Water Resources in a Climate-changing World I Concepts

Transcription

1 Water Resources in a Climate-changing World I Concepts Manfred Koch Department of Geohydraulics and Engineering Hydrology, University of Kassel, Germany kochm@uni-kassel.de SEAN-DEE Seminar, Kassel University, November 8, 2013

2 Overview 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions 1.2 Climate change: Hydrological impacts 1.3 Climate change: Groundwater resources sustainability 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management 2.2 Scales and variability of climate and hydrological systems 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related to hydro-climate modeling 3.2 From global to local scale: downscaling from the climate to the hydrological model. CONCLUSIONS /PART I CASE STUDIES / PART II 4.1 Upper Blue Nile River Basin / Ethiopia 4.2 Omo River Basin / Ethiopia 4.3 Thailand / Rayong 4.4 Glacier melting in the Andes/Peru 4.5 Fulda catchment /Germany

3 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions

4 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions

5 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions

6 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions

7 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions

8 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts / general aspects Climatechangeimpacts/ general The hydrological cycle Climate change impacts/ water resources

9 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts/global

10 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts /global

11 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts/europe * Most General Circulation Models (GCMs) predict a strong change in rainfall over the the Northern hemisphere with wetter winters and drier summers in Northern Europe GCM results indicate increases in both the frequency and intensity of heavy rainfall events under enhanced greenhouse conditions In Northern Europe these changes will cause a 10 to 30 percent increase in the magnitude of rainfall events up to a 50 year return period by the end of the century One of the most significant impacts of such changes may be on hydrological processes and, particularly, river flow Changes in seasonality and an increase in low and high rainfall extremes, such as droughts of the 1990s and floods of 2000/01 severely affect the water balance of river basins. * This will influence the rate of available water resources, as well as the frequency of flooding and ecologically damaging low-flows. Summer precipitation change in % for relative to

12 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts / Europe PRUDENCE: Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects project

13 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts / Germany Changes in annual mean temperature, smoothed with a 11- year window Changes in annual precipitation, (Baur curves) smoothed with a 11- year window.

14 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts/germany Regional Climate Model REMO (Meterological Institute, Hamburg, Germany) REMO model prediction of temperature changes in 0C and winter and summer precipiation for relative to

15 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.2 Climate change: Hydrological impacts / US/ Canada C G C M 1 C R C M 2100-change in winter precipitation in Canada Global and regional model 2100-change in median runoff in the US

16 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.3 Climate change: Groundwater resources sustainability / background Climate change could affect ground-water sustainability in several ways, including (1) changes in ground-water recharge resulting from changes in average precipitation and temperature or in the seasonal distribution of precipitation, (2) more severe and longer lasting droughts, (3) changes in evapotranspiration resulting from changes in vegetation, and (4) possible increased demands for ground water as a backup source of water supply. Surficial aquifers, which supply much of the flow to streams, lakes, wetlands, and springs, are likely to be the part of the ground-water system most sensitive to climate change; yet, limited attention has been directed at determining the possible effects of climate change on shallow aquifers and their interaction with surface water.

17 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.3 Climate change: Groundwater resources sustainability/ water use USA Trends in population, groundwater, and surface water withdrawals. Withdrawals by sector Hudson et al. (2004)

18 Overview 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions 1.2 Climate change: Hydrological impacts 1.3 Climate change: Groundwater resources sustainability 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management 2.2 Scales and variability of climate and hydrological systems 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related to hydro-climate modeling 3.2 From global to local scale: downscaling from the climate to the hydrological model. CONCLUSIONS /PART I CASE STUDIES /PART II 4.1 Upper Blue Nile River Basin / Ethiopia 4.2 Omo River Basin / Ethiopia 4.3 Thailand / Rayong 4.4 Glacier melting in the Andes/Peru 4.5 Fulda catchment /Germany

19 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management ds / dt = Qin - Qout Total water balance : ds T / dt = P + Qswi ET Qswi /Integrated model Surface water balance: ds S / dt = P ET O I /Surface water model Vadose zone balance: ds V / dt = I R /Vadose zone model Groundwater balance: ds G / dt = R + Gin Gout - Qp /Groundwater model

20 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management / IWRM

21 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management / IWRM

22 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management/ seawater intrusion under human and climate impact /concepts

23 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management/ seawater intrusion under human and climate impact / Florida case study

24 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.2 Scales and variability of climate and hydrological systems /atmosphere and ocean

