Sociohydrology: Scientific Challenges in Addressing the Sustainable Development Goals.

The Sustainable Development Goals (SDGs) of the United Nations Agenda 2030 represent an ambitious blueprint to reduce inequalities globally and achieve a sustainable future for all mankind. Meeting the SDGs for water requires an integrated approach to managing and allocating water resources, by involving all actors and stakeholders, and considering how water resources link different sectors of society. To date, water management practice is dominated by technocratic, scenario-based approaches that may work well in the short term but can result in unintended consequences in the long term due to limited accounting of dynamic feedbacks between the natural, technical, and social dimensions of human-water systems. The discipline of sociohydrology has an important role to play in informing policy by developing a generalizable understanding of phenomena that arise from interactions between water and human systems. To explain these phenomena, sociohydrology must address several scientific challenges to strengthen the field and broaden its scope. These include engagement with social scientists to accommodate social heterogeneity, power relations, trust, cultural beliefs, and cognitive biases, which strongly influence the way in which people alter, and adapt to, changing hydrological regimes. It also requires development of new methods to formulate and test alternative hypotheses for the explanation of emergent phenomena generated by feedbacks between water and society. Advancing sociohydrology in these ways therefore represents a major contribution toward meeting the targets set by the SDGs, the societal grand challenge of our time.

Valuing scarce observation of rainfall variability with flexible semi-distributed hydrological modelling - Mountainous Mediterranean context.

To represent spatial and temporal variability in rainfall adequately, rainfall-runoff models must compromise among modelling objectives, data availability, conceptualization options, and the actual variability in rainfall. This is of utmost importance for challenges of integrated water management in the rapidly changing Mediterranean context. We evaluated the sensitivity of the SWAT model to combinations of spatial rainfall variability and catchment subdivision in a data-scarce mesoscale mountainous Mediterranean context. The case study focused on the Joumine catchment, in northern Tunisia, which is emblematic of agro-hydro-chemical changes and challenges. The double-mass curve method was used to verify the consistency of rainfall time series from 1991 to 2003, indicating proportionality between annual rainfall at the reference gauge and that of the nearest gauge. The rainfall lapse rate at the Joumine catchment was 69.9 mm per 100 m of altitude. Seven sets of rain gauges and five subdivision configurations of the catchment were simulated. Differences between measured and predicted streamflow at the outlet were assessed using three indices of model fit. Predicted streamflow was extremely sensitive to spatial rainfall variability but relatively insensitive to catchment subdivision. Daily predictions were most accurate for the wettest year (2002-2003) and least accurate for the driest year (1993-1994).

Modeling water quality to improve agricultural practices and land management in a tunisian catchment using the soil and water assessment tool.

Agriculture intensification has impaired water quality. In this study, the risk of pollution by nitrates was assessed by experimental monitoring, spatial integration of farm census, and modeling of water quality using the Soil and Water Assessment Tool (SWAT), version 2009, over the period of 1990 to 2006 for a catchment located northern Tunisia. Under a semiarid climate, the water quality is influenced by the predominating agriculture activities. The hydrological results are compared with the observed flows derived from measurements at the outlet of the Joumine watershed. Model performance showed good statistical agreements, with a Nash-Sutcliffe efficiency of 0.9 and a value of 0.92 after monthly calibration. The model predicted the timing of monthly peak flow values reasonably well. During the validation period, SWAT simulations were nearly as accurate, with Nash-Sutcliffe efficiency and values of 0.89 and 0.92, respectively. The model was used to simulate NO concentrations. The predicted NO concentration values were compared with in situ measured concentrations. The simulated and measured NO-N concentrations varied in the same range of 0 to 5 mg L at the E3 and E5 locations. The calibrated model was then used for simulating the impact of the best management practice scenarios to reduce NO loads to the river. The first set-up consisted of reducing the N fertilizer application by 20 and 100% from the current state. These two scenarios induced a reduction in NO loads by 22 and 72%, respectively. The second set-up consisted of using vegetation filter strips. The last scenario combined filter strips and a reduction of 20% in N fertilizer application. Results showed NO reduction rates of 20 and 36%, respectively. The SWAT model allowed managers to have several options to improve the water quality in the Joumine watershed.