die in der Lage ist, flächenhafte Niederschlagsinformationen mit hoher räumlicher und zeitlicher Auflösung zu erfassen und die Niederschlagsüberwachung auf ein neues Niveau zu heben. Radar ist jedoch eine indirekte Fernerkundungstechnik. Niederschlagsraten und -verteilungen werden aus gemessenen Reflektivitäten abgeleitet, die einer Reihe von potenziellen Fehlerquellen unterliegen. In den letzten Jahren überschritten mehrere nationale Radardatenarchive eine Zeitreihenlänge von zehn Jahren. Es wurden mehrere neue Radarklimatologie-Datensätze abgeleitet, die weitgehend konsistente, gut dokumentierte Radarprodukte zur quantitativen Niederschlagsschätzung liefern und neue klimatologische Anwendungsfelder für Radardaten eröffnen. Neben Unsicherheiten bezüglich der Datenqualität und der Niederschlagsquantifizierung gibt es jedoch eine Vielzahl technischer Barrieren, die potenzielle Nutzer von der Verwendung der Radardaten abhalten können. Zu den Herausforderungen gehören beispielsweise unterschiedliche proprietäre Datenformate, die Verarbeitung großer Datenmengen, ein Mangel an einfach zu bedienender und kostenloser Software, zusätzlicher Aufwand für die Bewertung der Datenqualität und Schwierigkeiten bei der Georeferenzierung der Daten. Diese Dissertation liefert einen Beitrag zur Verbesserung der Nutzbarkeit radarbasierter quantitativer Niederschlagsschätzungen, zur Sensibilisierung für deren Potenziale und Unsicherheiten und zur Überbrückung der Kluft zwischen der Radar-Community und anderen wissenschaftlichen Disziplinen, die der Nutzung der Daten immer noch eher zögerlich gegenüberstehen. Zunächst wurde eine GIS-kompatible Python-Bibliothek entwickelt, um die Verarbeitung von Wetterradardaten zu erleichtern. Die Bibliothek verwendet einen effizienten Workflow, der auf weit verbreiteten Werkzeugen und Datenstrukturen basiert, um die Rohdatenverarbeitung und das Zuschneiden der Daten zu automatisieren. Alle Routinen wurden für die operationellen deutschen RADOLAN-Kompositprodukte (“RADar OnLine Aneichung”) und den reanalysierten Radarklimatologie-Datensatz (RADKLIM) umgesetzt. Darüber hinaus bietet das Paket Funktionen für die zeitliche Datenaggregation, die Identifikation und Zählung von Starkregen sowie den Datenaustausch mit ArcGIS. Das Python-Paket wurde als Open-Source-Software namens radproc veröffentlicht. Radproc bildet die methodische Grundlage für alle nachfolgenden Analysen dieser Studie und wurde zudem bereits erfolgreich von mehreren wissenschaftlichen Arbeitsgruppen und Studenten zur Analyse von Starkregen und zeitlichen Aggregierung von Radardaten eingesetzt. Des Weiteren wurden in dieser Arbeit die Entwicklung, Unsicherheiten und Potentiale der stündlichen RADOLAN- und RADKLIM-Kompositprodukte im Vergleich zu Ombrometerdaten analysiert. Die Ergebnisse haben gezeigt, dass beide Radarprodukte die Gesamtniederschlagssummen und inbesondere Niederschläge hoher Intensität tendenziell unterschätzen. Die Analysen zeigten jedoch auch signifikante Verbesserungen im Verlauf der RADOLAN-Zeitreihe sowie deutliche Qualitätsverbesserungen durch die klimatologische Reanalyse, insbesondere im Hinblick auf die Korrektur typischer