Climate Change Impacts - Science topic

We have several crop, heat, and food indicators in our annual reports for Lancet Countdown https://lancetcountdown.org/ which are free to download, including the details appendices.

Thank you Jay I will see it!

Currently, water management remains heavily dominated by traditional, human‐built (i.e. ‘grey’) infrastructure and the enormous potential for NBS remains under‐utilized. Nature based Solutions for Water (NBS) include green infrastructure that can substitute, augment or work in parallel with grey infrastructure in a cost‐effective manner. The goal is to find the most appropriate blend of green and grey investments to maximize benefits and system efficiency while minimizing costs and trade‐offs.

Hi there Carolin,

sounds like a great topic for a BA thesis :-) I'm interested in your project - potentially also in participating as an interviewee. What precisely are you investigating in your research?

Feel free to contact me at Julius.Riese@web.de

With best wishes from Berlin,

Julius

Climate Change Performance Index is an independent monitoring tool for tracking the climate protection performance of 57 countries and the EU. It aims to enhance transparency in international climate politics and enables comparison of climate protection efforts and progress made by individual countries.

Source: https://germanwatch.org/en/19552

With the departure of Donald Trump, new president has entered the Paris climate agreement.

Draft Order of the Ministry of Economic Development of the Russian Federation "On approval of methodological recommendations and targets on adaptation to climate change"

The guidelines for assessing climate risks establish common approaches for their assessment.

Thus, for territories, a unified list of natural hazards and phenomena that occur in the atmosphere, hydrosphere, cryosphere and lithosphere is considered as a source of risk. For each of these risk sources, quantitative indicators of the intensity, prevalence, and duration of exposure are provided. In addition to the territorial reference, an approach is used based on the threshold (critical) values of climatic factors, exceeding which with a high degree of probability will lead to the loss of operability (termination of normal functioning) or the elimination of infrastructure and other objects.

Methodological recommendations for ranking adaptation measures according to their priority level are based on the need to identify adaptation needs and develop appropriate adaptation measures.

At the same time, a risk-based approach (measures that reduce the overall level of climate risks for the territory and / or industry) and a benefit-based approach (measures that allow the regional and / or industry levels to use the opportunities arising from climate change) can be used to develop adaptation measures. The ranking of adaptation measures is based on the contribution of these measures to reduce the level of risk or vulnerability of the affected objects, or the effect of using new opportunities. At the same time, the weight of each of these factors is determined by the subject of adaptation independently based on the specifics of the characteristic climate risks and the activities carried out. Examples of adaptation measures depending on climate risk and an approximate form for ranking adaptation measures according to their priority are attached.

Methodological recommendations for the formation of industry, regional and corporate plans for adaptation to climate change combine the results of the use of the above recommendations.

The main task of the prepared adaptation plans is to determine the list of priority adaptation measures that are characterized by a high effect at a relatively low cost.

It also provides targets for achieving climate change adaptation goals, which include federal, sectoral and regional indicators and are designed to analyze the effectiveness of climate change adaptation measures and monitor the implementation of climate change adaptation plans (national, sectoral and regional).

I think it not possible to replace all fossil fuel combustion systems with renewable energy systems. First of all, most of the renewable energy system’s construction cost is comparatively higher than the conventional ones. Secondly, some of the renewable energy system (such as solar and wind energy system) depends on environment and weather conditions. So, it can not be able to provide a stable output throughout the day. Thirdly, it is too difficult to produce a large quantity of energy from renewable energy sources quickly.

Climate change which makes the rainfall intensity very low or very high coupled with increased impermeable surfaces due to land use change.

The most significant impact is obviously, the rise in existing sea levels and flooding of the coastal regions. This will lead to loss of life and property and will force millions of people to migrate from existing land to some other areas. This mass migration may lead to clashes between people even lead to disturbed political situations.

