Managing the environmental impacts of war: What can be learned from conflict-vulnerable communities?

Wars have serious negative effects on the total environment. This study reviews 193 case studies worldwide in order to better understand these impacts and their potential management before, during and after war. The synthesis of the evidence shows that military actions damage landscape resources. Aerial bombings have great negative impacts by damaging environmental conservation efforts, destroying trees, disturbing soilscapes and undermining soil health. In addition, war exterminates wildlife and their ecological niches and contributes to atmospheric and water pollution. Overall, military leaders and personnel have shown little concern about these impacts. Limited postwar restoration activities are also undertaken to reduce war-driven environmental impacts. The study highlights some good practices on how to manage the total environment during the warfare. Therefore, communities must share best lessons to remain in a sustainable peace, restore the war-damaged environment, and enhance sustainable economic development.

Mapping and modeling riverine sand and gravel mining at the sub-continental scale: A case study for India.

Sand and gravel are amongst the most mined and consumed resources in the world, especially in the Global South where the demand for sand increases due to urbanization. Large parts of this extraction occur in rivers, with adverse environmental consequences. Mitigation of riverine sand and gravel mining (RSM) impacts on freshwater systems requires a robust understanding of the scale and controlling factors of RSM. However, very limited data exist on the occurrence of this process. This is especially true over larger spatial scales. Here we aim to bridge this gap and gain more insight into the occurrence of RSM at a subcontinental scale. More specifically, we (1) develop a systematic mapping procedure of RSM to collect the first large-scale dataset of RSM occurrence focusing on India as a case study. Using this dataset, we then (2) statistically analyze the factors potentially controlling spatial patterns of RSM across India. Factors were included that represent both the demand and supply of sand. Based on these results, we (3) develop a logistic regression model to estimate the probability of RSM occurrence. Overall, our work shows the enormous scale of RSM in India: of the 808 randomly selected and investigated river reaches (with lengths of ca. 10 km), 61.6 % showed clear evidence of RSM. Statistical analyses revealed that the presence of RSM is mainly linked to variables describing the demand for sand (e.g. distance to city, percentage of built-up area around the river reach), while variables relating to supply (e.g. soil texture, expected sediment discharge) showed much weaker correlations. Only rainfall variability was a clearly significant factor, which may relate to river reach accessibility. Based on these findings, we present a first model and map that predicts the susceptibility to RSM in India.

EUSEDcollab: a network of data from European catchments to monitor net soil erosion by water.

As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

A data driven gully head susceptibility map of Africa at 30 m resolution.

Predicting gully erosion at the continental scale is challenging with current generation models. Moreover, datasets reflecting gully erosion processes are still rather scarce, especially in Africa. This study aims to bridge this gap by collecting an extensive dataset and developing a robust, empirical model that predicts gully head density at high resolution for the African continent. We developed a logistic probability model at 30 m resolution that predicts the likelihood of gully head occurrence using currently available GIS data sources. To calibrate and validate this model, we used a new database of 31,531 gully heads, mapped over 1216 sites across Africa. The exact location of all gully heads was manually mapped by trained experts using high-resolution imagery available from Google Earth. This allowed the extraction of detailed information at the gully head scale, such as the local soil surface slope. Variables included in our empirical model are topography, climate, vegetation, soil characteristics and tectonic context. They are consistent with our current process-based understanding of gully formation and evolution. The model shows that gully occurrences mainly depend on slope steepness, soil texture and vegetation cover and to a lesser extent on rainfall intensity and tectonic activity. The combination of these factors allows for robust and fairly reliable predictions of gully head occurrences, with Areas Under the Curve for validation around 0.8. Based on these results, we present the first gully head susceptibility map for Africa at a 30 m resolution.

Progress and challenges in sustainable land management initiatives: A global review.

Sustainable land management (SLM) is widely recognized as the key to reducing rates of land degradation, and preventing desertification. Many efforts have been made worldwide by various stakeholders to adopt and/or develop various SLM practices. Nevertheless, a comprehensive review on the spatial distribution, prospects, and challenges of SLM practices and research is lacking. To address this gap, we gathered information from a global SLM database provided by the World Overview of Conservation Approaches and Technologies (WOCAT) and two bibliographic databases of academic research. Over 1900 SLM practices and 1181 academic research papers from 129 and 90 countries were compiled and analyzed. Relatively better SLM dissemination was observed in dry subhumid countries and countries with medium scores on the Human Development Index (HDI), whereas dissemination and research were both lower in humid countries with low HDI values. Cropland was the main land use type targeted in both dissemination and research; degradation caused by water erosion and mitigation aimed at water erosion were also the main focus areas. Other dominant land use types (e.g., grazing) and SLM purposes (e.g., economic benefits) have received relatively less research attention compared to their dissemination. Overall, over 75 % of the 60 countries experiencing high soil erosion rates (>10 t ha-1 yr-1) also have low HDI scores, as well as poor SLM dissemination and research implying the limited evidence-based SLM dissemination in these countries. The limitation of research evidence can be addressed in the short term through integrating existing scientific research and SLM databases by adopting the proposed Research Evidence for SLM framework. There is, however, a great need for additional detailed studies of country-specific SLM challenges and prospects to create appropriate evidence-based SLM dissemination strategies to achieve multiple SLM benefits.

Land susceptibility to water and wind erosion risks in the East Africa region.

