Satellite Remote Sensing of Woody and Herbaceous Leaf Area for Improved Understanding of Forage Resources and Fire in Africa

In sub-Saharan Africa (SSA) tree-grass systems commonly referred to as savannas dominating drylands, play a critical role in social, cultural, economic and environmental systems. These coupled natural-human systems support millions of people through pastoralism, are important global biodiversity hotspots and play a critical role in global biogeochemical cycles. Despite the importance of SSA savannas, they have been marginalized for years as most governments neglect dryland resources in favor of agricultural research and development assistance. Hence, lack of spatially and temporally accurate information on the status and trends in savanna resources has led to poor planning and management. This scenario calls for research to derive information that can be used to guide development, management and conservation of savannas for enhanced human wellbeing, livestock productivity and wildlife management. The above considerations motivated a more detailed study of the composition, temporal and spatial variability of savannas, comprising of three components. Remote sensing data was combined with field and literature data to: partition Moderate Resolution Imaging Spectroradiometer (MODIS) total leaf area index (LAIA) time series into its woody (LAIW) and herbaceous (LAIH) constituents for SSA; and application of the partitioned LAI to determine how changes in herbaceous and woody LAI, affect fire regimes and livestock herbivory in SSA. The results of this analysis include presentation of algorithm for partitioning of MODIS LAIA from 2003-2015. Biome phenologies, seasonality and distribution of woody and herbaceous LAI are presented and the long-term average 8-day phenologies availed for evaluation and research application. In determining how changes in herbaceous and woody LAI affect fire regimes in SSA, we found that herbaceous fuelload (indexed as LAIH) correlated more closely with fire, than with LAIW, providing more explanatory power than overall biomass in fire activity. We observed an asymptotic relationship between herbaceous fuel-load and fire with trees promoting fires in dry ecosystems but suppressing fires in wetter regions. In the livestock herbivory analysis we found that the more refined forage indices (LAIH and LAIW) explained more of the variability in livestock distribution than the aggregate biomass, with livestock favoring moderate to nutrient rich forage resources dependent on animal body size

Disentangling the Regional Climate Impacts of Competing Vegetation Responses to Elevated Atmospheric CO₂

Biophysical vegetation responses to elevated atmospheric carbon dioxide (CO(2)) affect regional hydroclimate through two competing mechanisms. Higher CO(2) increases leaf area (LAI), thereby increasing transpiration and water losses. Simultaneously, elevated CO(2) reduces stomatal conductance and transpiration, thereby increasing rootzone soil moisture. Which mechanism dominates in the future is highly uncertain, partly because these two processes are difficult to explicitly separate within dynamic vegetation models. We address this challenge by using the GISS ModelE global climate model to conduct a novel set of idealized 2×CO(2) sensitivity experiments to: evaluate the total vegetation biophysical contribution to regional climate change under high CO(2); and quantify the separate contributions of enhanced LAI and reduced stomatal conductance to regional hydroclimate responses. We find that increased LAI exacerbates soil moisture deficits across the sub‐tropics and more water‐limited regions, but also attenuates warming by ∼0.5–1°C in the US Southwest, Central Asia, Southeast Asia, and northern South America. Reduced stomatal conductance effects contribute ∼1°C of summertime warming. For some regions, enhanced LAI and reduced stomatal conductance produce nonlinear and either competing or mutually amplifying hydroclimate responses. In northeastern Australia, these effects combine to exacerbate radiation‐forced warming and contribute to year‐round water limitation. Conversely, at higher latitudes these combined effects result in less warming than would otherwise be predicted due to nonlinear responses. These results highlight substantial regional variation in CO(2)‐driven vegetation responses and the importance of improving model representations of these processes to better quantify regional hydroclimate impacts

Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies

Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012. Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes

