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

Improving usability of weather radar data in environmental sciences : potentials, challenges, uncertainties and applications

Precipitation is a crucial driver for many environmental processes and exhibits a high spatiotemporal variability. The traditional, widely-used point-scale measurements by rain gauges are not able to detect the spatial rainfall distribution in a comprehensive way. Throughout the last decades, weather radars have emerged as a new measurement technique that is capable of providing areal precipitation information with high spatial and temporal resolution and put precipitation monitoring on a new level. However, radar is an indirect remote sensing technique. Rain rates and distributions are inferred from measured reflectivities, which are subject to a series of potential error sources. In the last years, several operational national radar data archives exceeded a time series length of ten years and several new radar climatology datasets have been derived, which provide largely consistent, well-documented radar quantitative precipitation estimate (QPE) products and open up new climatological application fields for radar data. However, beside uncertainties regarding data quality and precipitation quantification, several technical barriers exist that can prevent potential users from working with radar data. Challenges include for instance different proprietary data formats, the processing of large data volumes and a scarcity of easy-to-use and free-of-charge software, additional effort for data quality evaluation and difficulties concerning data georeferencing. This thesis provides a contribution to improve the usability of radar-based QPE products, to raise awareness on their potentials and uncertainties and to bridge the gap between the radar community and other scientific disciplines which are still rather reluctant to use these highly resolved data. First, a GIS-compatible Python package was developed to facilitate weather radar data processing. The package uses an efficient workflow based on widely used tools and data structures to automate raw data processing and data clipping for the operational German radar-based and gauge-adjusted QPE called RADOLAN (“RADar OnLine Aneichung”) and the reanalysed radar climatology dataset named RADKLIM. Moreover, the package provides functions for temporal aggregation, heavy rainfall detection and data exchange with ArcGIS. The Python package was published as an Open Source Software called radproc. It was used as a basis for all subsequent analyses conducted in this study and has already been applied successfully by several scientific working groups and students conducting heavy rainfall analysis and data aggregation tasks. Second, this study explored the development, uncertainties and potentials of the hourly RADOLAN and RADKLIM QPE products in comparison to ground-truth rain gauge data. Results revealed that both QPE products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, the analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation and range-dependent attenuation. The applicability of the evaluation results was underpinned by the publication of a rainfall inter-comparison geodataset for the RADOLAN, RADKLIM and rain gauge datasets. The intercomparison dataset is a collection of precipitation statistics and several parameters that can potentially influence radar data quality. It allows for a straightforward comparison and analysis of the different precipitation datasets and can support a user’s decision on which dataset is best suited for the given application and study area. The data processing workflow for the derivation of the intercomparison dataset is described in detail and can serve as a guideline for individual data processing tasks and as a case study for the application of the radproc library. Finally, in a case study on radar composite data application for rainfall erosivity estimation, RADKLIM data with a 5-minute temporal resolution were used alongside rain