Author(s): Auffhammer, M | Abstract: Climate scientists have spent billions of dollars and eons of supercomputer time studying how increased concentrations of greenhouse gases and changes in the reflectivity of the earth’s surface affect dimensions of the climate system relevant to human society: surface temperature, precipitation, humidity, and sea levels. Recent incarnations of physical climate models have become sophisticated enough to be able to simulate intensities and frequencies of some extreme events, like tropical storms, under different warming scenarios. In a stark juxtaposition, the efforts involved in and the public resources targeted at understanding how these physical changes translate into economic impacts are disproportionately smaller, with most of the major models being developed and maintained with little to no public funding support. The goal of this paper is first to shed light on how (mostly) economists have gone about calculating the “social cost of carbon” for regulatory purposes and to provide an overview of the past and currently used estimates. In the second part, I will focus on where empirical economists may have the highest value added in this enterprise: specifically, the calibration and estimation of economic damage functions, which map weather patterns transformed by climate change into economic benefits and damages. A broad variety of econometric methods have recently been used to parameterize the dose (climate) response (economic outcome) functions. The paper seeks to provide an accessible and comprehensive overview of how economists think about parameterizing damage functions and quantifying the economic damages of climate change.

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Quantifying Economic Damages from Climate Change

Daily runoff forecasting by deep recursive neural network

Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific.

Recently, deep learning models have been widely used in water conservancy engineering forecasting problems, due to their excellent ability to deal with the complex interactions between various hydrological factors. However, the existing research mainly focus on model structure adjustment and input feature selection, ignoring the influence of model ensemble on prediction. In this paper, a novel multi-model ensemble method, namely deep learning multi-dimensional ensemble method, is proposed to deal with multi-step runoff prediction problem. The method, which can significantly improve the runoff prediction performance, couples two ensemble techniques with different functional dimensions, namely snapshot ensemble and attention ensemble. Snapshot ensemble technique is used to enhance model generalization capabilities from single-model dimension. While, attention ensemble technique is employed to increase model prediction accuracy from multi-model dimension. Furthermore, a novel data-driven model, called deep learning multi-dimensional ensemble model, is proposed by combining three different deep learning neural networks with deep learning multi-dimensional ensemble method. The proposed model is applied in a real-world case study in the upstream of Yangtze River basin. Three evaluation indicators and ten comparative models are used to test the model performance. The test results not only show the superiority of proposed model over other comparison models, but also prove the effectiveness of the deep learning multi-dimensional ensemble method. The study highlights the power of the ensemble of deep learning model and the promising prospect of our deep learning multi-dimensional ensemble method in hydrological predictions. 

Short-term runoff prediction using deep learning multi-dimensional ensemble method

Appraising climate change impacts on future water resources and agricultural productivity in agro-urban river basins.

In the present study, assessment of the impact of climate change on the availability of water resources and crop yield of Warangal district of Telangana state, India has been carried out using Soil and Water Assessment Tool (SWAT). The importance of bias correction methods in regional forecasts with multiple Regional Climate Models (RCMs) along with projected uncertainties have been emphasized, and regionalization of parameters in ungauged watersheds have been dealt with. SWAT model was run using observed data and then calibrated using observed streamflow of Akeru watershed, Warangal district, India. The R2 and NSE values for calibration (0.72 and 0.84, respectively) and validation periods (0.7 and 0.56, respectively) indicated a significant correlation between observed and simulated streamflow. Then the model was run for historical and future scenarios (early, mid, and end of the 21st century) for four RCMs. Variables such as rainfall, surface runoff, water yield, evapotranspiration, and intensity of rainfall showed an increasing trend under future scenarios, while crop yields (corn, cotton and rice) showed a decreasing trend. The models predicted an increase in the extremity of rainfall events, especially in the months of July and August, for the mid and end of the 21st century. The results showed that the production of cotton is under threat in the district in future. The results obtained can be used to plan the mitigation and adaptation strategies for the region.