Mapping gains and losses in woody vegetation across global tropical drylands

Woody vegetation in global tropical drylands is of significant importance for both the interannual variability of the carbon cycle and local livelihoods. Satellite observations over the past decades provide a unique way to assess the vegetation long-term dynamics across biomes worldwide. Yet, the actual changes in the woody vegetation are always hidden by interannual fluctuations of the leaf density, because the most widely used remote sensing data are primarily related to the photosynthetically active vegetation components. Here, we quantify the temporal trends of the nonphotosynthetic woody components (i.e., stems and branches) in global tropical drylands during 2000–2012 using the vegetation optical depth (VOD), retrieved from passive microwave observations. This is achieved by a novel method focusing on the dry season period to minimize the influence of herbaceous vegetation and using MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to remove the interannual fluctuations of the woody leaf component. We revealed significant trends (P < 0.05) in the woody component (VODwood) in 35% of the areas characterized by a nonsignificant trend in the leaf component (VODleaf modeled from NDVI), indicating pronounced gradual growth/decline in woody vegetation not captured by traditional assessments. The method is validated using a unique record of ground measurements from the semiarid Sahel and shows a strong agreement between changes in VODwood and changes in ground observed woody cover (r2 = 0.78). Reliability of the obtained woody component trends is also supported by a review of relevant literatures for eight hot spot regions of change. The proposed approach is expected to contribute to an improved assessment of, for example, changes in dryland carbon pools

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Precipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance

Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3&amp;ndash;5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations

Holoceno climate, vegetation and human aimpact in the Western Mediterranean inferred from Pyrenean lake records and climate models