25 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.2 Scales and variability of climate and hydrological systems /precipitation and streamflow Monthly precipitation and wavelet spectrum in Hamburg Monthly discharge and wavelet spectrum of Elbe river close to Hamburg

26 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.2 Scales and variability of climate and hydrological systems/ groundwater aquifer Effects of drought on ground-water levels and associated subsidence in the San Joaquin Valley, California. Regional groundwater flow system with subsystems at different scales Water elevations in three wells In a surficial aquifer in Florida

27 Overview 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions 1.2 Climate change: Hydrological impacts 1.3 Climate change: Groundwater resources sustainability 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management 2.2 Scales and variability of climate and hydrological systems 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related to hydro-climate modeling 3.2 From global to local scale: downscaling from the climate to the hydrological model. CONCLUSIONS /PART I CASE STUDIES /PART II 4.1 Upper Blue Nile River/ Ethiopia 4.2 Omo River / Ethiopia 4.3 Thailand / Rayong Province 4.4 Glacier melting in the Andes/Peru 4.5 Fulda catchment /Germany

28 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related of hydro-climate modeling /climate and hydrological models Types of climate models Atmosphere general circulation models (AGCMs) Ocean general circulation models (OGCMs) Coupled atmosphere-ocean general circulation models (AOGCMs) Fundamental equations in climate models Numerical discretization in AOGCMs

29 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related of hydro-climate modeling /climate models Historical development of climate models Synopsis of GCMs (starting point for all impacts studies, due to anthropogenic forcing): Advantages: information physically consistent, long simulations + different SRES scenarios, many variables, data readily available; Disadvantages: coarse-scale information where regional or local-scale climate information is essential, daily characteristics may be unrealistic except for very large regions, computationally expensive

30 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related of hydro-climate modeling / hydrological models

31 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.2 From global to local scale: downscaling from the climate to the hydrological model Why downscaling? Because there is a mismatch of scales between what global climate models can supply and what hydrological impact models require.

32 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.2 From global to local scale: downscaling from the climate to the hydrological model # 8 7 # 6 # # 1 # # 4 Rayong Province 3 # 12 # ; 11 # # 5 #

33 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.2 From global to local scale: downscaling from the climate to the hydrological model 1. High resolution and variable resolution AOGCM time-slice experiments - numerical modelling 2. Regional Climate Models (RCMs) - dynamic downscaling 3. Empirical/statistical and statistical/dynamical models - statistical downscaling Fowler, H. J., S. Blenkinsopa and C. Tebaldi, Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, , 2007

34 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.2 From global to local scale: downscaling from the climate to the hydrological model /SDSM SDSM (transfer function/ weather typing)

35 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.2 From global to local scale: downscaling from the climate to the hydrological model /LARS LARS-WG/stochastic weather generator allows the generation of daily data from monthly climate change scenario information. Advantage of using a stochastic weather generator is that a number of different daily time series representing the scenario can be generated permits risk analyses to be undertaken. Steps involved: 1) Analysis of the weather generator parameters, i.e. the statistical characteristics of the data, precipitation, maximum and minimum temperature, sunshine radiation. Use of semiempirical distributions calculated from the observed data, for wet and dry series duration, precipitation amount and solar radiation. Temperatures described using Fourier series. 2) Generation of synthetic weather data using the parameter files generated in step (1) for the observed data record length, or to simulate longer time series of data. For generation of daily data for a particular climate change scenario the appropriate monthly changes are included. 3) Statistical test (chi-squared test, Student's t-test and F-test) of the characteristics of the observed data are compared with those of synthetic data generated using the parameters derived from the observed station data.

36 CONCLUSIONS Effects of potential long-term changes in climate, caused either by man-made adverse activities or by natural climate variability of internal or external origins on water resources, namely, groundwater - which is most likely become the major source of water for human consumption, as surface water resources are on the demise has been discussed with regard to its availability, sustainability and its counterpart, vulnerability. Climate change could affect groundwater sustainability in several ways, including (1) changes in groundwater recharge resulting from seasonal and decadal changes in precipitation and temperature, (2) more severe and longer lasting droughts, (3) changes in evapotranspiration due to changes in temperature and vegetation, (4) possible increased demands for ground water as a backup source of water supply or for further economical (agricultural) development, (5) sea water intrusion in low-lying coastal areas due to rising sea levels and reduced groundwater recharge that may lead a deterioration of the groundwater quality there. This appears to be situation for the Bangkok coastal aquifer system Because groundwater systems tend to respond much more slowly to long-term variability in climate conditions than surface-water systems, their management requires special long-term ahead-planning.

