The demand for solar radiation forecasting has become a significant feature in the design of photovoltaic (PV) systems. Currently, the artificial neural network (ANN) is the most popular model that is used to estimate solar radiation. However, a new approach, called the extreme learning machine (ELM) algorithm, has been introduced by Huang et al. In this research, ELM and a multilayer feed-forward network with back propagation were used to predict daily global solar radiation. Metrological parameters such as air temperature, humidity and date code have been used as inputs for the ANN and ELM models. The accuracy and performance of these techniques were evaluated by comparing their outputs. ELM is faster than ANN, and results in a high generalization capability. / It is a comperison between ANN and ELM

The mangroves forests have a high social, economical and environmental importance because they are consists of a rich faun and flora. Brazil has one of the biggest mangrove areas in the world which expands from its North to the South coast. Actually, one of the main environmental issues is the deforesting, such as, the mangrove conversion into degraded area, exchanging the net radiation. The mangrove transformation in degraded area influences directly the regional microclimate for the soil is exposed to direct solar radiation altering the net radiation. Solar radiation is all electromagnetic radiation coming from the Sun, reaching the earth. Therefore, the knowledge above the Solar Global Radiation behavior, and its components (Direct (RD) and Diffuse (Rd)), within and outside forests, is essential to understand of the various processes in this system. The study has been carried out with data of originally from two experimental sites, with different vegetal cover (natural and degraded mangrove), located in Marechal Deodoro city (Alagoas), inside an environmental protection area (APA) called Santa Rita, which coordinates are between 9º37 30 - 9º47 30 S and 35º45 00 - 35º55 00 W, ranging from October 2004 to September 2005, where has been installed, in which one of this sites, an equipments set to measure the necessaries variability. The result shows high oscillation between dry and wet season in concerning the maximum Rg, influencing its components, too. From the inside, we can observe the dependence that the solar rays penetration have due to the zenith angle and 80% of attenuation. The analyses of these data in allowed them to generate mathematical models with R2, varying between 0,8 and 0,97, becoming possible the estimate of the amount of radiation that reaches the interior of this ecosystem. / Fundação de Amparo a Pesquisa do Estado de Alagoas / As florestas de manguezais são de extrema importância social, econômica e ambiental, pois as mesmas são detentoras de uma rica fauna e flora. O Brasil possui uma das maiores áreas de manguezais do mundo que se estende desde o extremo Norte até o Sul do país. Atualmente uma das grandes questões ambientais diz respeito ao desmatamento das áreas de florestas nativas como, por exemplo, os manguezais. Os efeitos da alteração de manguezal em área degradada influenciam também diretamente no microclima da região, pois no aspecto meteorológico é necessário que seja limitada à forma de exploração desse ecossistema, pois a conversão de florestas de mangues em áreas degradadas expõe à superfície à radiação solar direta alterando o balanço de radiação. Então, conhecer o comportamento da Rg e suas componentes, Radiação Difusa (Rd) e Radiação Direta (RD), dentro das florestas, é primordial para o entendimento da disponibilidade de energia para os diversos processos desse sistema. O principal objetivo deste trabalho foi avaliar e comparar o comportamento da Radiação Solar Global (Rg) e suas componentes Direta (RD) e Difusa (Rd), dentro e acima de uma floresta de mangue. O estudo foi realizado com a obtenção de dados provenientes de dois sítios experimentais, com cobertura vegetal diferente (mangue natural e degradado), localizados no município de Marechal Deodoro, dentro de uma área de Proteção Ambiental (APA) de Santa Rita, situada entre as latitudes de 9º37 30 e 9º47 30 S e as longitudes 35º45 00 e 35º55 00 W, no período de outubro de 2004 a setembro de 2005. Os resultados mostram uma grande variação entre as estações seca - chuvosa no tocante as máximas atingidas pela Rg, influenciando também suas componentes. Internamente observa-se a grande dependência que a penetração dos raios solares tem do ângulo zenital (Z). A análise desses dados nos permitiu gerar modelos matemáticos com R2 variando entre 0,8 e 0,97 tornando possível à estimativa da quantidade de radiação que atinge o interior desse ecossistema.