gauge data to compare different erosivity estimation methods used in erosion control practice. The aim was to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Moreover, correction factors proposed in other studies were tested with regard to their ability to compensate for different temporal resolutions of rainfall input data and the underestimation of precipitation by radar data. The results clearly showed that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. The radar climatology data showed a high potential to improve rainfall erosivity estimations, but also a certain bias in the spatial distribution of the R-factor due to the rather short time series and a few radar artefacts. The application of correction factors to compensate for the underestimation of the radar led to an improvement of the results, but a possible overcorrection could not be excluded, which indicated a need for further research on data correction approaches. This thesis concludes with a discussion of the role of open source software, open data and of the implementation of the FAIR (Findable, Accessible, Interoperable, Re-usable) principles for the German radar QPE products in order to improve data usability. Finally, practical recommendations on how to approach the assessment of QPE quality in a specific study area are provided and potential future research developments are pointed out.Niederschlag ist ein wesentlicher Antrieb vieler Umweltprozesse und weist eine hohe räumliche und zeitliche Variabilität auf. Die traditionellen, weit verbreiteten punktuellen Messungen mit Ombrometern sind nicht in der Lage, die räumliche Niederschlagsverteilung flächendeckend zu erfassen. Im Laufe der letzten Jahrzehnte hat sich mit dem Wetterradar eine neue Messtechnik etabliert, die in der Lage ist, flächenhafte Niederschlagsinformationen mit hoher räumlicher und zeitlicher Auflösung zu erfassen und die Niederschlagsüberwachung auf ein neues Niveau zu heben. Radar ist jedoch eine indirekte Fernerkundungstechnik. Niederschlagsraten und -verteilungen werden aus gemessenen Reflektivitäten abgeleitet, die einer Reihe von potenziellen Fehlerquellen unterliegen. In den letzten Jahren überschritten mehrere nationale Radardatenarchive eine Zeitreihenlänge von zehn Jahren. Es wurden mehrere neue Radarklimatologie-Datensätze abgeleitet, die weitgehend konsistente, gut dokumentierte Radarprodukte zur quantitativen Niederschlagsschätzung liefern und neue klimatologische Anwendungsfelder für Radardaten eröffnen. Neben Unsicherheiten bezüglich der Datenqualität und der Niederschlagsquantifizierung gibt es jedoch eine Vielzahl technischer Barrieren, die potenzielle Nutzer von der Verwendung der Radardaten abhalten können. Zu den Herausforderungen gehören beispielsweise unterschiedliche proprietäre Datenformate, die Verarbeitung großer Datenmengen, ein Mangel an einfach zu bedienender und kostenloser Software, zusätzlicher Aufwand für die Bewertung der Datenqualität und Schwierigkeiten bei der Georeferenzierung der Daten. Diese Dissertation liefert einen Beitrag zur Verbesserung der Nutzbarkeit radarbasierter quantitativer Niederschlagsschätzungen, zur Sensibilisierung für deren Potenziale und Unsicherheiten und zur Überbrückung der Kluft zwischen der Radar-Community und anderen wissenschaftlichen Disziplinen, die der Nutzung der Daten immer noch eher zögerlich gegenüberstehen. Zunächst wurde eine GIS-kompatible Python-Bibliothek entwickelt, um die Verarbeitung von Wetterradardaten zu erleichtern. Die Bibliothek verwendet einen effizienten Workflow, der auf weit verbreiteten Werkzeugen und Datenstrukturen basiert, um die Rohdatenverarbeitung und das Zuschneiden der Daten zu automatisieren. Alle Routinen wurden für die operationellen deutschen RADOLAN-Kompositprodukte (“RADar OnLine Aneichung”) und den reanalysierten Radarklimatologie-Datensatz (RADKLIM) umgesetzt. Darüber hinaus bietet das Paket Funktionen für die zeitliche Datenaggregation, die Identifikation und Zählung von Starkregen sowie den Datenaustausch mit ArcGIS. Das Python-Paket