37 CONCLUSIONS (cont. 2) Coupled hydro-climate models are needed to predict climate one or more decades ahead into the future to assist in rational planning of water resource systems as water needs change. It is important that these models predict trends at the decadal time scale, but also provide an indication of the permanence of these changes to distinguish permanent changes from rather temporary excursions. Some challenges and further research needs to better understand the effects of climate variability and change on future water resources availability may be summarized as follows: evaluating and improving global climate models in terms of the most critical parameters for hydrology, such as extremes of precipitation, and evapotranspiration incorporating humidity, cloudiness, and radiation; better representation of yearly- to decadal-scale climate variability in global climate models through representations of driving mechanisms such as El Nino, the Inter-decadal Pacific Oscillation for the Pacific region and the Northern Atlantic Oscillation (NAO) and the Arctic Oscillation for the northern Atlantic hemisphere reducing uncertainty in climate projections through further research into methods of determining and narrowing uncertainty for particular applications such as hydrology; studies to separate anthropogenic-induced changes from natural climate change and variability by also including high resolution paleo-records of hydrological and climatological parameters and analyzing them with modern methods of stochastic time series analysis;

38 CONCLUSIONS (cont. 3) further development and application of downscaling methods that represent climate at the relatively fine spatial and temporal scales of landscape hydrology; better representation of the continental physical hydrology in global climate models to simulate the interactions between climate and hydrology; better understanding of large-scale physical hydrology and its effects on the subsurface recharge process to be able to project future hydrological behavior for unprecedented climate conditions; improving the understanding of the interactions of groundwater with land and surface water resources by developing better integrated surface water/groundwater models; get a better hold on the importance of feedbacks through vegetation on hydrology and how these might change under future climate and CO 2 concentrations; increased use of remotely sensed data for climatological and hydrological applications as from the very promising GRACE earth satellite project. In conclusion, proper consideration of climate variability and change will be a key at present still underemphasized - factor in ensuring the sustainability and proper management of water resources and groundwater in particular. The achievement of this goal will require more collaboration across the fields of climatology and hydrology, as more reliable methods for water planning that provide planning certainty for water users under the impact of climate change must be developed.

39 End of Part I

40 Water Resources in a Climate-changing World II Applications and Case Studies Manfred Koch Department of Geohydraulics and Engineering Hydrology, University of Kassel, Germany kochm@uni-kassel.de SEAN-DEE Seminar, Kassel University, November 8, 2013

41 Overview 1. WATER RESOURCES IN THE FACE OF CLIMATE CHANGE 1.1 Climate change: Observations and predictions 1.2 Climate change: Hydrological impacts 1.3 Climate change: Groundwater resources sustainability 2. SYSTEM ANALYSIS OF SURFACE - AND GROUNDWATER RESERVOIRS 2.1 Basics of water budget analysis and implications on sustainable water management 2.2 Scales and variability of climate and hydrological systems 3. MODELING THE IMPACT OF CLIMATE CHANGE ON SURFACE- AND GROUNDWATER 3.1 General issues related to hydro-climate modeling 3.2 From global to local scale: downscaling from the climate to the hydrological model. CONCLUSIONS /PART I CASE STUDIES /PART II 4.1 Upper Blue Nile River/ Ethiopia 4.2 Omo River / Ethiopia 4.3 Thailand / Rayong Province 4.4 Glacier melting in the Andes/Peru 4.5 Fulda catchment /Germany

42 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

43 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

44 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

45 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

46 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

47 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Observed meteorological data / sub-basin wise

48 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

49 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

50 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

51 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

52 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

53 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem SDSM- downscaled predictions for max temperatures

54 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem SDSM- downscaled rainfall predictions

55 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem LARS-WG downscaled predictions of temperatures and precipitation

56 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Comparison of SDSM- and LARS-WG downscaled rainfall predictions

57 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem LARS-WG- multimodal downscaled dry-spell lengths

58 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Climate prediction changes (precipitation, minimum and maximum temperature), relative to the 20 th century reference period for the UBNRB using three downscaling methods

59 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Summary of climate predictions Temperatures For the 2050s future time period the downscaled climate predictions indicate rise of C to C for the seasonal maximum temperatures Tmax, and of C to C for the minimum temperatures Tmin. Similarly, during the 2090s the seasonal Tmax increases by C to C and Tmin by 1 0 C to C, whereby these increases are generally higher for the A2 than for the A1B scenario. For most sub-basins of the UBNRB, the predicted changes of Tmin are larger than those of Tmax. Precipitation Both downscaling tools predict large changes which, depending on the GCM employed, are such that the spring and summer seasons will be experiencing decreases between -36% to 1% Forthe autumn and winter seasons an increase of -8% to 126% for the two future time periods Results more or less independent of the SRES scenario used.

