scholarly journals Seasonal streamflow forecasts for Europe – Part 2: Sources of skill

2019 ◽  
Vol 23 (1) ◽  
pp. 371-391 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Ronald W. A. Hutjes
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Companion Paper ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Annual Variations ◽  
Run Off

Abstract. This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill.

scholarly journals Seasonal streamflow forecasts for Europe – II. Explanation of the skill

2016 ◽  
Author(s):  
Wouter Greuell ◽  
Wietse H. P. Franssen ◽  
Ronald W. A. Hutjes
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Irrigation Management ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Streamflow Forecasts ◽  
Hydrological Prediction

Abstract. Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the second of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981–2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing.

Seasonal forecasts of runoff and river discharge in South America: skill and post-processing

2020 ◽  
Author(s):  
Wouter Greuell ◽  
Ronald Hutjes
Keyword(s):  
South America ◽  
Lead Time ◽  
Initial Conditions ◽  
Seasonal Forecasts ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Discharge Data ◽  
Continental Scale ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

<p>This contribution deals with the skill of a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to South America and earlier to Europe (see  Greuell et al., hess-23-371-2019). The system basically consists of the Variable Infiltration Capacity (VIC) hydrological model forced with output from ECMWF’s Seasonal Forecasting System 5 (SEAS5). We analyse skill in runoff and discharge hindcasts both with real observations and with so-called pseudo-observations, i.e. with discharge data generated with VIC forced with historical meteorological observations (1981-2015). At the continental scale discrimination skill in runoff shows characteristics that are similar to Europe. Especially, even at the longest lead time (7 months) significant skill remains in 20-30% of both continents. However, in the first lead month there is less significant skill in South America, due to absence of skill in its very dry and very wet regions, than in Europe, where similar extremes do not exist. To explain the skill in runoff, we performed two suites of specific hydrological hindcasts akin to Ensemble Streamflow Predictions (ESP), which each isolate a different source of skill (meteorological forcing and initial conditions). We find that in South America the contribution to skill by forcing is larger than in Europe, which can be ascribed to differences in the skill in the precipitation forcing. Even at a lead time of 7 months, the precipitation hindcasts have significant skill in 15-30% of South America while in Europe skill is almost confined to the first lead month. Discharge hindcasts for grid cells with a sufficient amount of observations were post-processed with ensemble model output statistics (EMOS). This procedure successfully increased reliability but resulted in a small decrease of discrimination skill. Nevertheless, for the location of the Itaipu dam, used to produce 18% of Brazil’s electricity, discrimination skill is highly significant for the post-processed discharge, e.g. at all lead times in the last two months of the year.</p>

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface Temperature

2014 ◽  
Vol 27 (24) ◽  
pp. 9253-9271 ◽  
Author(s):  
Stefano Materia ◽  
Andrea Borrelli ◽  
Alessio Bellucci ◽  
Andrea Alessandri ◽  
Pierluigi Di Pietro ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Predictive Skill ◽  
The Impact ◽  
Initialization Strategy

Abstract The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts.

scholarly journals Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 2: Experimental campaigns in Northern Africa

2011 ◽  
Vol 11 (11) ◽  
pp. 30273-30331 ◽  
Author(s):  
K. Haustein ◽  
C. Pérez ◽  
J. M. Baldasano ◽  
O. Jorba ◽  
S. Basart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Regional Scale ◽  
Surface Wind ◽  
Companion Paper ◽  
Northern Africa ◽  
Saharan Dust ◽  
Temperature Cycle ◽  
Daily Maximum ◽  
Model Initialization

Abstract. The new online NMMB/BSC-Dust model is intended to provide short to medium-range weather and dust forecasts from regional to global scales. The companion paper Pérez et al., 2011 develops the dust model parameterizations and provides daily to annual evaluations of the model for its global and regional configurations. Modeled aerosol optical depth (AOD) was evaluated against AERONET Sun photometers over Northern Africa, Middle East and Europe with correlations around 0.6–0.7 on average without dust data assimilation. In this paper we analyze in detail the behavior of the model using data from the Saharan Mineral dUst experiment (SAMUM-1) in 2006 and the Bodélé Dust Experiment (BoDEx) in 2005. AOD from satellites and Sun photometers, vertically resolved extinction coefficients from lidars and particle size distributions at the ground and in the troposphere are used, complemented by wind profile data and surface meteorological measurements. All simulations were performed at the regional scale for the Northern African domain at the expected operational resolution of 25 km. Model results for SAMUM-1 generally show good agreement with satellite data over the most active Saharan dust sources. The model reproduces the AOD from Sun photometers close to sources and after long-range transport, and the dust size spectra at different height levels. At this resolution, the model is not able to reproduce a large haboob occurred during the campaign. Some deficiencies are found concerning the vertical distribution. The mixing height is underestimated which may be attributed to poor soil initial conditions. For the BoDEx period, particular attention is paid to understand the dust model behavior in relation with the low level jet (LLJ) in the Bodélé. The diurnal temperature cycle depends strongly on the soil moisture, which is underestimated in the NCEP analysis used for model initialization. The daily maximum surface wind speeds are underestimated up to 50% in some days even when using a more accurate soil moisture initialization. The dust plume over the Bodélé is well reproduced by the model and the reductions in the threshold friction velocity substantially reduce the negative AOD bias in the model due to wind speed underestimation. The LLJ is also well reproduced, which is remarkable given the rather poor model initialization with NCEP-FNL data.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 10 (2) ◽  
pp. 1987-2013 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Lead Time ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Evaluating the Impacts of Climate Change and Vegetation Restoration on the Hydrological Cycle over the Loess Plateau, China

