Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow

A test of Snowmelt Runoff Model (SRM) for the Gongnaisi River basin in the western Tianshan Mountains, China

Solar and wind assets are climate-dependent and changes in climate will result in variations in their generation and intermittency. Developers of solar and wind parks in India have observed changes in climate conditions and variability in solar irradiation and wind profiles at the seasonal and year-to-year timescales. Future climate change perturbations, including monsoon shifts, could lead to lower-than-predicted wind and solar energy production and affect the economics of solar and wind assets. Regional climate models (RCMs) are the basis of climate impact assessments and the most trusted source of information to extract knowledge about future trends in climate variables. However, RCM projections are tainted with variability and uncertainty about the future trends. For India as a case study, we use the RCMs generated by the Coordinated Regional Climate Downscaling Experiment West Asia project (CORDEX WAS) to calculate individual wind, radiation, and temperature trends at selected sites; estimate wind and solar PV energy time series; and embed them in portfolio methods to test the impact of combining wind and solar assets on the variability of the total production and the uncertainty about the predicted production. We include a comparison of CORDEX RCMs with the ERA5 reanalysis dataset and conclude that all available RCMs reasonably simulate the main annual and seasonality features of wind speed, surface solar radiation, and temperature in India. The analysis demonstrates that the uncertainty about the portfolio return can be reduced by optimizing the combination of wind and solar assets in a producer portfolio, thus mitigating the economic impact of climate change. We find that the reduction obtained with a mixed portfolio ranges from 33 to 50% compared to a wind only portfolio, and from 30 to 96% compared to a solar only portfolio.

Projected snow cover and river flows are important for planning and managing water resources in snow-dominated basins of the Himalayas. To quantify the impacts of climate change in the data scarce Panjshir River basin of Afghanistan, this study simulated present and future snow cover area (SCA) distributions with the snow model (SM), and river flows with the snowmelt runoff model (SRM). The SRM used the degree-day factor and precipitation gradient optimized by the SM to simulate river flows. Temperature and precipitation data from eight kinds of general circulation models (GCMs) were used for bias correction. The SM and SRM were first calibrated and validated using 2009–2015 data, and then bias-corrected future climate data were input to the models to simulate future SCA and river flows. Under both the representative concentration pathways (RCP) 4.5 and 8.5, the annual average SCA and river flow were projected to decrease in the mid and late 21st century, although seasonal increases were simulated in some instances. Uncertainty ranges in projected SCA and river flow under RCP 8.5 were small in the mid 21st century and large in the late 21st century. Therefore, climate change is projected to alter high-altitude stream sources in the Hindukush mountains and reduce the amount of water reaching downstream areas.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM’s seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R < 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources.

The Earth’s climate is not static; it changes according to the natural and anthropogenic climate variability. Anthropogenic forcing due to increase of greenhouse gases in the atmosphere has driven changes in climate variables globally. Changes in climatological variables have severe impact on global hydrological cycle affecting the severity and occurrence of natural hazards such as floods and droughts. Estimation of projections under climate signals with statistical and dynamic downscaling models and integration with water resource management models for the impact assessment have gained much attention. The fine-resolution climate change predictions of dynamic regional climate model (RCM) outputs, which include regional parameterization, have been widely applied in the hydrological impact assessment studies. Advancement of the Coordinated Regional Downscaling Experiment (CORDEX) program has enabled the use of RCMs in regional impact assessment which has progressed in recent years. CORDEX model outputs were considered to be valuable in terms of establishing large ensembles of climate projections based on regional climate downscaling all over the world. However, the simulations of RCM outputs have to be evaluated to check the reliability in reproducing the observed climate variability over a region. The present study demonstrates the use of bias-corrected CORDEX model simulation in analyzing the regional-scale climatology at river basin scale, Krishna river basin (KRB), India. The precipitation and temperature simulations from CORDEX models with RCP 4.5 were evaluated for the historical data for the period of 1965 to 2014 with India Meteorological Department (IMD) gridded rainfall and temperature data sets cropped over the basin. The projected increase of precipitation under climate signals was predicted to be from 74.4 to 136.7 mm over KRB for the future time period of 2041–2060 compared to the observed periods of 1966–2003. About 1.06 °C to 1.35 °C of increase in temperatures was predicted for the periods of 2021–2040 and 2041–2060, respectively, compared to the observed period of 1966–2014 over KRB. The climate variable projections obtained based on RCM outputs can provide insights toward the variations of water-energy variables and consequent impact on basin yields and losses in river basin management.