A energia radiante absorvida pelas plantas é a principal determinante da taxa fotossintética, condicionando o crescimento e desenvolvimento vegetal, produção e qualidade dos produtos, sendo ainda uma variável fundamental na determinação da transpiração, possibilitando estabelecer relações entre essa variável e a evapotranspiração de referência, podendo-se determinar o coeficiente basal de culturas que orientarão nos processos de irrigação. Em vista da sua importância e do reduzido número de estudos de medida e estimativa do saldo de radiação da folhagem de arbóreas, objetivou-se realizar medidas do saldo de radiação de uma laranjeira (Rnl), cv. Pêra do Rio, com um sistema móvel de integração espaço-temporal para árvores "isoladas", instalando-se oito saldo-radiômetros na armação circular vertical em torno da copa, que através do movimento de três rotações por minuto, criava uma esfera "sensora" de medida ao redor da planta, e de trechos de renques de um cafezal (Rnc) formado, variedade Mundo Novo Apuatã, e de cafezal em formação, variedade Obatã IAC 1669-20, utilizando-se um sistema móvel de integração espaço-temporal para cultivos em renques, no qual oito saldo-radiômetros, fixados em uma armação circular vertical em torno da folhagem, movimentavam-se ao longo de 4,7 m de renque, criando um cilindro "nocional" de medida. O estudo no laranjal ocorreu entre outubro/04 e junho/05, e nos cafezais entre março/04 e maio/05, com ocorrência de diferentes áreas foliares. Os dois sistemas apresentaram-se eficientes para a medida do saldo de radiação. Essas medidas foram correlacionadas à irradiância solar global (Rg) e ao saldo de radiação de gramado (Rng), medidos em estações meteorológicas automáticas. Obtiveram-se melhores ajustes das análises de regressão entre os valores de Rnl e Rg, nas escala de 15 min e horária, verificando-se ainda bom ajuste entre essas variáveis na escala diária para as diferentes áreas foliares (AF). Também na escala diária, bom ajuste foi obtido entre Rnl por área unitária de projeção da copa no solo (Rng/AP) e Rng. Bons ajustes foram também obtidos entre o saldo de radiação dos cafeeiros em formação (Rnc) com Rg e Rng, nas escalas de 15 min e horária, e também para cafeeiros formados, inclusive na escala diária. Porém, não se verificou um ajuste único de Rnc/AF com Rg ou Rng para as diferentes áreas foliares dos cafeeiros em formação, na escala diária. O uso da lei de Beer para estimativa de Rnl e Rnc no período de 6-18h levou a valores subestimados em 17% para a laranjeira e em 8% para os cafeeiros em formação, comparados aos valores medidos, devendo-se atentar, portanto, para as limitações do uso da lei. Foram propostos modelos físicomatemáticos para estimativa de Rnl e Rnc, que apresentaram boa concordância com os valores medidos em ambas as espécies, para diferentes áreas foliares, na escala de 15 min. Ao correlacionar-se os valores de saldo de radiação medidos e estimados pelos modelos na escala diária, para todos os valores de área foliar, obteve-se superestimativas de 6% para a laranjeira e de 5% para o renque de cafeeiros formados, indicando o bom desempenho dos modelos. / The solar energy absorbed by plants is the main determinant factor of photosyntetic rate, conditioning growth and crop development, production and products qualities, still being a fundamental variable to determine transpiration. In relation to transpiration, it is possible to establish relationships between this variable and reference evapotranspiration, allowing to determine the basal crop coefficient, which will guide irrigation management. Regarding to the importance and the few number of studies about measurements and estimates of net radiation of arboreal canopy, this project aimed to accomplish net radiation measurements in an orange tree canopy within an orchard (Rnl), cultivar Pêra do Rio, with a device for measuring all wave net radiation absorbed by "single" trees, deploying eight net radiometers on a circular frame which rotates around the canopy, at 3 rpm, creating a "sensing sphere", and