Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent‐based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154949/1/risa13428_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154949/2/risa13428.pd

Building Climate Resilience in the Blue Nile/Abay Highlands: A Framework for Action

Ethiopia has become warmer over the past century and human induced climate change will bring further warming over the next century at unprecedented rates. On the average, climate models show a tendency for higher mean annual rainfall and for wetter conditions, in particular during October, November and December, but there is much uncertainty about the future amount, distribution, timing and intensity of rainfall. Ethiopia’s low level of economic development, combined with its heavy dependence on agriculture and high population growth rate make the country particularly susceptible to the adverse effects of climate change. Nearly 90% of Ethiopia’s population lives in the Highlands, which include the critical Blue Nile (Abay) Highlands—a region that holds special importance due to its role in domestic agricultural production and international water resources. A five year study of climate vulnerability and adaptation strategies in communities of Choke Mountain, located in the center of the Abay Highlands, has informed a proposed framework for enhancing climate resilience in communities across the region. The framework is motivated by the critical need to enhance capacity to cope with climate change and, subsequently, to advance a carbon neutral and climate resilient economy in Ethiopia. The implicit hypothesis in applying a research framework for this effort is that science-based information, generated through improved understanding of impacts and vulnerabilities of local communities, can contribute to enhanced resilience strategies. We view adaptation to climate change in a wider context of changes, including, among others, market conditions, the political-institutional framework, and population dynamics. From a livelihood perspective, culture, historical settings, the diversity of income generation strategies, knowledge, and education are important factors that contribute to adaptive capacities. This paper reviews key findings of the Choke Mountain study, describes the principles of the climate resilience framework, and proposes an implementation strategy for climate resilient development to be applied in the Abay Highlands, with potential expansion to agricultural communities across the region and beyond

Explaining National Trends in Terrestrial Water Storage

Access to fresh water is critical for human well-being, economic activity and, in some cases, political stability. Data from the Gravity Recovery and Climate Experiment (GRACE) has been used to monitor variability and trends in total water storage. This makes it possible to associate changes in water storage with both climate variability and large scale water management. Recent research has shown that these trends can be associated, globally, with rainfall, irrigation, and climate model predictions. This research indicates a need for further investigation into specific human predictors of trends in terrestrial water storage. This paper presents the first global scale analysis of GRACE trends focused on national scale socio-economic predictors of terrestrial water storage. We show that rainfall, irrigation, agricultural characteristics, and energy practices all contribute to GRACE trends, and the importance of each differs by country and region. Additionally, this work suggests that other factors such as GDP, population density, urbanization, and forest cover do not explain GRACE trends at a national level. Identifying these key predictors aids in understanding trends in water availability and for informing water management policy in a changing climate

Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July– September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented

Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July– September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented

A Data‐Driven Framework to Characterize State‐Level Water Use in the U.S.

Access to credible estimates of water-use are critical for making optimal operational de-15 cisions and investment plans to ensure reliable and affordable provisioning of water. Fur-16 thermore, identifying the key predictors of water use is important for regulators to pro-17 mote sustainable development policies to reduce water use. In this paper, we propose18 a data-driven framework, grounded in statistical learning theory, to develop a rigorously19 evaluated predictive model of state-level, per capita water use in the US as a function20 of various geographic, climatic and socioeconomic variables. Specifically, we compare the21 accuracy of various statistical methods in predicting the state-level, per capita water use22 and find that the model based on the Random Forest algorithm outperforms all other23 models. We then leverage the Random Forest model to identify key factors associated24 with high water-usage intensity among different sectors in the US. More specifically, ir-25 rigated farming, thermoelectric energy generation, and urbanization were identified as26 the most water-intensive anthropogenic activities, on a per capita basis. Among the cli-27 mate factors, precipitation was found to be a key predictor of per capita water use, with28 drier conditions associated with higher water usage. Overall, our study highlights the29 utility of leveraging data-driven modeling to gain valuable insights related to the water30 use patterns across expansive geographical areas

Impacts of anthropogenic heat on summertime rainfall in Beijing

Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL

EARTH OBSERVATION AND CLIMATE SERVICES FOR FOOD SECURITY AND AGRICULTURAL DECISION MAKING IN SOUTH AND SOUTHEAST ASIA

Deliberations on new developments in the use of Earth observation for agriculture and food security in South and Southeast Asia gathered 120 participants to discuss i) agricultural and hydrological drought monitoring and early warning systems, ii) crop mapping and yield estimation, and iii) risk financing and agrometeorological advisory services

An Integrated Hydrological and Water Management Study of the Entire Nile River System - Lake Victoria to Nile Delta

The Nile basin River system spans 3 million km(exp 2) distributed over ten nations. The eight upstream riparian nations, Ethiopia, Eretria, Uganda, Rwanda, Burundi, Congo, Tanzania and Kenya are the source of approximately 86% of the water inputs to the Nile, while the two downstream riparian countries Sudan and Egypt, presently rely on the river's flow for most of the their needs. Both climate and agriculture contribute to the complicated nature of Nile River management: precipitation in the headwaters regions of Ethiopia and Lake Victoria is variable on a seasonal and inter-annual basis, while demand for irrigation water in the arid downstream region is consistently high. The Nile is, perhaps, one of the most difficult trans-boundary water issue in the world, and this study would be the first initiative to combine NASA satellite observations with the hydrologic models study the overall water balance in a to comprehensive manner. The cornerstone application of NASA's Earth Science Research Results under this project are the NASA Land Data Assimilation System (LDAS) and the USDA Atmosphere-land Exchange Inverse (ALEXI) model. These two complementary research results are methodologically independent methods for using NASA observations to support water resource analysis in data poor regions. Where an LDAS uses multiple sources of satellite data to inform prognostic simulations of hydrological process, ALEXI diagnoses evapotranspiration and water stress on the basis of thermal infrared satellite imagery. Specifically, this work integrates NASA Land Data Assimilation systems into the water management decision support systems that member countries of the Nile Basin Initiative (NBI) and Regional Center for Mapping of Resources for Development (RCMRD, located in Nairobi, Kenya) use in water resource analysis, agricultural planning, and acute drought response to support sustainable development of Nile Basin water resources. The project is motivated by the recognition that accurate, frequent, and spatially distributed estimates of the water balance are necessary for effective water management. This creates a challenge for watersheds that are large, include data poor regions, and/or span multiple nations. All of these descriptors apply to the Nile River basin, yet successful management of the Nile is critical for development and political stability in the region. For this reason, improved hydrological data to support cooperative water management in the Nile basin is a priority for USAID, the US State Department, the World Bank and other international organizations. In this project, the U.S. based research team is working with partners at RCMRD, Nile Basin Initiative (NBI), and their member national-level agencies to develop satellite-based land cover maps, satellite-derived evapotranspiration estimates (using the ALEXI algorithm), and NASA's Land Data Assimilation System (LDAS) customized to match identified information needs. The cornerstone applied sciences product of the project is the development of a customized "Nile LDAS" that will produce optimal estimates of hydrological states and fluxes, as vetted against the in situ observations of NBI and RCMRD member organizations and independent satellite-derived hydrological estimates. Nile LDAS will be applied to improve the reliability of emerging Decision Support Systems in applications that include drought monitoring, reservoir management, and irrigation planning. The end-users such as RCMRD, NBI, Ethiopian and Kenya Meteorological and Famine Early Warning System Network (FEWSNet) will be the eventual benefactors of this work. There will be a capacity building process involving the above end-user organizations and transfer the models and the results for these organizations to execute for future use. The team has already initiated this study and the early results of first years' work are shown. The plan is to complete this work by late 2013