Detecting vulnerability of humid tropical forests to multiple stressors

Chapter 1 - Remote sensing of climate variability: An introduction

I discuss three aspects of research necessary for investigating possible effects of changes in climatic variability on crop yields. Additional information on changed variability effects is needed to further elucidate uncertainties in our knowledge of possible impacts of climate change on agriculture. First, sensitivity analyses of crop responses to shifting change in variability must be performed. Second, investigations of how climatic variability may change under perturbed (e.g., greenhouse gas-warmed) climate conditions should be undertaken. If one has some confidence in estimates of how variability may change, then a third research task is the formation of climate change scenarios that incorporate changes in climatic variability and their application to crop-climate models to determine crop responses. In this chapter, these research tasks are discussed regarding one climate variable, precipitation. I summarize two research projects that have been undertaken to investigate the sensitivity of the CERES-wheat (Triticum aestivum L.) crop model to changes in climatic variability, on daily to annual time scales, for sites in the central Great Plains. I also provide an example of determining possible changes in daily variability of precipitation through analysis of results from two regional climate model experiments, and then go on to describe an example of forming a climate change scenario that incorporates changes in daily precipitation variability estimated from the regional model runs. The CERES-wheat model exhibits considerable sensitivity to changes in precipitation variability, and variability of yields is positively correlated with variability in precipitation. Consideration of changes on both daily and interannual time scales is important. Estimates of changes in wheat yields simulated from a scenario that includes changes in variability are very different from changes in yield determined from a scenario that considers only mean changes in precipitation.

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.

ABSTRACT: The snowmelt‐runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April‐September) for ten years on the Rio Grande Basin (3419 km2) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi‐forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six‐month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi‐tation data, SRM produced a forecast hydrograph that had a r2= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R2= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.

Background: Providing persistent employment in the agricultural sector is one of the most significant economic and social challenges faced by many countries. The agricultural sector's high dependence on weather conditions makes it particularly vulnerable to climate change. Consequently, investigating the effects of climate change on the economy of this sector, especially concerning rural employment, is of paramount importance. This research aims to analyze the impact of climate change on citrus production in Mazandaran Province, utilizing the Ricardian approach. By examining the relationship between climatic variables and agricultural output, this study seeks to provide insights that can help enhance the resilience of the agricultural sector against climate fluctuations. Methods: The climatic variables studied in this research include temperature and rainfall data for Mazandaran Province from 2000 to 2017. These data were obtained from the Iran Meteorological Organization. The research focused on seven synoptic stations: Amol, Babolsar, Qarakhil, Nowshehr, Ramsar, Sari, and Siyabisheh. Among the various citrus products, orange and tangerine were selected due to their significant cultivated area and economic importance. The climatic variables analyzed include minimum temperature, maximum temperature, and monthly rainfall. These variables were downscaled for three distinct periods: the baseline period (2000 to 2017), as well as projected scenarios for 2020 and 2080, under three different greenhouse gas emission scenarios: optimistic (RCP2.6), medium (RCP4.5), and pessimistic (RCP8.5). The findings reveal that in orange production, changes in temperature and annual rainfall, compared to the average net income during the baseline period, will result in increased net income by 13.4%, 14.5%, and 25.2% under the three scenarios, respectively. For the years 2050 and 2080, the increases in net income are projected to be 14.3%, 27.8%, and 29.1%. In terms of tangerine production, the net income under the investigated scenarios is expected to rise by 5.2%, 6.7%, and 17.7%, with further increases of 6.2%, 15.0%, and 29.2% anticipated in 2050 and 2080, respectively. These results indicate a positive correlation between changing climatic conditions and the economic viability of citrus production in Mazandaran Province. In conclusion, the results obtained from examining the effects of climate change in 2050 and 2080 demonstrate a positive impact of changing climate variables on the net income derived from both orange and tangerine production. The findings suggest that climate change may lead to an upward trend in net income from citrus production, which could, in turn, foster employment development in this sector. This upward trend indicates that, while climate change poses significant challenges to agriculture, it may also create opportunities for increased productivity and economic growth in specific areas such as citrus farming. The implications of this research are significant for policymakers and agricultural stakeholders. Understanding how climate change can enhance or hinder agricultural productivity allows for better planning and resource allocation. It highlights the need for adaptive strategies that can help farmers maximize their yields while mitigating the adverse effects of climate variability. Moreover, the positive outlook regarding citrus production under changing climatic conditions emphasizes the importance of investing in agricultural research and development. By focusing on climate-resilient