Radarartefakte, orographischer und winterlicher Niederschläge sowie der entfernungsabhängigen Abschwächung des Radarsignals. Die Anwendbarkeit der Auswertungsergebnisse wurde durch die Veröffentlichung eines Geodatensatzes zum Niederschlagsvergleich für die RADOLAN-, RADKLIM- und Ombrometer-Datensätze untermauert. Der Vergleichsdatensatz ist eine Sammlung von Niederschlagsstatistiken sowie verschiedener Parameter, die die Qualität der Radardaten potenziell beeinflussen können. Er ermöglicht einen einfachen Vergleich und eine Analyse der verschiedenen Niederschlagsdatensätze und kann die Entscheidung von Anwendern unterstützen, welcher Niederschlagsdatensatz für die jeweilige Anwendung und das jeweilige Untersuchungsgebiet am besten geeignet ist. Der Workflow für die Ableitung des Vergleichsdatensatzes wurde ausführlich beschrieben und kann als Leitfaden für individuelle Datenverarbeitungsaufgaben und als Fallstudie für die Anwendung der radproc-Bibliothek dienen. Darüber hinaus wurde eine Fallstudie zur Anwendung von Radar-Komposits für die Abschätzung der Erosivität des Niederschlags durchgeführt. Dazu wurden RADKLIM-Daten und Ombrometerdaten mit einer zeitlichen Auflösung von 5 Minuten verwendet, um verschiedene Methoden zur Abschätzung der Niederschlagserosivität zu vergleichen, die in der Erosionsschutzpraxis Anwendung finden. Ziel war es, die Auswirkungen der Methodik und des Klimawandels sowie der Auflösung, Qualität und der räumlichen Ausdehnung der Eingabedaten auf den R-Faktor der Allgemeinen Bodenabtragsgleichung zu bewerten. Darüber hinaus wurden von anderen Studien vorgeschlagene Korrekturfaktoren im Hinblick auf ihre Fähigkeit getestet, unterschiedliche zeitliche Auflösungen von Niederschlagsdaten und die Unterschätzung des Niederschlags durch Radardaten zu kompensieren. Die Ergebnisse haben deutlich gezeigt, dass die R-Faktoren aufgrund des Klimawandels erheblich zugenommen haben und dass die aktuellen R-Faktor-Karten unter Verwendung neuerer, flächendeckender und räumlich höher aufgelöster Niederschlagsdaten aktualisiert werden müssen. Die Radarklimatologiedaten zeigten ein hohes Potenzial zur Verbesserung der Abschätzung der Niederschlagserosivität, aber aufgrund der vergleichsweise kurzen Zeitreihe und einiger Radarartefakte auch gewisse Unsicherheiten in der räumlichen Verteilung des R-Faktors. Die Anwendung von Korrekturfaktoren zur Kompensation der Unterschätzung des Radars führte zu einer Verbesserung der Ergebnisse, allerdings konnte eine mögliche Überkorrektur nicht ausgeschlossen werden, wodurch weiterer Forschungsbedarf bezüglich der Datenkorrektur aufgezeigt wurde. Diese Arbeit schließt mit einer Diskussion der Rolle von Open-Source-Software, frei verfügbarer Daten und der Umsetzung der FAIR-Prinzipien (Findable, Accessible, Interoperable, Re-usable) für die deutschen Radar-Produkte zur Verbesserung der Nutzbarkeit von Radarniederschlagsdaten. Abschließend werden praktische Empfehlungen zur Vorgehensweise bei der Bewertung der Qualität radarbasierter quantitativer Niederschlagsschätzungen in einem bestimmten Untersuchungsgebiet gegeben und mögliche zukünftige Forschungsentwicklungen aufgezeigt

