Potential and limitations of using soil mapping information to understand landscape hydrology

Abstract. This paper addresses the following points: how can whole soil data from normally available soil mapping databases (both conventional and those integrated by digital soil mapping procedures) be usefully employed in hydrology? Answering this question requires a detailed knowledge of the quality and quantity of information embedded in and behind a soil map. To this end a description of the process of drafting soil maps was prepared (which is included in Appendix A of this paper). Then a detailed screening of content and availability of soil maps and database was performed, with the objective of an analytical evaluation of the potential and the limitations of soil data obtained through soil surveys and soil mapping. Then we reclassified the soil features according to their direct, indirect or low hydrologic relevance. During this phase, we also included information regarding whether this data was obtained by qualitative, semi-quantitative or quantitative methods. The analysis was performed according to two main points of concern: (i) the hydrological interpretation of the soil data and (ii) the quality of the estimate or measurement of the soil feature. The interaction between pedology and hydrology processes representation was developed through the following Italian case studies with different hydropedological inputs: (i) comparative land evaluation models, by means of an exhaustive itinerary from simple to complex modelling applications depending on soil data availability, (ii) mapping of soil hydrological behaviour for irrigation management at the district scale, where the main hydropedological input was the application of calibrated pedo-transfer functions and the Hydrological Function Unit concept, and (iii) flood event simulation in an ungauged basin, with the functional aggregation of different soil units for a simplified soil pattern. In conclusion, we show that special care is required in handling data from soil databases if full potential is to be achieved. Further, all the case studies agree on the appropriate degree of complexity of the soil hydrological model to be applied. We also emphasise that effective interaction between pedology and hydrology to address landscape hydrology requires (i) greater awareness of the hydrological community about the type of soil information behind a soil map or a soil database, (ii) the development of a better quantitative framework by the pedological community for evaluating hydrological features, and (iii) quantitative information on soil spatial variability

Short Proof of a Cardinal Inequality involving the Weak Extent

We are presenting a short and self contained proof of the cardinal inequality $\mid X \mid ≤ we(X)^{psw(X)}, by using the Pol-\v{S}apirovski\u{ı}’s technique

Soil hydrology in agriculture

Understanding the hydrological behavior of soils is essential for managing and protecting agricultural (and natural) ecosystems. Soil hydrological behavior not only mainly determines crop responses to water and nutrients provided by irrigation and fertilization, but also the timing for soil tillage, environmental conditions for plant diseases, among other factors. In the sound management of irrigation water, in relation to specific environmental conditions and cropping systems, the knowledge of local water flow conditions in zones explored by the root systems is indispensable. Once the irrigation method has been established, only the knowledge of the laws governing water flow allows for the establishment of the necessary irrigation frequencies and rates to optimize the distribution of soil moisture, reducing the effects of water stress within the established limits and containing water wastage. Soil hydrology also controls deep percolation fluxes of water and nutrients, as well as water and nutrient runoff. Only by studying water dynamics in soil can the contribution of groundwater to water consumption be quantitatively determined. Moreover, the water volumes infiltrating into the soil due to rainfall are strictly linked and governed by the laws of water flow in the soil. No evaluation of water quantities being added to groundwater circulation can be made without first determining the water volumes moving in the zone between the soil surface and aquifer. The use of process-based soil-plant-atmosphere models, relating soil hydrology to crop growth, dates back several decades ago [1]. More recently, models are incorporated in decision support systems to be used for quantifying the effect of alternative farm managements [2], among many other decisions, such as landscape planning [3] and crop yield responses as affected by climatic change [4]. This, in turn, may allow for site-specific management of spatially variable soil (Agriculture 4.0). It is thus evident that soil hydrology is a key factor in food security and sustainable development goals (SDG2) [5,6]. The crucial link between soil hydrology and optimal management of water and solutes in agriculture calls for advancements in field-based monitoring and prediction tools for a better understanding of water and nutrient balance and, specifically, of all the functional processes involved (namely, evapotranspiration, groundwater recharge, nutrient and salt transport) [7,8]. Understanding these processes has obvious consequences on the water and solute management in agriculture, suggesting optimal irrigation methods, water volumes and fertilizer amounts to keep crop yields, while minimizing environmental problems (e.g., nitrate leaching towards groundwater, soil salinization). The complexity of soil water flow and solute transport processes has encouraged the widespread use of mathematical models, corresponding as closely as possible to real phenomena [9]. Efforts have been mainly devoted to develop increasingly sophisticated parameterizations of the interaction between soil, vegetation and the atmosphere in the so-called soil–plant–atmosphere continuum (SPAC) transfer schemes [10,11]. The estimation of water and solute balances at different spatial and temporal scales is a fundamental task of these models. Under most climatic conditions, the ability of the root zone to match evapotranspiration and precipitation depends on the soil’s infiltration capacity, root zone storage and water-holding capacity, as well as on the temporal dynamics of the precipitation process, relative to that of evapotranspiration. Knowledge of the physical and hydraulic properties of the shallow vadose zone is, therefore, a key element in correctly modeling the soil–groundwater–atmosphere exchange processes. Moreover, for large-scale applications, an evaluation in statistical terms of the variability of these properties is also necessary [12]. The body of knowledge on the link between hydrology and agriculture available at present, both theoretical and experimental, is extensive. Nevertheless, knowledge gaps still exist. The purpose of this special issue is to fill some of these gaps. In this sense, the papers selected for this special issue address a range of issues—all deal with the interaction of soil hydrology and agriculture in seeking effective management of water and nutrients. Most of the contributions integrate monitoring and modeling components at applicative scales, from field to district scales. Specifically, this special issue deals with the following major topics: Hydrological properties for model applications and their changes over time; Model calibration and water balance; Irrigation management and effects on soil hydrological processes and salinity. In the following paragraphs, details of each of the papers included in each of these major topics will be provided

ProTestA:Identifying and Extracting Protest Events in News Notebook for ProtestNews Lab at CLEF 2019

This notebook describes our participation to the Protest- New Lab, identifying protest events in news articles in English. Systems are challenged to perform unsupervised domain adaptation against three sub-tasks: document classification, sentence classification, and event ex- traction. We describe the final submitted systems for all sub-tasks, as well as a series of negative results. Results indicate pretty robust perfor- mances in all tasks (average F1 of 0.705 for the document classification sub-task, average F1 of 0.592 for the sentence classification sub-task; av- erage F1 0.528 for the event extraction sub-task), ranking in the top 4 systems, although drops in the out-of-domain test sets are not minimal