How to stop this, is the real situation on which researchers are still working in different fields (for ex. CCR & CCU). However, in my opinion, the solution to control global warming can be solved, if man is willing. Since there are a lot of economic constraints, and the world economy and existing machines and infrastructure can work on fossil fuel as a source of energy (to a major extent). Hence it will take time to switch to green and renewable sources of energy. Again here, the economy and availability of technology will be a major constraint, and the balance (i.e. the consequences) will shift again to the side to which process is fast (i.e. rate of global warming rise vs rate of switching to renewable sources of energy).

The easiest way to control global warming is still, planting more trees to maintain and control the world's CO2 levels.

Convey my thanks for such a relevant article that nicely elaborated of the carbon draining from the terrestrial forest floors arrested in the mangroves ecosystems and that is seen at a glance, I will go through the paper later on and use as citation in my work

Dear Sami Gören , Thank you very much for your assistance. I will find the book.

Thank you all Daphnee Michel , S. Béatrice Marianne Ewalds-Kvist , Andrew Paul McKenzie Pegman , Alexandra Pierre . I apologize for the delay in reacting and thanking you for your contributions.

I work in all three languages i.e., MATLAB, Python and R on a regular basis. However, when it comes to make suggestion to someone who is about to start his/her research in hydrology, I always recommend Python.

One of the major advantage of Python and R over MATLAB is no licensing fee.

A hydrological modeler may work in a consultancy or may start his/her consultancy. If s/he work in a consultancy or even in an MNC, s/he might be asked to work on Python or R, as the commercial licence of MATLAB is too expensive to afford when equally efficient free options are available. Imagine starting your own consultancy and purchasing commercial license of MATLAB (it will burn the hole in your pocket).

Another advantage of Python is that in case one want to automate any process in ArcGIS/ QGIS one can use it easily, whereas MATLAB and R cannot be used. One may say that this can also be done using modeler in ArcGIS, however, I would say that modeler can better be used to integrate different tools but when it comes to analyse thousands of images and tens of operations, scripting becomes the only solution.

In case somebody is working in Linux system or uses Linux/Windows/Mac on need basis, getting license of MATLAB (or sometime even pirated version) becomes difficult. On the other hand Python and R are available free in all platforms.

If one want to automate any program or .exe file in Linux terminal or Windows Command Prompt, Python can be used to automate the process by converting the scripts into a executable.

These days, Google Earth Engine another powerful application also have the provision of using Python scripts. So, if one has to do computation on powerful Google Cloud, python is the only choice among the discussed three.

The single advantage of R over Python and Matlab is the availability of sophisticated and advanced statistics packages. These packages have very good and elaborate documentation and are unanimously accepted in the scientific community. However, if one has expertise in Python switching to R would not be a problem.

So if you ask which one to choose, I would answer, choose Python as first option and MATLAB as last option.

Hope it helps!

Regards

DS Bisht

Please go through the following RG links.

Santosh Kaini : thank you. Very interesting points of view. We are working on a model performance and trend scenaries approach.Thanks again.

I think this review really good

https://iwaponline.com/hr/article/50/6/1577/69068/Hydrological-modelling-of-climate-change-impacts

Le sujet est scientifique et seul l'exploitation des résultats réels. La modélisation de ces résultats nous permet de tirer les bonnes conclusions.

There are many emerging and re-emerging zoonoses, such as SARS, Bird flu, Swine flu, Hanta virus, West Nile fever, Dengue fever, Nipah virus, Rift Valley fever etc. are significant cause of morbidity and mortality in developing as well as developed nations.Further research should be conducted on molecular epidemiology, chemotherapy and vaccinology.

Please take a look at this useful RG link.

If interested in mean changes and want to cover full spectrum of future projections (Cold-Dry, Cold-Wet, Warm-Dry, Warm-Wet), use envelope-based approach (four corners with combinations of 10th and 90th percentiles of precipitation and temperature)

If interested in extremes, then use GCM-history vs. GCM-future. (Based on ETCCDI, e.g., CDD, CSDI, CWD, R99pTOT, WSDI, etc.) --- Rclimdex can be used to calculate ETCCDI

If you want to see whether GCMs can represent observed climate at present and near past then you need to compare GCM-history and Observed-history. Statistical indices can be used for this purpose.

If interested in Bias Correction, CMhyd is a handy tool to do that.

Hey Faci,

I just attached a picture and hope it will help you.

Best

Resilience is a tendency to return to a pre-disturbance condition. What pre-disturbance condition are you expecting to return to?

If we look at Doyal & Gough's Theory of Human Needs, the most fundamental human need is to function effectively in one's community. So the pre-disturbance condition may be a community that all of the people are able to function effectively within? That might be ambitious, if that is not available right now.

How would you measure that? You could measure how much time they use today to meet their needs, how much time they would use today if they could meet all of their needs, and how much time they would need at some unknown future date to meet their needs as the result of a finite change in climate. You could then find a relationship between amount of climate change and magnitude of impact on their time use. How much can the climate change for them still be able to meet all of their needs in 24h/d/ca or less? What is obstructing people from being able to meet their needs more effectively and efficiently? What would be obstructing them in that unknown future? How do you remove those obstructions, so that more people can have all their needs met?

There is a relationship between efficiency and resilience - one tends to drop as the other increases, often because of extra redundancy. We can try to learn from the past, to design to be just strong enough (hard to do with changing targets). Another approach is to rely on adaptive management, to respond to changing conditions. That can be as simple as ensuring that a weather proof electrical receptacle is put near where the bypass pump would have to go (or any other example where the want of a nail would lose a kingdom). In our modern culture, we expect very high efficiency and introducing resilience often takes a change in the way of looking at the problem. That often means educating the client... troublesome, that, but worth it. The results can be higher performance, lower overall cost, better dynamic responses.

It is not only the water but soils as well that are affected - dried out soils are prone to erosion, especially with sudden heavy precipitation (occurring with higher frequency as the climate conditions are drying out). Add to that deforestation and you have a real crisis. High summer temperatures are also very big problem, not only because of evaporation but because some plants/trees emit carbon rather than oxygen when certain temperature limit is crossed. So, yes, climate change can provoke great impact on agriculture and economy in general. What is probably confusing is the fact that we don't see the change equally distributed around the globe - some areas are more affected by drought and some by excess of precipitation. But in both cases agriculture will be affected, especially traditional ways of food production.

Hello Amjad Khan ,

I agree with Mohamed Salem Nashwan . Each grid point consists of a time series - analogous to data from a station. So, you can test for serial correlation for each of the grid points in your data set.

Best, Michel

Please have a look at this useful RG link.

Flood can define ss per the geographical area and intensity of rain. Whenever that created interference in daily activity. We can say it is flood.

Overall, I would say "Yes". However, Land use planning has so many other implications that are social and economic (besides the typical environmental implications that immediately come to mind when thinking about LUP). It depends also on the type of investigation but perhaps the LUP as part of the 17 sustainable development goals is/can be a a valuable metric in mitigating global climate change.

Dear Bayan Hussein!

This is true!

Our time series starts in 1989, so it is 30 y now.

We also measured the intraannual variation.

Most surprising are the effects of drought (2003 and 2018) on the hydrochemistry of discharge, enhancing the pH values!

I you would consider short time series, you can be mislead!

Best

Carl

This is a multi-factor problem. Good question, but the answer is not so simple

You'll have to model each (sub-region) individually for future effects of rainfall and temperature ranges but also look at wind patterns and storm events.

You will then need to look at the heritage stock and assess how the building fabric responds to the new (modelled) climatic realities. You can do so by looking at the performance and response to climatic events over the past 30 years . Look at the extreme events and possibly use them as new normal.

The next issue will be the state of the heritage assets and the funding that currently goes into maintenance, both private funding for private buildings and public monies for community-owned assets. What is the trend here? That trend can be forward projected.

You will then also need to take into account the projected impact of climate change on the economic capacity of the private citizens and of the local governments. That will vary from region to region, but will have an impact on the future capacity and willingness to pay for repairs.

When you have all of this in hand, you ned to develop a set of evaluation criteria with relative weighting , which then provide you with a numeric value of future risk that incorporates environmnetal and socio-economic factors.

Breeders must start working on finding more abiotic stress-tolerant varieties because of increasing temperature, salinity and drought. Plants will have shorter life cycles because of increasing temperature which means low yield. flower and fruit drop will increase because of higher temperature and strong winds which will contribute to lower yield production. fruit quality will also be reduced. cereal and fruit crops will be the most affected ones.

@ Dr. Vinesh Kumar

Please take a look at this useful link.

Thanks!

Donkor Emmanuel. Thank you for your assistance I will look over the paper.

We should change some habit in the developing countries

climate change refers to the change to the climate system such as average temperature over long time, rainfall, etc. climate change impacts refers to the results / consequences of these change, e.g. that cyclones become more intense, low lying areas get submerged by water, corals die, etc........

Refer the following and get details directly from them.

Your question has raised a lot more questions which must be answered by asking questions that a scientific research have to provide answers to. I believe if this research must be undertaken (I doubt) so much funds must be made available.

I believe we need to fully deal with our Climate Change issues on Earth (which several people are still disputing) before we start worrying about other planets.

Dear Yosef mekonnen Ali

I think yes you can. there are many studies using this model to assessment of climate change.

Hi,

Just google Prof. John Sterman+climate change, then you find several SD works regarding climate change and specially with focus on human interaction.

Best.

Hi Sodiq,

Thanks for your good line of thoughts! The issue of adaptation to climate change is context-specific and a whole lot of damage could be done if someone makes a mistake of generalization. Some farmers in West Africa, are already adapting to the effects of climate change but their efforts are sometimes insufficient. In certain cases, some farmers do take wrong coping or adaptation approaches, which could lead to maladaptation.

Concerning the questions on "who, what, and how" of adaptation with effect on farmers' productivity, we need to look at the various stakeholders and their roles. However, ways to ensure synergies and effective collaboration between these group of stakeholders, which include the farmers, is the key challenge in this perspective.

Hi,

check

Hi Ajay Raghavendra,

thank you very much for your suggested publications. Particular, the paper of Durack is spot-on.

best regards,

Triet Nguyen

LARS-WG and SDSM is very good.

do not try to choose a GCM but use multiple GCMs. There is no good GCM but the use of ensemble average of the GCMs is recommended in several research studies

Dear Aamir,

the official portal for CMIP5 data is ESGF :

It has "everything" but is a bit harsh for non-specialists. You would also need to bias-adjust the simulations by yourself to use them in crop models.

You can also check us out at https://theclimatedatafactory.com/

We provide "ready to use" CMIP5 data for climate change impact modeling as a commercial service. The methods and process are described in this report: https://www.researchgate.net/publication/322888233_Bias_Adjusting_Climate_Model_Projections

All the best

Some parts of New Zealand's South Island had consecutive five days of 5 C above average summer temperatures this summer. This is becoming a trend and in a few decades countries will have to either change the demography of crop species or breed crops to meet the environmental challenges. While these changes may be bearable for colder countries in the 'short term', for tropical countries already suffering from droughts, high temperatures etc., even a degree of average temperature increase will have larger consequences I guess. Higher night temperatures will seriously affect crop productivity overall.

I agree with Kenneth Towe. No one can control or change climate. All that humans can do is to adapt to it. Note that during the Ice Ages, or earlier during the Paleocene -Eocene Thermal Maximum, tropical climate extended to Europe, yet man was not there. The climatic regimes ran into thousands and millions of years. Climate change is just normal to the earth. Man can destroy his environment but cannot control climate. Fill up the land lying below sea level and build cities. Millenia later, global warming, ice-melting causing sea level rise will simply drown the city.

Obianuju P. Umeji

Please have a look at this useful RG link.

Dear Saima,

You could use CFSR_World Weather Database to fill all missing data.

I hope you succeed!

ND Liem

SWAT

In case of such situation, the affect countries should have plan to accommodate citizens in friend countries.

Dear Prof. Qin Liu

I'm working on different project related to drought monitoring and prediction. Recently, I've recently published two papers in this regard:

1- Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data,

Journal of Arid Land, DOI:10.1007/s40333-017-0070-y

2- Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment.Theoretical and Applied Climatology, DOI:10.1007/s00704-017-2315-2

Also, I've developed a website that all my softwares are uploaded in it:

If I can help you in your project, I appreciate.

Regards,

Dr. Nasrin Salehnia

Here are some papers that may be of help in your search:

Toniolo, H. and Parker, G.: 1-D Numerical modeling of reservoir sedimentation, in: IAHR Symposium on River, Coastal and Estuarine Morphodynamics, Barcelona, Spain, 457–468, 2003

Hu, C., Guo, Q., Chen, J., and Cao, W.: Applications of numerical simulation to the sedimentation in the Sanmenxia reservoir and the Lower Yellow River, Internat. J. Environ. Pollut., 42, 148– 165, 2010.

Also, check the Delft-3D model which has been used for various applications: https://oss.deltares.nl/web/delft3d

Here's an example:

Omer, A. Y. A., Ali, Y. S. A., Roelvink, J. A., Dastgheib, A., Paron, P., and Crosato, A.: Modelling of sedimentation processes inside Roseires Reservoir (Sudan), Earth Surf. Dynam., 3, 223-238, https://doi.org/10.5194/esurf-3-223-2015, 2015.

Best regards,

Tom

Dear Saima,

In addition to Martijn's answer, what I understand from SWAT model is that it will take closest meteorological stations from the center of each sub-catchment as the model forcing. Hence, your SWAT simulation can be mainly driven by one meteorological station depending on their proximity from sub-catchments. In addition to that, you should provide meteorological time series with an equal time length, otherwise the SWAT system will reject your simulation automatically (with a pop up warning on the screen).

For the second question, I agree with the previous answers. You may use your 20-years observed time series to perform bias correction. However, I will do what Martijn has said by means splitting the time series into two division for calibration and validation.

Good luck!

I am not sure, if the focus shoul dbe too much on scientific aspects of agricultural production. India is producing sufficient food to feed everybody well. India has become a major exporter of food, e.g. rice as outside consumer shave better purchasing power than th epoor in India.

Food science e.g. has a role to play to achieve food security, but its importance is often seen too high. The assumption that too little food is produced to feed everybody on the planet is incorrect. Actually enough food statistically is around, but not everybody has access to what is available. Food science can cause increase in the cost of food making it unaffordable for poor people.

The Green Revolution is an example for this. High Yielding Varieties cost money, fertilizers, pesticides and other high costs for external inputs make agriculture costly. Still plant science can help to better adapt agriculture for example to a changing climate. One aspect that brings issues around food security together is the sustainable livelihood approach. Of course livelihood security is a concept much broader than food security, but there are a lot of overlaps, especially in times of climate change.....

Crucial is to keep the basis of food production is good shape. Environmental degradation is very dangerous from such perspective. Second is the distribution / access to food, which is to remove poverty. To better understand what is needed one needs to be aware of the different facets of food security.

This is how the FAO defines food security. It combines food production, distribution and consumption, looks into cultural and individual food preferences and also at quality aspects of food both from a nutrition perspective as well as from the perspective of food safety. Climate / environmental changes can have impacts on all these aspects, on food production, food distribution / access and food consumption.

Food production and climate change

all food crops have their optimum what climatic conditions such as temperature, rainfall, etc are concerned. Especially extremes (too cold, too hot, too wet, too dry) can have devastating ing impacts. Some plants can tolerate a wide range of conditions, while other plants are rather little flexible to adapt to changing climatic conditions. In addition natural hazards (floods, cyclones, droughts) can have devastating impacts destroying crops in the fields. This all does not mean that only negative impacts are from climate change to agriculture. E.G. the temperate zones towards the poles will have warmer climates allowing longer vegetation periods, longer for crops like wheat. The impacts thus have to be established in regional or even sub-regional dimensions.

Access to food and climate change

this addresses in particular the changes of food prices as a result of climate change. Here in particular countries are affected that already now import big parts of their food requirements, like Kiribati, Tuvalu and other atoll states. Already today food imports make much of their imports and have very negative impacts on their BoP situation. On the micro-level afford rising food prices. They have to change to cheaper foods, often of lesser quality.

McCarthy, J. F., & Obidzinski, K. (2017). Framing the food poverty question: Policy choices and livelihood consequences in Indonesia. Journal of Rural Studies, 54, 344-354.

Cramer, L., Huyer, S., Lavado, A., Loboguerrero, A. M., Martínez Barón, D., Nyasimi, M., ... & van Wijk, M. (2017). Methods Proposed to Evaluate the Potential Impact of Climate Change on Food and Nutrition Security in Central America and the Dominican Republic.

Moragues-Faus, A., Sonnino, R., & Marsden, T. (2017). Exploring European food system vulnerabilities: Towards integrated food security governance. Environmental Science & Policy, 75, 184-215.

Chandra, A., McNamara, K. E., Dargusch, P., Caspe, A. M., & Dalabajan, D. (2017). Gendered vulnerabilities of smallholder farmers to climate change in conflict-prone areas: A case study from Mindanao, Philippines. Journal of Rural Studies, 50, 45-59.

CAPE is fine, but it is not designed for tropical latitudes, for those lower latitudes, I would suggest using the Gálvez-Davison Index (GDI).  A paper on the GDI along with coding you can use from GrADDS (I have a Matlab version I created and have attached it here), I would suggest going to http://www.wpc.ncep.noaa.gov/international/gdi/ for more information.  The GDI requires RH and Air Temperature data at the following 4 levels:  950-hPa, 850-hPa, 700-hPa, and 500-hPa.  If 950-hPa data is not available, then 925-hPA data can be used, and while this will give you the right variability, the amplitudes may be a bit less.

Dear Saima Nauman

Here are few links, hope you will find these links helpful.

Regards,   

---- Amit

It is a very interesting question, where I recommend to discuss the issue of 'indigenous'. What is meant by this? People's minority status in a society? Their difference from the mainstream? Their special exposure? Or is it that you mean just "local exposure"?. Here in Fiji the indigenous population is the majority of the population. Usually the impacts / exposure to climate change / natural hazards are not discuss under a label of 'indigenous', but under 'local', 'exposed' 'vulnerable' , 'poor' population. Often these and similar categories play a much bigger role even when there are overlaps between these categories and the category 'indigenous'.

I agree with Dr.Singh

Good information but it is unpublished.

The "study" isn't peer reviewed. One can guess why the authors choose to avoid having their references and conclusions verified by peers.

May be,but its not continuous and you cannot level  and classify their effect and results, in fact many factors correlate well with desertification classes(slight,moderate and sever).

Abstract : This paper explores the value of using community risk assessments (CRAs) for climate change adaptation. CRA refers to participatory methods to assess hazards, vulnerabilities and capacities in support of community-based disaster risk reduction, used by many NGOs, community-based organizations, and the Red Cross/Red Crescent. We review the evolution of climate change adaptation and community-based disaster risk reduction, and highlight the challenges of integrating global climate change into a bottom-up and place-based approach. Our analysis of CRAs carried out by various national Red Cross societies shows that CRAs can help address those challenges by fostering community engagement in climate risk reduction, particularly given that many strategies to deal with current climate risks also help to reduce vulnerability to climate change. Climate change can also be explicitly incorporated in CRAs by making better use of CRA tools to assess trends, and by addressing the notion of changing risks. However, a key challenge is to keep CRAs simple enough for wide application. This demands special attention in the modification of CRA tools; in the background materials and trainings for CRA facilitators; and in the guidance for interpretation of CRA outcomes. A second challenge is the application of a limited set of CRA results to guide risk reduction in other communities and to inform national and international adaptation policy. This requires specific attention for sampling and care in scaling up qualitative findings. Finally, stronger linkages are needed between organizations facilitating CRAs and suppliers of climate information, particularly addressing the translation of climate information to the community level.

