Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.

Evaluation of Soil Erosion and Its Prediction Protocols around the Hilly Areas of Mubi Region, Northeast Nigeria

Soil erosion is a severe degradation phenomena that has since received huge attention among earth scientists in the developed worlds, and same efforts are now extending to Africa and other parts of underdeveloped worlds. This chapter focuses on collation, analyzing and appraising of soil ero¬sion studies around Mubi region, Northeast Nigeria, where the Mandara mountain ranges is notably responsible for spurring soil erosion. This chapter reviewed reports on the: (a) Mubi regional soil properties, erosion processes and principles of their occurrence, (b) soil erosion predictions using empirical and physically-based models by researchers, and, (c) economicimplications and managements of soil erosion in the region. This chapter reveals that classical and rill/ephemeral gully (EG) erosion features received more research attention than surface erosion such as splash and sheet. No information was reported on effects of landslides/slumping noticeable along rivers/stream banks around the region. The few economic analysis reported for soil nutrient and sediments entrained by concentrated flow channels were very high and intolerable to the predominantly peasant farmers in the region. It is hoped that the considerable volumes of erosion researches and recommendations assembled in this chapter shall be carefully implemented by prospective farmers, organizations, and residents in the Mubi region.

LAND-SUITE V1.0: a suite of tools for statistically-based landslide susceptibility zonation

In the past 50 years, a large variety of statistically-based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations and several approaches to evaluate the models performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modelling and zonation is still missing. The literature search revealed that several articles describe tools that apply physically based models for susceptibility zonation, but only few use statistically-based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models, and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically-based landslide susceptibility modelling which integrate, extend and complete LAND-SE. LAND-SUITE is able to: i) facilitate input data preparation; ii) perform preliminary and exploratory analysis of the available data; iii) test different combinations of variables and select the optimal thematic/explanatory set; iv) test different model types and their combinations; and v) evaluate the models performance and uncertainty. LAND-SUITE provides a tool that can assist the user to reduce some common source of errors coming from the data preparatory phase, and to perform more easily, more flexible and more informed statistically-based landslide susceptibility applications.

Soil Hydrology of Agricultural Landscapes: Quantitative Description, Research Methods, and Availability of Soil Water

Soil hydrology has deep Russian roots, which are primarily related to the theory of soil hydrological constants and their practical application. These constants have been used to assess the hydrological soil conditions in stationary observations, for which attempts to arrange regular hydrological observations in the landscape faced impracticable complexity of work and calculations and provided unreliable quantitative predictions. At present, there are new opportunities for experimental research, digital analysis, and prediction of hydrological indicators of soils in the landscape. A new quantitative approach to the use of digital technologies for monitoring soil water and temperature in the soils of agricultural landscapes, their dynamics, and their probabilistic calculations has been developed. Based on the soil map, it is proposed to create an information and measurement system with the studied thermal and hydrophysical characteristics of soils using mathematical models to calculate the dynamics of moisture and temperature for given periods and conditions of different availability of heat and precipitation, which allows us to quantify the availability of moisture reserves in the soils of the agricultural landscape. This system of observations, assessment, and forecast includes the use of modern technologies for determining soil water content and temperature, the adaptation of predictive physically based models for calculating the dynamics of moisture reserves depending on the availability of precipitation and conditions at the lower boundary of soil profiles. The paper deals with the hydrological analysis of soils by the example of the agricultural landscape of the Zelenograd station of the Dokuchaev Soil Science Institute in the village of El’digino, Pushkino district, Moscow oblast.

Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling

Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Flood4castRTF: A Real-Time Urban Flood Forecasting Model

Worldwide, climate change increases the frequency and intensity of heavy rainstorms. The increasing severity of consequent floods has major socio-economic impacts, especially in urban environments. Urban flood modelling supports the assessment of these impacts, both in current climate conditions and for forecasted climate change scenarios. Over the past decade, model frameworks that allow flood modelling in real-time have been gaining widespread popularity. Flood4castRTF is a novel urban flood model that applies a grid-based approach at a modelling scale coarser than most recent detailed physically based models. Automatic model set-up based on commonly available GIS data facilitates quick model building in contrast with detailed physically based models. The coarser grid scale applied in Flood4castRTF pursues a better agreement with the resolution of the forcing rainfall data and allows speeding up of the calculations. The modelling approach conceptualises cell-to-cell interactions while at the same time maintaining relevant and interpretable physical descriptions of flow drivers and resistances. A case study comparison of Flood4castRTF results with flood results from two detailed models shows that detailed models do not necessarily outperform the accuracy of Flood4castRTF with flooded areas in-between the two detailed models. A successful model application for a high climate change scenario is demonstrated. The reduced data need, consisting mainly of widely available data, makes the presented modelling approach applicable in data scarce regions with no terrain inventories. Moreover, the method is cost effective for applications which do not require detailed physically based modelling.