Land degradation by water and wind erosion is a serious problem worldwide. Despite the significant amount of research on this topic, quantifying these processes at large- or regional-scale remains difficult. Furthermore, very few studies provide integrated assessments of land susceptibility to both water and wind erosion. Therefore, this study investigated the spatial patterns of water and wind erosion risks, first separately and then combined, in the drought-prone region of East Africa using the best available datasets. As to water erosion, we adopted the spatially distributed version of the Revised Universal Soil Loss Equation and compared our estimates with plot-scale measurements and watershed sediment yield (SY) data. The order of magnitude of our soil loss estimates by water erosion is within the range of measured plot-scale data. Moreover, despite the fact that SY integrates different soil erosion and sediment deposition processes within watersheds, we observed a strong correlation of SY with our estimated soil loss rates (r2 = 0.4). For wind erosion, we developed a wind erosion index by integrating five relevant factors using fuzzy logic technique. We compared this index with estimates of the frequency of dust storms, derived from long-term Sea-Viewing Wide Field-of-View Sensor Level-3 daily data. This comparison revealed an overall accuracy of 70%. According to our estimates, mean annual gross soil loss by water erosion amounts to 4 billion t, with a mean soil loss rate of 6.3 t ha-1 yr-1, of which ca. 50% was found to originate in Ethiopia. In terms of land cover, ca. 50% of the soil loss by water erosion originates from cropland (with a mean soil loss rate of 18.4 t ha-1 yr-1), which covers ca. 15% of the total area in the study region. Model results showed that nearly 10% of the East Africa region is subject to moderate or elevated water erosion risks (>10 t ha-1 yr-1). With respect to wind erosion, we estimated that around 25% of the study area is experiencing moderate or elevated wind erosion risks (equivalent to a frequency of dust storms >45 days yr-1), of which Sudan and Somalia (which are dominated by bare/sparse vegetation cover) have the largest share (ca. 90%). In total, an estimated 8 million ha is exposed to moderate or elevated risks of soil erosion by both water and wind. The results of this study provide new insights on the spatial patterns of water and wind erosion risks in East Africa and can be used to prioritize areas where further investigations are needed and where remedial actions should be implemented.

Modelling sediment fluxes in the Danube River Basin with SWAT.

Sediment management is of prior concern in the Danube Basin for provision of economic and environmental services. This study aimed at assessing current (1995-2009) sediment fluxes of the Danube Basin with SWAT model and identifying sediment budget knowledge gaps. After hydrologic calibration, hillslope gross erosion and sediment yields were broadly calibrated using ancillary data (measurements in plots and small catchments, and national and European erosion maps). Mean annual sediment concentrations (SSC) from 269 gauging stations (2968 station-year entries; median 19mg/L, interquartile range IQR 10-36mg/L) were used for calibrating in-stream sediments. SSC residuals (simulations-observations) median was 2mg/L (IQR -14; +22mg/L). In the validation dataset (172 gauging stations; 1457 data-entries, median 17mg/L, IQR 10-28), median residual was 9mg/L (IQR -9; +39mg/L). Percent bias in an independent dataset of annual sediment yields (SSY; 689 data-entries in 95 stations; median 52t/km2/y, IQR 20-151t/km2/y) was -21.5%. Overall, basin-wide model performance was considered satisfactory. Sediment fluxes appeared overestimated in some regions (Sava and Velika Morava), and underestimated in others (Siret-Prut and Romanian Danube), but unbiased elsewhere. According to the model, most sediments were generated by hillslope erosion. Streambank degradation contributed about 5% of sediments, and appeared important in high stream power Alpine reaches. Sediment trapping in reservoirs and floodplain deposition was probably underestimated and counterbalanced by high stream deposition. Factor analysis showed that model underestimations were correlated to Alpine and karst areas, whereas underestimations occurred in high seismicity areas of the Lower Danube. Contemporary sediment fluxes were about one third of values reported for the 1980s for several tributaries of the Middle and Lower Danube. Knowledge gaps affecting the sediment budget were identified in the contributions of some erosion processes (glacier erosion, gully erosion and mass movements), and in-stream sediment dynamics.

Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins.

The Soil and Water Assessment Tool (SWAT) is used worldwide for water quality assessment and planning. This paper aimed to assess and adapt SWAT hillslope sediment yield model (Modified Universal Soil Loss Equation, MUSLE) for applications in large basins, i.e. when spatial data is coarse and model units are large; and to develop a robust sediment calibration method for large regions. The Upper Danube Basin (132,000km(2)) was used as case study representative of large European Basins. The MUSLE was modified to reduce sensitivity of sediment yields to the Hydrologic Response Unit (HRU) size, and to identify appropriate algorithms for estimating hillslope length (L) and slope-length factor (LS). HRUs gross erosion was broadly calibrated against plot data and soil erosion map estimates. Next, mean annual SWAT suspended sediment concentrations (SSC, mg/L) were calibrated and validated against SSC data at 55 gauging stations (622 station-years). SWAT annual specific sediment yields in subbasin reaches (RSSY, t/km(2)/year) were compared to yields measured at 33 gauging stations (87station-years). The best SWAT configuration combined a MUSLE equation modified by the introduction of a threshold area of 0.01km(2) where L and LS were estimated with flow accumulation algorithms. For this configuration, the SSC residual interquartile was less than +/-15mg/L both for the calibration (1995-2004) and the validation (2005-2009) periods. The mean SSC percent bias for 1995-2009 was 24%. RSSY residual interquartile was within +/-10t/km(2)/year, with a mean RSSY percent bias of 12%. Residuals showed no bias with respect to drainage area, slope, or spatial distribution. The use of multiple data types at multiple sites enabled robust simulation of sediment concentrations and yields of the region. The MUSLE modifications are recommended for use in large basins. Based on SWAT simulations, we present a sediment budget for the Upper Danube Basin.