scholarly journals A bootstrap-based differential split-sample test to assess the transferability of conceptual rainfall-runoff models under past and future climate variability

2019 ◽  
Vol 575 ◽  
pp. 470-486 ◽  
Author(s):  
Hamouda Dakhlaoui ◽  
Denis Ruelland ◽  
Yves Tramblay
Keyword(s):  
Climate Variability ◽  
Future Climate ◽  
Rainfall Runoff ◽  
Split Sample ◽  
Sample Test

scholarly journals Responses of runoff to historical and future climate variability over China

2017 ◽  
Author(s):  
Chuanhao Wu ◽  
Bill X. Hu ◽  
Guoru Huang ◽  
Peng Wang ◽  
Kai Xu
Keyword(s):  
Climate Variability ◽  
Arid Regions ◽  
Southern China ◽  
Aridity Index ◽  
Global Climate Models ◽  
Future Climate ◽  
Western China ◽  
Climate Projections ◽  
Positive Contribution ◽  
The Mean

Abstract. China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal-spatial patterns of water resources. Based on the long-term (1960–2012) water budget data and climate projections from 28 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.2 to 3.3) and potential evaporation (PET) elasticity (from −2.3 to −0.2) across China. The P elasticity is larger in northeast and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960–2012, the climate contribution to R ranges from −2.4 % a−1 to 3.3 % a−1 across China, with the negative contribution in the North China plain and the positive contribution in western China and some parts of the southwest. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071–2100, the mean annual P will likely increase in most parts of China, especially the western regions, while the mean annual PET will likely increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China will likely become wetter and drier in the period 2071–2100, respectively (relative to the baseline 1971–2000).

scholarly journals Climate and hydrological variability: the catchment filtering role

2015 ◽  
Vol 19 (1) ◽  
pp. 379-387 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Annual Maximum ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Maximum Peak ◽  
Hydrological Variability ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

RAT (Robustness Assessment Test): a straightforward evaluation of hydrological model robustness to a changing climate

2021 ◽  
Author(s):  
Vazken Andréassian ◽  
Léonard Santos ◽  
Torben Sonnenborg ◽  
Alban de Lavenne ◽  
Göran Lindström ◽  
...  
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Climate Variables ◽  
Data Set ◽  
Hydrological Characteristics ◽  
Changing Climate ◽  
Split Sample ◽  
Sample Test ◽  
Model Robustness

<p>Hydrological models are increasingly used under evolving climatic conditions. They should thus be evaluated regarding their temporal transferability (application in different time periods) and extrapolation capacity (application beyond the range of known past conditions). In theory, parameters of hydrological models are independent of climate. In practice, however, many published studies based on the Split-Sample Test (Klemeš, 1986), have shown that model performances decrease systematically when it is used out of its calibration period. The RAT test proposed here aims at evaluating model robustness to a changing climate by assessing potential undesirable dependencies of hydrological model performances to climate variables. The test compares, over a long data period, the annual value of several climate variables (temperature, precipitation and aridity index) and the bias of the model over each year. If a significant relation exists between the climatic variable and the bias, the model is not considered to be robust to climate change on the catchment. The test has been compared to the Generalized Split-Sample Test (Coron et al., 2012) and showed similar results.</p><p>Here, we report on a large scale application of the test for three hydrological models with different level of complexity (GR6J, HYPE, MIKE-SHE) on a data set of 352 catchments in Denmark, France and Sweden. The results show that the test behaves differently given the evaluated variable (be temperature, precipitation or aridity) and the hydrological characteristics of each catchment. They also show that, although of different level of complexity, the robustness of the three models is similar on the overall data set. However, they are not robust on the same catchments and, then, are not sensitive to the same hydrological characteristics. This example highlights the applicability of the RAT test regardless of the model set-up and calibration procedure and its ability to provide a first evaluation of the model robustness to climate change.</p><p> </p><p><strong>References</strong></p><p>Coron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, doi:10.1029/2011WR011721</p><p>Klemeš, V., 1986. Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13–24, doi:10.1080/02626668609491024</p><p> </p>

scholarly journals Evaluating the Yield Response of Maize (Zea mays L.) and Rice (Oryza sativa L.) to Future Climate Variability in The Gambia

2019 ◽  
Vol 7 (2) ◽  
pp. 11
Author(s):  
Ebrima Sonko ◽  
Sampson K. Agodzo ◽  
Philip Antwi-Agyei
Keyword(s):  
Climate Variability ◽  
Rice Yield ◽  
Oryza Sativa L ◽  
Maize Yield ◽  
Future Climate ◽  
Yield Response ◽  
Climate Data ◽  
Negative Effects ◽  
Yield Data ◽  
Maize Cultivars

Climate change and variability impact on staple food crops present a daunting challenge in the 21st century. The study assesses future climate variability on maize and rice yield over a 30-year period by comparing the outcomes under two GCM models, namely, CSIRO_RCP4.5 and NOAA_RCP4.5 of Australia’s Commonwealth Scientific and National Oceanic and Atmospheric Administration respectively. Historical climate data and yield data were used to establish correlations and then subsequently used to project future yields between 2021 and 2050. Using the average yield data for the period 1987-2016 as baseline yield data, future yield predictions for 2021-2030, 2031-2040 and 2041-2050 were then compared with the baseline data. The results showed that the future maize and rice yield would be vulnerable to climate variability with CSIRO_RCP4.5 showing increase in maize yield whilst CSIRO_RCP4.5 gives a better projection for rice yield. Furthermore, the results estimated the percentage mean yield gain for maize under CSIRO_RCP4.5 and NOAA_ RCP4.5 by about 17 %, 31 % and 48 % for the period 2021-2030, 2031-2040 and 2041-2050 respectively. Mean rice yield lossess of -23 %, -19 % and -23 % were expected for the same period respectively. The study recommended the use of improved rice and maize cultivars to offset the negative effects of climate variability in future.

scholarly journals Process-based modelling to evaluate simulated groundwater levels and frequencies in a Chalk catchment in south-western England

2018 ◽  
Vol 18 (2) ◽  
pp. 445-461 ◽  
Author(s):  
Simon Brenner ◽  
Gemma Coxon ◽  
Nicholas J. K. Howden ◽  
Jim Freer ◽  
Andreas Hartmann
Keyword(s):  
Groundwater Level ◽  
Climate Changes ◽  
Data Availability ◽  
Model Complexity ◽  
High Porosity ◽  
Groundwater Levels ◽  
Subsurface Heterogeneity ◽  
Split Sample ◽  
Sample Test ◽  
Simulated Groundwater

Abstract. Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

Sign in / Sign up

Export Citation Format

Share Document