60 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

61 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem

62 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Streamflow analysis Filling in of missing data at one station by data from another Decadal trends in the annual streamflows

63 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Streamflow analysis/decomposition

64 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Streamflow analysis/ SWAT calibration and validation

65 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Streamflow analysis/ Future predictions for different SRES

66 4. CASE STUDIES 4.1 Upper Blue Nile River / Ethiopia / Ph.D. Project Netsanet Zelalem Summary of streamflow predictions SWAT-predicted streamflow using SDSM downscaled output shows declines of -44% (- 142mm) to -61% (-194mm), depending on the time slice and the SRES-scenario considered. Streamflow reduction is larger for the 2090s than for the 2050s, and larger for SRES-scenario A1B than for A2. When using LARS-WG mono-modal output, in the SWAT model, the future predicted streamflow shows a decline by only -10% (-33mm) to -20% (-64mm), whereby the predicted streamflow is lower in the 2050s than in the 2090s, particularly, for the A1B-scenario. This holds for both LARS-WG options and is due to a lower autumn precipitation predicted for the 2050s than for 2090s. In contrast, the higher SDSM streamflow decline predicted for the 2090s than for the 2050s is due to a higher predicted precipitation reduction in the former period. Streamflow values predicted by LARS-WG using multi-modal are generally lying between those of SDSM and LARS-WG mono-modal. Hence streamflow decreases predicted by this downscaling option are between -18% (-57mm) to -25% (-80mm).

67 4. CASE STUDIES 4.2 Omo River Basin / Ethiopia / Ph.D. Project Teshome Seyoum

68 4. CASE STUDIES 4.2 Omo River Basin / Ethiopia / Ph.D. Project Teshome Seyoum

69 4. CASE STUDIES 4.2 Omo River Basin / Ethiopia / Ph.D. Project Teshome Seyoum

70 4. CASE STUDIES 4.2 Omo River Basin / Ethopia / Ph.D. Project Teshome Seyoum Objectives of research (1) Main objectives model cascade dams & reservoirs operation in the Omo river basin for optimal water use. network cascade dams & reservoirs, using the HEC-ResSim model. (2) Specific objectives simulate runoff & inflow to reservoirs in Omo river basin with the SWAT model. develop & recommend optimal dam & reservoir operation rule curves for cascade dams & reservoirs. evaluate the effects of various reservoir operating alternatives for either preventing flooding or for avoiding precarious low flows downstream of the reservoirs.

71 4. CASE STUDIES 4.2 Omo River Basin / Ethopia / Ph.D. Project Teshome Seyoum Research Methodology Models: 1) SWAT model: Basin daily streamflow model 2) HEC-ResSim model: Reservoir operations model Data: 1) SWAT: Daily values of precipitation, max & min air temperature, solar radiation, RH, & wind speed. DEM, land-use, soil-coverage 2) HEC-ResSim: Observed and SWAT-modeled flow hydrographs, reservoir pool elevations, physical & operational reservoir data Final Delivery * Development of a model that represents the cascade dams & reservoirs, * Delivery of optimal water use operational model for Omo river basin

72 4. CASE STUDIES 4.3 Thailand/ Rayong Province # 8 7 # 6 # # 1 # # 4 3 ; # 11 # 12 # # 5 #

73 4. CASE STUDIES 4.3 Thailand/ Rayong Province 8 %U 9 %U Streams %U Raingages # Sub-basin outlet # Watershed outlet Reservoir Subbasins # %U 2 6 # 1%U %U # 7 %U ## %U 5 # # %U 4 %U 3 # # 11%U # # %U 12 # %U 10 Map of Khlong Yai basin and the streamflow network with reservoir locations

74 4. CASE STUDIES 4.3 Thailand/ Rayong Province Conceptual methodology of the study

75 4. CASE STUDIES 4.3 Thailand/ Rayong Province Monthly mean temperature (ºC) NCEP surface air temperature / Grid 12.5N-13.5N/101E-102.5E monthly mean temperature Year temperature anomaly trend line y = x Temperature anomaly (ºC) relative to Anomalies of monthly mean air temperature at surface relative to and the trend of monthly mean temperature in Rayong and Chonburi provinces

