Identifying and assessing vegetation behaviour in riparian zones at large scale in the Brazilian Savannah

Riparian zones (RZs) have a clear distinct behaviour than the rest of the landscape. Particularly in water-limited regions, such as the Brazilian Savannah (Cerrado biome), where dry season may extend 5 months, the difference between riparian and upland zones is highly pronounced due to vegetation water access to groundwater, and this can have implications on the climatic and hydrological cycles. In order to quantify this difference at large-scale, it was herein proposed to (1) map RZs using topographical information, (2) investigate how land cover is distributed among topographic gradients and (3) investigate vegetation behaviour through remote sensing vegetation measurements and evapotranspiration (ET) estimation. A 140,000 km² upland region inside the Cerrado biome, called the Urucuia aquifer system, was chosen as study site. The region has seen a huge agricultural expansion during the last decades, with mechanized and irrigated crops increasingly using water from its underground reserves, which associated with climate change can have a big impact on the ecosystem, and understanding the role of RZs can be essential to quantify this impact. The height above nearest drainage (HAND) index was used to map RZs, by visually assessing bellow which values the index provided a reasonable RZ buffer comparing with Google Earth imagery. We also used HAND to quantify across its values the historical land cover distribution obtained by the MapBiomas database, and analyse vegetation behaviour in RZs and upland zones (UZs) using remote sensing vegetation measurements of normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) and ET estimation from the surface energy balance algorithm for land (SEBAL). A necessary step for HAND computation is a defined stream network, for which the main challenge is identifying channel heads. Herein it was developed an algorithm that produced a varying draining area threshold (vDAT) map for channel initiation, using the topographic position index (TPI) as an auxiliary variable. This algorithm was tested using MERIT-DEM. With the stream network, HAND values bellow 5 m provided the best RZ buffer. As for land cover distribution, we captured that forests naturally occur more densely in the extreme values of HAND (very shallow and very deep) and that farmland historical occupation in the Urucuia region occur more in the upper portions of the terrain, possibly due to soil conditions stablished during landscape formation and evolution. As for vegetation activity, the land cover class seems to have more influence on vegetation behaviour than topographic position, for all indicators computed. Yet, NDMI values in Riparian Forests are greater than in Upland Forests, particularly towards drier conditions, in terms of both seasonality (drier months) and inter-annual variability (drier years). Despite this indication of more water available in RZs than UZs, the ET estimation could not capture these differences, possibly due to difficulties in estimating this variable in natural vegetation with high degree of water stress

Variabilidade do armazenamento de água no Brasil

Brazil hosts a large amount of freshwater. Knowing how this stored water is partitioned in space and time between surface and subsurface components is a crucial step towards a more correct depiction of the country’s water cycle, which has major implications for decision making related to water resources management. Here, we extracted monthly water storage (WS) variability, from 2003 to 2020, based on multiple state-of-the-art datasets representing different WS components – groundwater (GW), soil moisture (SM), surface waters (SW), and artificial reservoirs (RS) – in all Brazilian Hydrographic Regions (BHRs), and computed each component’s contribution to the total variability. Most of the variability can be attributed to SM (40-68%), followed by GW (18-40%). SW has great influence in the north-western BHRs (humid monsoon influenced) with 18-40% and the southern BHRs (subtropical system influenced) with 5-10%. RS has important contributions in the Paraná with 12.1%, São Francisco with 3.5%, and Tocantins-Araguaia with 2.1%. In terms of long-term variability, water storages have been generally decreasing in the eastern and increasing in north-western and southern BHRs, with GW and RS being the most affected, although it can also be observed in SW peaks. Comparisons made with previous studies show that the approach and datasets used can have a considerable impact in the results. Such analysis can have broad implications in identifying the nature of amplitude and phase variability across regions in order to better characterize them and to obtain better evaluations of hydrological trends under a changing environment.O Brasil abriga uma grande quantidade de água doce. Saber como essa água armazenada é repartida no espaço e no tempo entre os componentes superficiais e subsuperficiais é crucial para uma representação mais correta do ciclo hídrico do país, o que tem grandes implicações para a tomada de decisões relacionadas à gestão dos recursos hídricos. Neste estudo, extraímos a variabilidade mensal do armazenamento de água, de 2003 a 2020, com base em diferentes fontes que representam o estado da arte da informação sobre diferentes componentes de armazenamento águas subterrâneas, umidade do solo, águas superficiais, e reservatórios artificiais – em todas as regiões hidrográficas brasileiras, e computamos a contribuição de cada componente em relação a variabilidade total. A maior parte da variabilidade pode ser atribuída a umidade do solo (4068%), seguida por águas subterrâneas (18-40%). Águas superficiais tem grande influência nas regiões hidrográficas do noroeste (influência de sistemas de monção) com 18-40% e nas BHRs do sul (influência de sistemas subtropicais) com 5-10%. O estoque em reservatórios artificiais tem contribuições importantes nas regiões do Paraná com 12,1%, do São Francisco com 3,5% e do Tocantins-Araguaia com 2,1%. Em termos de variabilidade de longo prazo, os estoques de água têm geralmente diminuído nas regiões leste e aumentado no noroeste e no sul, sendo os estoques de águas subterrâneas e reservatórios os mais afetados, embora essa tendência também possa ser observada nos picos de água superficial. Comparações feitas com estudos anteriores mostram que a abordagem e os conjuntos de dados utilizados podem ter um impacto considerável nos resultados. Tal análise pode ter amplas implicações na identificação da natureza da variabilidade de amplitude e fase entre as regiões, a fim de melhor caracterizá-las e obter melhores avaliações das tendências hidrológicas