Hydrological Point Research Articles

Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania

Study regionPrahova river basin located in the central-southern region of Romania. Study focusThis study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the regionThe PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin.

Low-regret climate change adaptation in coastal megacities – evaluating large-scale flood protection and small-scale rainwater detention measures for Ho Chi Minh City, Vietnam

Abstract. Urban flooding is a major challenge for many megacities in low-elevation coastal zones (LECZs), especially in Southeast Asia. In these regions, the effects of environmental stressors overlap with rapid urbanization, which significantly aggravates the hazard potential. Ho Chi Minh City (HCMC) in southern Vietnam is a prime example of this set of problems and therefore a suitable case study to apply the concept of low-regret disaster risk adaptation as defined by the Intergovernmental Panel on Climate Change (IPCC). In order to explore and evaluate potential options of hazard mitigation, a hydro-numerical model was employed to scrutinize the effectiveness of two adaptation strategies: (1) a classic flood protection scheme including a large-scale ring dike as currently constructed in HCMC and (2) the widespread installation of small-scale rainwater detention as envisioned in the framework of the Chinese Sponge City Program (SCP). A third adaptation scenario (3) assesses the combination of both approaches (1) and (2). From a hydrological point of view, the reduction in various flood intensity proxies that were computed within this study suggests that large-scale flood protection is comparable but slightly more effective than small-scale rainwater storage: for instance, the two adaptation options could reduce the normalized flood severity index (INFS), which is a measure combining flood depth and duration, by 17.9 % and 17.7 %, respectively. The number of flood-prone manufacturing firms that would be protected after adaptation, in turn, is nearly 2 times higher for the ring dike than for the Sponge City approach. However, the numerical results also reveal that both response options can be implemented in parallel, not only without reducing their individual effectiveness but also complementarily with considerable added value. Additionally, from a governance perspective, decentralized rainwater storage conforms ideally to the low-regret paradigm: while the existing large-scale ring dike depends on a binary commitment (to build or not to build), decentralized small- and micro-scale solutions can be implemented gradually (for example through targeted subsidies) and add technical redundancy to the overall system. In the end, both strategies are highly complementary in their spatial and temporal reduction in flood intensity. Local decision-makers may hence specifically seek combined strategies, adding to singular approaches, and design multi-faceted adaptation pathways in order to successfully prepare for a deeply uncertain future.

Tipping Bucket Rain Gauges in Hydrological Research: Summary on Measurement Uncertainties, Calibration, and Error Reduction Strategies

Tipping bucket rain gauges (TBRs) continue to be one of the most widely used pieces of equipment for rainfall monitoring; they are frequently used for the calibration, validation, and downscaling of radar and remote sensing data, due to their major advantages—low cost, simplicity and low-energy consumption. Thus, many works have focused and continue to focus on their main disadvantage—measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks’ operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques, summarizing TBR measurement uncertainties, focusing on calibration and error reduction strategies, discussing the state of the art and providing future perspectives of the technology.

Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Long short-term memory (LSTM) networks have demonstrated successful applications in accurately and efficiently predicting reservoir releases from hydrometeorological drivers including reservoir storage, inflow, precipitation, and temperature. However, due to its black-box nature and lack of process-based implementation, we are unsure whether LSTM makes good predictions for the right reasons. In this work, we use an explainable machine learning (ML) method, called SHapley Additive exPlanations (SHAP), to evaluate the variable importance and variable-wise temporal importance in the LSTM model prediction. In application to 30 reservoirs over the Upper Colorado River Basin, United States, we show that LSTM can accurately predict the reservoir releases with NSE ≥ 0.69 for all the considered reservoirs despite of their diverse storage sizes, functionality, elevations, etc. Additionally, SHAP indicates that storage and inflow are more influential than precipitation and temperature. Moreover, the storage and inflow show a relatively long-term influence on the release up to 7 days and this influence decreases as the lag time increases for most reservoirs. These findings from SHAP are consistent with our physical understanding. However, in a few reservoirs, SHAP gives some temporal importances that are difficult to interpret from a hydrological point of view, probably because of its ignorance of the variable interactions. SHAP is a useful tool for black-box ML model explanations, but the hydrological processes inferred from its results should be interpreted cautiously. More investigations of SHAP and its applications in hydrological modeling is needed and will be pursued in our future study.