wurde als Open-Source-Software namens radproc veröffentlicht. Radproc bildet die methodische Grundlage für alle nachfolgenden Analysen dieser Studie und wurde zudem bereits erfolgreich von mehreren wissenschaftlichen Arbeitsgruppen und Studenten zur Analyse von Starkregen und zeitlichen Aggregierung von Radardaten eingesetzt. Des Weiteren wurden in dieser Arbeit die Entwicklung, Unsicherheiten und Potentiale der stündlichen RADOLAN- und RADKLIM-Kompositprodukte im Vergleich zu Ombrometerdaten analysiert. Die Ergebnisse haben gezeigt, dass beide Radarprodukte die Gesamtniederschlagssummen und inbesondere Niederschläge hoher Intensität tendenziell unterschätzen. Die Analysen zeigten jedoch auch signifikante Verbesserungen im Verlauf der RADOLAN-Zeitreihe sowie deutliche Qualitätsverbesserungen durch die klimatologische Reanalyse, insbesondere im Hinblick auf die Korrektur typischer Radarartefakte, orographischer und winterlicher Niederschläge sowie der entfernungsabhängigen Abschwächung des Radarsignals. Die Anwendbarkeit der Auswertungsergebnisse wurde durch die Veröffentlichung eines Geodatensatzes zum Niederschlagsvergleich für die RADOLAN-, RADKLIM- und Ombrometer-Datensätze untermauert. Der Vergleichsdatensatz ist eine Sammlung von Niederschlagsstatistiken sowie verschiedener Parameter, die die Qualität der Radardaten potenziell beeinflussen können. Er ermöglicht einen einfachen Vergleich und eine Analyse der verschiedenen Niederschlagsdatensätze und kann die Entscheidung von Anwendern unterstützen, welcher Niederschlagsdatensatz für die jeweilige Anwendung und das jeweilige Untersuchungsgebiet am besten geeignet ist. Der Workflow für die Ableitung des Vergleichsdatensatzes wurde ausführlich beschrieben und kann als Leitfaden für individuelle Datenverarbeitungsaufgaben und als Fallstudie für die Anwendung der radproc-Bibliothek dienen. Darüber hinaus wurde eine Fallstudie zur Anwendung von Radar-Komposits für die Abschätzung der Erosivität des Niederschlags durchgeführt. Dazu wurden RADKLIM-Daten und Ombrometerdaten mit einer zeitlichen Auflösung von 5 Minuten verwendet, um verschiedene Methoden zur Abschätzung der Niederschlagserosivität zu vergleichen, die in der Erosionsschutzpraxis Anwendung finden. Ziel war es, die Auswirkungen der Methodik und des Klimawandels sowie der Auflösung, Qualität und der räumlichen Ausdehnung der Eingabedaten auf den R-Faktor der Allgemeinen Bodenabtragsgleichung zu bewerten. Darüber hinaus wurden von anderen Studien vorgeschlagene Korrekturfaktoren im Hinblick auf ihre Fähigkeit getestet, unterschiedliche zeitliche Auflösungen von Niederschlagsdaten und die Unterschätzung des Niederschlags durch Radardaten zu kompensieren. Die Ergebnisse haben deutlich gezeigt, dass die R-Faktoren aufgrund des Klimawandels erheblich zugenommen haben und dass die aktuellen R-Faktor-Karten unter Verwendung neuerer, flächendeckender und räumlich höher aufgelöster Niederschlagsdaten aktualisiert werden müssen. Die Radarklimatologiedaten zeigten ein hohes Potenzial zur Verbesserung der Abschätzung der Niederschlagserosivität, aber aufgrund der vergleichsweise kurzen Zeitreihe und einiger Radarartefakte auch gewisse Unsicherheiten in der räumlichen Verteilung des R-Faktors. Die Anwendung von Korrekturfaktoren zur Kompensation der Unterschätzung des Radars führte zu einer Verbesserung der Ergebnisse, allerdings konnte eine mögliche Überkorrektur nicht ausgeschlossen werden, wodurch weiterer Forschungsbedarf bezüglich der Datenkorrektur aufgezeigt wurde. Diese Arbeit schließt mit einer Diskussion der Rolle von Open-Source-Software, frei verfügbarer Daten und der Umsetzung der FAIR-Prinzipien (Findable, Accessible, Interoperable, Re-usable) für die deutschen Radar-Produkte zur Verbesserung der Nutzbarkeit von Radarniederschlagsdaten. Abschließend werden praktische Empfehlungen zur Vorgehensweise bei der Bewertung der Qualität radarbasierter quantitativer Niederschlagsschätzungen in einem bestimmten Untersuchungsgebiet gegeben und mögliche zukünftige Forschungsentwicklungen aufgezeigt

