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It is widely admitted that changes in land use land cover (LULC) influence the hydrology of the catchment. However, how these changes affect hydrological model prediction uncertainty is still a raising question. In this paper we addressed this question by investigating the impacts of the rapid change in LULC in the Siliana catchment in Tunisia on monthly flow and magnitude of flow extremes using the SWAT hydrological model while quantifying the contribution of LULC to the model parameter and prediction uncertainty. At a first step, the SWAT model parameter and prediction uncertainty were estimated using the GLUE method and acceptable parameter sets were identified. Subsequently, the SWAT model was fed with historical LULC as derived from Landsat 5 and 8 satellite images for the years 1990, 2000, 2013 and 2019, and run with the acceptable parameter sets. The results show that the increase in olive plantations (+380 %), urban area (+200 %), and irrigated lands (+309 %) from 1990 to 2019, has LULC decreased monthly flow, high flows magnitude but did not impact low flows in particular over the previous two decades. The findings also suggest that model prediction uncertainty can mask LULC effects, suggesting that model results can be misleading without explicit consideration of uncertainty when assessing the hydrological impacts of changes in LULC.

Rain-fed agriculture is the main source of livelihood for most of Burundi’s population, especially in the northeastern part of the country. This research is aimed at examining how smallholder farmers in the Northeastern region of Burundi perceive climate change and variability and at identifying the methods that are used to adapt, based on data from 200 small farmers and on actual weather data recorded between 1986 and 2017. We find that the majority of farmers (54%) perceive significant increases in temperature and unpredictability of rainfall duration and intensity and are making adjustments to adapt their agriculture in response to changes in climate. Over 80% of farmers have implemented at least one adaptation strategy among the nine evaluated. Changing crop varieties, changing fertilizers, and planting shade trees are the main adaptation strategies that were being implemented by farmers across the study area. The results of a binary regression model showed that the agricultural education and experience of farmers, as well as farm and family size, livestock ownership, climate information access, credit access, and farm income, strongly influence smallholder farmers’ decisions to adapt to climate change. The main obstacles are the lack of information on climate and adaptation strategies, and poverty, which makes it difficult to cope with the increased costs of farming. Understanding farmers’ perceptions of climate change and variability on a local level would provide information on how to develop adaptation strategies. The present study suggests the need for strengthening farmers’ capacities and improving the policy framework for adaptation to climate change in order to improve farmers’ livelihoods. Implications for policymakers will, therefore, include making flexible credit facilities, and investing in training extension agents on both climate change outreach and coping strategies.

La Belgique a enregistré de nombreux progrès sur le plan environnemental. Mais l’empreinte écologique globale du pays soulève bien d’autres questions.

We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time series. Four CIMs are selected from two criteria, linear or nonlinear and bivariate or multivariate. A priori, multivariate, and nonlinear CIMs are best suited for revealing hydrological connections because they fit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic case study. The synthetic experiment confirms our expectation: unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, for the real case study, the multivariate nonlinear method was unstable because of the uneven distribution of missing values affecting the final sample size for the multivariate analyses, forcing us to cope with the results' robustness. Nevertheless, if we recommend a nonlinear multivariate framework to reveal actual hydrological connections, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable comparative tool for exploring data. Still, causal inference remains attached to subjective choices, operational constraints, and hypotheses challenging to test. As a result, the robustness of the conclusions that the CIMs can draw always deserves caution, especially with real, imperfect, and limited data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs without omitting any other approach that aims at the causal understanding of a system.

Water users in northwestern Tunisia, like the Medjerda watershed, are increasingly withdrawing water from hydro-systems. This is a response to the effects of climate change that are causing strong variability. These water withdrawals result in an increase in the state of stress over time (Fehri et al., 2020). The involvement of citizens, in a participatory manner, is likely to help raise awareness among water users. The use of Mobile application has been validated in the region of Medjez El Bab by the involvement of different citizen groups (Fehri et al., 2020a; Fehri, 2021). The objective of this sheet is to capitalize on these achievements and to be used in the north-west of Tunisia for monitoring droughts or floods.

Ouvrir son robinet et se servir un verre d'eau, prendre une douche, ou encore laver son linge… Des gestes anodins du quotidien dans les pays développés. Et si un jour on ne pouvait plus le faire aussi facilement ? Depuis le début du siècle, les organisations mondiales alertent : la majeure partie de la population mondiale sera soumise à une pénurie d’eau d’ici quelques années, en raison de l’accroissement démographique, mais surtout du réchauffement climatique, qui perturbe le cycle de l’eau. Cette ressource essentielle pour le monde vivant pourrait-elle venir à manquer ? Dans le cadre de son dossier "C’est pour demain", La Libre dresse un état des lieux.