La presente Tesis Doctoral aborda una detallada reconstrucción paleoclimática en el Pirineo Central durante el Holoceno a partir del estudio palinológico de dos secuencias lacustre localizadas a diferentes altitudes y que representan dos pisos de vegetación muy bien diferenciados: en primer lugar, la Basa de la Mora, localizado en el piso subalpino y, en segundo lugar, en el lago de Estaña, en piso basal del Pre-Pirineo. Además, se ha seleccionado el Holoceno Medio para estudiar la fiabilidad de los modelos climáticos a la hora de reconstruir los climas del pasado en el Mediterráneo, a partir del análisis de la expresión estacional de los climas en las simulaciones del Coupled Model Intercomparison Project (CMIP5). El estudio multiproxy (polen, sedimentología, geoquímica, quironómidos y microcarbón) de alta resolución de la secuencia de la Basa de la Mora (BSM) (42º32¿ N, 0º19¿ E, 1914 m s.n.m) muestra una marcada variabilidad ambiental en el Pirineo Central durante el Holoceno. El robusto modelo de edad, basado en 15 dataciones radiocarbónicas, respalda la primera reconstrucción precisa de cambios climáticos rápidos durante el Holoceno en esta área. En el Holoceno temprano se registra una cuenca altamente arbolada, con unos niveles lacustres altos y procesos intensos de run-off en la cuenca favoreció la existencia de comunidad de quironómidos dominados por taxones no lacustres (Orthocladiinae) relacionados con la entrada de arroyos fluviales. Este escenario es coherente con la alta estacionalidad en latitudes medias en el Hemisferio Norte causada por la configuración de los parámetros orbitales durante el Holoceno Temprano, que provocaría un aumento en la acumulación de nieve en las cumbres pirenaicas durante el invierno así como unas altas tasas de fusión de la nieve durante el verano. Entre 9.8 y 8.1 cal yr BP, se reconoce una gran inestabilidad climática debido al registro de profundos cambios en la cubierta vegetal y de una alta fluctuación en los procesos de erosión en la cuenca. Las variaciones entre coníferas y mesofitos has revelado la ocurrencia de al menos cuatro eventos rápidos y de corta duración registrados aproximadamente a 9.7, 9.3, 8.8 y 8.3 cal Ka BP. Entre 8.1 y 5.7, durante el Holoceno Medio, un clima más estable con abundante precipitación dio lugar a los máximos niveles lacustres, la expansión del bosque de caducifolios, la retirada de las coníferas y la intensificación de los fuegos. Hacia el 5.7 cal Ka BP un cambio climático hacia condiciones más secas contribuyó al declive regional de los arboles caducifolios, la expansión de los pinos y Juniperus y un descenso notable de los niveles del lago. A pesar de las condiciones más secas, la actividad del fuego se redujo debido a una disminución de la biomasa disponible. Dos intervalos especialmente áridos tuvieron lugar entre 2.9 y 2.4 cal Ka BP y entre 1.2 y 0.7 cal Ka BP (800-1300 AD). El segundo coincide con la Anomalía Climática Medieval y en la secuencia BSM se registra como unos de los periodos más áridos del Holoceno. La actividad antrópica es escasa e incluso nula durante la mayor parte del Holoceno, hasta al menos los últimos 700 años, cuando se reconocen los primeros signos de deforestación. La Pequeña Edad de Hielo se registra por un aumento de los niveles lacustres y por lun abandono de las actividades humanas debido a las condiciones frías en las cumbres pirenaicas. El registro palinológico del lago de Estaña (EST) (670 m s.n.m., 42°02¿N, 0°32¿E) proporciona la primera reconstrucción Holocena de la vegetación en piso basal de los Pirineos. La presente Tesis Doctoral presenta una comparación de la secuencia de Estaña con otras secuencias polínicas pirenaicas localizadas en pisos de vegetación más altos, permitiendo ilustrar el papel de los cambios en temperatura y precipitación que dieron lugar a un ajuste vertical de los pisos de vegetación en los Pirineos durante el Holoceno. Durante el comienzo del Holoceno, una estacionalidad alta y unas condiciones extremadme áridas dieron lugar a un paisaje estépico en Estaña, impidiendo las expansión del bosque en altitud. Entre 9.2 y 8.2 cal Ka BP, un aumento de las temperaturas de invierno junto a una mayor disponibilidad hídrica permitieron la expansión de los taxones arbóreos, principalmente Corylus, en Estaña. Este paisaje dominado por taxones mesófilos sugiere una distribución uniforme de la precipitación a lo largo del año en el piso basal de los Pirineos. Sin embargo, contrasta con un patrón de precipitación con una estación seca establecido en cotas más altas del Pirineo, indicando la existencia de un patrón hidrológico muy complejo en la región durante este periodo. Entre 8.2 y 6 cal Ka BP, la ocurrencia de inviernos cálidos y condiciones muy húmedas con una distribución de la precipitación uniforme, dio lugar al desarrollo de un bosque de tipo Mediterráneo, formado por Quercus semi-caducifolios, en Estanya y favoreció la expansión en altitud del bosque de caducifolios, el cual pudo establecerse en el piso subalpino. El periodo entre 6 y 4.8 cal Ka BP fue una fase de transición a nivel regional en el que se empezó a establecer una estacionalidad en la precipitación caracterizada por la existencia de una estación árida. Dado el carácter mediterráneo de la vegetación en Estaña, este cambio en el patrón de la vegetación sólo afecto a la vegetación mesófila del piso subalpino. El establecimiento final de unas condiciones áridas en torno al 4.8 cal Ka BP, causó la desaparición de importantes masas de árboles caducifolios en el área y favoreció la expansión de Quercus semi-caucifolio y perennifolio en Estaña y la expansión de Pinus a mayores altitudes. Los primeros signos de actividad antrópico en Estaña se registran hacia el años 3.1 cal Ka BP con la ocurrencia de la primera fase de deforestación y la aparición de polen de tipo Cerealia. El aumento del manejo del paisaje se produjo en torno al 0.8 cal ka BP debido a la expansión de las actividades agrícolas y ganaderas. Además, en la presente Tesis Doctoral también se ha analizado la expresión estacional de los climas del Mediterráneo y norte de África en las simulaciones del Coupled Model Intercomparison Project (CMIP5) para el Holoceno-Medio y el periodo Pre-Industrial. Las observaciones climáticas actuales muestran cuatro tipos distintos de regímenes de precipitación caracterizados por una distribución estacional y una cantidad total de precipitación diferente: una banda ecuatorial, caracterizada por un pico doble en la precipitación; la zona del Monzón, caracterizada por la concentración de la lluvia en verano; el desierto, caracterizado por una baja estacionalidad y cantidad total de lluvia; y la zona del Mediterráneo, caracterizado por sequía estival. En las simulaciones para el periodo PreIndustrial, la mayoría de los modelos simulan adecuadamente la posición de los climas del Mediterráneo y del ecuador pero sobrestiman la extensión de la influencia del monzón y subestiman la expansión del desierto. Sin embargo, la mayoría de los modelos fallan a la hora de reproducir la cantidad total de precipitación en cada zona. En las simulaciones para el Holoceno-Medio, los modelos simulan una reducción de la precipitación de invierno en la zona ecuatorial, y una expansión hacia el norte del monzón con un aumento significativo de la precipitación de verano y otoño. La precipitación aumenta ligeramente en el desierto, principalmente en verano y otoño, debido a una expansión hacia el norte del monzón. Por su parte los cambios en el Mediterráneo son muy pequeños, aunque hay un ligero aumento de la precipitación en primavera consistente con los datos paleoclimáticos que muestran una expansión de los arboles caducifolios y por tanto un aumento de la precipitación en la estación de crecimiento durante el Holoceno Medio. La comparación con las reconstrucciones también sugieren que la mayoría de los modelos subestiman los cambios anuales en precipitación durante el Holoceno Medio en todas las zonas salvo en la banda ecuatorial