Assessment of water resources and crop yield under future climate scenarios: A case study in a Warangal district of Telangana, India

A real-time autoregressive updating model is proposed in this study to forecast the flow in a watershed. The model has two components: (1) Finite Element-Event based distributed rainfall runoff model for runoff simulation and (2) Autoregressive model for updating the error forecast. The efficiency of the runoff updating model depends on the accuracy of the rainfall. Forecasting plays a major role in view of the lead time. In the present study, forecasting is carried out with a lead period of 1 to 3 h. The performance of the integrated model is tested using Nash Sutcliffe efficiency (E) and correlation coefficient (r). The integrated model is applied for Banha, Harsul and Khadakohol watersheds in India. From the results, it can be concluded that the developed model is efficient in flow forecasting on real-time basis in the watersheds.

Flow Forecasting in a Watershed using Autoregressive Updating Model

Impacts of climate change on water provisional services in Tungabhadra basin using InVEST Model

In the semi-arid regions of South Asia, tank systems are the major source of irrigation. In India, the Telangana state government has initiated the Mission Kakatiya program to rejuvenate irrigation tank systems. Understanding the hydrological processes that supply water to these systems is critical to the success of these types of programs in India. The current study attempted to comprehend the hydrological processes and flow routing in the Salivagu watershed tank cascade system in Telangana. There are a lot of ungauged tank cascade systems in this region. Soil Water Assessment Tool (SWAT), a physically-based model, was used to simulate flow patterns in the Salivagu watershed with and without tank systems. The geospatially extracted area and volume were used for this study provided by WBIS-Bhuvan-NRSC. Additionally, the Katakshapur Tank Cascade System (KTCS) was chosen to analyze the water availability in each tank using the water balance approach. The Salivagu watershed flow simulation without tanks overestimated streamflow. The volume difference in flow between with and without tank was 606 Mm3, 615.9 Mm3, and 1011 Mm3 in 2017, 2018, and 2019, respectively. The SWAT simulated volumes of the Ramchandrapur and Dharmaraopalle tanks in KTCS were merely satisfied because the tank size was less than 0.7 km2 and the storage capacity was up to 1 Mm3. Due to tank sizes more than 0.8 km2 and capacities greater than 2 Mm3, the Mallampalli and Katakshapur tank simulation findings were in good agreement with WBIS-Bhuvan-NRSC. This research advances our understanding of the hydrological processes in ungauged cascading tank systems in tropical semi-arid regions.

Flow Simulation and Storage Assessment in an Ungauged Irrigation Tank Cascade System Using the SWAT Model

Reinforced cement concrete (RCC) is universally acknowledged as a low-cost, rigid, and high-strength construction material. Major structures like buildings, bridges, dams, etc., are made of RCC and subjected to repetitive loading during their service life for which structural performance deteriorates with time. Bridges and high-rise structures, being above ground level, are hard to equip with the contact mechanical methods to inspect strains and displacements for structural health monitoring (SHM). A non-contact, optical and computer vision based full field measuring technique called digital image correlation (DIC) technique was developed in the recent past to specifically evaluate bridge decks. Generally, optical images of structure in field conditions are not acquired precisely perpendicular to the object, which instinctively affects the deformation results obtained during loading conditions. An unmanned aerial vehicle (UAV) equipped with DIC vision-based technique acts as a rapid and cost-effective tool to quantify the serviceability of bridges by measuring strains and displacements at inaccessible locations. In this study, a non-contact unmanned aerial vehicle image correlation (UAVIC) technique is used on a scaled bridge girder and a contact method of measuring deformations with a dial gauge. Both investigations are correlated for accuracy assessment, and it is understood that results in laboratory conditions are 90% accurate. Similarly, the UAVIC technique is also performed on a rail over the bridge in the field conditions to understand the feasibility of the proposed method and evaluate damage quantification of it.

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Venkata Reddy Keesara

Papers

Assessment of water resources and crop yield under future climate scenarios: A case study in a Warangal district of Telangana, India

Flow Forecasting in a Watershed using Autoregressive Updating Model

Impacts of climate change on water provisional services in Tungabhadra basin using InVEST Model

Flow Simulation and Storage Assessment in an Ungauged Irrigation Tank Cascade System Using the SWAT Model

RCC Structural Deformation and Damage Quantification Using Unmanned Aerial Vehicle Image Correlation Technique