in hedgerows (Rnc):of a mature coffee plantation, variety Mundo Novo Apuatã; and of a young coffee plantation, variety Obatã IAC 1669-20, with a device for measuring total radiation absorbed by hedgerows, deploying eight net radiometers on a vertical circular frame around the plants, moving in a rail of 4.7 m along the hedgerow, creating a "notional cylinder" of measurement. The study in the orange orchard was carried out from October/2004 to June/2005, and in the coffee plantation was from March/2004 to May/2005. Both devices for net radiation measurements worked well. These measurements were correlated to global solar radiation (Rg) and grass net radiation (Rng), routinely measured in automatic weather stations. The best fits between Rnl and Rg, using regression analyses, were obtained for data of 15 min and hourly periods. Good agreement between these was also observed when the different leaf areas (AF) were considered, at daily scale. The same was observed for the relationship between Rnl by canopy drip line (Rnl/AP) and Rng. For the young coffee hedgerow, a good correlation was observed between Rnc and Rg or Rng, for both 15 min and hourly scales, and for mature coffee hedgerow, even at daily scale. An unique relationship between Rnc and Rg or Rng was not verified for different leaf areas of young coffee hedgerow, at daily scale. The use of Beer´s law to estimate Rnl and Rnc from 6 a.m. to 6 p.m. resulted in an underestimation of 17% to the orange tree and 8% to the young coffee hedgerow, when compared to measured values, which suggest caution to the limitations of this law. The physical-mathematical models proposed to estimate Rnl and Rnc presented good levels of agreement between measured and estimated values for both species, at different leaf areas, at 15 min scale. When correlating the measured and estimated values, for all leaf areas, at daily scale, it was obtained an overestimation of 6% for orange tree, and of 5% for young coffee hedgerow, showing a good performance of these models.

A energia radiante absorvida pelas plantas é a principal determinante da taxa fotossintética, condicionando o crescimento e desenvolvimento vegetal, produção e qualidade dos produtos, sendo ainda uma variável fundamental na determinação da transpiração, possibilitando estabelecer relações entre essa variável e a evapotranspiração de referência, podendo-se determinar o coeficiente basal de culturas que orientarão nos processos de irrigação. Em vista da sua importância e do reduzido número de estudos de medida e estimativa do saldo de radiação da folhagem de arbóreas, objetivou-se realizar medidas do saldo de radiação de uma laranjeira (Rnl), cv. Pêra do Rio, com um sistema móvel de integração espaço-temporal para árvores “isoladas”, instalando-se oito saldo-radiômetros na armação circular vertical em torno da copa, que através do movimento de três rotações por minuto, criava uma esfera “sensora” de medida ao redor da planta, e de trechos de renques de um cafezal (Rnc) formado, variedade Mundo Novo Apuatã, e de cafezal em formação, variedade Obatã IAC 1669-20, utilizando-se um sistema móvel de integração espaço-temporal para cultivos em renques, no qual oito saldo-radiômetros, fixados em uma armação circular vertical em torno da folhagem, movimentavam-se ao longo de 4,7 m de renque, criando um cilindro “nocional” de medida. O estudo no laranjal ocorreu entre outubro/04 e junho/05, e nos cafezais entre março/04 e maio/05, com ocorrência de diferentes áreas foliares. Os dois sistemas apresentaram-se eficientes para a medida do saldo de radiação. Essas medidas foram correlacionadas à irradiância solar global (Rg) e ao saldo de radiação de gramado (Rng), medidos em estações meteorológicas automáticas. Obtiveram-se melhores ajustes das análises de regressão entre os valores de Rnl e Rg, nas escala de 15 min e horária, verificando-se ainda bom ajuste entre essas variáveis na escala diária para as diferentes áreas foliares (AF). Também na escala diária, bom ajuste foi obtido entre Rnl por área unitária de