Landslide Susceptibility Assessment Using the Scoops3D Model in a Tropical Mountainous Terrain

Many physically-based distributed models study the landslide occurrence using an infinite slope stability analysis, simulating a planar failure, which is not usually applicable to rotational failures and deep landslides. Recently, some three-dimensional distributed physically-based models have been developed that have been applied in different parts of the world. In this research, the Scoops3D model is implemented for a landslide susceptibility analysis in a tropical mountainous terrain of the Colombian Andes (Medellín, Colombia). In addition to identifying the areas susceptible to the occurrence of rotational landslides, the results of the safety factor are analyzed with the areas of associated critical failure surfaces to provide an interpretation and explanation of the simulation results. This is to have a better understanding of how the model works and to facilitate its implementation in landslide hazard assessment. The Scoops3D physicallybased model can be a very useful tool for mass movement risk management projects.

The role of lateral erosion in the evolution of nondendritic drainage networks to dendricity and the persistence of dynamic networks

Dendritic, i.e., tree-like, river networks are ubiquitous features on Earth’s landscapes; however, how and why river networks organize themselves into this form are incompletely understood. A branching pattern has been argued to be an optimal state. Therefore, we should expect models of river evolution to drastically reorganize (suboptimal) purely nondendritic networks into (more optimal) dendritic networks. To date, current physically based models of river basin evolution are incapable of achieving this result without substantial allogenic forcing. Here, we present a model that does indeed accomplish massive drainage reorganization. The key feature in our model is basin-wide lateral incision of bedrock channels. The addition of this submodel allows for channels to laterally migrate, which generates river capture events and drainage migration. An important factor in the model that dictates the rate and frequency of drainage network reorganization is the ratio of two parameters, the lateral and vertical rock erodibility constants. In addition, our model is unique from others because its simulations approach a dynamic steady state. At a dynamic steady state, drainage networks persistently reorganize instead of approaching a stable configuration. Our model results suggest that lateral bedrock incision processes can drive major drainage reorganization and explain apparent long-lived transience in landscapes on Earth.

Rainfall Thresholds for Shallow Landslides by coupled Physically-Based Models and Machine Learning methods in Colombian Andes Basins

&lt;p&gt;Landslides have taken thousands of lives worldwide in the last decades, especially in developing countries. In the Colombian Andes, tropical rainfall conditions and steep terrains are the most common triggering factors of landslides. According to DESINVENTAR in Colombia between 1921-2020, 10.438 landslides have been registered and left almost 7.313 fatalities and destructive outcomes to the economic system. Rainfall thresholds have been used to forecast the occurrence of landslides. Physically-based rainfall thresholds take into account the effects of rainfall coupling hydrological and geotechnical models providing a wide understanding of the physical behavior of the rainfall throw the hillslope and infiltration processes. On the other hand, Machine Learning methods have been implemented to evaluate the correlation between the spatial distribution of the landslide hazard and the morphometric parameters of the basin (e.g. average slope, area, and Melton ratio).&lt;/p&gt;&lt;p&gt;This work was performed implementing the physically-based model TRIGRS to analyze the distribution of the safety factor under different combinations of intensity and duration from gauge-based IDF curves. And, morphometric parameters were calculated to 14 basins distributed along the Colombian Andes; all them were processed by machine learning methods to correlate the influence of each parameter with the rainfall threshold.&amp;#160; The results of coupling physically-based models and machine learning methods could provide criteria that allow setting up a procedure that defines a condition of instability based on the distribution of the safety factor in a basin.&lt;/p&gt;&lt;p&gt;Keywords: Rainfall thresholds, Shallow Landslides, Morphometric Parameters, IDF Curves, TRIGRS&lt;/p&gt;