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year

Threats to Soil Quality in Europe

During the recent years, there has been a surge of concern and attention in Europe to soil degradation processes. One of the most innovative aspects of the newly proposed Soil Thematic Strategy for the EU is the recognition of the multifunctionality of soils. This report is summarizing the reserch results on the fields of soil degradation and soil quality reserach. Chapters of the report include: Preface Characterisation of soil degradation risk: an overview Soil quality in the European Union Main threats to soil quality in Europe The Natural Susceptibility on European Soils to Compaction Soil Erosion: a main threats to the soils in Europe Soil Erosion risk assessment in the alpine area according to the IPCC scenarios An example of the threat of wind erosion using DSM techniques Updated map of salt affected soils in the European Union A framework to estimate the distribution of heavy metals in European Soils Application of Soil Organic Carbon Status Indicators for policy-decision making in the EU Main threats on soil biodiversity: The case of agricultural activities impacts on soil microarthropods Implications of soil threats on agricultural areas in Europe MEUSIS, a Multi-Scale European Soil Information System (MEUSIS): novel ways to derive soil indicators through UpscalingJRC.H.7-Land management and natural hazard

Soil erosion modelling as a tool for future land management and conservation planning

Maintaining future agricultural productivity and ensuring soil security is of global concern and requires evidence-based management practices. Moreover, understanding where and when land is at risk of erosion is a fundamental step to combatting future soil loss and reach Land Degradation Neutrality (LDN). However, this is a difficult task because of the high spatial and temporal variability of the controlling factors involved. Therefore, tools investigating the impact and frequency of extreme erosive events are crucial for land managers and policymakers to apply corrective measures for better erosion management in the future. While the utility of using wind and water erosion models for management is well established, there is a paucity of work on the impact of climate change and extreme environmental conditions (e.g. wildfires) on soil erosion by wind and water simultaneously. Both erosion types are controlled by different environmental variable that vary highly in space and time. Therefore, the overarching aim of this study was to develop a joint wind-water erosion modelling method and demonstrate the utility of this approach to identify (1) the spatio-temporal variability of extreme erosion events in the South Australian agricultural zone (Australia) and (2) assess the likely increase of this variability in the face of climate change and the recurrence of wildfires. To fulfil the aim of the research project, we adapted two state-of-the-art wind and water (hillslope) erosion models to integrate modern high-resolution datasets for spatial and temporal analysis of erosion. The adaptation of these models to local conditions and the use of high-resolution datasets was essential to ensure reliable erosion assessment. First, we applied these models separately in the Eyre Peninsula and Mid-North agricultural regions. We evaluated the spatio-temporal variability of extreme erosion events between 2001 and 2017 and described the complex interactions between each erosional process and their influencing factors (e.g. soil types, climate conditions, and vegetation cover). Hillslope erosion was very low for most of the Eyre Peninsula; however, a large proportion of the central Mid-North region frequently recorded severe erosion (> 0.022 t ha-1) two to three months per year, for most of the years in the time-series. The most severe erosion events were primarily driven by topography, low ground cover ( 500 MJ mm ha-1 h-1). Average annual wind erosion was very low and comparable in the two regions. Nonetheless, most of the west coast of the Eyre Peninsula frequently registered severe erosion (> 0.000945 t ha-1 or 0.945 kg ha-1) two to three months per year, for most of the years. The most severe erosion events were largely driven by the soil type (sandy soils), recurring low ground cover ( 68 km h-1). We identified that erosion severity was low for the vast majority of the study area, while 4% and 9% of the total area suffered severe erosion by water and wind respectively, demonstrating an extreme spatial and temporal skewness of soil erosion processes. Then we combined the modelling outputs from the wind and water erosion models and tested the models’ response to major wildfire events. This research demonstrated how erosion modelling could be used to predict the impact of severe wildfire events on soil erosion. The two models satisfactorily captured the spatial and temporal variability of post-fire erosion. However, a very small fraction of the region (0.7%) was severely impacted by both wind and water erosion. We observed that soil erosion increased immediately after the wildfires or within the first six months for the ten fire-affected regions. For three of the wildfire events, the models showed an increase in wind and water erosion in consecutive months or at the same time. These results highlighted the importance to consider wind and water erosion simultaneously for post-fire erosion assessment in dryland agricultural regions. Finally, we had the rare opportunity to assess the impact of a catastrophic wildfire event on wind erosion in an agricultural landscape by examining the influence of unburnt stubble patches on adjacent burnt or bare plots using a spatio-temporal sampling design. The field study allowed a quantitative assessment of spatial and temporal patterns of wind erosion and sediment transport after a catastrophic wildfire event. It showed very high levels of spatial variability of erosion processes between burnt and bare patches and demonstrated how measuring field-scale sediment transport could complement fine-scale experimental studies to assess environmental processes at the field scale. This research highlights the utility of erosion models to inform corrective measures for future land management. We have implemented tools that allow a realistic assessment of the influence of climate change and extreme environmental conditions scenarios on soil erosion for a wide range of land cover over large regions. Here, the models enabled the identification of the relative post-fire wind or water erosion risk in dryland agricultural landscapes, making them particularly useful for land management under future uncertainty. Spatial patterns compared well with previous modelling approaches and underpinned the benefit of erosion models to assess spatial differences in erosion risk and evaluate corrective measures at the regional scale. However, modelled soil erosion magnitudes strongly depend on how the influence of soils is implemented in the models, making it difficult to set absolute quantitative soil loss targets for land management. The thesis has provided a proof of concept of the approach for South Australia. However, all input data can be freely sourced Australia-wide and similar dataset are available globally.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