projeção da copa no solo (Rng/AP) e Rng. Bons ajustes foram também obtidos entre o saldo de radiação dos cafeeiros em formação (Rnc) com Rg e Rng, nas escalas de 15 min e horária, e também para cafeeiros formados, inclusive na escala diária. Porém, não se verificou um ajuste único de Rnc/AF com Rg ou Rng para as diferentes áreas foliares dos cafeeiros em formação, na escala diária. O uso da lei de Beer para estimativa de Rnl e Rnc no período de 6-18h levou a valores subestimados em 17% para a laranjeira e em 8% para os cafeeiros em formação, comparados aos valores medidos, devendo-se atentar, portanto, para as limitações do uso da lei. Foram propostos modelos físicomatemáticos para estimativa de Rnl e Rnc, que apresentaram boa concordância com os valores medidos em ambas as espécies, para diferentes áreas foliares, na escala de 15 min. Ao correlacionar-se os valores de saldo de radiação medidos e estimados pelos modelos na escala diária, para todos os valores de área foliar, obteve-se superestimativas de 6% para a laranjeira e de 5% para o renque de cafeeiros formados, indicando o bom desempenho dos modelos. / The solar energy absorbed by plants is the main determinant factor of photosyntetic rate, conditioning growth and crop development, production and products qualities, still being a fundamental variable to determine transpiration. In relation to transpiration, it is possible to establish relationships between this variable and reference evapotranspiration, allowing to determine the basal crop coefficient, which will guide irrigation management. Regarding to the importance and the few number of studies about measurements and estimates of net radiation of arboreal canopy, this project aimed to accomplish net radiation measurements in an orange tree canopy within an orchard (Rnl), cultivar Pêra do Rio, with a device for measuring all wave net radiation absorbed by “single” trees, deploying eight net radiometers on a circular frame which rotates around the canopy, at 3 rpm, creating a “sensing sphere”, and in hedgerows (Rnc):of a mature coffee plantation, variety Mundo Novo Apuatã; and of a young coffee plantation, variety Obatã IAC 1669-20, with a device for measuring total radiation absorbed by hedgerows, deploying eight net radiometers on a vertical circular frame around the plants, moving in a rail of 4.7 m along the hedgerow, creating a “notional cylinder” of measurement. The study in the orange orchard was carried out from October/2004 to June/2005, and in the coffee plantation was from March/2004 to May/2005. Both devices for net radiation measurements worked well. These measurements were correlated to global solar radiation (Rg) and grass net radiation (Rng), routinely measured in automatic weather stations. The best fits between Rnl and Rg, using regression analyses, were obtained for data of 15 min and hourly periods. Good agreement between these was also observed when the different leaf areas (AF) were considered, at daily scale. The same was observed for the relationship between Rnl by canopy drip line (Rnl/AP) and Rng. For the young coffee hedgerow, a good correlation was observed between Rnc and Rg or Rng, for both 15 min and hourly scales, and for mature coffee hedgerow, even at daily scale. An unique relationship between Rnc and Rg or Rng was not verified for different leaf areas of young coffee hedgerow, at daily scale. The use of Beer´s law to estimate Rnl and Rnc from 6 a.m. to 6 p.m. resulted in an underestimation of 17% to the orange tree and 8% to the young coffee hedgerow, when compared to measured values, which suggest caution to the limitations of this law. The physical-mathematical models proposed to estimate Rnl and Rnc presented good levels of agreement between measured and estimated values for both species, at different leaf areas, at 15 min scale. When correlating the measured and estimated values, for all leaf areas, at daily scale, it was obtained an overestimation of 6% for orange tree, and of 5% for young coffee hedgerow, showing a good performance of these models.