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

A Model for Continental-Scale Water Erosion and Sediment Transport and Its Application to the Yellow River Basin

Quantifying suspended sediment discharge at large catchment scales has significant implications for various research fields such as water quality, global carbon and nutrient cycle, agriculture sustainability, and landscape evolution. There is growing evidence that climate warming is accelerating the water cycle, leading to changes in precipitation and runoff and increasing the frequency and intensity of extreme weather events, which could lead to intensive erosion and sediment discharge. However, suspended sediment discharge is still rarely represented in regional climate models because it depends not only on the sediment transport capacity based on streamflow characteristics but also on the sediment availability in the upstream basin. This thesis introduces a continental-scale Atmospheric and Hydrological-Sediment Modelling System (AHMS-SED), which overcomes the limitations of previous large-scale water erosion models. Specifically, AHMS-SED includes a complete representation of key hydrological, erosion and sediment transport processes such as runoff and sediment generation, flow and sediment routing, sediment deposition, gully erosion and river irrigation. In this thesis, we focus on developing and applying AHMS-SED in the Yellow River Basin of China, an arid and semi-arid region known for its wide distribution of loess and the highest soil erosion rate in the world. There are three key issues involving the model development and application: human perturbation (irrigation) of the water cycle, the uncertainty of precipitation forcing on the water discharge and the large-scale water erosion and sediment transport. This thesis addresses all these three issues in the following way. First, a new irrigation module is integrated into the Atmospheric and Hydrological Modelling System (AHMS). The model is calibrated and validated using in-situ and remote sensing observations. By incorporating the irrigation module into the simulation, a more realistic hydrological response was obtained near the outlet of the Yellow River Basin. Second, an evaluation of six precipitation-reanalysis products is performed based on observed precipitation and model-simulated river discharge by the AHMS for the Yellow River Basin. The hydrological model is driven with each of the precipitation-reanalysis products in two ways, one with the rainfall-runoff parameters recalibrated and the other without. Our analysis contributes to better quantifying the reliability of hydrological simulations and the improvement of future precipitation-reanalysis products. Third, a regional-scale water erosion and sediment transport model, referred to as AHMS-SED, is developed and applied to predicting continental-scale fluvial transport in the Yellow River Basin. This model couples the AHMS with the CASCade 2-Dimensional SEDiment (CASC2D-SED) and takes into account gully erosion, a process that strongly affects the sediment supply in the Chinese Loess Plateau. The AHMS-SED is then applied to simulate water erosion and sediment processes in the Yellow River Basin for a period of eight years, from 1979 to 1987. Overall, the results demonstrate the good performance of the AHMS-SED and the upland sediment discharge equation based on rainfall erosivity and gully area index. AHMS-SED is also used to predict the evolution of sediment transport in the Yellow River Basin under specific climate change scenarios. The model results indicate that changes in precipitation will have a significant impact on sediment discharge, while increased irrigation will reduce the sediment discharge from the Yellow River

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

Surface water and energy fluxes in South America : an integrated approach based on remote sensing and flux measurements