We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time series. Four CIMs are selected from two criteria, linear or nonlinear and bivariate or multivariate. A priori, multivariate, and nonlinear CIMs are best suited for revealing hydrological connections because they fit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic case study. The synthetic experiment confirms our expectation: unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, for the real case study, the multivariate nonlinear method was unstable because of the uneven distribution of missing values affecting the final sample size for the multivariate analyses, forcing us to cope with the results' robustness. Nevertheless, if we recommend a nonlinear multivariate framework to reveal actual hydrological connections, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable comparative tool for exploring data. Still, causal inference remains attached to subjective choices, operational constraints, and hypotheses challenging to test. As a result, the robustness of the conclusions that the CIMs can draw always deserves caution, especially with real, imperfect, and limited data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs without omitting any other approach that aims at the causal understanding of a system.

Various efforts to reverse the increasing concentration of nitrates and pesticides in our groundwaters such as the Programme de Gestion Durable de l’Azote (PGDA) have been implemented in Wallonia (Belgium), driven by the EU Nitrates Directive (91/676/CEE), the Water Framework Directive (2000/60/EC) and the Groundwater Directive (2006/118/EC). The effects of these efforts on groundwater quality are complex to assess because of the non-linearity and the spatial heterogeneity of the hydrological fluxes, the numerous potential pollution sources, and the temporal delays due to the infiltration time in the vadose zone. The objective of our research is to define the environmental and anthropogenic factors affecting the effectiveness of the regulations and management practices. We use the monitoring data of 52 groundwater wells and galleries to assess the evolution of nitrate and pesticide concentration in four different water bodies. We use as indicators the contamination levels before and after the introduction of regulations and good management practices, as well as the actual trend. We then seek to predict these indicators and explain their variability by looking into characteristics which could support or undermine the regulations and good practices such as water depth, soil type, crop area or urban development. Our results should contribute to a better understanding and prediction of the groundwater quality trends at the catchment scale in Wallonia, providing guidance to catchment management.

Over the last decades, various regulations and good management practices have been implemented to reverse the increasing concentration of nitrates and pesticides in our groundwaters. The effects of these efforts on groundwater quality are complex to assess because of the non-linearity and the spatial heterogeneity of the hydrological fluxes, the numerous potential pollution sources, and the temporal delays due to the infiltration time in the vadose zone. We use the information encoded in the monitoring data of 52 groundwater wells and galleries in Wallonia (Belgium) to assess the effectiveness of the regulations and management practices. We analyze the trends of the time series and subsequently seek to explain their spatial and temporal variability by looking into the characteristics likely to support or undermine the regulations and good practices such as water depth or urban development. Our results should contribute to a better understanding and prediction of the groundwater quality trends at the catchment scale in Wallonia, providing guidance to catchment management.

Robust methods are needed to assess tillage-induced changes in the physical quality of agricultural soils. Most soil physical quality (SPQ) evaluation procedures are based on the analysis of a limited number of soil cores randomly sampled in the soil profile. Such procedures likely fail to properly capture the spatial patterns in soil structure induced by tillage implements and vehicle traffic, resulting in a misleading SPQ diagnosis. We propose a hybrid method that couples high-resolution soil penetration resistance measurements with soil core sampling to derive high-resolution data grids of SPQ indicators encompassing the soil profile structural heterogeneity. The method builds on strong experimental relationships between soil penetration resistance and bulk density, and between bulk density and the soil hydraulic properties. We tested our method on a silt-loam soil with three tillage treatments (moldboard plowing, deep loosening with a tine cultivator, and minimum tillage), each including a zone impacted by the wheel traffic. The results allow visualization and analysissis of of the effect of tillage treatments and vehicle traffic on the soil SPQ indicators with a high level of spatial detail. The proposed methodology can be used to compare various soil management techniques or to monitor the temporal evolution of SPQ. As such, it can be a valuable tool to guide agricultural soil management. Furthermore, the generated high-resolution soil physical parameter grids could be used to parameterize numerical hydrodynamic models and estimate water and solute fluxes with distributed parameters representing the actual soil profile heterogeneity.

