Determinants of City Growth in Brazil

In this paper, we examine the determinants of Brazilian city growth between 1970 and 2000. We consider a model of a city, which combines aspects of standard urban economics and the new economic geography literatures. For the empirical analysis, we constructed a dataset of 123 Brazilian agglomerations, and estimate aspects of the demand and supply side as well as a reduced form specification that describes city sizes and their growth. Our main findings are that increases in rural population supply, improvements in inter-regional transport connectivity and education attainment of the labor force have strong impacts on city growth. We also find that local crime and violence, measured by homicide rates impinge on growth. In contrast, a higher share of private sector industrial capital in the local economy stimulates growth. Using the residuals from the growth estimation, we also find that cities who better administer local land use and zoning laws have higher growth. Finally, our policy simulations show that diverting transport investments from large cities towards secondary cities do not provide significant gains in terms of national urban performance.

Projecting Changes in Tanzania Rainfall for the 21st century: Scenarios, Downscaling & Analysis

A Non-Homogeneous hidden Markov Model (NHMM) is developed using a 40-years record (1950-1990) of daily rainfall at eleven stations in Tanzania and re-analysis atmospheric fields of Temperature (T) at 1000 hPa, Geo Potential Height (GPH) at 1000 hPa, Meridional Winds (MW) and Zonal Winds (ZW) at 850 hPa, and Zonal Winds at the Equator(ZWE) from 10 to 1000 hPa. The NHMM is then used to predict future rainfall patterns under a global warming scenario (RCP8.5), using predictors from the CMCC-CMS simulations from 1950-2100. The model directly considers seasonality through changes in the driving variables thus addressing the question of how future changes in seasonality of precipitation can be modeled. The future downscaled simulations from NHMM, with predictors derived from the simulations of the CMCC-CMS CGM, in the worst conditions of global warming as simulated by RCP8.5 scenario, indicate that, Tanzania may be subjected to to a reduction of total annual rainfall; this reduction is concentrated in the wet seasons, OND, mainly as a consequence of decreasing of seasonal number of wet days. . Frequency and Intensity of extreme events don’t show any evident trend during the 21 century

Local polynomial method for ensemble forecast of time series

International audienceWe present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D) with a delay time (?). To obtain a forecast from a given time point t, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (K=?*n, ?=fraction (0,1] of the data, n=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order p is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (D, ?, ?, p) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time t, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

A water risk index for portfolio exposure to climatic extremes: conceptualization and an application to the mining industry

Corporations, industries and non-governmental organizations have become increasingly concerned with growing water risks in many parts of the world. Most of the focus has been on water scarcity and competition for the resource between agriculture, urban users, ecology and industry. However, water risks are multi-dimensional. Water-related hazards include flooding due to extreme rainfall, persistent drought and pollution, either due to industrial operations themselves, or to the failure of infrastructure. Most companies have risk management plans at each operational location to address these risks to a certain design level. The residual risk may or may not be managed, and is typically not quantified at a portfolio scale, i.e. across many sites. Given that climate is the driver of many of these extreme events, and there is evidence of quasi-periodic climate regimes at inter-annual and decadal timescales, it is possible that a portfolio is subject to persistent, multi-year exceedances of the design level. In other words, for a multi-national corporation, it is possible that there is correlation in the climate-induced portfolio water risk across its operational sites as multiple sites may experience a hazard beyond the design level in a given year. Therefore, from an investor's perspective, a need exists for a water risk index that allows for an exploration of the possible space and/or time clustering in exposure across many sites contained in a portfolio. This paper represents a first attempt to develop an index for financial exposure of a geographically diversified, global portfolio to the time-varying risk of climatic extremes using long daily global rainfall datasets derived from climate re-analysis models. Focusing on extreme daily rainfall amounts and using examples from major mining companies, we illustrate how the index can be developed. We discuss how companies can use it to explore their corporate exposure, and what they may need to disclose to investors and regulators to promote transparency as to risk exposure and mitigation efforts. For the examples of mining companies provided, we note that the actual exposure is substantially higher than would be expected in the absence of space and time correlation of risk as is usually tacitly assumed. We also find evidence for the increasing exposure to climate-induced risk, and for decadal variability in exposure. The relative vulnerability of different portfolios to multiple extreme events in a given year is also demonstrated

