Denitrification in large tropical river systems is likely important for nitrogen retention estimates, but is limited by the need for measurements and the ability to scale these estimates to relate seasonal changes to river geomorphology and discharge. Geomorphic units (GUs), that describe the structure of a river system based on their inundation frequency and vegetation cover, may be useful to characterise features that influence denitrification rates. In this study, we tested the hypothesis that measurements of potential denitrification rate (PDR) using denitrification enzyme assays from different GUs could be used to1) relate PDR to soil, vegetation and different land use and land-cover (LULC) types as controlling factors and 2) that these characteristics could be assessed using remote sensing data to model PDR over a large spatial scale (along a 50 km reach) for the Padma River (Bangladesh). Specifically, 245 PDR measurements were made from the four LULC types with in eight GUs during the dry/winter season 2020. Linear regression using a mixed-modelling approach showed that PDR was highly related to vegetation cover and soil moisture across all GUs. Sentinel-2 data were then used to develop relationships between the Normalised Difference Vegetation Index. (NDVI) and vegetation cover and, specifically, between Sentinel-2 band 11 and soil moisture, which also reasonably described PDR rates. We then used this satellite data to estimate reach-scale PDR in post-monsoon, dry/winter and pre-monsoon seasons. The satellite-based model showed that PDR increased in GUs from post-monsoon 2019 to pre-monsoon 2020. The vegetation islands and the bars were the most important GUs for denitrification in all seasons. The satellite-assisted approach developed in this study can be applied to the GUs in large lowland rivers where inundation occurs frequently.

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Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation, often referred to as evapotranspiration (ET), over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 676 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using eddy-covariance (EC)-based ET estimates as a reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by root-mean-squared errors (RMSEs). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm d−1, with a mean of 1.18 mm d−1. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in the temporal and spatial scales of reference datasets to those of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and their statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment.@en

Geomorphic classification of large rivers identifies morphological patterns, as a foundation for estimating biogeochemical and ecological processes. In order to support the modelling of in-channel nutrient retention or export, the classification of geomorphic units (GUs) was done in the Padma River, Bangladesh, a large and geomorphically-complex lowland river. GUs were classified using the normalized difference vegetation index (NDVI) four times over a year, so as to cover the seasonal variation of water flows. GUs were categorized as primary and secondary channels (C & S); longitudinal bar (L); transverse bar (T); side bar (SB); unvegetated bank (EK); dry channel (ED); island (VI); and water depression (WD). All types of GUs were observed over the four distinct annual seasons, except ED, which was absent during the high flow, monsoon season. Seasonal variation of the surface area of GUs and discharge showed an inverse relation between discharge and exposed surface areas of VI, L, T, and SB. Nutrients mainly enter the river system through water and sediments, and during monsoon, the maximum portion of emergent GUs were submerged. Based on the assumption that nutrient retention is enhanced in the seasonally inundated portions of GUs, nutrient retention-/export-relevant geomorphic units (NREGUs) were identified. Seasonal variation in the area of NREGUs was similar to that of GUs. The mean NDVI values of the main identified NREGUs were different. The variation of NDVI values among seasons in these NREGUs resulted from changes of vegetation cover and type. The variation also occurred due to alteration of the surface area of GUs in different seasons. The changes of vegetation cover indicated by NDVI values across seasons are likely important drivers for biogeochemical and ecological processes.

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Evapotranspiration (ET), a key variable in both water and energy cycles. It is very challenging to measure or estimate in large regions. Among many approaches to estimate ET indirectly (e.g. through hydrological modelling), models that are based on satellite remote sensing data (RS) are increasingly being used. However, the RS-based models inherit uncertainty from many sources, such as the model’s algorithm and parameters, input satellite data, and processing techniques. It is challenging to assess this uncertainty due to limitations of validation data, high volume and high dimensionality of RS data. Many studies have evaluated uncertainty in RS-based estimation of ET using different methods and reference data. The suitability of methods and reference data subsequently affect the validity of these evaluations. Therefore, it is necessary to have an overview of different evaluation methods and their uses. This study aimed to systematically review original research papers that assessed uncertainty or accuracy of RS-ET model or data products. We categorized these papers and quantified based on (i) spatial and temporal scale of ET estimation, (ii) types of uncertainty, and (iii) methods used to assess uncertainty. Studies have been geographically concentrated in North Asia, North America, and Europe. Most studies used the validation method, which quantifies the discrepancy between pixel-based ET estimation with an in-situ estimation. Although a standardized validation approach for satellite-based ET estimates is not yet ready, most validation studies employed Eddy Covariance (EC) flux towers for reference estimation at field-scale. In regions where in-situ measurements are limited, many studies use the residual of the water balance as reference. However, few studies considered uncertainty in the reference estimation and mismatch of spatial and temporal scales. For monitoring agricultural fields, most RS-ET methods have been reported with high accuracy. When applying these methods to larger extent, additional assessments are required to better inform data users of the quality of RS-ET estimation. These include cross-validation, sensitivity, and uncertainty analyses. Overall, this review showed the progress in evapotranspiration estimation using satellite data in terms of uncertainty assessment.