Das drahtlose Sensornetzwerk (WSN) ist eine Technologie, die Umgebungsbedingungen oder physikalische Parameter misst, weiterleitet und per Fernüberwachung zur Verfügung stellt. Normalerweise werden die Sensorknoten, die diese Netzwerke bilden, von Batterien gespeist. Diese sollen aus verschiedenen Gründen nicht mehr verwendet werden, sondern es wird auf eine eigenständige Stromversorgung gesetzt. Dies soll den aufwendigen Austausch und die Wartung minimieren. Energy Harvesting kann mit den Knoten verwendet werden, um die Batterien zu unterstützen und die Lebensdauer der Netzwerke zu verlängern. Aufgrund der hohen Leistungsdichte der Solarenergie im Vergleich zu verschiedenen anderen Umweltenergien sind Solarzellen die am häufigsten eingesetzten Wandler, allerdings stellt die schwankende und intermittierende Natur der Solarenergie eine Herausforderung dar, einen funktionalen und zuverlässigen Sensorknoten zu versorgen. Um den Sensorknoten effektiv zu betreiben, sollte sein Energieverbrauch sinnvoll gesteuert werden. Ein interessanter Ansatz zu diesem Zweck ist die Steuerung der Aktivitäten des Knotens in Abhängigkeit von der zukünftig verfügbaren Energie. Dies erfordert eine Vorhersage der wandelbaren Sonnenenergie für die kommenden Betriebszeiten einschließlich der freien Zeiten der Sonne. Einige Vorhersagealgorithmen wurden mit stochastischen und statistischen Prinzipien sowie mit Methoden der künstlichen Intelligenz (KI) erstellt. Durch diese Algorithmen bleibt ein erheblicher Vorhersagefehler von 5-70%, der den zuverlässigen Betrieb der Knoten beeinträchtigt. Beispielsweise verwenden die stochastischen Methoden einen diskreten Energiezustand, der meist nicht zu den tatsächlichen Messwerten passt. Die statistischen Methoden verwenden einen Gewichtungsfaktor für die zuvor registrierten Messwerte. Daher sind sie nur geeignet, um Energieprofile bei konstanten Wetterbedingungen vorherzusagen. KI-Methoden erfordern große Beobachtungen im Trainingsprozess, die den benötigten Speicherplatz erhöhen. Dementsprechend ist die Leistung hinsichtlich der Vorhersagegenauigkeit dieser Algorithmen nicht ausreichend. In dieser Arbeit wird ein Vorhersagealgorithmus mit einem neuronalen Netzwerk entwickelt und eingebunden in einen Mikrocontroller, um die Verwaltung des Energieverbrauchs von solarzellengesteuerten Sensorknoten zu optimieren. Das verwendete neuronale Netzwerk wurde mit einer Kombination aus meteorologischen und statistischen Eingangsparametern realisiert. Dies hat zum Ziel, die erforderlichen Designkriterien für Sensorknoten zu erfüllen und eine Leistung zu erreichen, die in ihrer Genauigkeit die Leistung der oben genannten traditionellen Algorithmen übersteigt. Die Vorhersagegenauigkeit die durch den Korrelationskoeffizienten repräsentiert wird, wurde für das entwickelte neuronale Netzwerk auf 0,992 bestimmt. Das genaueste traditionelle Netzwerk erreicht nur einen Wert von 0,963. Das entwickelte neuronale Netzwerk wurde in einen Prototyp eines Sensorknotens integriert, um die Betriebszustände oder -modi über einen Simulationszeitraum von einer Woche anzupassen. Während dieser Zeit hat der Sensorknoten 6 Stunden zusätzlich im Normalbetrieb gearbeitet. Dies trug dazu bei, eine effektive Nutzung der verfügbaren Energie um ca. 3,6% besser zu erfüllen als das genaueste traditionelle Netz. Dadurch wird eine längere Lebensdauer und Zuverlässigkeit des Sensorknotens erreicht. / Wireless Sensor Network (WSN) is a technology that measures an environmental or physical parameters in order to use them by decision makers with a possibility of remote monitoring. Normally, sensor nodes that compose these networks are powered by batteries which are no longer feasible, especially when they used as fixed and standalone power source. This is due to the costly replacement and maintenance. Ambient energy harvesting systems can be used with these nodes to support the batteries and to prolong the lifetime of these networks. Due to the high power density of solar energy in comparison with different environmental energies, solar cells are the most utilized harvesting systems. Although that, the fluctuating and intermittent nature of solar energy causes a real challenge against fulfilling a functional and reliable sensor node. In order to operate the sensor node effectively, its energy consumption should be well managed. One interesting approach for this purpose is to control the future node’s activities according to the prospective energy available. This requires performing a prior prediction of the harvestable solar energy for the upcoming operation periods including the sun’s free times. A few prediction algorithms have been created using stochastic