Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador.

The Pambamarca fortress complex in northern Ecuador is a cultural and built heritage with 18 prehispanic fortresses known as Pucaras. They are mostly located on the ridge of the Pambamarca volcano, which is severely affected by erosion. In this research, we implemented a multiscale methodology to identify sheet, rill and gully erosion in the context of climate change for the prehistoric sites. In a first phase, we coupled the Revised Universal Soil Loss Equation (RUSLE) and four CMIP6 climate models to evaluate and prioritize which Pucaras are prone to sheet and rill erosion, after comparing historical and future climate scenarios. Then, we conducted field visits to collect geophotos and soil samples for validation purposes, as well as drone flight campaigns to derive high resolution digital elevation models and identify gully erosion with the stream power index. Our erosion maps achieved an overall accuracy of 0.75 when compared with geophotos and correlated positively with soil samples sand fraction. The Pucaras evaluated with the historical climate scenario obtained erosion rates ranging between 0 and 20 ton*ha-1*yr-1. These rates also varied from -15.7% to 39.1% for four future climate change models that reported extreme conditions. In addition, after identifying and overflying six Pucaras that showed the highest erosion rates in the future climate models, we mapped their gully-prone areas that represented between 0.9% and 3.2% of their analyzed areas. The proposed methodology allowed us to observe how the design of the Pucaras and their concentric terraces have managed to reduce gully erosion, but also to notice the pressures they suffer due to their susceptibility to erosion, anthropic pressures and climate change. To address this, we suggest management strategies to guide the protection of this cultural and built heritage landscapes.

Learning from mistakes-Assessing the performance and uncertainty in process-based models.

Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrised or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analysing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (1) training a machine-learning (ML) error model using the input data of the process-based model and other available variables, (2) estimation of local explanations (i.e., contributions of each variable to an individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analysing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ML model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation-emitted long wave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.

Automatic Regionalization of Model Parameters for Hydrological Models.

Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters "saturated hydraulic conductivity" and "field capacity," which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.

Observed and Potential Impacts of the COVID-19 Pandemic on the Environment.

Various environmental factors influence the outbreak and spread of epidemic or even pandemic events which, in turn, may cause feedbacks on the environment. The novel coronavirus disease (COVID-19) was declared a pandemic on 13 March 2020 and its rapid onset, spatial extent and complex consequences make it a once-in-a-century global disaster. Most countries responded by social distancing measures and severely diminished economic and other activities. Consequently, by the end of April 2020, the COVID-19 pandemic has led to numerous environmental impacts, both positive such as enhanced air and water quality in urban areas, and negative, such as shoreline pollution due to the disposal of sanitary consumables. This study presents an early overview of the observed and potential impacts of the COVID-19 on the environment. We argue that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.