State and Fate of Glaciers in the Val Veny (Mont-Blanc Range, Italy): Contribution of Optical Satellite Products

The glaciers of the Val Veny (Italian side of the Mont-Blanc Massif) have been the site of numerous field observations during the last decades, in particular for the study of glacial fluctuations or surface processes related to the debris cover. Here, we propose to examine how satellite observations can complement field measurements on the state and fate of the Val Veny glaciers. Indeed, satellite products obtained in a quasi-systematic way allow to account not only for the loss of surface and volume, but also for the changes in their flow velocities. The overall pattern we document is a glacier thinning and slowdown of the ice flow, with an estimated shrinkage of 25% by 2050 and a volume loss ranging between 30 and 43% depending on the data source used for the estimation of the initial volume. In such a context, a portion of the upper reaches of Brenva Glacier shows an unexpected pattern of thickening and increase in ice flow that rises questions on its origin. Finally, the uncertainties in the estimation of ice thicknesses remain important and have repercussions on the future evolution of the glaciers and their contribution from a hydrological point of view. By 2050, we estimate that the water contribution due to the volume loss of Val Veny glaciers could decrease by 40%.

Computation of Time of Concentration Based on Two-Dimensional Hydraulic Simulation

Time of concentration (TC) is a parameter in runoff estimation, used to study and design different types of projects in watersheds. Any error in TC calculation leads to an inaccurate estimation of the design flow, which can lead to over-sizing or under-sizing of designed facilities that can have great economic and environmental consequences. Therefore, choosing the correct method to estimate TC is of great importance. Due to the diversity of estimation methods in the literature, the obtained TC values are different. This study aims to present a new method to calculate TC, based on its main concept, i.e., the time required for a water parcel to reach its outlet from the farthest hydrological point of a watershed. A two-dimensional hydraulic simulation was used to model the water parcel travel. A watershed was selected as a case study, and its time of concentration was determined by salt solution tracing. The field measurement results were used for calibration of the numerical simulation model. Meanwhile, 31 empirical relations in the literature were reviewed to determine the most accurate ones. Estimated TC values were compared with the measured ones, and the relative error percentage was used to evaluate the accuracy of the result. In the empirical TC methods, the maximum error was above 300%, and the minimum error was 6.7% for the field studied area. The relative errors of hydraulic simulation outputs were between 3 and 27%. The results showed that only three empirical methods, namely Simas and Hawkins, SCSlag, and Yen and Chow, had the least errors respectively equal to 6.7%, 8.660%, and 13.5%, which can be recommended for the studied area and those with similar hydrological characteristics. On the other hand, hydraulic simulation is also introduced as an efficient method to determine TC which can be used in any desired watershed.

Organization Patterns of Complex River Networks in Chile: A Fractal Morphology

River networks are spatially complex systems difficult to describe by using simple morphological indices. To this concern, fractal theory arises as an interesting tool for quantifying such complexity. In this case of study, we have estimated for the first time the fractal dimension of Chilean networks distributed across the country, analysed at two different scales. These networks insert into variable environments, not only from a climatic and hydrological point of view, but also from a morphological point of view. We investigate to which extent the fractal dimension is able to describe the apparent disorganized character of landscape, by applying two methods. Striking patterns of organization related to Horton ratios and the fractal dimension are reported and discussed. This last parameter depends on the scale of the network, showing interesting groupings by tectonic and climatological factors. Our results suggest that under restricted conditions, the fractal dimension could help to capture the intricate morphology of Chilean networks and its links with the hydrological, climatic, and tectonic conditions present across the country.

Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations

Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to 6 h, which is a relevant lead time for emergency services in times of crisis. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occurred in southeastern France. Precipitation forecasts are evaluated at a 15- min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrological point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15 min and 1 h 30 min of forecast, the performance of PIAF was higher than AROME-NWC. For longer lead times (up to 3 h) their performances were generally equivalent. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash flood simulations and driven by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash flood forecasting in terms of peak intensity, timing, and first wave of discharge, with an anticipation of these phenomena that can reach several hours.

AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

Abstract. Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences.

Study of Physical-Chemical Characteristics of Discharge Wastewater in Lepenc Basin

Pollution caused by the discharge of wastewater into the Lepenc river basin is diverse and directly affects the quality of life of the inhabitants of this area. This paper aims to investigate the physicochemical characteristics of pollution from wastewater discharges, build the water balance of the entire basin, and identify the risk to human life due to lack of drinking water. From the hydrological point of view, the Lepenc river basin is divided into sub-basins, and the results obtained by experimental measurements show that wastewater pollution varies from one sub-basin to another and depends on the amount and origin of wastewater. Combined analysis of the available amount of water and the need for clean water, which is based on parameters that are out of the water balance below the water stress limit, the need for wastewater treatment is a necessity and that continuous water discharge of contaminants in the water basin is unacceptable or in other words very dangerous. The results showed that some analyzed parameters are outside the acceptable limits for drinking water such as TSS with values above 75 mg/L, BOD5 with values up to 9.42 mg/L, NH+4 up to 9.48 mg/L, and PO3-4 up to 1.76 mg/L. On the other hand, the waters of the Lepenci river basin had low heavy metal pollution during the observed period. Therefore, routine monitoring of wastewater in the Lepenc Basin is recommended to prevent the risk of pollution of the basin.

Analysis and evaluation of flood routing using Muskingum method

There are several mathematical procedures that deal with hydrologic flood routing. The Muskingum technique is one of the most common techniques used for flood routing for river reach. From the hydrologic point of view, flood routing in a stream is used to predict the flood discharge, or storage, at any downstream station in a stream channel from a known discharge, or stage, at an upstream station. Hydrologic routing is an approximate technique. However, it provides relatively easy alternative, for solving flood routing problems. It is based on the storage and the continuity equations. In principle hydrologic routing employs historical data on inflow and outflow discharges in the reach under study. The Muskingum method is the particular one to be considered in this paper, describing three procedures, other than the classical trial and error procedure, for solving flood routing.

A new transfer function model for the estimation of non-point-source solute travel times

The scope of this work is to present a new fast and reliable transfer function model, which simulates the spatio-temporal distribution of non-point-source solutes along the unsaturated zone, suitable to be used at large scales within a web-based Decision Support System. With the assumptions of a) a gravity induced water flow, b) a non-reactive solute and c) a purely convective flow, the model uses the transfer functions, i.e., the travel time (TT) probability density functions, derived from the unsaturated hydraulic conductivity curve k(θ). The output concentration of a solute is simply the convolution of the transfer functions with the input concentrations to the system. A model sensitivity analysis, based on Monte Carlo simulations, was carried out, showing that saturated water content and the tortuosity parameter τ were the parameters that affected the mean TT more. The model was validated against concentration experiments carried out on four large soil columns. Results were really good for all soils, with the best agreement with R2 = 0.97, RMSE = 0.11 and ME = −0.01. Moreover, the outputs obtained applying the model to 46 soil profiles sampled in the Valle Telesina, in Southern Italy, completely characterised from the hydrological point of view, were compared with those obtained from the Richard-based model Hydrus 1D. The result of the comparisons gave a very high correlation coefficient (above 0.8), a mean absolute error between the two models of around 40 days and a percent bias of −16%. Finally, the application of transfer function model to a large spatial extent is presented, to show its possible use for the groundwater vulnerability assessment.

An Analysis of Total Water Storage Changes Obtained from GRACE FO Observations over the Venezia Islands Area Supported with Additional Data

The Venezia Islands are a very special area from the hydrological point of view due to its water mass changes. Regular floods results in the need for the regular monitoring of water mass changes. For this purpose, a Gravity Recovery and Climate Experiment mission (GRACE) can be used as a source of data. The aim of the paper is to compare the latest results of the new GRACE FO observations. The comparisons were carried out all over Venezia Island using the L3 level, RL06 release data obtained with spherical harmonics degree and order extension of up to 120, by the three most important computational centres: JPL, GFZ, CSR. Results are compared to an average month values of precipitation and evapotranspiration and tide gauge data in the nearby area. Based on the research, no dependence between TWS and evapotranspiration and evapotranspiration change were found.