Colluvial soils consist of surface soils and/or parent materials locally transported from upper section of the slopes through water erosion, tillage erosion and construction of agricultural terraces. As a consequence of the recognition that colluvial soil stores large amounts of soil organic carbon (SOC), the SOC cycle in colluvial soils has received increasing attention. The aim of this thesis was to evaluate and understand the SOC dynamics and controlling factors in colluvial soils in the context of climate change. This was done for two specific colluvial environments: eroded and terraced agricultural systems. For the eroded colluvial soils, soil deposition rate is identified as the most important independent control on SOC stability and this is independent of climate zone. In addition, the rate of deposition also indirectly affects SOC stability by changing the average weathering status and mineral matrix of colluvial (sub)soil settings which affect mineral–dependent SOC physico-chemical protection mechanisms. The remaining part of this thesis focused on the colluvial SOC dynamics in terraced systems. Firstly, we demonstrate that the soil infrared spectroscopy, combined with compositional data analysis enables efficient and reliable ‘large–scale’ assessment of terracing soil properties relevant to SOC biogeochemical cycles. Furthermore, we confirm that the terraced landforms store more SOC than non-terracing landforms, mainly due to the preservation of SOC by topsoil burial in colluvial settings. Finally, we demonstrate that the terrace soil age is a key factor controlling soil heterotrophic respiration of subsoils (e.g., burial layers), likely through driving the mineral weathering and thus determining the availability of reactive mineral surfaces that stabilize SOC. In addition, the terrace soil age is also a fundamental regulator of SOC temperature sensitivity to decomposition by governing the temporal evolution of SOC fractions and soil C:N ratio (C quality)., (SC - Sciences) -- UCL, 2022

Paleosols, developed under the influence of past environmental conditions, are considerably valuable archives of past environments and soil formation. The Chinese Loess Plateau has a vertical alternation of Aeolian sediment (loess) and paleosols formed therein. These paleosols were formed during the interglacials, including the Quaternary. This thesis investigates how the expression of these paleosols can be related to the climatic conditions at the time of their formation. To this end, a climate model (LOVECLIM1.3) is linked to a soil formation model (SoilGen2). The climate model was corrected for bias, and the soil model was calibrated for various soil process parameters by confronting simulated and measured soil properties for interglacial soils formed in the CLP and test the effect of reconstructed dust addition patterns on soil development in the Loess Plateau. The results suggest that the calibrated soil model permits interglacial soil simulation in the CLP over long timescales. Subsequently, the response of the soil model to aspects of the simulated climate was determined for MIS 11 and MIS 13 interglacials. Results show that precipitation, Aeolian matter addition and potential evapotranspiration have the greatest impact on paleosols during MIS 11 and MIS 13 interglacials, but their relative importance varies between soil properties (e.g. mass of clay and calcite) and between interglacials. In addition, the response to orbital forced rhythms (precession) and to the NH ice volume during the interglacials was also investigated. Precession and NH ice volume both have indirect effects on paleosol formation through annual mean precipitation. Finally, the soil-climate model was used to quantify soil stocks and ecosystem services as a function of various low (natural) erosion rates. Results showed a negative performance of ecosystem services above 1.0 Mg ha-1 y-1 erosion rate as a threshold to the studied (semi-arid, monsoon) climate evolution., (SC - Sciences) -- UCL, 2022

Coral populations have declined dramatically worldwide under the combined effects of climate change and local anthropogenic stressors. In addition to ocean warming and acidification, threats to coral reefs in the Caribbean include frequent and virulent disease outbreaks, and intensifying hurricanes. In particular, Florida's Coral Reef (FCR) is currently facing a multi-year outbreak of the stony coral tissue loss disease (SCTLD). First observed in 2014 during the monitoring of the PortMiami Deep Dredge Project (PMDDP), the disease has now spread through the entire FCR and has been reported in numerous territories of the Caribbean. However, the propagation of the disease through FCR seemed to slow down when it reached its southwestern end, between the Marquesas and the Dry Tortugas (DRTO). Although the causative agent of the disease remains unknown, studies showed that its transmission was likely waterborne and that sediments could act as SCTLD vectors. The hydrodynamics should therefore be highly explanatory of its spread. Furthermore, Florida is a prime landfall target for hurricanes during the reproduction period of corals. In addition to causing wholesale destruction of the reefs, hurricanes might thus also impact larval dispersal through wind-wave-induced currents. Here, we use the high resolution coastal ocean model SLIM to capture transport processes in the FCR, and study the impact of SCTLD and hurricanes to coral reefs. First, a coupled epidemio-hydrodynamic model is developed to reproduce and understand the observed spread of SCTLD in Florida. A sediment transport model is then used to evaluate the impact of the PMDDP on the observed onset of the disease in 2014. Finally, a coupled wave-current model is implemented to study the impact of Hurricane Irma (2017) on transport processes over reefs in the Florida Keys. Assuming the dispersal of disease agents within neutrally buoyant material, our model successfully reproduced the observed spread of SCTLD through, (FSA - Sciences de l'ingénieur) -- UCL, 2022