and statistical principles as well as artificial intelligence (AI) methods. A considerable prediction error of 5-70% is realized by these algorithms affecting the reliable operation of the nodes. For example, the stochastic ones use a discrete energy states which are mostly do not fit the actual readings. The statistical methods use a weighting factors for the previous registered readings. Thus, they are convenient only to predict energy profiles under consistent weather conditions. AI methods require large observations to be used in the training process which increase the memory space needed. Accordingly, the performance concerning the prediction accuracy of these algorithms is not sufficient. In this thesis, a prediction algorithm using a neural network has been proposed and implemented in a microcontroller for managing energy consumption of solar cell driven sensor nodes. The utilized neural network has been developed using a combination of meteorological and statistical input parameters. This is to meet a required design criteria for the sensor nodes and to fulfill a performance exceeds in its accuracy the performance of aforementioned traditional algorithms. The prediction accuracy represented by the correlation coefficient has been registered for the developed neural network to be 0.992, which increases the most accurate traditional network which has a value 0.963. The developed neural network has been embedded into a sensor node prototype to adjust the operating states or modes over a simulation period of one week. During this period, the sensor node has worked 6 hours more towards normal operation mode. This in its role helped to fulfill an effective use of available energy approximately 3.6% better than the most accurate traditional network. Thus, longer lifetime and more reliable sensor node.

Les îles tropicales sont soumises à un ennuagement hétérogène et changeant rapidement. Par ailleurs, elles ont une ressource solaire importante mais significativement variable d’un jour à l’autre. Dans le sud-ouest de l’océan indien (SWIO), La Réunion fait partie de ces îles tropicales ayant un potentiel solaire colossal mais fortement intermittent. Dans cette étude, nous proposons une nouvelle approche de prévision déterministe des cartes journalières rayonnement solaire (SSR), basée sur quatre modèles de régression linéaire : une régression linéaire multiple (MLR), une régression en composantes principales (PCR), une régression des moindres carrés (PLSR) et une régression pas à pas (stepwise--SR). Ces quatre régressions sont appliquées sur les données satellites SARAH-E (CM SAF) à 5km de résolution entre 2007 et 2016, en vue d’en effectuer la prévision. Pour obtenir de meilleures performances, nous proposons d'inclure les paramètres multi-fractale (H,C_1 et α) comme nouveaux paramètres prédictifs. Ceux-ci sont obtenus à partir de l'analyse de l'intermittence du SSR basée sur la méthode d’analyse d’ordre spectral arbitraire de Hilbert. Cette analyse qui est une extension de la transformation d’Hilbert Huang (HHT) est utilisée afin d’estimer l’exposant d’échelle ξ(q). On effectue la combinaison d’une décomposition en mode empirique et de l’analyse spectrale de Hilbert (EMD + HSA). Dans une première étape, l’analyse multi-fractale est appliquée sur une mesure du SSR d'une seconde échelle à partir d'un pyranomètre SPN1 à Moufia en 2016. La moyenne infra journalière, journalière et saisonnière de la structure multi-fractale a été dérivée, et la loi d’échelle d’exposants ξ(q) a été analysée. Dans une seconde partie, l’analyse de l’intermittence est effectuée sur les mesures du SSR, d'une période d’une minute, à partir le réseau de SPN1 contenant 11 stations en 2014. Les modèles spatiaux pour toutes les stations avec les paramètres multi-fractales H,C_1 et α sont mis en évidence. La variabilité de la largeur du spectre de singularité est considérée pour étudier l'intermittence spatiale et la multi-fractalité dans l'échelle quotidienne et l'échelle saisonnière. Sur la base de ces analyses d'intermittence faites sur les mesures de plusieurs stations, les paramètres multi-fractaux universels (H,C_1 et α) pourraient être choisis comme de nouveaux prédicteurs afin d’indiquer les propriétés multi-fractales du SSR. / Due to the heterogeneous and rapidly-changing cloudiness, tropical islands, such as Reunion Island in the South-west Indian Ocean (SWIO), have significant solar resource that is highly variable from day-to-day. In this study, we propose a new approach for deterministic prediction of daily surface solar radiation (SSR) maps based on four linear regression models: multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), and