Quantifying the Impact on Stormwater Management of an Innovative Ceramic Permeable Pavement Solution

Stormwater management in cities has traditionally been based on centralized systems, evacuating runoff as quickly as possible through drainage networks that collect and convey the runoff to the final point of treatment or the receiving water body. In recent years, a different approach focused on the use of Sustainable Urban Drainage Systems (SUDS) represents a paradigm shift, promoting a decentralized management as close to the runoff source as possible. Among these techniques, permeable pavements represent an effective solution for reducing runoff and providing pollutant treatment. This contribution describes the results obtained from an innovative ceramic permeable pavement developed as part of the LIFE CERSUDS project in the city of Benicàssim (Spain). This pavement, composed by modules built from ceramic tiles in stock, allows water infiltration, runoff treatment and water reuse as part of a SUDS built in 2018 and monitored from September 2018 to September 2019. The purpose of the research was to demonstrate the hydraulic performance of the proposed solution through monitoring of runoff quantity and quality variables. Monitoring data analysis have shown positive results, reducing peak runoff rates and the volume of water which is conducted downstream. From the hydrological point of view, the system capacity shown a 100% runoff management for events up to 15–25 mm of precipitation. This is a very significant threshold since these values represent, respectively, the 81% and 91% percentiles for the study area. System performance was confirmed in terms of runoff management and water infiltration. This demonstration case study represents a reference example of urban retrofitting actions which integrate social, economic and environmental aspects.

Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

Оценка эластичности стока рек восточной части бассейна Амура

Taking into account the unstable moisture regime and the diversity of landscapes within the Amur River basin, the problem of assessing the impact of climatic changes on the processes of surface water cycle in the region becomes multifaceted, associated with the solution of particular problems. This work studied the reaction of a river runoff to changes in the amount of atmospheric precipitation on the basis of the elasticity coefficient. Small and medium-sized river basins (52 in total) belonging to the system of the Middle and Lower Amur are the objects of this study. The data of standard observations at hydrological posts and meteorological points for the summer-autumn flood hazard period (June-September) were used. The data series were selected to include different moisture conditions. It was found that with an increase in precipitation by 1 %, the river runoff in the flood hazard season has increased by 1.02–3.86 % in 48 cases, and decreased in 4 cases. The results of the work are fundamentally close to the regional estimates within the basin of the Amur River on the base of the analysis of factual material (including the near abroad) and simulation results, as well as to the values of the runoff elasticity coefficient obtained in different geographic zones and given in the specialized literature. The relationship between the elasticity coefficient values of runoff with various basin indicators has been studied. For the rivers in the Primorsky Kray (catchment area of the Ussuri River), the relationship between the elasticity coefficient values and the seasonal runoff and precipitation, the runoff coefficient, average height, catchments slopes and average river network slopes has been revealed. The influence of local conditions of runoff formation on the river basins response to changes in the amount of atmospheric precipitation is discussed.

A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models

Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs’ performance, such as 90-meters’ TanDEM-X and 30-meters’ SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).

Hydropluviometric variability in non-Sahelian West Africa: case of the Koliba/Corubal River Basin (Guinea and Guinea-Bissau)

Abstract. The Koliba/Corubal River watershed is poorly documented due to the hydrometric measurements shutdown and gaps in the very short hydropluviometric timeseries. The purpose of this study is to analyze the variability of rainfall in the Basin, by simulating flows using the GR2M rain-flow model and extending the discharge timeseries. From the regional vector method, the rainfall timeseries were homogenized, and the gaps filled by the estimated values. The rank correlation and Pettitt test on annual rainfall amounts (1924–2015) indicate breaks in 1958, 1967 and 1969, leading to rainfall deficits ranging from 9.7 % to 20.2 %. For some stations, the segmentation method shows a recovery of rainfall towards the end of the 1980s (Gaoual, Mali) and the early 2000s (Gabu). The analysis of the temporal distribution of the Monthly Rainfall Coefficients shows an improvement of the contributions of a few months during the period after rupture. From a hydrological point of view, the correlation between the mean annual rainfall and the runoff has allowed to extend the flow timeseries. The mean monthly rainfall calculated using the inverse square of distance method, the Potential Evapotranspiration, and the flow rates were used to calibrate and validate the model to determine the parameters that better transform rainfall in flow. The values of the Nash criteria close to 100 have made it possible to extend the monthly flow data from Koliba/Corubal to Gaoual, Cade and Tche-Tche until 2015.

