Model identification and accuracy for estimation of suspended sediment load

In the present study, three widely used modeling approaches: (1) sediment rating curve (SRC) and optimized OSRC, (2) machine learning models (ML) (random forest (RF) and Dagging-RF (DA-RF)) and (3) the semi-physically based soil and water assessment tool (SWAT) are applied to predict suspended sediment load (Qs) at the Talar watershed in Iran. Various graphical and quantitative methods were used to evaluate the goodness of fit. Results indicated that the RF model had the best prediction power in the training phase, while the dagging-RF hybrid algorithm outperformed all other models in the validation phase. The OSRC, RF and DA-RF had ‘very good’ performances based on the NSE in the validation phase, SRC showed ‘good’ performance, while the predicted values using SWAT were ‘satisfactory’. Our results suggest that the OSRC and ML models are more suitable for prediction of Qs in study catchments with poor data availability.</p

NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures

Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of MendozaEEA Rama CaídaFil: Brieva, Carlos. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Brieva, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; ArgentinaFil: Saco, Patricia, M. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Sandi, Steven G. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; AustraliaFil: Sandi, Steven G. Deakin University. School of Engineering; AustraliaFil: Mora, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; ArgentinaFil: Rodríguez, José F. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australi

The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems

Our current understanding of semiarid ecosystems is that they tend to display higher vegetation greenness on polar-facing slopes (PFS) than on equatorial-facing slopes (EFS). However, recent studies have argued that higher vegetation greenness can occur on EFS during part of the year. To assess whether this seasonal reversal of aspect-driven vegetation is a common occurrence, we conducted a global-scale analysis of vegetation greenness on a monthly time scale over an 18-year period (2000–2017). We examined the influence of climate seasonality on the normalized difference vegetation index (NDVI) values of PFS and EFS at 60 different catchments with aspect-controlled vegetation located across all continents except Antarctica. Our results show that an overwhelming majority of sites (70%) display seasonal reversal, associated with transitions from water-limited to energy-limited conditions during wet winters. These findings highlight the need to consider seasonal variations of aspect-driven vegetation patterns in ecohydrology, geomorphology, and Earth system models

Ecogeomorphic coevolution of semiarid hillslopes: Emergence of banded and striped vegetation patterns through interaction of biotic and abiotic processes

[1] Nonlinear interactions between physical and biological factors give rise to the emergence of remarkable landform‐vegetation patterns. Patterns of vegetation and resource redistribution are linked to productivity and carrying capacity of the land. As a consequence, growing concern over ecosystem resilience to perturbations that could lead to irreversible land degradation imposes a pressing need for understanding the processes, nonlinear interactions, and feedbacks, leading to the coevolution of these patterns. For arid and semiarid regions, causes for concern have increased at a rapid pace during the last few decades due to growing anthropic and climatic pressures that have resulted in the degradation of numerous areas worldwide. This paper aims at improving our understanding of the ecogeomorphic evolution of landscape patterns in semiarid areas with a sparse biomass cover through a modeling approach. A coupled vegetation‐pattern formation and landform evolution model is used to study the coevolution of vegetation and topography over centennial timescales. Results show that self‐organized vegetation patterns strongly depend on feedbacks with coevolving landforms. The resulting patterns depend on the erosion rate and mechanism (dominance of either fluvial or diffusive processes), which are affected by biotic factors. Moreover, results show that ecohydrologic processes leading to banded pattern formation, when coupled with landform processes, can also lead to completely different patterns (stripes of vegetation along drainage lines) that are equally common in semiarid areas. These findings reinforce the importance of analyzing the coevolution of landforms and vegetation to improve our understanding of the patterns and structures found in nature

Causality and the Entropy-Complexity Plane: Robustness and Missing Ordinal Patterns

We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, i) the "causal" entropy-complexity plane [Rosso et al. Phys. Rev. Lett. 99 (2007) 154102] and ii) the estimation of the decay rate of missing ordinal patterns [Amig\'o et al. Europhys. Lett. 79 (2007) 50001, and Carpi et al. Physica A 389 (2010) 2020-2029]. In this work we extend the use of these techniques to address the analysis of deterministic finite time series contaminated with additive noises of different degree of correlation. The chaotic series studied here was via the logistic map (r = 4) to which we added correlated noise (colored noise with f-k Power Spectrum, 0 {\leq} k {\leq} 2) of varying amplitudes. In such a fashion important insights pertaining to the deterministic component of the original time series can be gained. We find that in the entropy-complexity plane this goal can be achieved without additional computations.Comment: submitted to Physica

Soil-derived Nature’s Contributions to People and their contribution to the UN Sustainable Development Goals