Climate Adaptation Services for the Netherlands: an operational approach to support spatial adaptation planning, Regional Environmental Change,June 2014, Volume 14, Issue 3, pp 1035–1048

Abstract : There is a growing availability of climate change information, offered to scientists and policy makers through climate services. However, climate services are not well taken up by the policy-making and planning community. Climate services focus on primary impacts of climate change, e.g., the disclosure of precipitation and temperature data, and this seems insufficient in meeting their needs. In this paper, we argue that, in order to reach the spatial planning community, climate services should take on a wider perspective by translating climate data to policy-relevant indicators and by offering support in the design of adaptation strategies. We argue there should be more focus on translating consequences of climate change to land-use claims and subsequently discuss the validity, consequences and implications of these claims with stakeholders, so they can play a role in spatial planning processes where much of the climate adaptation takes place. The term Climate Adaptation Services is introduced as being a stepwise approach supporting the assessment of vulnerability in a wider perspective and include the design and appraisal of adaptation strategies in a multi-stakeholder setting. We developed the Climate Adaptation Atlas and the Climate Ateliers as tools within the Climate Adaptation Services approach to support decision-making and planning processes. In this paper, we describe the different steps of our approach and report how some of the challenges were addressed.

 Dear

in your observed tree it may be a falling fruit (fruit senescence),  because some species retain fruiting still the next flowering season sets OR - It depends on frequency of rainfall pattern and temperature, you must have data of leafing, flowering and fruiting, it may be because of intermittent rainfall, some species respond to leaf and flower bud but they may fail to set  fruiting due (lack of pollinator) 

Dear Emad,

I am agree with you! Yes, the climate change causes many problems for human person. 

Thanks a lot for your comments!

Sincerely, Bashkim

Check this CORDEX East-Asia site for RCM data. They have multiple RCM-GCM_RCP combinations.  https://cordex-ea.climate.go.kr/main/mainPage.do .

It would be helpful if you add further details on the kind of impact you want to assess. But you can use your observed Precipitation and Temperature data to assess the credibility of the RCMs during historical periods and look at future projections from those you feel comfortable with.  You can do whole lot of different analysis like trend, mean change (future vs historical), impact on extremes indices (like the ETCCDI), ....

Good luck

I do not know specifics or exact intent of research, but it takes a long term record to correlate wIth climate change, unless you have nearby studies.  So I would steer away from climate change term, even if you try to interface with it, unless you have substantial data or intent for comprehensive study through time.

Within a watershed or basin, land use changes as well as hydrologic modifications and soil disturbances when too wet can contribute to flooding, loss of soil structure than can contribute to waterlogging or puddling of soils.  You might consider something like Ecohydrological Effects of Natural and Anthropogenic Change to Rural Life in Mahanadi Delta, or simpler Ecohydrological Changes That Influence Rural Livelihood within Mahanadi Delta.

@Rajiv Pandey, thank you so much for your suggestions and article. i shall go through this article but i will also need a more physical approach because most of my data are physically based even though i know that social impacts should not be neglected. an approach that can use models (statistical, distributed or semi-distributed models etc.)

Explore here too..

Above both answers explains very well. I have worked on both GPM as well as TRMM data. TRMM gives a .25 degree resolution which is not very good. But If you make it as point data source along with your ground data sources and interpolate it into grids. It works really well in every conditions. It works better than working alone with ground data sources. The early run and late run of GPM has a latency period so if you have a long travel time , then only it will be benificial. For forecasting I would recommend to use GFS(Global forecast Systsm) or more accurate(according to me) ECMWF .