76 4. CASE STUDIES 4.3 Thailand/ Rayong Province streamflow anomaly monthly streamflow trend line y = x Monthly streamflow (cms) Year Streamflow anomaly (cms) relative to Streamflow at outlet of subbasin 2 / obs data of station Z.15 Observed monthly streamflow and trend at outlet of subbasin

77 4. CASE STUDIES 4.3 Thailand/ Rayong Province 30 Streamflow at outlet of sub-basin 1 / SWAT of Khlongyai basin error Monthly streamflow (cms) streamflow shallow percolation Observed flow Simulated flow calculation error Error (cms) Observed and simulated monthly streamflow at outlet of subbasin 1

78 4. CASE STUDIES 4.3 Thailand/ Rayong Province Monthly streamflow (cms) streamflow at outlet of sub-basin 2 / SWAT of Khlongyai basin error Observed flow Simulated flow calculation error streamflow Error (cms) Observed and simulated monthly streamflow at outlet of subbasin 2

79 4. CASE STUDIES 4.3 Thailand/ Rayong Province Streamflow at outlet of sub-basin 3 / SWAT of Khlongyai basin error Monthly (cms) Observed flow Simulated flow Shallow percolation calculation error streamflow Error (cms) Observed and simulated monthly streamflow at outlet of subbasin 3

80 4. CASE STUDIES 4.3 Thailand/ Rayong Province 30 Monthly rainfall/streamflow/precolation at outlet of subbasin 2 0 Streamflow (cms) Rainfall/Percolation Streamflow Observed flow Rainfall Simulated flow Percolation Rainfall/Precolation (mm/month) Rainfall, percolation and monthly streamflow at outlet of subbasin 2

81 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru

82 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Basins and areas specifically threatened by climate change

83 4. CASE STUDIES: 4.3 Glacier melting in the Andes/Peru CUENCA RIO SANTA HUARAZ TRUJILLO CHIMBOTE CANAL CHINECAS CANAL CHAVIMOCHIC Rio Santa catchment, Canon del Pato hydropower plant, and Chavimochic irrigation project

84 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Rio Santa and Chavimochic catchments Glaciers

85 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Cumulative mean specific mass balances (a) and cumulative total mass balances (b) of glaciers and ice caps, calculated for large regions (Dyurgerov and Meier, 2005).

86 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Rainfall predictions Average Dec-Jan-Feb rainfall Average Dec-Jan-Feb rainfall Source: SENAMHI

87 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Temperature predictions Average Mar-Apr-May temperature Average Mar-Apr-May temperature Source: SENAMHI

88 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Water balance predictions Water balance Water balance Source: SENAMHI

89 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Statistical analysis of climate indices across Northern Peru Mann-Kendall Test of monotonic trend: negative

90 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru Statistical analysis of climate indices across Northern Peru / Time series Correlation of temperature with El Nino SST Linear regression/prediction of temperature by El Nino SST

91 4. CASE STUDIES: 4.4 Glacier melting in the Andes/Peru/ Statistical analysis of climate indices Wavelet analysis of El Nino SST and station temperature

92 4. CASE STUDIES 4.4 Glacier melting in the Andes/Peru Statistical analysis of climate indices across Northern Peru Wavelet multiresolution analysis of station temperature and El Nino SST

93 4. CASE STUDIES 4.4 Fulda catchment / Germany Hessen Edertal reservoir System output Germany Fulda Germany N 20 km A E = 6930 km²

94 4. CASE STUDIES 4.4 Fulda catchment / Germany Fulda catchment with sub-basins, Eder lake and Eder dam

95 4. CASE STUDIES 4.4 Fulda catchment / Germany REMO --> SWAT- water balance predictions for the 21 st century /REMO-predictions/ Temperatures Future Temperatures Increase between 2 and 4 o C, depending on SRES Least increase for the optimistic B1 scenario Delay effect for the A1B scenario at the end of the 21 st century

96 4. CASE STUDIES 4.4 Fulda catchment / Germany REMO --> SWAT- water balance predictions for the 21 st century /REMO-predictions/ Precipitation Future Precipitation Periods of significantly higher yearly precipitation No long-term trend in yearly precipitation More precipitation in winter /less in summer

97 4. CASE STUDIES 4.4 Fulda catchment / Germany REMO --> SWAT- water balance predictions for the 21 st century /REMO-predictions / Runoff Future Runoff Increase of yearly flow in all scenarios Most runoff increase detected in the optimistic B1 scenario Significant increase of winter runoff in all scenarios Oscillation of the mean yearly runoff Moving average of yearly runoff (mean of 23 years)

98 End of Part II Thank you