Global climate models (GCMs) represent invaluable instrument for various purposes, most prominently studying climate system dynamics, evolution of past climates and climate change projections. The newest set of GCM simulations has been produced under CMIP6 initiative coordinated by the the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling. Due to their coarse spatial resolution, some kind of downscaling is necessary for applications of GCM outputs on regional and local scales, with dynamical downscaling using regional climate models (RCMs) being a common solution of this issue. Clearly, the outputs of RCMs are influenced by the boundary conditions provided at the lateral boundaries of the integration domain. The magnitude of this influence is subject of ongoing research and depends on various aspects including the geographical region, temporal scale, climatic variable etc. Nevertheless, previous studies proved that an analysis of boundary conditions is needed for for proper RCM evaluation. Especially with regard to potential error propagation.              In present study we evaluate the simulation of CNRM-ESM2-1, one of the CMIP6 GCMs, and compare it to other CMIP6 ensemble members. The CNRM-ESM2-1 is being used as driving model for convective-permitting simulation of Aladin-CLIMATE RCM within Czech national project PERUN aimed at creation of updated climate change scenarios for the Czech Republic. We concentrate on historical GCM simulations in the period of 1990-2014, and use reanalysis ERA5 as the reference. The analysis is conducted over the boundaries of Aladin-CLIMATE/CZ intergration domain, which covers Central Europe. The deviations of the driving GCM (CNRM-ESM2-1) from reanalysis are compared to other CMIP6 GCMs. We also evaluate uncertainty arising from natural climate variability using perturbed initial conditions ensemble of CNRM-ESM2-1.

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

To adapt forest ecosystems and forest management to climate change, it is essential to know which forest regions and which tree species are resilient to climate variability and which ones are possibly affected most by past and anticipated future changes. In this contribution, for the main forest regions of Türkiye and six tree species, recent climate variability and trends were quantified and statistically correlated to record tree defoliation and vitality. Climate variables considered are maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and total precipitation (Prcp), which are compared to forest health responses recorded as part of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) on 277 plots across forests along the Black Sea and Mediterranean regions. In addition, long-term data on satellite measurements of the normalized difference vegetation index (NDVI) were extracted for the same 277 plots for the period 2008–2020. Firstly, 30 years (1991–2020) of reanalysis of climate variables from ECMWF were extracted for all plots; secondly, individual correlations and cross-correlations of climate variables and tree health and vitality were computed for the period 2008–2020 (significance level of 95%) for the four most dominant species from the Black Sea forests (F. orientalis, Q. cerris, P. sylvestris, P. orientalis) and two species from Mediterranean forests (P. brutia and C. libani). Temperature showed a stronger effect on most species than precipitation. Finally, time-lagged correlations were analyzed for seven-time lags (significance level of 95%) to evaluate legacy effect. The analysis revealed that different tree species from the two regions show different responses to climate variables. Species in the Mediterranean region are more resistant to droughts and climatic variations. Legacy effects of defoliation and NDVI have lasted for at least 2 years.