stepwise regression (SR), that we have applied on the SARAH-E@5km satellite data (CM SAF) for the period during 2007-2016. To improve the accuracy of prediction, the multifractal parameters (H,C_1 and α) are proposed to include as new predictors in the predictive model. These parameters are obtained from the analysis of SSR intermittency based on arbitrary order Hilbert spectral analysis. This analysis is the extension of Hilbert Huang Transform (HHT) and it is used to estimate the generalized scaling exponent ξ(q). It is the combination of the Empirical Mode Decomposition and Hilbert spectral analysis (EMD+HSA). In a first step, the multifractal analysis is applied onto one-second SSR measurements form a SPN1 pyranometer in Moufia in 2016. The mean sub-daily, daily and seasonal daily multifractal patterns are derived, and the scaling exponent ξ(q) is analyzed. In a second step, the intermittency study is conducted on one-minute SSR measurements from a SPN1 network with 11 stations in 2014. The spatial patterns for all the stations with the multifractal parameters H,C_1 and α are shown. The variability of singularity spectrum width is considered to study the spatial intermittency at the daily and seasonal scale. Based on this intermittency analysis from measurements at several stations, the universal multifractal parameters (H,C_1 and α) could be taken as new predictors for indicating the multifractal properties of SSR.

Evapotranspiration is a nexus for planetary energy and carbon cycles, as yet poorly constrained. Here I use stable isotopes of oxygen and hydrogen to partition flux of water due to plant transpiration from the direct evaporative flux from soils, water bodies and plant. The study areas, Langat and Kelantan watersheds represent examples of domains dominated by the respective Southwest and Northeast monsoons on the two sides of the main orographic barrier (Titiwangsa mountain range). Mean annual rainfall for the Langat watershed, obtained from 30 years of hydrological data, is 2145 ± 237 mm. Tentatively, 48% of this precipitation returns to the atmosphere via transpiration (T), with 33% partitioned into discharge (Q), 8% into interception (In), and 11% into evaporation (Ed). In the Kelantan watershed, the mean annual rainfall, also based on the 30 year hydrological data, is 2383 ± 120 mm. Similar to Langat, the T accounts for 43% of precipitation (P), 45% is discharged into South China Sea (Q), 12% partitioned into interception (In) and tentatively 0% for evaporation (Ed). Ed for the Langat watershed represents only a small proportion in terms of volumetric significance, up to almost ~11% with strong effect on the isotopic fingerprints of waters associated with the summer Southwest Monsoon (SWM). Note, however, that insignificant Ed for the Kelantan watershed may be an artefact of rain and river water sampling at only coastal downstream portion of the watershed. High humidity (80%) also was recorded for the Malaysian Peninsula watershed. T appropriates about half of all solar energy absorbed by the continents, here ~1000*103 g H2O m-2 yr-1 similar to other tropical regions at 900-1200*103 g H2O m-2 yr-1. The associated carbon fluxes are ~ 1300 g C m-2yr-1, independent of P. Vegetation responses to solar irradiance, via T and photosynthesis reflects the importance of stomatal regulation of the water and carbon fluxes. In order to maintain high transpiration in the tropical region, “constant” water supply is required for continuous pumping of water that delivers nutrients to the plant, suggesting that water and carbon cycle are co-driven by the energy of the sun. The existence of the water conveyor belt may be precondition for nutrient delivery, hence operation of the carbon cycle. Potentially, this may change our perspective on the role that biology plays in the water cycle. In such perspective, the global water cycle is the medium that redistributes the incoming solar energy across the planet, and the anatomical structures of plants then help to optimize the loop of energy transfer via evaporation and precipitation in the hydrologic cycle. The main features of aquatic geochemistry of the Langat and Kelantan rivers inferred from the Principal Component Analysis are controlled by three components that explain 80% and 82% of total variances. These components are reflecting of the geogenic factor with superimposed pollution, the latter particularly pronounced in urbanized sections of the Langat river and dominant in downstream of the Kelantan river. There is no correlation between seasonal variations in major ion chemistry and environmental variables such as precipitation, discharge, temperature or solar activity.