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Many terrestrial biogeochemistry process models have been applied around the world at different scales and for a large range of ecosystems. Despite being essential ecosystems that sustain important ecological processes, only a few efforts have been made to estimate the gross primary production (GPP) and the hydrological budgets along an altitudinal gradient for grasslands in the Andean Region. One of the few previous studies in the region considered the heterogeneity of the main properties of the páramo vegetation and showed significant differences in plant functional types, site/soil parameters, and daily meteorology. This study extends the work previously mentioned by using the Biome-BGC model to simulate the GPP and the water fluxes in a representative area of the Ecuadorian Andean páramos. It focuses on three main growth forms of vegetation and is also extended to cells with similar properties. The responses of GPP and the water fluxes were dependent on environmental drivers, ecophysiology, and site-specific parameters. The results showed that the GPP estimations at lower elevations are more than twice the estimations at higher elevations, which might have a large implication during extrapolations at larger spatiotemporal scales. The assessment of the water fluxes in the páramo ecosystem was inaccurate, presumably due to the poor estimation of the soil processes, water storage, and evaporative processes. A further development in the soil and evaporative modeling process of Biome-BGC is needed in order to be fully applicable in the high-altitudinal páramo ecosystems. An accurate estimation of the temporal changes of carbon and water budgets can potentially assess the effect of the climate drivers in the biomass productivity of this terrestrial ecosystem.@en

Aims The importance of quantifying carbon stocks in terrestrial ecosystems is crucial for determining climate change dynamics. However, the present regional assessments of carbon stocks in tropical grasslands are extrapolated to unsampled areas with a high degree of uncertainty and without considering the carbon and nitrogen composition of vegetation and soil along altitudinal ranges. This study aims to assess carbon and nitrogen concentrations in soil and vegetation, aboveground carbon stocks distribution and soil organic carbon stocks along an altitudinal range in the páramo region in the Ecuadorian Andes. Methods The vegetation inventory was conducted using 15×15 m sampling plots distributed in three altitudinal ranges. Based on the patterns exhibited by the dominant vegetation growth forms, biomass and soil were sampled to quantify the corresponding carbon and nitrogen concentrations. Subsequently, the aboveground live biomass along the páramo altitudinal range was estimated using allometric equations. Finally, soil and vegetation carbon stocks were estimated for the entire basin. Important Findings Altitudinal analysis supported a potential distribution of carbon and nitrogen concentrations in soil, litter and live tissues, where higher concentrations were found in the low altitudinal range mainly for tussocks and acaulescent rosettes. Cellulose in litter showed higher concentrations at low altitudinal ranges for acaulescent rosettes and cushions only. For the same growth forms, lignin patterns in litter were higher in high altitudinal ranges. Soil texture provided complementary information: high percentage of silt was highly correlated to high soil nitrogen and carbon concentration. Tussocks were found to be responsive to altitude with their, highest aboveground carbon stocks occurring at the low altitudinal range, but cushions and acaulescent rosettes responded differently. The established relationships among soil, vegetation and altitude shown in this study must be taken into account to estimate both aboveground and soil organic carbon stocks in páramo regions - such estimates will be considerably inaccurate if these relationships are ignored.

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The Upper Blue Nile is home to a large human and livestock population that live in diverse biophysical and socio-economic environment. The basin is increasingly experiencing multi-dimensional pressures including population growth, climate change and variability, deforestation, land/soil degradation, as well as increasing upstream-downstream tension on water use rights. Understanding the dynamic interactions of land use and water resources in the basin comes at the forefront of any effort to improving the livelihood and sustainability in the basin. As part of a study to develop a decision support system for an integrated natural resources management for the Upper Blue Nile basin, methods and techniques are developed to identify land use suitably based on various biophysical and socio-economic factors, and water resources availability in the basin. Possible biophysical and socio-economic land use change drivers on a mesoscale catchment, Jedeb, were identified using historical remote sensing data as well as primary data sources such as field observations, questionnaires and interviewing key stakeholder informants. Then, major land use change drivers were ranked using regression analysis (PCA). A hydrological model was setup using the Soil and Water Assessment Tool (SWAT) for the Jedeb catchment. Based on the identified high impact land use change drivers and additional factors such as agro-ecological zones and water resources availability (output from the hydrological model), a land use suitability analysis for the catchment was developed using the SITE (SImulation of Terrestrial Environments) generic land use change modeling framework. The land use and hydrological models exchange yearly simulation results to determine land use suitability analysis and impacts of land use change on components of the basin's hydrology, and vice-versa. Land use scenario was analyzed by assuming a 20% increase in population in the catchment. Preliminary results indicate a clear shift mainly from grassland land use type to cultivation land use type. It is concluded that the techniques and methodologies used in this study especially for integrating the two models can be used for a more realistic and thorough analysis of land use suitability and water resources dynamics and assessment study. Outputs of this study are used as inputs to develop a spatial decision support system for an integrated assessment and management of land use and water resources in the basin.

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