Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio

Multidecadal variability in hydro-climate of Okavango river system, southwest Africa, in the past and under future climate

The focus of this paper is to understand the multi-decadal oscillatory component of variability in the Okavango River system, in southwestern Africa, and its potential evolution through the 21st century under climate change scenarios. Statistical analyses and hydrological modelling are used to show that the observed multi-decadal wet and dry phases in the Okavango River and Delta result from multi-decadal oscillations in rainfall, which are likely to be related to processes of internal variability in the climate system, rather than external natural or anthropogenic forcing. Analyses of changes in this aspect of variability under projected climate change scenarios are based on data from a multi-model ensemble of 19 General Circulation Models, which are used to drive hydrological models of the Okavango River and Delta. Projections for the 21st century indicate a progressive shift towards drier conditions attributed to the influence of increasing temperatures on water balance. It is, however, highly likely that multi-decadal oscillations, possibly of similar magnitude to that of 20th century, will be superimposed on the overall trend. These may periodically offset or amplify the mean drying trend. This effect should be accounted for in water and catchment management and climate change adaptation strategies

Development of a Downscaling Scheme for a Coarse Scale Soil Water Estimation Method

Many river basins worldwide, especially in semi-arid regions, are adversely impacted by poor hydrological infrastructure or are poorly characterized due to limited or no hydrologic data. This condition challenges water-management authorities, who benefit from reliable prediction of the hydrological dynamics that can be made by means of hydrological models. Because of the lack of sufficient or reliable data, often such models are difficult to calibrate and to validate. This study addresses this data limitation by formulating and testing an independent validation tool for hydrological models that can be applied to downscale macro-scale soil water data derived from a remotely sensed scatterometer dataset. This proposed method uses the concept of hydrological response units (HRU) to analyze the spatial variability within one scatterometer footprint. The HRUs are treated as model entities in the process oriented hydrological model J2000 that was applied to the Great Letaba River catchment (ca. 4.700 km²) in South Africa. The soil water time series results were then compared to the remotely sensed data set and the downscaling scheme derived. First, the analysis conducted on footprint scale highlights the similarities in predicting the soil water generation over the long term and in seasonal terms. It also exhibits that the absolute values of both time series can not be used for further investigation, due to differences in the observed soil water volume. Second, the resulted simulated soil water time series were used to establish the downscaling method. Here, the study provides promising results that allow the downscaling of the coarse scale soil water calculated dataset, based upon the landscape related parameters of land cover, soil group and precipitation. The study findings indicate that, by linking the two concepts, hydrological modeling and remote sensing, water management authorities should be able to reduce certain prediction uncertainties of the applied models

High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall, Enhanced Vegetation Index and Digital Elevation Model

Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. At last, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.JRC.H.1-Water Resource

Atmospheric circulation patterns that trigger heavy rainfall in West Africa

Classification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two-step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan-Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large-scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule-based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso-scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region

Atmospheric circulation patterns that trigger heavy rainfall in West Africa

Classification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two-step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan-Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large-scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule-based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso-scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region

Paleodistribution modeling in archaeology and paleoanthropology

abstract: Species distribution modeling (SDM) is a methodology that has been widely used in the past two decades for developing quantitative, empirical, predictive models of species–environment relationships. SDM methods could be more broadly applied than they currently are to address research questions in archaeology and paleoanthropology. Specifically, SDM can be used to hindcast paleodistributions of species and ecological communities (paleo-SDM) for time periods and locations of prehistoric human occupation. Paleo-SDM may be a powerful tool for understanding human prehistory if used to hindcast the distributions of plants, animals and ecological communities that were key resources for prehistoric humans and to use this information to reconstruct the resource landscapes (paleoscapes) of prehistoric people. Components of the resource paleoscape include species (game animals, food plants), habitats, and geologic features and landforms associated with stone materials for tools, pigments, and so forth. We first review recent advances in SDM as it has been used to hindcast paleodistributions of plants and animals in the field of paleobiology. We then compare the paleo-SDM approach to paleoenvironmental reconstructions modeled from zooarchaeological and archaeobotanical records, widely used in archaeology and paleoanthropology. Next, we describe the less well developed but promising approach of using paleo-SDM methods to reconstruct resource paleoscapes. We argue that paleo-SDM offers an explicitly deductive strategy that generates spatial predictions grounded in strong theoretical understandings of the relation between species, habitat distributions and environment. Because of their limited sampling of space and time, archaeobiological records may be better suited for paleo-SDM validation than directly for paleoenvironmental reconstruction. We conclude by discussing the data requirements, limitations and potential for using predictive modeling to reconstruct resource paleoscapes. There is a need for improved paleoclimate models, improved paleoclimate proxy and species paleodistribution data for model validation, attention to scale issues, and rigorous modeling methods including mechanistic models.This is the accepted author manuscript, accepted for publication 12/17/1

Review of Downscaling Methodologies for Africa Climate Applications

Downscaling is the term used to describe the various methods used to translate the climate projections from coarse resolution GCMs to finer resolutions deemed more useful for assessing impacts. Projections of future climate are produced using complex, coupled atmosphere-ocean models (GCMs). The GCMs are most reliable at the continental scale. Due to the inherent uncertainty of the climate system and the inevitable existence of model errors, multi-model ensembling is the recommended approach for characterizing expected climate changes. As downscaling is dependent on the ability of GCMs to successfully project the climate change signal, it is limited to where that signal is clear. Assessments of climate change in Africa indicate some consensus of reduced winter rainfall in southern Africa, increased annual rainfall in east Africa and uncertainty for the rest of Africa. Selection of GCMs that "do better" over Africa, or any region, is difficult and probably not warranted, given the general parity in model skill and the difficulty in identifying which models are more skillful. Ensemble means or medians offer the highest level of projection accuracy. Downscaling approaches are generally categorized as dynamical, using regional climate models, and statistical, using empirical relationships. However, dynamical downscaling often includes statistical modeling in the form of "bias correction." Dynamical downscaling is useful for incorporating topographic features, such as strong orography, and land use and vegetation. It is recommended where those features play a significant role in regional climate. However, computational time and the uncertainties that accompany complex models outweigh the benefits of dynamical downscaling where these features are not significant. The spatial resolution that can be achieved is on the order of tens of kilometers. Statistical downscaling is simpler and more efficient than dynamical downscaling. It is preferred where estimates of specific variables, especially at point locations, are sought for input to sector models (e.g., hydrologic models) or decision making. However, statistical modeling can mask a true understanding of regional climate dynamics and estimates may be overconfident. In summary, downscaling is best understood as an attempt to increase the understanding of climate change influences at the regional scale. In that context, a variety of methodologies should be explored, using all tools possible to increase that understanding. A set of "Best Practices" is recommended for pursuing this effort