South America is a water-abundant continent, home to the world's largest river basins and rainforest, which plays a crucial role in providing moisture to other regions of the continent through evapotranspiration (). is a crucial indicator of the earth's ecosystem functioning, linking the water, energy, and carbon cycles. Due to the great challenge of obtaining information based on in situ measurements, remote sensing data has become a great opportunity to obtain estimations. Based on measurements and estimations based on remote sensing data, this study aimed to evaluate the dynamics, patterns and controls of water and energy fluxes in South America, seeking to answer three main questions: i) can remote sensing data provide accurate information on the water balance?; ii) how do the factors controlling vary across different biomes and land use and land cover (LULC) conditions? and iii) can remote sensing models represent accurately patterns and its components under different LULC conditions? To answer the first question, we performed a water balance analysis, evaluating the uncertainties of precipitation and estimations using in situ measurements, and conducting an analysis to understand how much these uncertainties can be affected due to the basin’s scales. The results showed that due to the uncertainties related to each of the variable from remote sensing it is not yet possible to achieve the water balance closing. However, the approach proved to be a great alternative to evaluate the dynamics of water fluxes from small to large basins, especially in those where in situ measurement is still scarce. To seek to answer the second question, we evaluated the influence of biotic and abiotic factors on control processes, based on surface and aerodynamic conductances and the decoupling factor, at 20 flux measurement sites in South America. Through this analysis, different patterns of latent () and sensible () heat fluxes were verified, and different degrees of importance of biotic and abiotic controls on the process according to different LULC conditions. Finally, based on 11 flux measurement sites and four models (MOD16, GLEAM, PML and SSEBOP), we assessed the accuracy of estimates in the Amazon basin, and the representation of fluxes in forest, pasture, and soybean areas, in the Tapajós basin. The results showed that obtaining accurate estimates is still a major challenge in the Amazon basin, especially in humid and seasonally flooded sites. Significant discrepancies between the models and between measurements were found, and these discrepancies were even more significant when evaluated the individual components. However, even though each model did not perform significantly under all climatic and vegetation conditions, they present together a great opportunity to improve the accuracy of estimates, leading to an improved understanding of the impacts on water and energy fluxes due to human activities. Thus, these results demonstrate the potential and limitations of hydrological components obtained by remote sensing, especially for , and how LULC changes may modify this flux in South America.A América do Sul é um continente abundante em água, abrigando as maiores bacias hidrográficas e a maior floresta tropical do mundo, a floresta Amazônica. A Amazônia desempenha um papel crucial no fornecimento de umidade para outras regiões do continente por meio da evapotranspiração (). A é um indicador crucial do funcionamento do ecossistema terrestre, interligando os ciclos da água, energia e carbono. Devido ao grande desafio de obtenção de informações de por medições in situ, o uso de dados de sensoriamento remoto tem se mostrado uma grande alternativa para obter estimativas desta variável. Com base em dados medidos e estimados por sensoriamento remoto foi conduzido um estudo que visou analisar a dinâmica, os padrões e os controles dos fluxos de água e energia na América do Sul, buscando responder a três questões principais: i) os dados de sensoriamento remoto podem fornecer informações precisas sobre o balanço hídrico?; ii) como os fatores que controlam a variam em diferentes biomas e condições de uso e cobertura do solo (LULC)?; e iii) os modelos de sensoriamento remoto conseguem representar com acurácia os padrões de e das suas componentes em diferentes condições de LULC? Para responder a primeira pergunta realizou-se uma análise de balanço hídrico, na qual foi avaliada as incertezas das estimativas de precipitação e usando medições in situ, e uma análise do quanto essas incertezas podem ser afetadas devido ao efeito de escala das bacias analisadas. Os resultados mostraram que devido às incertezas relacionadas com cada uma das componentes estimadas por sensoriamento remoto ainda não é possível alcançar o fechamento do balanço hídrico. No entanto, a abordagem demonstrou ser uma grande alternativa para avaliar a dinâmica dos fluxos de água, de pequenas a grandes bacias, especialmente naquelas onde a medição in situ ainda é escassa. Para buscar responder a segunda pergunta analisou-se a influência dos fatores bióticos e abióticos no controle dos processos de , por meio da análise das condutâncias de superfície e aerodinâmica e do fator de desacoplamento em 20 locais de monitoramento de fluxo na América do Sul. Por meio desta análise verificou-se diferentes padrões dos fluxos de calor latente () e sensível (), além de diferentes graus de importância dos controles bióticos e abióticos sobre o processo de e de acordo com as diferentes condições de LULC. Por fim, com base em 11 locais de monitoramento de fluxo e quatro modelos de (MOD16, GLEAM, PML e SSEBOP), analisou-se a acurácia destas estimativas na bacia amazônica, e a representação dos fluxos de em áreas de floresta, pastagem e soja, na bacia do Tapajós. Os resultados mostraram que a obtenção de estimativas acuradas de ainda é um grande desafio na bacia Amazônica, principalmente em locais úmidos e sazonalmente inundados. Discrepâncias significativas entre os modelos e entre as medições foram encontradas, sendo estas discrepâncias ainda mais expressivas quando se analisou as componentes individuais de . No entanto, os resultados deste estudo demonstraram que apesar de cada modelo não apresentar um desempenho significativo em todas as condições climáticas e de vegetação, estes apresentam em conjunto, uma grande oportunidade para melhorar a acurácia das estimativas de , propiciando um aprimoramento na compreensão dos impactos nos fluxos de água e energia devido a atividades antrópicas. Deste modo, estes resultados enfatizam os potenciais e limitações das variáveis hidrológicos obtidas por sensoriamento remoto, especialmente para a , e como as mudanças LULC podem modificar este fluxo na América do Sul