Hazard Assessment from Storm Tides and Rainfall on a Tidal River Estuary

Here, we report on methods and results for a model-based flood hazard assessment we have conducted for the Hudson River from New York City to Troy/Albany at the head of tide. Our recent work showed that neglecting freshwater flows leads to underestimation of peak water levels at up-river sites and neglecting stratification (typical with two-dimensional modeling) leads to underestimation all along the Hudson. As a result, we use a three-dimensional hydrodynamic model and merge streamflows and storm tides from tropical and extratropical cyclones (TCs, ETCs), as well as wet extratropical cyclone (WETC) floods (e.g. freshets, rain-on-snow events). We validate the modeled flood levels and quantify error with comparisons to 76 historical events. A Bayesian statistical method is developed for tropical cyclone streamflows using historical data and consisting in the evaluation of (1) the peak discharge and its pdf as a function of TC characteristics, and (2) the temporal trend of the hydrograph as a function of temporal evolution of the cyclone track, its intensity and the response characteristics of the specific basin. A k-nearest-neighbors method is employed to determine the hydrograph shape. Out of sample validation tests demonstrate the effectiveness of the method. Thus, the combined effects of storm surge and runoff produced by tropical cyclones hitting the New York area can be included in flood hazard assessment. Results for the upper Hudson (Albany) suggest a dominance of WETCs, for the lower Hudson (at New York Harbor) a case where ETCs are dominant for shorter return periods and TCs are more important for longer return periods (over 150 years), and for the middle-Hudson (Poughkeepsie) a mix of all three flood events types is important. However, a possible low-bias for TC flood levels is inferred from a lower importance in the assessment results, versus historical event top-20 lists, and this will be further evaluated as these preliminary methods and results are finalized. Future funded work will quantify the influences of sea level rise and flood adaptation plans (e.g. surge barriers). It would also be valuable to examine how streamflows from tropical cyclones and wet cool-season storms will change, as this factor will dominate at upriver locations

Local polynomial method for ensemble forecast of time series

We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (<i>D</i>) with a delay time (&tau;). To obtain a forecast from a given time point <i>t</i>, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (<i>K</i>=&alpha;*<i>n</i>, &alpha;=fraction (0,1] of the data, <i>n</i>=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order <i>p</i> is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (<i>D</i>, &tau;, &alpha;, <i>p</i>) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time <i>t</i>, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

Development of Mountain Climate Generator and Snowpack Model for Erosion Predictions in the Western United States Using WEPP: Phase IV

Executive Summary: Introduction: This report summarizes work conducted during the funding period (December 1, 1991 through September 30, 1992) of a Research Joint Venture Agreement between the Intermountain Research Station, Forest Service, U. S. Department of Agriculture and the Utah Water Research Laboratory (UWRL), Utah State University (USU). The purpose of the agreement is to develop a Western Mountain Cilmate Generator (MCLIGEN) similar in function to the existing (non-orographic area) Climate Generator (CLIGEN), which is part of the Water Erosion Prediciton Project (WEPP) procedure. Aso, we are developing a Western U.S. Snowpack Simulation Model for includsion in WEPP. In the western U.S., topographic influences on climate make the climate too variable to be captured by one representatbie station per 100 km, as is done in CLIGEN. Also, few meteorological observations exist in high-elevation areas where Forest Service properties are located. Therefore, a procedure for estimating climatological variables in mountainous areas is needed to apply WEPP in these regions. A physically based approach, using an expanded and improved orographic precipitation model, is being utilized. It will use radiosonde lightning data to estimate historical weather sequences. Climatological sequences estimated at ungaged locations will be represented using stochastic models, similar to the approach used in the existing CLIGEN. By using these stochastic models, WEPP users will be able to synthesize climate sequences for input to WEPP. MCLIGEN will depend on historically based, physically interpolated weather sequences from a mesoscale-climate modeling system which is comprised of four nested layers: 1. an existing synoptic scale forecast model (200 x 300 km) 2. a regional scale slimate model (60 x60 km) 3. a local scale climate model (10 x 10 km); and 4. a specific point climate predictor, referred to as ZOOM. Two additional MCLIGEN components are: 5. a local scalses stochastic climate generator; and 6. a point energy balance snowmelt model Progress made during the reporting period in developing the physically based interpolation climate modeling system stochastic models, and snowpack models is summareized below

Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.</p