Review of Soil Erosion Assessment using RUSLE Model and GIS

Soil erosion is one of the world environmental problems the world is facing in the 21st century affecting human society and is listed amongst the top environmental issues facing the world including increasing human population, water shortages, loss of biodiversity, energy and human diseases. An estimated 10 million hectares of agricultural lands are degraded and turned into un-farmable areas due to soil erosion thus resulting in reduced food production for the 3.7 billion malnourished people as reported by World Health Organization. Estimation of soil erosion loss and evaluation of soil erosion risk has become an urgent task by many nations before implementing soil conservation practices. There is now a large published literature on the application of the Revised Universal Soil Loss Equation known as the RUSLE model in combination with GIS technology for predicting soil loss and erosion risks in different regions.  This review paper assesses the current literature on the combined application of RUSLE and GIS, examining new developments in deriving the five RUSLE components. The literature review shows that using the traditional RUSLE model in mapping out soil erosion in large watersheds poses challenges. The combined effect of RUSLE and GIS provides a useful and efficient tool for predicting long-term soil erosion potential and assessing soil erosion impacts. However, there is a need to further investigate better ways of deriving the conservation and management factor (P) in the RUSLE for better on future studies. Data source and quality is also another key issue in GIS application, thus great care must be given in checking and pre-processing GIS data, including conversion to different formats, geo-referencing, data interpolation and registration. Finally, validation of the soil erosion loss using reference data is also a valuable input towards improving the quality and correctness of the results. Keywords: Soil erosion, RUSLE, watershed, GI

GIS-based time series study of soil erosion risk using the Revised Universal Soil Loss Equation (RUSLE) model in a micro-catchment on Mount Elgon, Uganda

Land degradation has already been treated as one of the most serious problem all around the world. This study is a GIS-based time series study which devotes to calculate annual soil loss value, seek for soil erosion trends linked with precipitation and land use in Manafwa micro-catchment, Mount Elgon region, Uganda. Two different versions of Revised Universal Soil loss Equation (RUSLE) are implemented and compared, one using flow length and the other using flow accumulation to estimate the slope length and steepness (LS) factor. The modeling is carried out for the years 2000, 2006, and 2012, and is based on ASTER remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. After running RUSLE model and analyzing the result maps, no significant soil erosion trends or patterns are found, as well as significant trends in precipitation and land cover changes during last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through analysis of climate data, nor by trends in estimated soil loss.Land degradation has already been treated as one of the most serious problem all around the world. This study is a GIS-based time series study which devotes to calculate annual soil loss value, seek for soil erosion trends linked with precipitation and land use in Manafwa micro-catchment, Mount Elgon region, Uganda. Revised Universal Soil loss Equation (RUSLE) is a world popular soil erosion model with five influencing factors, rainfall erosivity, soil erodability, slope length and steepness factor, cover management factor, and conservation practice factor. Two different versions of RUSLE which present two different calculation methods for slope length and steepness factor are implemented and compared. The modeling is carried out for the years 2000, 2006, and 2012, and is based on remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. After running RUSLE model six result maps showing soil erosion risk level are obtained, two for each year with two different methods. By analyzing the result maps, no significant soil erosion trends or patterns are found, as well as significant trends in precipitation and land cover changes during last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through analysis of climate data, nor by trends in estimated soil loss

Wildfire Hazard and Risk Assessment

Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes

Mitigating source water risks with improved wildfire containment

Includes bibliographical references (pages 20-25).In many fire-prone watersheds, wildfire threatens surface drinking water sources with eroded contaminants. We evaluated the potential to mitigate the risk of degraded water quality by limiting fire sizes and contaminant loads with a containment network of manager-developed Potential fire Operational Delineations (PODs) using wildfire risk transmission methods to partition the effects of stochastically simulated wildfires to within and out of POD burning. We assessed water impacts with two metrics—total sediment load and frequency of exceeding turbidity limits for treatment—using a linked fire-erosion-sediment transport model. We found that improved fire containment could reduce wildfire risk to the water source by 13.0 to 55.3% depending on impact measure and post-fire rainfall. Containment based on PODs had greater potential in our study system to reduce total sediment load than it did to avoid degraded water quality. After containment, most turbidity exceedances originated from less than 20% of the PODs, suggesting strategic investments to further compartmentalize these areas could improve the effectiveness of the containment network. Similarly, risk transmission varied across the POD boundaries, indicating that efforts to increase containment probability with fuels reduction would have a disproportionate effect if prioritized along high transmission boundaries