Data-Driven Models for Groundwater Management in Irrigated Cropland

The goal of this thesis is to develop a forecast-based framework to support groundwater management in irrigated cropland. To do so, the forecasting capabilities of seven data-driven models (artificial neural networks, support vector machines, random forests, extreme learning machines, genetic programming, autoregressive and naïve) are first tested for different lead-times (one to five months), hydrogeological regimes and water availability conditions. Then, the most accurate among the models is selected and evaluated at the aquifer scale across the High Plains. When non-satisfactory forecasts are obtained, a Multi Model Combination (MuMoC) based on a hybrid of an artificial neural network and instance-based learning method is applied. This alternative model uses forecasts from neighboring wells to improve the accuracy obtained with a single model. Finally, sensitivity analysis is applied to assess the contribution of the observational input uncertainty on the error. Analysis of the results allows the identification of artificial neural network (ANN) as the most accurate among the predictors, with good forecasting skills across lead-times, hydrological regimes and water availability conditions. When ANN is used at the aquifer-scale the results show high average forecasting skills, with metrics of performance illustrating higher error in areas of strong interaction between hydro-meteorological forcings, irrigation, and the aquifer. In those areas, the implementation of MuMoC lead to an increase in forecasting accuracy by about 25%. Sensitivity analysis results shows that modelling error is particularly sensitive to evapotranspiration uncertainties (followed by rainfall), especially during the crop growing season, while inputs as snowmelt and streamflow has a significant effect in modeling performances only for few times steps. We can conclude that the proposed framework can provide water managers with the proper information: (1) to select the most accurate data-driven model; (2) to assess how the model forecasting accuracy changes across hydrogeological regimes and lead times; (3) to determine how input uncertainties affect modeling performance. We therefore believe that an operational implementation of the proposed methodology can support decision making for managing water in irrigated areas

Disentangling Sources of Future Uncertainties for Water Management in Sub-Saharan River Basins

Water management in sub-Saharan African river basins is challenged by an uncertain future climatic, social and economical patterns potentially causing diverging water demands and availability, and by multi-stakeholder dynamics, resulting in evolving conflicts and tradeoffs. In such contexts, a better understanding of the sensitivity of water management to the different sources of uncertainty can support policymakers in identifying robust water supply policies balancing optimality and low vulnerability against likely adverse future conditions. This paper contributes an integrated decision-analytic framework combining an optimization, robustness, sensitivity, and uncertainty analysis to retrieve the main sources of vulnerability to optimal and robust reservoir operating policies across multi-dimensional objective spaces. We demonstrate our approach on the lower Umbeluzi river basin, Mozambique, which an archetypal example of sub-Saharan river basin, where surface water scarcity compounded by substantial climatic variability, uncontrolled urbanization rate, and agricultural expansion are hampering the Pequenos Libombos dam's ability to supply the agricultural, energy, and urban sectors. We adopt an Evolutionary Multi-Objective Direct Policy Search (EMODPS) optimization approach for designing optimal operating policies, whose robustness against social, agricultural, infrastructural, and climatic uncertainties is assessed via robustness analysis. We then implement the generalized likelihood uncertainty estimation (GLUE) and PAWN uncertainty and sensitivity analysis methods for disentangling the main challenges to the sustainability of the operating policies and quantifying their impacts on the urban, agricultural, and energy sectors. Numerical results highlight the importance of a robustness analysis when dealing with uncertain scenarios, with optimal non-robust reservoir operating policies largely being dominated by robust control strategies across all stakeholders. Furthermore, while robust policies are usually vulnerable only to hydrological perturbations and are able to sustain the majority of population growth and agricultural expansion scenarios, non-robust policies are sensitive also to social and agricultural changes and require structural interventions to ensure stable supply.ISSN:1027-5606ISSN:1607-793

B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology

In this manuscript, we present B-AMA (Basic dAta-driven Models for All), an easy, flexible, fully coded Python-written protocol for the application of data-driven models (DDM) in hydrology. The protocol, which is open source and freely available for academic and non-commercial purposes, has been realized to allow early career scientists, with a basic background in programming, to develop DDM ensuring that no stones are left unturned through their implementation. B-AMA embeds data splitting, feature selection, hyperparameter optimization, and performance metrics. A Jupyter notebook with a practical workflow is available to guide the users through the protocol employment, while visualization tools allow efficient investigation and communication of results. We tested B-AMA across four hydrological applications to explore DDM applicability across temporal resolutions, time series lengths, and autocorrelations. B-AMA showed great accuracy and reasonable computational time, making the protocol ideal for educational purposes and for the development of DDM-based forecasts of hydrological time-series

Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands

In the last decades, advancements in computational science have greatly expanded the use of artificial neural networks (ANNs) in hydrogeology, including applications on groundwater forecast, variable selection, extended lead-times, and regime-specific analysis. However, ANN-model performance often omits the sensitivity to ob- servational uncertainties in hydroclimate forcings. The goal of this paper is to implement a data-driven modeling framework for assessing the sensitivity of ANN-based groundwater forecasts to the uncertainties in observational inputs across space, time, and hydrological regimes. The objectives are two-folded. The first objective is to couple an ANN model with the PAWN sensitivity analysis (SA). The second objective is to evaluate the scale- and process-dependent sensitivities of groundwater forecasts to hydroclimate inputs, computing the sensitivity index in groundwater wells (1) across the whole time-series (for the global sensitivity analysis); (2) across the output sub-regions with conditions of water deficit and water surplus (for the ‘regional’ sensitivity analysis); and (3) at each time step (for the time-varying sensitivity analysis). The implementation of the ANN-PAWN occurs in 68 wells across the Northern High Plains aquifer, USA, with pre-time-step rainfall, evapotranspiration, snowmelt, streamflow, and groundwater measurements as inputs. Results show that evapotranspiration and rainfall are the major sources of uncertainty, with the latter being particularly relevant in water surplus conditions and the former in water deficit conditions. The time-varying sensitivity analysis leads to the identification of localized sensitivities to other sources of uncertainty, as snowmelt in spring or river flow during the annual peak period at the groundwater level

Common Pool Resource Management: Assessing Water Resources Planning for Hydrologically Connected Surface and Groundwater Systems

Common pool resource (CPR) management has the potential to overcome the collective action dilemma, defined as the tendency for individual users to exploit natural resources and contribute to a tragedy of the commons. Design principles associated with effective CPR management help to ensure that arrangements work to the mutual benefit of water users. This study contributes to current research on CPR management by examining the process of implementing integrated management planning through the lens of CPR design principles. Integrated management plans facilitate the management of a complex common pool resource, ground and surface water resources having a hydrological connection. Water governance structures were evaluated through the use of participatory methods and observed records of interannual changes in rainfall, evapotranspiration, and ground water levels across the Northern High Plains. The findings, documented in statutes, field interviews and observed hydrologic variables, point to the potential for addressing large-scale collective action dilemmas, while building on the strengths of local control and participation. The feasibility of a “bottom up” system to foster groundwater resilience was evidenced by reductions in groundwater depths of 2 m in less than a decade