Rewilding and restoring cultural landscapes in Mediterranean mountains: Opportunities and challenges

Farmland abandonment and the decline of livestock activity in the Mediterranean mountains have resulted in dramatic landscape changes, including the generalized expansion of shrublands and forests, and the homogenization of the old cultural landscapes. This process has a variety of consequences from ecological, geomorphological and hydrological points of view, as well as from the perspectives of land management and public awareness. An intense debate currently surrounds the discussion and evaluation of rewilding (the process of passively allowing woody encroachment, as well as the reintroduction of large mammals) as an opportunity for enhancing biodiversity and restoring original landscapes after centuries of human activity versus ecological restoration (activities leading to the recovery of degraded ecosystems, including clearing and light human activity). There is no clear consensus regarding the best way to improve the ecological relationships and functioning within an ecosystem. Biodiversity and sustainability can be seen under different levels of human pressure and landscape transformation; total farmland abandonment is not always the best alternative, particularly when local inhabitants aim to sustain themselves using local resources. Many geographers and ecologists consider that extensive stockbreeding in a partially open landscape is a rational way to (i) improve landscape organization, (ii) increase flows and turnover within the ecosystems, (iii) increase the diversity of plants and animals that benefit from a relatively light human presence, and (iv) reduce wildfire risk. However, it has proven challenging for land managers and stockbreeders to clear the best old abandoned fields and “construct” a sustainable, balanced landscape that combines forests, shrublands and open lands. Private landowner involvement and support from the general public is crucial for both funding and the long-term maintenance of benefits. The best old fields should be cleared in the context of high-resolution knowledge of the topography, grassland characteristics, grassland cycles and livestock management. Such efforts are likely to be an excellent opportunity to introduce compatibility between light human activity and increases in biodiversity and sustainability for many marginal mountains, where land abandonment and general forest/shrub recovery are the inevitable tendencies. This paper examines some of the contrasting positions of the scientific community regarding the rewilding or ecological restoration of mountain landscapes, and briefly highlights some experiences in which intentional clearing of old abandoned fields has benefited stockbreeding, biodiversity, runoff generation and wildfire risks. Notably, we describe a sub-Mediterranean valley of the Iberian Range, Northern Spain, as an example. In the long term, we find that the intentional clearing of the best old fields allows the slow organization of a final landscape that will be more useful for local inhabitants, thereby helping to reverse human depopulation in these regions.

Parametric Terracing as Optimization of Controlled Slope Intervention

With the introduction of mechanization in agriculture, the area of terraced slopes has increased. However, in most cases, the planning of terracing in practice remains experience-based, which is no longer effective from an agricultural, geological, and hydrological point of view. The usual method of building terraces, especially terraces with earth risers, is therefore outdated, and a new method must be found for planning and building terraced areas. In addition to geographical information system (GIS) tools, parametric design tools for planning terraced landscapes are now available. Based on the design approaches for a selected plot in the Gorizia Hills in Slovenia, where we used a trial-and-error method, we improved previous results by defining a model using a computer algorithm that generates a terraced landscape on a selected slope depending on various input parameters such as the height of the terrace slope, the inclination of the terrace slope, the width of the terrace platform, and the number of terraces. For the definition of the algorithm we used the visual program tool Grasshopper. By changing the values of the input data parameters, the algorithm was able to present combinatorial simulations through a variety of different solutions with all the corresponding statistics. With such results it is much easier to make a conscious decision on which combination of parameters is optimal to prevent landslides, plan adequate drainage, and control soil movements when building terraces. The controlled slope intervention is further optimized by the introduction of a usage index (Tx), defined as the quotient of the sum of all flat areas (terrace platforms) and the total area of the plot.