Evapotranspiration is a nexus for planetary energy and carbon cycles, as yet poorly constrained. Here I use stable isotopes of oxygen and hydrogen to partition flux of water due to plant transpiration from the direct evaporative flux from soils, water bodies and plant. The study areas, Langat and Kelantan watersheds represent examples of domains dominated by the respective Southwest and Northeast monsoons on the two sides of the main orographic barrier (Titiwangsa mountain range). Mean annual rainfall for the Langat watershed, obtained from 30 years of hydrological data, is 2145 ± 237 mm. Tentatively, 48% of this precipitation returns to the atmosphere via transpiration (T), with 33% partitioned into discharge (Q), 8% into interception (In), and 11% into evaporation (Ed). In the Kelantan watershed, the mean annual rainfall, also based on the 30 year hydrological data, is 2383 ± 120 mm. Similar to Langat, the T accounts for 43% of precipitation (P), 45% is discharged into South China Sea (Q), 12% partitioned into interception (In) and tentatively 0% for evaporation (Ed). Ed for the Langat watershed represents only a small proportion in terms of volumetric significance, up to almost ~11% with strong effect on the isotopic fingerprints of waters associated with the summer Southwest Monsoon (SWM). Note, however, that insignificant Ed for the Kelantan watershed may be an artefact of rain and river water sampling at only coastal downstream portion of the watershed. High humidity (80%) also was recorded for the Malaysian Peninsula watershed. T appropriates about half of all solar energy absorbed by the continents, here ~1000*103 g H2O m-2 yr-1 similar to other tropical regions at 900-1200*103 g H2O m-2 yr-1. The associated carbon fluxes are ~ 1300 g C m-2yr-1, independent of P. Vegetation responses to solar irradiance, via T and photosynthesis reflects the importance of stomatal regulation of the water and carbon fluxes. In order to maintain high transpiration in the tropical region, “constant” water supply is required for continuous pumping of water that delivers nutrients to the plant, suggesting that water and carbon cycle are co-driven by the energy of the sun. The existence of the water conveyor belt may be precondition for nutrient delivery, hence operation of the carbon cycle. Potentially, this may change our perspective on the role that biology plays in the water cycle. In such perspective, the global water cycle is the medium that redistributes the incoming solar energy across the planet, and the anatomical structures of plants then help to optimize the loop of energy transfer via evaporation and precipitation in the hydrologic cycle. The main features of aquatic geochemistry of the Langat and Kelantan rivers inferred from the Principal Component Analysis are controlled by three components that explain 80% and 82% of total variances. These components are reflecting of the geogenic factor with superimposed pollution, the latter particularly pronounced in urbanized sections of the Langat river and dominant in downstream of the Kelantan river. There is no correlation between seasonal variations in major ion chemistry and environmental variables such as precipitation, discharge, temperature or solar activity.