Rainfall Erosivity in Soil Erosion Processes

This book gathers recent international research on the association between aggressive rainfall and soil loss and landscape degradation. Different contributions explore these complex relationships and highlight the importance of the spatial patterns of precipitation intensity on land flow under erosive storms, with the support of observational and modelling data. This is a large and multifaceted area of research of growing importance that outlines the challenge of protecting land from natural hazards. The increase in the number of high temporal resolution rainfall records together with the development of new modelling capabilities has opened up new opportunities for the use of large-scale planning and risk prevention methods. These new perspectives should no longer be considered as an independent research topic, but should, above all, support comprehensive land use planning, which is at the core of environmental decision-making and operations. Textbooks such as this one demonstrate the significance of how hydrological science can enable tangible progress in understanding the complexity of water management and its current and future challenges

Modeling impacts of climate change on aridity and crop water demand in Syria

Rising temperature and changing rainfall patterns due to global warming would cause a shift in aridity, particularly in the dry regions of the world which may subsequently affect several sectors particularly the agricultural and water resources. The effect of climate change may severely affect the livelihood of the vast population depending on agriculture in dry regions if proper adaptation and mitigation measures are not taken. The major objective of the present study is to develop a framework for the projection of climate change using general circulation model (GCM) and assess the impacts of climate change aridity and crop water demand (CWD) in dry regions for different representative concentration pathways (RCP) scenarios. Syria, located in a predominantly arid region and one of the most vulnerable countries of the world to climate change was considered as the case study area. Considering scarcity of data, a gauge based gridded rainfall data of global precipitation climatology center (GPCC) and temperature data of climate research unit (CRU) for the period 1951-2010 were used. The temporal variations in aridity were estimated using the UNESCO aridity index while the CWD was estimated using a simple water balance model. A novel entropy-based method known as symmetrical uncertainty (SU) was used for the selection of GCMs to reduce uncertainties in climate change projections. The performance of four state-of-the-art bias correction approaches was compared for selecting the best method for reliable downscaling of climate using model output statistics (MOS) approach. A random forest (RF) based regression was used for the generation of the multi-model ensemble (MME) mean projections of climate. Results revealed an increase in aridity and crop water demand in Syria during 1981-2010 compared to 1951-1980. The temperature was found to be the dominating factor for defining aridity in semi-arid regions in the north while rainfall as the dominating factor in the arid south. Four GCMs namely HadGEM2-AO, NorESM1-M, CSIRO-Mk3.6.0, and CESM1-CAM5 were found to be the most suitable for the projection of rainfall and temperature in Syria. Performance assessment of bias correction methods revealed linear scaling (LS) as the most suitable for downscaling of both precipitation and temperature using the MOS approach. The LS downscaled GCM simulations were found to replicate the mean, variability and temporal distribution of GPCC/CRU precipitation/temperature reliably. Future projection of rainfall and temperature using MME for the period 2010 – 2100 revealed a decrease in precipitation in the range of -30 – 85.2% mostly in the coastal areas while it was projected to increase next to those areas in the range of 18 – 87.3%. The MME projected an increase in temperature in the range of 0.0 – 5.1°C over the entire country for different RCPs. All the RCPs projected a higher increase in average temperature in the east, particularly northeast and least in the western coastal region. The change in precipitation and temperature would cause an increase in aridity and CWD in Syria. The aridity and CWD were projected to increase more in the western coastal region where precipitation was projected to decrease more. Besides, those were projected to increase in most of the areas of the country used for agriculture. It is expected that the methodology proposed in this study can be used as a tool for providing the information required for climate change adaptation and mitigation planning