Rationalizing Systems Analysis for the Evaluation of Adaptation Strategies in Complex Human-Water Systems

Water resources management is a nontrivial process requiring a holistic understanding of the factors driving the dynamics of human-water systems. Policy-induced or autonomous behavioral changes in human systems may affect water and land management, which may affect water systems and feedback to human systems, further impacting water and land management. Currently, hydro-economic models lack the ability to describe such dynamics either because they do not account for the multifactor/multioutput nature of these systems and/or are not designed to operate at a river basin scale. This paper presents a flexible and replicable methodological framework for integrating a microeconomic multifactor/multioutput Positive Multi-Attribute Utility Programming (PMAUP) model with an eco-hydrologic model, the Soil and Water Assessment Tool (SWAT). The connection between the models occurs in a sequential modular approach through a common spatial unit, the “hydrologic-economic representative units” (HERUs), derived from the boundaries of decision-making entities and hydrologic responsive units. The resulting SWAT-PMAUP model aims to provide the means for exploring the dynamics between the behavior of socio-economic agents and their connection with the water system through water and land management. The integrated model is illustrated by simulating the impacts of irrigation restriction policies on the Río Mundo subbasin in south-eastern Spain. The results suggest that agents' adaptation strategies in response to the irrigation restrictions have broad economic impacts and subsequent consequences on surface and groundwater hydrology. We suggest that the integrated modeling framework can be a valuable tool to support decision-making in water resources management across a wide range of scales

Compounding COVID-19 and climate risks: The interplay of banks’ lending and government's policy in the shock recovery

We assess the individual and compounding impacts of COVID-19 and climate physical risks in the economy and finance, using the EIRIN Stock-Flow Consistent model. We study the interplay between banks’ lending decisions and government's policy effectiveness in the economic recovery process. We calibrate EIRIN on Mexico, being a country highly exposed to COVID-19 and hurricanes risks. By embedding financial actors and the credit market, and by endogenising investors’ expectations, EIRIN analyses the finance-economy feedbacks, providing an accurate assessment of risks and policy co-benefits. We quantify the impacts of compounding COVID-19 and hurricanes on GDP through time using a compound risk indicator. We find that procyclical lending and credit market constraints amplify the initial shocks by limiting firms’ recovery investments, thus mining the effectiveness of higher government spending. When COVID-19 and hurricanes compound, non-linear dynamics that amplify losses emerge, negatively affecting the economic recovery, banks’ financial stability and public debt sustainability

Irrigation technology and water conservation: A review of the theory and evidence

Climate change, population growth, and economic development increase competition for water and exacerbate water scarcity- and drought-related losses (IPCC 2014), resulting in the identification of water crises as the greatest global societal threat (WEF 2019). Farming currently accounts for roughly 70 percent of freshwater withdrawals worldwide (FAO 2019) and often constitutes the least productive (i.e., lowest value) use of freshwater resources (Damania et al. 2017). In this context, providing safe, stable, and profitable food production while making incremental water available to alternative uses, including the environment, requires efficiency improvements in agricultural water management (UN 2015)

Testing empirical and synthetic flood damage models: The case of Italy

Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models

Cost-benefit analysis of coastal flood defence measures in the North Adriatic Sea

The combined effect of global sea level rise and land subsidence phenomena poses a major threat to coastal settlements. Coastal flooding events are expected to grow in frequency and magnitude, increasing the potential economic losses and costs of adaptation. In Italy, a large share of the population and economic activities are located along the low-lying coastal plain of the North Adriatic coast, one of the most sensitive areas to relative sea level changes. Over the last half a century, this stretch of coast has experienced a significant rise in relative sea level, the main component of which was land subsidence; in the forthcoming decades, climate-induced sea level rise is expected to become the first driver of coastal inundation hazard. We propose an assessment of flood hazard and risk linked with extreme sea level scenarios, under both historical conditions and sea level rise projections in 2050 and 2100. We run a hydrodynamic inundation model on two pilot sites located along the North Adriatic coast of Emilia-Romagna: Rimini and Cesenatico. Here, we compare alternative extreme sea level scenarios accounting for the effect of planned and hypothetical seaside renovation projects against the historical baseline. We apply a flood damage model to estimate the potential economic damage linked to flood scenarios, and we calculate the change in expected annual damage according to changes in the relative sea level. Finally, damage reduction benefits are evaluated by means of cost-benefit analysis. Results suggest an overall profitability of the investigated projects over time, with increasing benefits due to increased probability of intense flooding in the near future

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Safer_RAIN: A DEM-based hierarchical filling-&-spilling algorithm for pluvial flood hazard assessment and mapping across large urban areas

The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments

Extreme and long-term drought in the La Plata Basin: event evolution and impact assessment until September 2022

The current drought conditions across the Parana-La Plata Basin (LPB) in Brazil-Argentina have been the worst since 1944. While this area is characterized by a rainy season with a peak from October to April, the hydrological year 2020-2021 was very deficient in rainfall, and the situation extended into the 2021-2022 hydrological year. Below-normal rainfall was dominant in south-eastern Brazil, northern Argentina, Paraguay, and Uruguay, suggesting a late onset and weaker South American Monsoon and the continuation of drier conditions since 2021. In fact, in 2021 Brazilian south and south-east regions faced their worst droughts in nine decades, raising the spectre of possible power rationing given the grid dependence on hydroelectric plants. The Paraná-La Plata Basin drought induced damages to agriculture and reduced crop production, including soybeans and maize, with effects on global crop markets. The drought situation continued in 2022 in the Pantanal region. Dry meteorological conditions are still present in the region at the end of September 2022 with below-average precipitation anomalies. Soil moisture anomaly and vegetation conditions are worst in the lower part of the La Plata Basin, in the southern regions. Conversely, upper and central part of the basin show partial and temporary recovery

Identifying the factors influencing the total external hydraulic loads to the dese-zero watershed

Some watersheds are heavily influenced by human factors that affect their overall water balance and dynamics. This is the case of the Dese-Zero watershed, part of the Venice Lagoon watershed - VLW, located in North-East Italy. Such watershed is characterised by a highly modified environment, with the presence of several hydraulic works and devices, which, ultimately, ensure optimal flow and water availability conditions for different water needs. In addition to this artificial-hydraulic influence, the VLW is also heavily affected by groundwater contributions coming from a mainly external aquifer located in the Venetian high plains. Hydrological modelling under these circumstances is particularly challenging and requires detailed quantified information regarding the total external contributions (i.e. groundwater and deviated surface water from/to bordering watersheds). In order to cope with such complex dynamics, this study proposes a framework contemplating a coupled mechanistic-empirical modelling approach. Under such framework, the physically-based model simulates the internal processes of the studied watershed while the empirical model accounts for the total external hydraulic influences. Data pre-processing is performed with Principal Component Analysis - PCA aiming at the identification of the main factors contributing to the total external hydraulic contribution. The SWAT model is used as the mechanistic component while two alternative modelling techniques are tested for the empirical counterpart, namely: i) Multiple Linear Regression - MLR, and ii) Artificial Neural Networks - ANN. The results suggest that, among the studied weather variables, precipitation plays a major role in the estimation of the total external hydraulic loads. Moreover, both temporal (e.g. season of the year) and stream flow (e.g. SWAT simulated output) information is also relevant for the estimation of the studied process. Finally, it is concluded that the studied coupled mechanistic-empirical model is capable of simulating the hydrology of the Dese-Zero watershed while the non-liner neural networks model is the best option for estimating the total external hydraulic loads