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2241 ◽  
Author(s):  
Yang ◽  
Kang ◽  
Bu ◽  
Chen ◽  
Gao
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Cover ◽  
Loess Plateau ◽  
Hydrological Cycle ◽  
Management Strategies ◽  
The Loess Plateau ◽  
Infiltration Capacity ◽  
Deep Soil ◽  
The Impact

In recent decades, both observation and simulation data have demonstrated an obvious decrease in runoff and soil moisture, with increasing evapotranspiration, over the Loess Plateau. In this study, we employed a Variable Infiltration Capacity model coupled with scenario simulation to explore the impact of change in climate and land cover on four hydrological variables (HVs) over the Loess Plateau, i.e., evapotranspiration (ET), runoff (Runoff), shallow soil moisture (SM1), and deep soil moisture (SM2). Results showed precipitation, rather than temperature, had the closest relationship with the four HVs, with r ranging from 0.76 to 0.97 (p < 0.01), and this was therefore presumed to be the dominant climate-based driving factor in the variation of hydrological regimes. Vegetation conversion, from cropland and grassland to woodland, significantly reduced runoff and increased soil moisture consumption, to sustain an increased ET, and, assuming that the reduction of SM2 is entirely evaporated, we can attribute 71.28% ± 18.64%, 65.89% ± 24.14% of the ET increase to the water loss of SM2 in the two conversion modes, respectively. The variation in HVs, induced by land cover change, were higher than the expected climate change with respect to SM1, while different factors were selected to determine HVs variation in six catchments, due to differences in the mode and intensity of vegetation conversion, and the degree of climate change. Our findings are critical for understanding and quantifying the impact of climate change and vegetation conversions, and provide a further basis for the design of water resources and land-use management strategies with respect to climate change, especially in the water-limited Loess Plateau.

How Much Can A Priori Hydrologic Model Predictability Help in Optimal Merging of Satellite Precipitation Products?

2011 ◽  
Vol 12 (6) ◽  
pp. 1287-1298 ◽  
Author(s):  
Abebe Gebregiorgis ◽  
Faisal Hossain
Keyword(s):  
Soil Moisture ◽  
Mississippi River ◽  
A Priori ◽  
Tropical Rainfall Measuring Mission ◽  
Hydrologic Model ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Satellite Precipitation ◽  
Precipitation Product ◽  
Predictive Skill

Abstract In this study, the authors ask the question: Can a more superior precipitation product be developed by merging individual products according to their a priori hydrologic predictability? The performance of three widely used high-resolution satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) real-time precipitation product 3B42 (3B42-RT), the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)] was evaluated in terms streamflow predictability for the entire Mississippi River basin using the Variable Infiltration Capacity (VIC) macroscale hydrologic model. A merging concept that was not based on a single universal merging formula for the whole basin but rather used a “localized” (grid box by grid box) approach for merging precipitation products was then explored. In this merging technique, the a priori (historical) hydrologic predictive skill of each product for each grid box was first identified. Prior to streamflow routing, the corresponding accuracy of the spatially distributed simulations of soil moisture and runoff were used as proxy for weights in merging the precipitation products. It was found that the merged product derived on the basis of runoff predictability outperformed its counterpart merged product derived on the basis of soil moisture simulation. Results indicate that such a grid box by grid box merging concept that leverages a priori information on predictability of individual products has the potential to yield a more superior product for streamflow prediction than what the individual products can deliver for hydrologic prediction.

scholarly journals Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1053
Author(s):  
Yuan Yao ◽  
Wei Qu ◽  
Jingxuan Lu ◽  
Hui Cheng ◽  
Zhiguo Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Climate Models ◽  
Global Climate Models ◽  
Hydrological Response ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Change Analysis ◽  
Average Annual Runoff

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

scholarly journals Historical climate controls soil respiration responses to current soil moisture

2017 ◽  
Vol 114 (24) ◽  
pp. 6322-6327 ◽  
Author(s):  
Christine V. Hawkes ◽  
Bonnie G. Waring ◽  
Jennifer D. Rocca ◽  
Stephanie N. Kivlin
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Soil Respiration ◽  
Carbon Cycling ◽  
Large Scale ◽  
Microbial Respiration ◽  
Rainfall Gradient ◽  
Soil Microbial ◽  
Historical Climate ◽  
Moisture Dependence

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration–moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.

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