Acknowledgments The input of PS contributes to Soils-R-GRREAT (NE/P019455/1) and the input of PS and SK contributes to the European Union's Horizon 2020 Research and Innovation Programme through project CIRCASA (grant agreement no. 774378). PR acknowledges funding from UK Greenhouse Gas Removal Programme (NE/P01982X/2). GB De Deyn acknowledges FoodShot Global for its support. TKA acknowledges the support of “Towards Integrated Nitrogen Management System (INMS) funded by the Global Environment Facility (GEF), executed through the UK’s Natural Environment Research Council (NERC). The input of DG was supported by the New Zealand Ministry of Business, Innovation and Employment (MBIE) strategic science investment fund (SSIF). PMS acknowledges support from the Australian Research Council (Project FT140100610). PM’s work on ecosystem services is supported by a National Science Foundation grant #1853759, “Understanding the Use of Ecosystem Services Concepts in Environmental Policy”. LGC is funded by National Council for Scientific and Technological Development (CNPq, Brazil – grants 421668/2018-0 and 305157/2018-3) and by Lisboa2020 FCT/EU (project 028360). BS acknowledges support from the Lancaster Environment Centre Project.Peer reviewedPostprin

Variations in hydrological connectivity of Australian semiarid landscapes indicate abrupt changes in rainfall-use efficiency of vegetation

[1] Dryland vegetation frequently shows self‐organized spatial patterns as mosaic‐like structures of sources (bare areas) and sinks (vegetation patches) of water runoff and sediments with variable interconnection. Good examples are banded landscapes displayed by Mulga in semiarid Australia, where the spatial organization of vegetation optimizes the redistribution and use of water (and other scarce resources) at the landscape scale. Disturbances can disrupt the spatial distribution of vegetation causing a substantial loss of water by increasing landscape hydrological connectivity and consequently, affecting ecosystem function (e.g., decreasing the rainfall‐use efficiency of the landscape). We analyze (i) connectivity trends obtained from coupled analysis of remotely sensed vegetation patterns and terrain elevations in several Mulga landscapes subjected to different levels of disturbance, and (ii) the rainfall‐use efficiency of these landscapes, exploring the relationship between rainfall and remotely sensed Normalized Difference Vegetation Index. Our analyses indicate that small reductions in the fractional cover of vegetation near a particular threshold can cause abrupt changes in ecosystem function, driven by large nonlinear increases in the length of the connected flowpaths. In addition, simulations with simple vegetation‐thinning algorithms show that these nonlinear changes are especially sensitive to the type of disturbance, suggesting that the amount of alterations that an ecosystem can absorb and still remain functional largely depends on disturbance type. In fact, selective thinning of the vegetation patches from their edges can cause a higher impact on the landscape hydrological connectivity than spatially random disturbances. These results highlight surface connectivity patterns as practical indicators for monitoring landscape health

SARS-CoV-2 Catalonia contact tracing program : evaluation of key performance indicators

Background: Guidance on SARS-CoV-2 contact tracing indicators have been recently revised by international public health agencies. The aim of the study is to describe and analyse contact tracing indicators based on Catalonia's (Spain) real data and proposing to update them according to recommendations. Methods: Retrospective cohort analysis including Catalonia's contact tracing dataset from 20 May until 31 December 2020. Descriptive statistics are performed including sociodemographic stratification by age, and differences are assessed over the study period. Results: We analysed 923,072 contacts from 301,522 SARS-CoV-2 cases with identified contacts (67.1% contact tracing coverage). The average number of contacts per case was 4.6 (median 3, range 1-243). A total of 403,377 contacts accepted follow-up through three phone calls over a 14-day quarantine period (84.5% of contacts requiring follow-up). The percentage of new cases declared as contacts 14 days prior to diagnosis evolved from 33.9% in May to 57.9% in November. All indicators significantly improved towards the target over time (p < 0.05 for all four indicators). Conclusions: Catalonia's SARS-CoV-2 contact tracing indicators improved over time despite challenging context. The critical revision of the indicator's framework aims to provide essential information in control policies, new indicators proposed will improve system delay's follow-up. The study provides information on COVID-19 indicators framework experience from country's real data, allowing to improve monitoring tools in 2021-2022. With the SARS-CoV-2 pandemic being so harmful to health systems and globally, is important to analyse and share contact tracing data with the scientific community

Hydrologic dispersion in fluvial networks

This chapter attempts to bring together and summarize the results from recent research analysing the role of hillslope, channel and network processes on the hydrologic response of basins. In doing so, particular emphasis is placed on understanding how different processes act at various scales, from individual channels to the network scale, to produce the dispersive, or 'spreading', effects that shape the basin's hydrologic response. These processes not only have an impact on the hydrograph's shape by determining the waywater is routed to the outlet but also on the way sediments, nutrients, chemicals, aquatic organisms, seeds, bacteria and a number of other substances are redistributed along the basin and/or transported to the outlet by the flow. Consequently, the advances presented in this chapter are relevant not only for hydrology and other fields like fluvial geomorphology and ecology but also for interdisciplinary research in a number of emerging fields, like ecohydrology hydroecology and ecogeomorphology