Spatio-temporal variations in global surface soil moisture based on multiple datasets: intercomparison and climate drivers

Accurate soil moisture datasets are essential to understand the impacts of climate change. However, few studies have evaluated the consistency and drivers of long-term trends in soil moisture among different dataset types (satellite, assimilation, reanalysis, and climate model) at the global scale. Here we analyze the spatio-temporal variations of global surface soil moisture and associated climate dynamics over 1980–2020 using multiple soil moisture datasets, i.e., multi-satellite assimilated remote sensing datasets (ESA CCI), simulated soil moisture based on LSMs (GLDAS, GLEAM, CMIP6), and reanalysis (ECMWF ERA5, MERRA2, CRA-Land). Most of these datasets indicate pervasive drying of global surface soil moisture over the last four decades. Prominent soil moisture drying is detected in North America, Europe, northeastern Asia, North Africa, and the Arabian Peninsula. The cross-correlations among the five synthetic soil moisture datasets are the highest between GLEAM and the reanalysis datasets. Using the Aridity Index (AI, the ratio between annual total precipitation and potential evapotranspiration), we find that soil moisture drying is the most intensive in the humid-arid transitional regions with AI ranging 0.8–1.2. Surface soil moisture drying is primarily driven by increases in temperature, followed by ENSO, as indicated by Maximum Covariance Analysis (MCA). However, the significance of the impact of ENSO on soil moisture variability is sensitive to the choice of soil moisture dataset used in the MCA

Land Ecosystems and Hydrology

The terrestrial biosphere is an integral component of the Earth Observing System (EOS) science objectives concerning climate change, hydrologic cycle change, and changes in terrestrial productivity. The fluxes o f CO2 and other greenhouse gases from the land surface influence the global circulation models directly, and changes in land cover change the land surface biophysical properties o f energy and mass exchange. Hydrologic cycle perturbations result from terrestrially-induced climate changes, and more directly from changes in land cover acting on surface hydrologic balances. Finally, both climate and hydrology jointly control biospheric productivity, the source o f food, fuel, and fiber for humankind. The role of the land system in each of these three topics is somewhat different, so this chapter is organized into the subtopics of Land-Climate, Land-Hydrology, and Land-Vegetation interactions (Figures 5.1, 5.2, and 5.3)

Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing

Grazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions